Smackdown's theme tonight is destruction. The reunion of arguably the most dominant tag team in Smackdown history, The Brothers of Destruction, reunite. Can the on again/off again partnership of Undertaker and Kane last this time around? Also, in preparation for this weekend's Cyber Sunday pay-per-view GM Teddy Long has assigned a special bodyguard for his World Heavyweight champion, King Booker. A farewell speech from Rey Mysterio and a challenge from Batista cap off another chaotic show!
The program starts out this evening from Milwaukee with Rey Mysterio announcing his retirement due to injuries sustained in the "I Quit Match" with Chavo Guerrero. A recap is shown of Rey getting his leg smashed to bits by Chavo in that match. Mysterio states that he didn't quit on the fans, he only said the words because of the pain in his knee and the hope to come back and face Guerrero again. Strangely enough although this is supposed to be a retirement, Mysterio says he will be back. Chavo and Vickie Guerrero now come down to ringside and bring the chair that Chavo used on Rey's knee. Guerrero wrote the words "I Ouit" ... that's right not "quit" but a misspelling of "ouit". In any case Mysterio lunges at Chavo but Guerrero slams away with the steel chair on the injured leg. Chris Benoit runs in for the save before Vickie can add to the damage with shots of her own.
Kendrick and London connect with simultaneous flying crossbodys to the outside of opposite ends of the ring. Soon James regains control and tries to ground Kendrick with submissions and a near fall following a back breaker. Kendrick narrowly escapes the finisher and makes the hot tag to London who goes wild. One half of the tag champions delivers impressive drop saults and a standing hurracarrana. A splash by Kendrick is followed immediately by a standing moonsault from London who covers James for the win.
GM Teddy Long says he wants to keep King Booker safe for Cyber Sunday so that he can become the Champion of Champions. Teddy has assigned a special bodyguard for Booker tonight, and it is ... The Animal, Batista! The King tells Batista that he does not need his help, and that he beat the big man before. Batista holds Booker up against the wall and says the only reason Book beat him was because of Finlay ... who The Animal will take care of later tonight.
MVP and Kennedy bicker in the locker room about their tag match versus The Undertaker and Kane tonight. Wow MVP really needs a less Power Ranger looking wrestling outfit.
MVP and Kennedy keep running away initially and get counted out. GM Teddy Long comes out and makes the bout a no countout. The two teams battle it out further until Kennedy tries to get disqualified with a low blow on Taker. GM Long comes out once again and said he will now add a no DQ stipulation to the matchup. The brothers of destruction go on a long double team run trying to one up each other by decimating both MVP and Kennedy. After tossing them on tables and clotheslining their opponents all over the ring, finally the referee gets the match back to a tag team format. MVP and Kennedy finally get some momentum on Kane but it is quickly stopped with a sidewalk slam following an Undertaker tag in. MVP distracts the referee so that Kennedy can score a low blow and a swinging neck breaker on the Dead Man. All of a sudden both brothers are down and just as suddenly sit right back up. A double chokeslam follows and then Taker scores a big Tombstone on Kennedy. 1-2-3 and the brothers of destruction win.
Nothing too much to this match but once again Kristal's wrestling is improving greatly ... unfortunately I cannot say the same for Layla. The two teams battle back and forth until Kristal rolls up Layla for the three count by pulling the tights. All of a sudden The Boogeyman's music hits and out comes the freak. Only Miz and Kristal are left in the ring. The Miz is able to sneak away leaving Kristal to get a dose of worms for the second week in a row.
Some strong technical wrestling combined with some high flying spots are what start off this one. Both of these guys can really go and hopefully this will be the start of making the Cruiserweight championship important once again. Yang had the match all but won when Sylvan comes and interferes. This allows Helms to gain control and win the contest, therefore retaining his title once again. Following the victory Helms and Sylvan begin putting the beat donwdown on a helpless Yang. All of a sudden Matt Hardy runs down with the save, after which, all four men begin brawling.
Before the bell rings Tatanka comes out in new full face make up. This doesn't distract Lashley as he works Noble's right leg and nails an awesome belly-to-belly over the top suplex. Jamie Noble attempts a flying crossbody but is caught by the big man and turns it into a British Bulldog running powerslam. Lashley gets the pinfall. Tatanka then grabs the mic and says that him and his people have suffered many injustices. A vengeful warrior has been awoken inside of him and he challenges anyone to take him on.
Batista walks King Booker and Queen Sharmell to their limo for protection. The two are about to have a confrontation when some unknown attackers in Raw shirts try to get at the World Heavyweight champion. The Animal swats all of them aside and the royal couple are able to escape. Batista then says he's going to take care of something personal tonight.
Batista comes out to the ring, calls out Finlay and the Irishman walks out ... then hides behind the curtain and jump attacks The Animal. Many officials come out and pull the two apart. They break free several times and club away on one another until finally being separated permanently. These two warriors will face off next week, and the show goes off the air with the two glaring at one another.
Andy McNamara is a freelance writer and writes for SLAM! Wrestling. Make sure to listen to Andy live at 8pm on November 7 on www.thefreq.ca for play-by-play broadcasting of the Durham College Lords basketball team. Andy can be reached at andymcnamara@hotmail.com.


text:
Posted by Guest Author on September 24, 2014
More than 100 heads of state and government were joined by more than 800 business, finance and civil leaders in New York on Tuesday for the UN Climate Summit, a one-day forum dedicated to non-binding pledges for climate action.
The aim of the Summit was to gain political momentum for the UN's 2015 Climate Change Conference, where – for the first time in over 20 years of negotiations – the UN aims to propose a binding and universal agreement, by all nations of the world, to reduce emissions and build resilience to the adverse impacts of climate change.
"Today we must set the world on a new course," United Nations Secretary-General Ban Ki-Moon said in opening remarks. "Climate change is the defining issue of our age. It is defining our present. Our response will define our future."
Ki-Moon and other prominent speakers, including actor Leonardo DiCaprio, former U.S. Vice President Al Gore and Rajendra K. Pachauri, who headed the Nobel Prize-winning panel of scientists that studied the issue, warned that time for action was short:
By 2020, Ban said, the world must reduce greenhouse gases to prevent an escalating level of warming that world leaders five years ago called dangerous. Leaders in 2009 pledged to keep world temperatures from increasing by another 2 degrees Celsius. [SOURCE]
Ki-Moon asked the leaders in attendance for a global vision for low-carbon economic growth and to advance climate action on five fronts: cutting emissions, mobilizing money and markets, pricing carbon, strengthening resilience and mobilizing new coalitions.
According to Ki-Moon's official Chair's Summary, below is detailed list of the most significant commitments made at the Climate Summit.
Cutting emissions
Many leaders, from all regions and all levels of economic development advocated for a peak in greenhouse gas emissions before 2020, dramatically reduced emissions thereafter and climate neutrality in the second half of the century
European Union countries committed to a target of reducing emissions to 40 per cent below 1990 levels by 2030
Leaders from more than 40 countries, 30 cities and dozens of corporations launched large-scale commitment to double the rate of global energy efficiency by 2030 through vehicle fuel efficiency, lighting, appliances, buildings and district energy
The New York Declaration on Forests, launched and supported by more than 150 partners, including 28 government, 8 subnational governments, 35 companies, 16 indigenous peoples groups and 45 NGO and civil society groups, aims to halve the loss of natural forests globally by 2030
Twenty-four leading global producers of palm oil as well as commodities traders committed to contribute to the goal of zero net deforestation by 2020 and to work with governments, private sector partners and indigenous peoples to ensure a sustainable supply chain
The transport sector brought substantial emissions reduction commitments linked to trains, public transportation, freight, aviation and electric cars
Some of the world's largest retailers of meat and agricultural products committed to adapt their supply chains to reduce emissions and build resilience to climate change. They will assist 500 million farmers in the process
Mobilizing money and markets
A new coalition of governments, business, finance, multilateral development banks and civil society leaders announced their intent to mobilise over $200 billion for financing low-carbon and climate-resilient development
Countries strongly reaffirmed their support for mobilizing public and private finance to meet the $100 billion dollar goal per annum by 2020
Leaders expressed strong support for the Green Climate Fund and many called for the Fund's initial capitalization at an amount no less than $10 billion. There was a total of $2.3 billion in pledges to the Fund's initial capitalization from six countries. Six others committed to allocate contributions by November 2014
The European Union committed $3 billion for mitigation efforts in developing countries between 2014 and 2020
The International Development Finance Club (IDFC) announced that it is on track to increase direct green/climate financing to $100 billion a year for new climate finance activities by the end of 2015
Significant new announcements were made on support for South-South cooperation on climate change
Leaders from private finance called for the creation of an enabling environment to undertake the required investments in low-carbon climate resilient growth. They announced the following commitments:
Leading commercial banks announced their plans to issue $30 billion of Green Bonds by 2015, and announced their intention to increase the amount placed in climate-smart development to 10 times the current amount by 2020
A coalition of institutional investors, committed to decarbonizing $100 billion by December 2015 and to measure and disclose the carbon footprint of at least $500 billion in investments
The insurance industry committed to double its green investments to $84 billion by the end of 2015
Three major pension funds from North America and Europe announced plans to accelerate their investments in low-carbon investments across asset classes up to more than $31 billion by 2020
Pricing carbon
Seventy-three national governments, 11 regional governments and more than 1000 businesses and investors signalled their support for pricing carbon. Together these leaders represent 52 per cent of global GDP, 54 per cent of global greenhouse gas emissions and almost half of the world's population
Some leaders agreed to join a new Carbon Pricing Leadership Coalition to drive action aimed at strengthening carbon pricing policies and redirecting investment
More than 30 leading companies announced their alignment with the Caring for Climate Business Leadership Criteria on Carbon Pricing
Strengthening resilience
A variety of innovative resilience initiatives were announced at the Summit, including many that will strengthen countries and communities on the climate front lines. These include an initiative to provide user-friendly "news you can use" climate information for countries around the world
Leaders agreed to strengthen and scale up the risk financing mechanisms for Africa and the Caribbean
The African Risk Capacity announced an expansion of its services and coverage, including the introduction of Catastrophe Bonds
An initiative to integrate climate risk into the financial system by 2020 was launched by a coalition of investors, credit ratings agencies, insurers and financial regulators in response to the growing number of extreme weather events
Leaders from the insurance industry, representing $30 trillion in assets and investments committed to creating a Climate Risk Investment Framework by Paris in 2015
Mobilizing new coalitions
Leaders welcomed multilateral and multi-stakeholder actions between governments, finance, the private sector, and civil society to address emissions in critical sectors and support adaptation and resilience, especially in Small Island Developing States, Africa and the Least Developed Countries
Leaders from 19 countries and 32 partners from government, regional organisations, development institutions and private investors committed to creating an 8,000 kilometre-long African Clean Energy Corridor
The Global Alliance for Climate-Smart Agriculture, comprised of 16 countries and 37 organisations, was launched to enable 500 million farmers worldwide to practice climate-smart agriculture by 2030
Leaders of the oil and gas industry, along with national governments and civil society organisations, made an historic commitment to identify and reduce methane emissions by 2020. A second industry-led initiative was launched by leading producers of petroleum who committed to address methane as well as other key climate challenges, followed by regular reporting on ongoing efforts. Industry leaders and governments also committed to reduce HFCs in refrigeration and food storage
A new Global Mayors Compact, representing well over 2,000 cities pledged new commitments on climate action supported by new funding from public and private sources — 228 cities have voluntary targets and strategies for greenhouse gas reductions – that could avoid up to 2.1 gigatonnes of greenhouse gas emissions per year
A new coalition of more than 160 institutions and local governments and more than 500 individuals committed to divesting $50 billion from fossil fuel investments within the next three-five years and reinvest in new energy sources
Panels comprised of eminent global leaders, policy experts and citizen activists discussed the need for, and multiple benefits of, accelerated climate action. Panellists focussed on the need for science-based decision making; strengthening economic performance while cutting emissions, generating jobs and enhancing resilience; pricing and reducing pollution for improved health; mobilizing new coalitions to help move markets; and ensuring that the most affected are at the centre of the global response to climate change
Filed under: Sustainability 101

redpajama_set_name: RedPajamaCommonCrawl


text:
Home News PS5 India Pre-Order Registrations Now Live on Amazon, Flipkart
PS5 India Pre-Order Registrations Now Live on Amazon, Flipkart
Rishi Alwani
Update on July 4, 2020 at 2:55pm – added Games The Shop PS5 pre-order registration details.
The PS5 is yet to get a release date or price but Indian retailers have updated their sites to allow for pre-order registrations. Both Amazon India and Flipkart now sport PS5 listings along with a 'Notify Me' button for users to register their interest in Sony's upcoming console. We were tipped off by avid readers of the site.
In addition to this, the PS5 listing mentions upcoming accessories which should see an India launch as well. They are as follows:
PS5 accessories for India launch
Pulse 3D Wireless Headset
Furthermore, information on upcoming PS5 games has been listed as well. Considering most of these don't have concrete release dates, it's unlikely that all of them would be out during the console's launch window.
PS5 games India list
Ratchet and Clank: Rift Apart
It's interesting to see PS5 information go live on Amazon India and Flipkart first before Sony's own retail site or other independent retail chains like Games The Shop which put up its own PS5 listing a day later complete with a registration link that directs you to a Google Docs page unlike Amazon or Flipkart.
Hopefully the console isn't exclusive to Amazon and Flipkart (Sony should ask Microsoft how that worked out with the Xbox One). We've reached out to Sony for comment and we'll update this story if we hear from the company.
When will PS5 pre-orders begin in India?
It's quite likely that pre-order registrations exist to judge if audiences are interested in the console before its price has been revealed and pre-orders can begin in earnest.
According to Sony pre-orders for the PS5 have not gone live anywhere in the world and India is no different.
"At this point in time, pre-orders are neither encouraged nor allowed from Sony," confirmed a spokesperson for Sony India to The Mako Reactor via email nearly two weeks ago.
Makes sense when you consider a price hasn't been announced yet. Furthermore, the likelihood of supply being widespread for India is also a concern.
With the PS4, BIS rules ensured that initial supply was inconsistent at best and the PS4 Pro had stock shortages for nearly a year since its India release date.
PS5 India release date
This time around, we speculate that not every Indian retailer may stock the PS5 at launch if we're to go by the scant few online stores that have PS5 banners from Sony, most of which have changed the 'Holiday 2020' release window to 'Late 2020' including Amazon India.
It's unlikely this means anything more than Sony being accurate with its communication as 'Holiday 2020' is in line with the Western calendar of Thanksgiving and Christmas rather than the rest of the world. A listing on Amazon France over the weekend had a November 20 release date but was later removed.
PS5 price
Several listings have emerged for the PS5 price over last month. One from PlayAsia suggested a $699 price and another from Amazon France claimed it would be EUR 499 onwards. Both were removed. It's pretty obvious that Sony is keeping its cards close to its chest regarding how much you'd eventually pay for the PS5 much like Microsoft is with the Xbox Series X.
Just don't expect the PS5 price to be cheap by any means.
"We wanted to list the PS5 on our social channels a few months ago," said one Delhi-based retailer on the condition of anonymity . "No price has been given to us and we just wanted to do this so customers could tell us if they're interested. However Sony India told us it would be on the expensive side when it hits and has asked us to focus on PS4 instead."
Previous articleMonster Hunter World: Iceborne Version 14.00 Update Patch Notes Revealed
Next articleThe World Ends With You The Animation Announced for 2021, First Trailer Released
Nioh 2 Complete Edition PC Preview – Graphics Settings, Performance, Control Options, and More
Yakuza: Like a Dragon Version 1.08 Update Out Now, Patch Notes Released


This is a placeholder page for Erin Amendelore, which means this person is not currently on this site. We do suggest using the tools below to find Erin Amendelore.
You are visiting the placeholder page for Erin Amendelore. This page is here because someone used our placeholder utility to look for Erin Amendelore. We created this page automatically in hopes Erin Amendelore would find it. If you are not Erin Amendelore, but are an alumni of Leesburg High School, register on this site for free now.


text:
Leonardo L. Ferreira
Alain Couvineau
Institut National de la Santé et de la Recherche Médicale (INSERM), France
Yingxia Li
Result and Discussion
ORIGINAL RESEARCH article
Front. Pharmacol., 05 March 2018
Sec. Experimental Pharmacology and Drug Discovery
Volume 9 - 2018 | https://doi.org/10.3389/fphar.2018.00096
LeadOp+R: Structure-Based Lead Optimization With Synthetic Accessibility
Fang-Yu Lin1, Emilio Xavier Esposito2 and Yufeng J. Tseng1,3*
1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
2exeResearch, LLC, East Lansing, MI, United States
3Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
We previously described a structure-based fragment hopping for lead optimization using a pre-docked fragment database, "LeadOp," that conceptually replaced "bad" fragments of a ligand with "good" fragments while leaving the core of the ligand intact thus improving the compound's activity. LeadOp was proven to optimize the query molecules and systematically developed improved analogs for each of our example systems. However, even with the fragment-based design from common building blocks, it is still a challenge for synthesis. In this work, "LeadOp+R" was developed based on 198 classical chemical reactions to consider the synthetic accessibility while optimizing leads. LeadOp+R first allows user to identify a preserved space defined by the volume occupied by a fragment of the query molecule to be preserved. Then LeadOp+R searches for building blocks with the same preserved space as initial reactants and grows molecules toward the preferred receptor-ligand interactions according to reaction rules from reaction database in LeadOp+R. Multiple conformers of each intermediate product were considered and evaluated at each step. The conformer with the best group efficiency score would be selected as the initial conformer of the next building block until the program finished optimization for all selected receptor-ligand interactions. The LeadOp+R method was tested with two biomolecular systems: Tie-2 kinase and human 5-lipoxygenase. The LeadOp+R methodology was able to optimize the query molecules and systematically developed improved analogs for each of our example systems. The suggested synthetic routes for compounds proposed by LeadOp+R were the same as the published synthetic routes devised by the synthetic/organic chemists.
We recently reported a new structure-based fragment hopping method in lead optimization, LeadOp, (Lin and Tseng, 2011). Our lead optimization method works by decomposing a chemical structure into fragments of different parts, either by chemical or user-defined rules. The fragments are evaluated in a pre-docked fragment database and ranked according to specific fragment-receptor binding interactions. The ranked fragments provide the ability to replace fragments possessing less favorable contributions to binding. With optimal fragments selected, LeadOp reassembles the fragments to form the new drug-like compound. LeadOp is an algorithm that can automatically optimize a query molecule by searching and replacing fragments from a pre-docked fragment database in the active site to generate structures with better binding without prior knowledge of better fragments. Additionally, users can specify parts of structures to be optimized based on known interactions or the user's preference. However, the proposed compounds are not always easy to synthesize. In this study, we demonstrate lead optimization with synthetic accessibility, LeadOp+R, an advanced approach for lead optimization with synthetic accessibility.
A basic difficulty in most applications of computer-aided drug design is that designed (suggested) molecules are often of uncertain synthetic accessibility, leading to a slow feedback-improvement loop between the experimental syntheses and modeling design (Hopkins et al., 2004). Various synthetic planning software, WODCA (Ihlenfeldt and Gasteiger, 1996), SYNGEN (Hendrickson and Toczko, 1988), and ROBIA, 4 (Socorro and Goodman, 2006) were developed to provide the synthetic route generation, that involves either searching a database of chemical reactions or transformation rules for reaction centers that match the target compound to propose analogous transformations. Tools in route generation, mostly retrosynthetic software, can suggest routes based on encoded generalized reaction rules to identify those bond disconnections most apt to lead to synthetically accessible precursor structures (Corey et al., 1975; Corey and Jorgensen, 1976) while Hendrickson's group (Hendrickson et al., 1985) developed a logic-based synthesis design method with formalized reaction constraints. A good example of route generation is Route Designer (Law et al., 2009), that use rules describing retrosynthetic transformations automatically generated from reaction database and generates complete synthetic routes for target molecules starting from available reactants. Applications combining the synthetic route designing and de-novo design for the target binding sites have also been developed, such as SPROUT (Mata et al., 1995), which starts from generation of a skeleton followed by atom substitution to convert the solution skeletons to molecules and rank the output from SPROUT according to ease of synthesis. However, the molecules are generated from the ease of synthesis, the desired core of potential inhibitors could not be easily preserved.
To make the synthetic-modeling feedback loop more straightforward, we develop and implement "LeadOp+R"—Lead Optimization with synthetic accessibility based on chemical Reaction route. LeadOp+R is an algorithm that performs structure-based lead optimization while considering the synthetic reactions from reactants to products according to reaction rules. It takes into consideration the chemical reaction environment; this information is based on known chemical synthesis. The synthetic routes suggested by LeadOp+R are examined to ensure the validity of transformation from one starting reactant into the final product through the use of the LeadOp+R reaction database. The extracted reaction rules in LeadOp+R reaction database do not take into account temporarily or unwanted chemical reactions; on the contrary, these extracted reaction rules consider direct chemical reactions that transform the starting reactants into products. LeadOp+R's algorithm consists of the following five steps: (i) identify a preserved space (defined by the volume occupied by a fragment of the query molecule to be preserved by the user) and searches for building blocks with the same preserved space as initial reactants, (ii) search the reaction rules for each reactants identified, (iii) generate reaction products based on reaction rules, (iv) evaluate the conformations of each products of each reaction, and (v) select the conformer from previous steps that would be selected as the reactant to grow molecules until optimizations are fulfilled for each selected inhibitor–receptor interactions by users. Multiple conformers of each product for each step were considered and evaluated. The conformer with the best group efficiency score would be selected as the next reactant, wherein the group efficiency score is calculated based on binding energy divided by the number of heavy atoms. Thus, this evaluation would favor the conformers with stronger binding toward the specified receptor-ligand interactions with less heavy atoms (Hopkins et al., 2004; Ciulli et al., 2006; Alex and Flocco, 2007; Saxty et al., 2007; Congreve et al., 2008; Orita et al., 2009). Compounds passing the molecular property filters comprised the final list of proposed compounds. The compounds were then energy-minimized and ranked on the basis of the overall ligand–receptor binding energy. To investigate the interactions between the newly assembled molecules and their receptor, molecular dynamics simulations were performed to explore the compounds' poses and interactions with the solved crystal structure of the receptor.
To demonstrate the LeadOp+R algorithm, we selected the Tie-2 kinase (Hodous et al., 2007) and human 5-lipoxygenase (5-LOX) (Ducharme et al., 2010) protein systems and their associated inhibitors as model systems. The endothelium-specific receptor tyrosine kinase Tie-2 (tyrosine kinase containing Ig and EGF homology domains) is primarily expressed in the vascular endothelium and is involved in vessel branching, sprouting, remodeling, maturation, and stability (Yu, 2005). The role of tyrosine kinases in angiogenesis and in the vascularization of solid tumors has drawn considerable interest (Hasegawa et al., 2007) and is considered to be angiogenesis-dependent in cancer. Interference with the Tie-2 pathway by diverse blocking agents has been shown to suppress tumor growth in xenograft studies (Oliner et al., 2004). The development of Tie-2 kinase inhibitors may block the beneficial anti-inflammatory and vascular stabilizing effects, thus the discovery of potent Tie-2 kinase inhibitors has advanced into clinical studies (Huang et al., 2010). Lipoxygenases are a family of iron-containing enzymes found in a large variety of organisms, including bacteria and animals. It catalyzes the dioxygenation of polyunsaturated fatty acids containing a cis-1,4-pentadiene structure—the first committed structure in a metabolic pathway cascade—and involved in the initiation of signaling molecule synthesis and inducing structural or metabolic changes (Steele et al., 1999). Four major isozymes of lipoxygenases have been identified (Ivanov et al., 2010), including 5-, 8-, 12-, and 15-LOX, that are key enzymes in the metabolism of prostaglandins and leukotrienes. In particular, leukotrienes are produced through the 5-LOX pathway and the increased activity of the 5-LOX pathway is strongly associated with atherosclerosis (Woods et al., 1993). As the 5-LOX biological pathways and byproducts lead to inflammation, discovering a 5-lipoxygenase inhibitor is important to the fields of inflammatory and allergic diseases (Shaffer and Mansmann, 1997).
Overall Procedure
The general protocol for LeadOp+R is illustrated in Figure 1 and the details of each step are described in the following sections. Prior to applying the LeadOp+R optimization procedure, a reaction rule database is constructed, containing reaction rules for the reactant moiety, the product moiety, and the building blocks of each reaction. Thus, participants involved in each reaction are known for synthetic assessment in LeadOp+R. The initial step of LeadOp+R requires the user to select the favored inhibitor-receptor interaction positions for optimization. The inhibitor-receptor interaction positions determine the "direction" for virtual synthesis and optimizations. LeadOp+R will systematically optimize and grow a structure until all the user-defined directions are processed. LeadOp+R initiates the analysis with the inhibitor-receptor complex from docking studies or crystal structures. The user can determine which fragment(s) in the query inhibitor (initial compound) to preserve during optimization. To ensure that the initial synthesis is accessible, the starting building block—containing the preserved fragment—is used as the initial building block. LeadOp+R then searches the reaction rule database with this building block to identify associated reactions rules. Once the reactions rules and associated participants are identified, the products of each reaction rule are generated virtually. The best binding conformation of the proposed compound is selected from an ensemble of conformers are constructed of each compound. The conformer of each compound with the lowest group efficiency value is selected as the initial conformer of the next building block until the program reaches the termination condition. By evaluating the contribution of each product upon binding with group efficiency, LeadOp+R selects compounds that bind stronger yet possess less heavy atoms. The compounds passing a set of molecular property filters comprised the final list of proposed compounds. Following a short molecular dynamics simulation, the compounds are energy-minimized and ranked on the basis of the overall ligand–receptor binding (interaction) energy. This provides a series of new and more potent compounds that are chemical accessibility.
Figure 1. Illustration of the LeadOp+R optimization workflow.
Example Systems
Tie-2 kinase (PDB: 2p4i), an endothelium-specific receptor tyrosine kinase (Hodous et al., 2007) and human 5-LOX enzyme (Charlier et al., 2006) a key enzyme in leukotriene biosynthesis, were selected as model systems to examine the LeadOp+R approach. One Tie-2 kinase inhibitor, compound 46 in reference 16 (denoted as compound rA in this study) and a human 5-LOX inhibitor, compound 7 (substituted coumarins) in reference 17 (denoted as compound rB in this study), were selected as the LeadOp+R optimization examples.
Construction of the LeadOp+R Reaction Database
LeadOp+R collects chemical reactions, building blocks, and reaction rules with reactant moieties and product moieties of each reaction to construct the LeadOp+R reaction database. LeadOp+R includes 198 classic chemical reactions from the Reaxy Database and 2,091 organic building blocks from the commercially available Sigma-Alderich Co1. product library. These building blocks include the typical building blocks in a chemical synthesis such as various nitrogen compounds (amines, isocyanides) and carbonyl compounds (amides, aldehydes, and ketones). A reaction rule in LeadOp+R includes the reactant moieties and product moieties extracted from the full structure of reactants and products of each reaction collected. In LeadOp+R, the reaction moieties were defined and extracted from a chemical reaction according the following steps (see Figure 2 for the illustration of the steps):
(1) Identification of reaction core. A collection of atoms that take part in the chemical transformation (reaction) have their atom type changed (element, number and type of bonds, and number of neighboring atoms) are considered the reaction core. These atoms are determined by comparing the atoms of the starting compound and product to those within the LeadOp+R reaction database; atoms that differ are part of the reaction core. Since the reaction core does not contain enough chemical information to accurately describe the reaction, additional information is gathered from atoms bound to the reaction core.
(2) Extraction of the reactant and product moieties for a reaction. The initial reaction cores typically do not include enough atoms and thus their "chemical environment" is expanded. The reaction core is increased to bonded (neighboring) atoms until the minimum reactant and product substructures are included to fully represent the reaction. Within a reaction, the reactant portion is denoted as the "reactant moiety" and as expected the product portion is denoted as "product moiety." The extension step is performed by traversing the atom types within the reaction core, as discussed in Step 1, until a single sp3 carbon is found and the atoms searched during the extension step are considered part of the same moiety. For cases where the searched atoms are in an aromatic ring, the extension was terminated when all the atoms in the aromatic ring are included in the moiety, thus all the atoms in the aromatic ring are considered part of the moiety.
Figure 2. Example of three steps used to construct the table of reaction rules. (A) Identification of reaction cores. The atoms with changed atom attributes are highlighted in red and blue within the two reactants. (B) Extraction of the moieties. (C) Identification of building blocks containing the reactant moieties. (D) Illustration of the steps in generating products.
Finally, the building blocks with the same reactant moiety for each reaction rule are collected (through a JChem application-programming interface; JChem API) and classified by the reaction. Building blocks for each reaction rule are recorded and used for virtual synthesis in the LeadOp+R algorithm.
Identify Reactant
LeadOp+R initiates the analysis of a complexed structure (inhibitor-receptor) taken from a docking study or crystal structure. Initially, the user identifies and preserves the "fragment space" of a query molecule defined by a fragment's volume. LeadOp+R then searches for building blocks with the same volume as the potential initial reactants. Products of each potential initial reactant are virtually synthesized according to the steps below. For each product molecule that passes the evaluation step, that product molecule becomes the next reactant in the next synthesis step.
Determine Reaction Rules for Each Reactant Identified
When a reactant is identified in the previous step, there are many potential reactant moieties and reactions associated with this reactant. Each reactant is subjected to sub-structure searching2 to identify atom arrangements (moieties) that are part of a chemical reaction rule within the LeadOP+R reaction database. This is done to determine potential chemical reactions for this specific reactant.
Generation of Reaction Products Based on Reaction Rules
Once all the potential reaction rules of a reactant are identified, the corresponding products are generated by "reacting" the reactant moieties and participant reactants (Figure 2D). In LeadOp+R, each reactant has two parts: one structure matches the reactant moiety and the other structure—excluding the reactant moiety—is denoted as the "clipped reactant." The same definition is used for other building blocks (participants) involved in a reaction. Each product is generated by combining the clipped portion of the reactant and the clipped portion of the participants as well as the product moiety based on the search of the reaction rule.
Evaluation of the Products for Each Reaction
Thirty conformers of each product are generated using the Java and JChem application-programming interface (Imre et al., 2006). Each conformer is aligned with the preserved space of the query molecule, while maximizing the overlap volumes, using the flexible 3D alignment tool of Marvin3 (see Figure 3). A conformer for each product was selected for the next step if the following criteria are met: (1) the binding mode of each conformer, aligned with the query molecule within the receptor site, has the same inhibitor-receptor interaction direction, and (2) the new moiety has a group efficiency value <−0.1.
Figure 3. Evaluation of each product for each reaction. Thirty conformers are generated (colored in yellow, green, orange, and gray sticks) and overlaid with the reactant within the binding site (colored in red stick). The user-defined inhibitor-receptor interaction direction (location) is indicated by the dotted red line.
Final Selection by Structure-Based Analysis
The selected conformer for each product is the reactants for the next reaction in the selected inhibitor-receptor interaction direction. The molecule continues to grow until all the inhibitor-receptor interaction directions are exhausted. The collection of potential new compounds is reduced using the following criteria: molecular weight <600 g mol−1 and a calculated lipophilicity (cLogP) <5, which is taken into account based on the Lipinski's Rule-of-Five (Lipinski et al., 2001). The compounds that pass the molecular property filters comprised the final list of proposed compounds. These compounds are then energy-minimized within the binding site and ranked based on the overall ligand-receptor binding energy.
The bound pose of the newly "constructed" compound, as determined with AutoDock Vina (Trott and Olson, 2010), is refined from the lowest binding free energy and the largest number of favorable ligand-receptor interactions within the binding site. The unfavorable contacts between the docked pose of the energy minimized "constructed" compound (fragments connected to the initial core of the compound) and the residues within the binding site are alleviated using molecular dynamics simulations; allowing the ligand-receptor complex to explore the local energy landscape. The best complex pose (ligand-receptor interaction) was selected and molecular dynamics was performed using GROMACS version 4.03 (Hess et al., 2008) and the GROMOS 53A6 force field (Oostenbrink et al., 2005). The complexes are placed in a simple cubic periodic box of SPC216 type water molecules (Berendsen et al., 1981), and the distance between the protein and each edge of the box was set to 0.9 nm. To maintain overall electrostatic neutrality and isotonic conditions, Na+ and Cl− ions were randomly positioned within the solvation box. To maintain the proper structure and remove unfavorable van der Waals contacts, a 1,000-step steepest descent energy minimization was employed and terminated when the convergence criteria of an energy difference between subsequent steps differ <1,000 kJ mol−1 nm−1. Following the energy minimization, the system is subjected to a 1,200 ps molecular dynamics simulation at constant temperature (300 K), pressure (1 atm), and a time step of 0.002 ps (2 fs) with the coordinates of the system recorded every ps.
LeadOp+R Optimization for Tie-2 Kinase Inhibitors
Structure-Based Lead Optimization With Synthetic Routes
From the literature (Bridges, 2001), it is known that a good kinase inhibitors should possess a hydrogen-bond donor/acceptor/donor motif to best interact with the backbone carbonyl/NH(amide)/carbonyl presented in the ATP-binding cleft. In the case of Tie-2 kinase, the residues in the active site of the ATP-binding cleft are Ala905 (carbonyl and amide NH) and Glu903 (carbonyl). Additionally, two hydrophobic pockets are part of the active site in the Tie-2 receptor and are designated as the first hydrophobic pocket (HP) and the extended hydrophobic pocket (EHP). We selected a series of Tie-2 inhibitors from the literature (Hodous et al., 2007) containing a co-crystal structure of inhibitor compound 47 with Tie-2 receptor (PDB code: 2p4i). In this co-crystal structure, the 2-(methylamino)pyrimidine ring of inhibitor compound 47 interacts with residue Ala905 via two hydrogen bonds and the pyrimidine is also within van der Waals contact of the Glu903. The central methyl-substituted aryl ring of compound 47 resides in the first hydrophobic pocket (HP), while the pyridine ring forms an edge-to-face π-stacking interaction with Phe983 of the DFG-motif. The carbonyl oxygen makes a hydrogen bond with the backbone NH of Asp982 (DFG motif) and the aryl amide moiety directs the terminal CF3-substituted aromatic ring into the EHP. Figure 4A illustrates the ligand-protein interaction of this co-crystal structure.
Figure 4. LeadOp+R result for the Tie-2 model system. (A) Chemical characteristic of each residue and interaction within the complex of compound 47 from the co-crystal structure (PDB code: 2p4i). (B–D) Chemical structure (left) and MDS result (right) of the generated compound rA1 (B), the generated compound rA2 (C), and the generated compound rA3 (D). Carbon atoms are colored pink. Amino acid residues that participate in hydrogen-bonding interactions (labeled red) with the proposed compound at the binding site are depicted with cyan molecular surface.
To demonstrate how LeadOp+R optimizes a compound automatically while considering the potential synthetic route, compound 46 is the query molecule for lead optimization (denoted as compound rA in this study) with a biologically determined IC50 value of 399 nM (Hodous et al., 2007). Compound rA was docked into the Tie-2 binding site and the lowest energy conformation was selected. The selected conformation possessed similar molecular interactions, as discussed earlier, with the Tie-2 active site (Figure 4A). The amide functional group of compound rA forms a hydrogen bond with the backbone amide of Asp982, while the pyridine and benzene rings extend into the hydrophobic pocket (HP) and EHP, respectively. The aminobenzoic fragment was designated as the preserved space in this example of LeadOp+R due to the important hydrogen bonding.
To evaluate our algorithm, we compared all of the LeadOp+R generated compounds to Tie-2 kinase inhibitor from the literature and found nine of the LeadOp+R compounds have also been synthesized and their ability to inhibit Tie-2 kinase measured. The inclusive synthesis of proposed products in each LeadOp+R step combined with systematically examining the proposed ligand-receptor interactions resulted in nine compounds with more potent IC50 values than the original compound (compound rA). All the LeadOp+R generated compounds were energy minimized in the active site of Tie-2, and then ranked on the basis of the overall ligand–receptor interaction energy. Among all LeadOp+R suggested compounds, nine compounds were previously studied in the literature (Hodous et al., 2007), and the priority suggested by the calculated binding energy had same trend as the experimentally determined IC50 values. In this study of Tie-2 kinase inhibitor design—three compounds, denoted as compounds rA1, rA2, and rA3 of the nine LeadOp+R generated compounds—were selected for further investigation. For these three compounds we found detailed synthetic route information (Hodous et al., 2007) and inhibition potency in the literature. These three compound rA1-rA3, have a higher potency than the query compound rA and the suggested priority of the new compounds with the calculated binding energy have a similar IC50 potency trend. Depicted representations of compounds rA1-rA3, as well as the corresponding inhibition data from the biological experiments and their predicted binding energy are provided in Table 1.
Table 1. Rank of the proposed LeadOp+R compounds based on the calculated binding energy, inhibition concentration (IC50) of Tie-2 from the literature (Hodous et al., 2007).
Molecular dynamics simulations were performed with three LeadOp+R generated compounds, rA1–rA3, to further analyze the ligand-protein interactions within the Tie-2 kinase active site. Following geometry optimization of the compounds with respect to Tie-2, molecular dynamics simulation studies were performed and the unique low-energy conformations of the complexes, from the final 50 ps of the MDS (50 configurations), are shown in Figures 4B–D.
In the generated compounds (rA1, rA2, and rA3) both amide arrangements are engaged in strong hydrogen bonds with Asp982 of the DFG-motif (first three residues of the activation loop). The pyrimidine ring in compounds rA1 and rA2 makes key hydrogen bonds with the backbone amide of the linker residue Ala905, situating the pyridine rings in alignment and within edge-to-face π-stacking distance of Phe983 of the DFG-motif. Additionally, the central and terminal aryl rings overlap with only slight differences in orientation for compounds rA1, rA2, and rA3. The additional a hydrogen bond forms between the methoxy group of compound rA1 and residue Asp982, while the CF3-groups are placed in essentially the same location within the EHP for compounds rA2 and rA3. These optimized results indicate the hydrogen-bonding and hydrophobic interactions are important for ligands binding to and inhibiting Tie-2, as previously reported (Hodous et al., 2007).
Synthetic Routes Suggested by LeadOp+R
For Tie-2 kinase inhibitors, favorable interactions occur between the ligand and the specific receptor residues Glu 872, Asp 982, Phe983, Ala905, and Glu903 (see Figure 4A). In this example, these interactions are selected as preferred inhibitor-receptor interactions for LeadOp+R to optimize based on the provided query molecule in a selective and systematic process. Experimental synthetic routes from the literature (Hodous et al., 2007; Figures 5A, 6A, 7A) and the reaction routes suggested by LeadOp+R (Figures 5B, 6B, 7B) to generate compound rA1, rA2, and rA3 are summarized below to demonstrate how LeadOP+R suggests the synthetic reaction routes that are similar to those proposed by organic and medicinal chemists. Matched reaction rules are listed to the right of Figures 4C, 5C, 6C with details of each synthetic step identified by LeadOp+R, for each product, described below.
Figure 5. Synthetic routes for compound rA1 and sub-structure searching to identify atom arrangements (moieties) that are part of a chemical reaction rule within the LeadOp+R reaction database. (A) Synthetic routes with reagents and condition (a–d) from experimental studies (Hodous et al., 2007). (B) Synthetic routes and (C) matched reaction rules provided by LeadOp+R.
Figure 6. Synthetic routes for compound rA2 and sub-structure searching to identify atom arrangements (moieties) that are part of a chemical reaction rule within the LeadOp+R reaction database. (A) Synthetic routes with reagents and condition (a–g) from experimental studies (Hodous et al., 2007). (B) Synthetic routes and (C) matched reaction rules provided by LeadOp+R.
Figure 7. Synthetic routes for compound rA3 and sub-structure searching to identify atom arrangements (moieties) that are part of a chemical reaction rule within the LeadOp+R reaction database. (A) Synthetic routes with reagents and condition (a–f) from experimental studies (Hodous et al., 2007). (B) Synthetic routes and (C) matched reaction rules provided by LeadOp+R.
Figure 5A illustrates the experimental reactions required to synthesize compound rA1 (compound 7) by reacting 5 (which was generated through transforming 2 into 4) followed by reacting 1 with 6. To compare LeadOp+R's suggested virtual synthesis of compound rA1 to proven synthetic routes; we compared the key reaction rules from experimental synthetic steps in the literature.
Figure 5B shows the LeadOp+R suggested synthetic routes to generate compound rA1 using the selected and preferred inhibitor-receptor interactions that allowed LeadOp+R to selectively and systematically optimize the query molecule. Initially, compound 1 was identified as the first reactant by searching all building blocks with the preserved fragment. LeadOp+R then proceed to produce product 8 by coupling 1 with 6 with the reaction rule (i) that conserves the preferred interaction with Glu872 specified. The reaction rule suggested by LeadOp+R matched the synthetic steps in the literature that forms compound 7 by combining compound 5 and fragment 6. Next, product 8 was considered as the reactant to interact with compound 2 to generate product 9 by growing molecules with preferred interaction toward Phe983. The second reaction rule (ii) suggested by LeadOp+R lead to product 9 that matched the same synthetic steps as those in the literature to synthesize compound 5 by reacting 1 with 4. It is interesting to note that at this step, the structure marked in red is the current structure 9, the same partial structure highlighted in red within the final product 7 (compound rA1) in the experimental synthesis. LeadOp+R continued the recursive optimization toward the cavity near Phe983 and Ala905 to transform 9 to 7 (compound rA1) with the third reaction rule, Figure 5C. This reaction route suggested by LeadOp+R also matches the experimental synthetic route in the literature to transform 2 into 4. To this end, LeadOp+R has successfully optimized the query compound rA to compound rA1 and suggested corresponding synthetic routes. In this example, we demonstrated how LeadOp+R controls the synthetic flow by extending the molecules with preferred interactions, available building blocks and associated reactions rules to reach fragment based optimization and synthetic accessible. Thus, the sequence of reactions to "grow" molecules may not be the same as those verified in experimental synthesis.
Figure 6A shows the experimental reaction to synthesize compound rA2 (compound 19) by reacting 18 (which was generated through the transformation of 13–18) with 12 (which was generated through the reaction of 10 with 11). To compare the LeadOp+R suggested virtual synthesis route for compound rA2 with the experimental synthetic route, we compared the key reaction rules from the experimental synthetic steps in the literature with the LeadOp+R suggested synthetic routes.
Figure 6B shows the LeadOp+R suggested synthetic routes for compound rA2, using the selected and preferred inhibitor-receptor interactions to optimize the query molecule in a selective and systematic manner. Initially, a hydroxy benzoic acid of 10 was identified as the first reactant by searching all building blocks with the preserved fragment. LeadOp+R then proceed to suggest product 12 by reacting 10 with 11 via the first reaction rule (i) that preserves the ligand's interaction with Glu972 of the active site. The reaction rule suggested by LeadOp+R matched the synthetic steps in the literature that forms compound 12 from compounds 10 and 11. Next, product 12 was considered as the reactant to react with compound 13 to generate product 20, by growing molecules with preferred interaction toward Phe983. The second reaction rule (ii) generates product 20 and the reaction route suggested by LeadOp+R matches the synthetic steps in the literature to synthesize compound 19 through the reaction of 12 with 18. LeadOp+R's recursive optimization continues toward the cavity near Phe983 and Ala905 to transform 20 to 19 (compound rA2) via the third reaction rule (iii), Figure 6C. This reaction route suggested by LeadOp+R also matched the experimental synthetic step in the literature to transform compound 13–18.
Figure 7A shows the experimental reaction to synthesize compound rA3 (compound 22) by reacting 21 (which was generated through the reaction of 1 with 11) with 18 (which was synthesized from 13). To compare LeadOp+R's suggested synthesis route for compound rA3 with the experimental synthetic routes, we compared the key reaction rules from the experimental synthetic steps in the literature with the LeadOp+R suggested synthetic routes.
Figure 7B depicts the LeadOp+R suggested synthetic routes to generate compound rA3, using the selected and preferred inhibitor-receptor interactions to optimize the query molecule. Initially, compound 1, a hydroxybenzoic acid, was identified as the first reactant by searching all building blocks with the preserved fragment indicated in red, Figure 7B. LeadOp+R then proceeded to produce compound 21 by reacting 1 with 11 via the first reaction rule (i) directing the growth of the compound (inhibitor) toward the preferred ligand interaction with Glu972. The reaction rule suggested by LeadOp+R matched the synthetic steps in the literature that forms compound 21 via the transformation of compound 1 with fragment 11. Next, product 21 was reacted with compound 13 to generate product 23, growing the transformed molecule toward Phe983. The second reaction rule (ii) generated product 22 as suggested by LeadOp+R and matches the same synthetic steps as those in the literature to synthesize compound 22 through the reaction of compound 21 with fragment 18. The recursive optimization of the initial query compound toward the cavity near Phe983 and Ala905 by LeadOp+R transformed compound 23 to 22 (compound rA3) with the third reaction rule (iii) as illustrated in Figure 7C. This reaction rule, suggested by LeadOp+R, also matches the experimental synthetic step in the literature to transform 13–18.
LeadOp+R has successfully optimized the query compound rA to compounds rA1, rA2, and rA3 with synthetic routes that match experimental synthetic routes for each compound. Through the systematic synthesis and constant evaluation of intermediate products via group efficiency, LeadOp+R searched each product and discovered higher binding inhibitors. Increased hydrophobic interactions between compound rA1 and the receptor were observed between the compound's aromatic group that resides in the EHP pocket (Figure 4B) and the methylpyrimidine. This corresponds to the experimental results and rA1 exhibits stronger inhibitor potency than compounds rA2 and rA3.
In the example of Tie-2 inhibitor design, LeadOp+R demonstrates its ability to control the synthetic flow by extending the query molecules to optimize the preferred ligand-receptor interactions while using the available building blocks and associated reactions rules to find the most feasible synthetic accessibility.
LeadOp+R for Human 5-Lipoxygenase Inhibitor
The human 5-Lipoxygenase (5-LOX) enzyme with the well-known 5-LOX inhibitors was selected as the second LeadOp+R test case. To design better 5-LOX inhibitors, structural insight of the 5-LOX active site and its associated interactions with ligands would be helpful; therefore we selected a theoretical model (comparative/homology protein structure/model) of 5-LOX (Charlier et al., 2006) that has good agreement with mutagenesis studies (Hammarberg et al., 1995; Schwarz et al., 2001). The proposed active site of 5-LOX forms a deep and bent cleft (channel) that extends from Phe177 and Tyr181 at the top of the cleft to the Trp599 and Leu420 amino acid residues at the bottom of the cleft (shown in Figure 8A). Most of the residues lining the cleft are hydrophobic with several key polar residues (Gln363, Asn425, Gln557, Ser608, and Arg411) distributed along the channel with the ability to interact with the ligand during the binding process. A small side pocket off of the main channel is composed of hydrophobic residues (Phe421, Gln363, and Lue368) and it is postulated that the lipophilic interactions between the ligand and receptor may enhance activity. The purported major pharmacophore interactions needed for a ligand to bind to 5-LOX includes: (i) two hydrophobic groups, (ii) a hydrogen bond acceptor, (iii) an aromatic ring, and (iv) two secondary interactions. The two secondary interactions are between the ligand and an acidic moiety (amino acid residue) and a hydrogen bond acceptor within the binding pocket of the receptor. The hydrogen bond acceptor of the ligand most likely interacts with the key anchoring points of the receptor (Tyr181, Asn425, and Arg411) to form hydrogen bonds, while Leu414 and Phe421 form a hydrophobic interaction between the ligand and the binding cavity (Charlier et al., 2006).
Figure 8. LeadOp+R result for the 5-LOX kinase model system. (A) Schematic representation of the human 5-LOX active site (left) and the binding pocket (right). The purported pharmacophores of the binding site of 5-LOX involving two hydrophobic groups (blue ovals), two hydrogen bond acceptors (green ovals), and an aromatic ring (orange oval) for ligand binding at the binding cavity. (B–D) Chemical structure (left) and MDS result (right) of the generated compound rB1 (B), the generated compound rB2 (C), and the generated compound rB3 (D). Carbon atoms are colored pink. Amino acid residues that participate in hydrogen-bonding interactions (labeled red) with the proposed compound within the binding site are depicted with gray molecular surfaces.
The 5-LOX inhibitor, compound 7 in the literature (Ducharme et al., 2010), was selected as our initial query molecule (denoted as compound rB in this study), which had a biologically determined IC50 value of 145 nM. Compound rB was docked into the 5-LOX computationally derived binding site and the lowest energy conformation was submitted to LeadOp+R. This selected pose (conformation) possesses similar ligand-receptor interactions as previously reported (Charlier et al., 2006). The oxochromen ring favorably interacts with the hydrophobic residue Leu414 (CH-π interaction) in the middle of the cavity, while the fluoro phenyl group extends into the hydrogen-bond acceptor region in the lower cleft of the active site. The docked conformation of compound rB was selected as the reference inhibitor with the oxochromen ring serving as the template structure.
To evaluate our algorithm, we compared all of the LeadOp+R generated compounds for 5-LOX to the analogs described in the literature and found that six of the LeadOp+R proposed compounds have been synthesized and their biological activities measured (Schwarz et al., 2001). The inclusive synthesis of products at each step combined along with systematically examining the interactions of the proposed compounds with the receptor generated six compounds with more potent IC50 values than the original compound (compound rB). All the LeadOp+R generated compounds were energy minimized within the active site of 5-LOX and then ranked based on the predicted binding energy of the complex and the suggested priority has the same trend as the IC50 potency values from the experimental study (Schwarz et al., 2001). In this study of 5-LOX inhibitor design, three compounds (denoted as compounds rB1, rB2, and rB3) of the nine LeadOp+R generated compounds, were selected for further investigation. For these three compounds detailed synthetic information (Ducharme et al., 2010) and inhibition potency is available from the literature (Ducharme et al., 2010). Additionally, these three compound rB1, rB2, and rB3 have a higher potency than the query compound rB and their suggested priority, based on predicted binding energy, as well as a similar IC50 trend. Depicted representations of the compounds rB1, rB2, and rB3, the corresponding inhibition data from the biological experiments, and their predicted binding energy are listed in Table 2.
Table 2. Rank of the proposed LeadOp+R compounds based on the calculated binding energy, inhibition contraction (IC50) of 5-LOX from the literature (Ducharme et al., 2010).
Molecular dynamics simulation studies were performed with the final poses of compounds rB1, rB2, and rB3 with respect to 5-LOX. The unique low-energy conformations of the complexes, from the last 50 ps of the MDS (50 configurations), are shown in Figures 8B–D.
The interactions of compounds rB1, rB2, and rB3 all reside within the hydrophobic pocket and contain participate in hydrogen bonding interactions between the oxygen or nitrogen atoms of the thiazol group with Lys409 and Tyr181. For compounds rB1 and B3, the fluoro group extends to the hydrogen-bond acceptor in the upper domain of the active site and interacts with Lys409. In addition, the oxochromen ring is in close proximity to Leu414 and is potentially an important CH-π contact as indicated in the literature (Charlier et al., 2006). Also, the thiazole structure of compound rB1 interacts with the 5-LOX hydrophobic residues Leu420 and Leu607 and it has been suggested that these interactions improve ligand binding via complementary hydrophobic interaction between the ligand and receptor. Additionally, favorable interactions occur between the fluoro group and residues Lys409, Arg411, and Tyr181. These contributions to the ligand-protein binding probably accounts for compound rB1's better inhibition compared to compounds rB, rB2, and rB3. These optimized results indicate that hydrogen bonding and hydrophobic interactions are important for ligands binding to and inhibiting, 5-LOX as previous report (Hodous et al., 2007).
The favorable interactions between inhibitors and 5-LOX, as stated in the literature, are two hydrogen-bond acceptor interactions within the binding pocket (including ligand interactions with Asn425 and Tyr181), two hydrophobic interaction pockets (including ligand interactions with Leu368, Gln363, Phe421, Arg411, Ile406, Lys409, and Phe177), and aromatic interactions (between the ligand and residues Leu414 and Leu607). In this example, ligand interactions with Asn425, Leu414, Leu607, and Tyr181 are indicated as "preferred" inhibitor-receptor interactions for LeadOp+R to selectively and systematically optimize. Experimental synthetic routes from the literature (Schwarz et al., 2001) (Figures 9A, 10A, 11A) and the synthetic reaction routes suggested by LeadOp+R (Figures 9B, 10B, 11B) to generate compound rB1, rB2, and rB3 are summarized below. To demonstrate LeadOp+R's ability to suggest reaction routes similar—or exactly the same—to those proposed and executed by synthetic chemists, the matched reaction rules are listed to the right of Figures 9C, 10C, 11C. Details of each synthetic step, identified by LeadOp+R for each product (proposed compounds/inhibitor), are described below.
Figure 9. Synthetic routes for compound rB1 and sub-structure searching to identify atom arrangements (moieties) that are part of a chemical reaction rule within the LeadOp+R reaction database. (A) Synthetic routes with reagents and condition (a–c) from experimental studies (Ducharme et al., 2010). (B) Synthetic routes and (C) matched reaction rules provided by LeadOp+R.
Figure 10. Synthetic routes for compound rB2 and sub-structure searching to identify atom arrangements (moieties) that are part of a chemical reaction rule within the LeadOp+R reaction database. (A) Synthetic routes with reagents and condition (a–e) from experimental studies (Ducharme et al., 2010). (B) Synthetic routes and (C) matched reaction rules provided by LeadOp+R.
Figure 11. Synthetic routes for compound rB3 and sub-structure searching to identify atom arrangements (moieties) that are part of a chemical reaction rule within the LeadOp+R reaction database. (A) Synthetic routes with reagents and condition (a–d) from experimental studies (Ducharme et al., 2010). (B) Synthetic routes and (C) matched reaction rules provided by LeadOp+R.
Figure 9A shows the experimental reaction route (Schwarz et al., 2001) to synthesize compound rB1 (compound 30) by reacting compound 26 (which was generated through the reaction of 24 with 25) with 29 (which was generated through the reaction of 27 with 28). To compare the LeadOp+R suggested synthesis with the experimental synthetic route for compound rB1, we compared the key reaction rules for the experimental synthetic steps in the literature with those suggested by LeadOp+R.
Figure 9B shows the LeadOp+R suggested synthetic routes to generate compound rB1 using the selected preferred inhibitor-receptor interactions. Compound 24 was identified as the initial reactant by searching all the available building blocks and preserving the molecular fragment. LeadOp+R suggested product 26 by reacting 24 with 25 with the first reaction rule (i) "growing" the compound toward the preferred interaction with Asn425. The reaction rule suggested by LeadOp+R matches the synthetic steps in the literature that yields compounds 26, 24, and 25. Next, product 26 was considered as the reactant to interact with compound 28 to generate product compound 31 by extending the ligand toward preferred interactions with Leu414. The second reaction rule (ii) to generate compound 31, as suggested by LeadOp+R, matches the synthetic routes presented in the literature to synthesize the thioether bond in compound 30 through the reaction of 26 with 29. It should be indicated that in this step, the structure marked in red is compound 31 and it is the same as the partial structure denoted in red for the final product 30 (compound rB1) in the experimental synthesis. The recursive optimization continues via LeadOp+R toward the cavity near Ile406 and the synthesis of compound 30 (compound rB1) by reacting 31 with 27 and the third reaction rule (iii) in Figure 9C. The LeadOp+R suggested reaction route also matches the experimental synthetic step in the literature to synthesize compound 29 through the reaction of 27 with 28. To this end, LeadOp+R has successfully optimized the query compound rB to compound rB1 and suggested feasible synthetic routes. In this example, we demonstrated LeadOp+R's control of the synthetic flow by extending the molecules to exploit preferred interactions, available building blocks, and associated reactions rules to achieve fragment based optimization and synthetic accessibility. For these reasons, the sequence of steps to "grow" molecules may not be the same as the published experimental synthesis.
Figure 10A depicts the experimental reaction scheme (Schwarz et al., 2001) to synthesize compound rB2 (compound 38) by reacting 26 (which was generated through the reaction of 24 with 25) with 37 (which was synthesized through a series of reaction starting with compound 32 to formed 37). To compare LeadOp+R's suggested synthesis of compound rB2 to the experimental synthetic routes, we explored the key reaction rules of the experimental synthetic steps in the literature for the proposed compound.
Figure 10B shows the LeadOp+R selective and systematically suggested synthetic routes to generate compound rB2 based on the user specified preferred inhibitor-receptor interactions. Initially, compound 24 was identified as the first reactant by searching all building blocks with the preserved fragment. LeadOp+R then proceed to produce compound 26 by reacting 24 with 25 via the first reaction rule (i) that directs the suggested compound toward the preferred interaction with Leu414. The reaction rule suggested by LeadOp+R matches the synthetic steps in the literature for the synthesis of compound 26 from compound 24 and 25. Next, product 26 was considered as the reactant to react with compound 32 to generate product 39; again by growing the molecule toward the preferred interaction with Leu414. The second reaction rule (ii) to generate product 39 suggests the same synthetic steps as the literature to synthesize compound 38 by reacting 26 and 27. The recursive optimization continues to explore the potential ligand interactions with Leu414 and Ile406 to generate compound 38 (compound rB2) by reacting 39 with 35 via the third reaction rule (iii) to synthesize compound 36 by the reaction of 34 and 35, resulting in the final product compound rB2.
Figure 11A shows the experimental synthesis route (Schwarz et al., 2001) to synthesize compound rB3 (compound 43) by reacting 40 with 42 (which was generated through the reaction of 35 with 41). To compare the LeadOp+R suggested route to the experimental route for rB3, we look at the key reaction rules in the literature.
Figure 11B shows the LeadOp+R suggested synthetic routes for compound rB3 using the selected preferred inhibitor-receptor interactions. Initially, compound 24 was identified as the first reactant by searching all building blocks with the preserved fragment that is indicated in Figure 11B as the red structure. LeadOp+R proceeded to generate compound 26 by reacting 24 with 25 via the first reaction rule (i) suggested by LeadOp+R. Again, this methodology directs the growth of the new ligand toward the preferred interaction of the ligand interacting with Leu414. The synthetic reactions suggested by LeadOp+R match the synthetic steps presented in the literature that forms compound 26. Next, product 26 was considered the reactant and transformed into product 40 by growing the ligand toward Ile406 of 5-LOX. The second reaction rule (ii) generates compound 40 and matches the synthetic steps discussed in the literature; compound 40 is identified as the same product that is discussed in the literature to synthesize compound 44. Continuing the recursive optimization to initiate the ligand's interaction with Ile 406 and Tyr181 results in the third reaction rule (iii), Figure 11C, and leads to compound 43. Compound 44 was identified as the reactant and reacted with 35 based on the fourth reaction rule (iv), generating compound 42 by reacting 35 with 41.
LeadOp+R has successfully optimized the query compound rB into compounds rB1, rB2, and rB3 and has suggested corresponding synthetic route for each compound. Through systematic synthesis and evaluation of intermediates using group efficiency, LeadOp+R searches for "products" with higher calculated binding affinities and improved interactions with the receptor. The more hydrogen-bonding interactions between compound rB1's oxygen or nitrogen atoms of the thiazol group and the receptor (shown in Figure 8B) corresponds to the experimental results of stronger inhibitor potency then the proposed compounds rB2 and rB3. In the example of 5-LOX inhibitor design, we demonstrate LeadOp+R's ability to controls the synthetic flow by extending the ligands with preferred interactions, available building blocks, and associated reactions rules.
LeadOp+R is an optimization algorithm that starts with a query reactant (compound) and better lead optimization occurs when starting the optimization process with a good binder that is advantageously positioned in the binding site. The LeadOp+R algorithm does not consider experimental product yield rate, reaction rate, and reaction conditions of a chemical synthesis but does propose potential synthetic routes purely based on the chemical reaction rules contained in the chemical reaction database. However, incompatibility of the reaction with specific substituents in the core may happen, the proposed synthetic routes are meant to provide a fast, systematic, and preliminary suggestion based on general reaction—synthesis—rules and structure-based (receptor) ligand optimization. The diversity of the reactant database is a critical factor when searching for the participant reactants along with the number of different poses sampled at each reaction site. The greater the number and diversity of reaction rules and reactant available for LeadOp+R to explore—for the system of interest—the greater the possibility to identify and generate new compounds.
In this work, we have implemented a structure-based lead optimization with synthetic accessibility algorithm called "LeadOp+R." Two model systems, Tie-2 kinase and human 5-LOX enzyme with their associated inhibitors, were selected to demonstrate the abilities of the LeadOp+R algorithm. We demonstrated how a query molecule was enhanced through structured-based optimization and a potential synthetic route was proposed based on reaction rules extracted from in-house synthetic database. In the case of Tie-2, co-crystalized structure is available, while the human 5-LOX example was performed using a theoretical 5-LOX receptor model (comparative or homology protein model) and a known inhibitor. LeadOp+R generates a set of potential compounds that exhibit better-calculated inhibition, possess better efficiency score(s), along with providing synthesis routes based on published reaction mechanisms (contained in an in-house reaction and reactants database). The molecular dynamic simulation analysis further demonstrates that the generated structures preserve the important ligand-protein interactions as seen in the crystal structures or reported in the literature. For the proposed compounds with biological inhibition values (IC50) obtained from the literature, LeadOp+R calculated inhibition values corresponding (based on rankings) to the literature values. The interactions between the inhibitor and protein, as noted in the literature, were observed in the entire molecular dynamic simulation. Moreover, we identified fragments that created and retained ligand-receptor interactions that were stronger and more consistent than the original query compound; these fragments were selected based on reaction rules and discovered in our reactant database.
In short, LeadOp+R is an algorithm that can automatically optimize a query molecule based on reaction routes by searching and selecting reactants that can undergo chemical synthesis thus generating compounds with better binding affinity for the biological system (receptor) of interest. Additionally, users can indicate specific parts of the query compound to be optimized and assign the predicted binding space (portion of the binding site) for the generated products based on known ligand-receptor interactions or preference. LeadOp+R is an algorithm that cannot only optimize the lead compounds but also design favorable and practical synthetic routes based on known reaction mechanisms, leading to faster data feedback between experimental and computer-aided molecular design.
YT: Initiated the concepts; F-YL and YT: Drafted, programmed, and performed the analysis on the projects; EE: Edited and gave comments on the work.
EE was employed by exeResearch, LLC and The Chem21 Group, Inc.
The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This work was funded by the Taiwan National Science Council, grants number 98-2323-B-002-011-, 106-2622-B-002-008-, 105-3011-F-002-010-, 106-2911-I-002-533, 106-2321-B-002-041-, and 106-3114-B-038-001-. Resources of the Laboratory of Computational Molecular Design and Metabolomics and the Department of Computer Science and Information Engineering of National Taiwan University were used in performing these studies.
1. ^"Sigma-Aldrich Chemie GmbH." (Steinheim, Germany).
2. ^"JChem." 5.4.1.1 ed. (Budapest, Hungary: ChemAxon Ltd).
3. ^"Marvin." 5.4.0.1 ed., (Budapest, Hungary: ChemAxon Ltd).
Alex, A. A., and Flocco, M. M. (2007). Fragment-based drug discovery: what has it achieved so far? Curr. Top. Med. Chem. 7, 1544–1567. doi: 10.2174/156802607782341082
Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., and Hermans, J. (1981). "Interaction models for water in relation to protein hydration," in Intermolecular Forces. The Jerusalem Symposia on Quantum Chemistry and Biochemistry, Vol. 14, ed B. Pullman (Dordrecht: Springer), 331-342. doi: 10.1007/978-94-015-7658-1_21.
Bridges, A. J. (2001). Chemical inhibitors of protein kinases. Chem. Rev. 101, 2541–2572. doi: 10.1021/cr000250y
Charlier, C., Hénichart, J. P., Durant, F., and Wouters, J. (2006). Structural insights into human 5-lipoxygenase inhibition: combined ligand-based and target-based approach. J. Med. Chem. 49, 186–195. doi: 10.1021/jm050870x
Ciulli, A., Williams, G., Smith, A. G., Blundell, T. L., and Abell, C. (2006). Probing hot spots at protein-ligand binding sites: a fragment-based approach using biophysical methods. J. Med. Chem. 49, 4992–5000. doi: 10.1021/jm060490r
Congreve, M., Chessari, G., Tisi, D., and Woodhead, A. J. (2008). Recent developments in fragment-based drug discovery. J. Med. Chem. 51, 3661–3680. doi: 10.1021/jm8000373
Corey, E. J., Howe, W. J., Orf, H. W., Pensak, D. A., and Petersson, G. J. (1975). General methods of synthetic analysis. Strategic bond disconnections for bridged polycyclic structures. J. Am. Chem. Soc. 97, 6116–6124.
Corey, E. J., and Jorgensen, W. L. (1976). Computer-assisted synthetic analysis. Synthetic strategies based on appendages and the use of reconnective transforms. J. Am. Chem. Soc. 98, 189–203.
Ducharme, Y., Blouin, M., Brideau, C., Châteauneuf, A., Gareau, Y., Grimm, E. L., et al. (2010). The discovery of setileuton, a potent and selective 5-lipoxygenase inhibitor. ACS Med. Chem. Lett. 1, 170–174. doi: 10.1021/ml100029k
Hammarberg, T., Zhang, Y. Y., Lind, B., Radmark, O., and Samuelsson, B. (1995). Mutations at the C-terminal isoleucine and other potential iron ligands of 5-lipoxygenase. Eur. J. Biochem. 230, 401–407. doi: 10.1111/j.1432-1033.1995.0401h.x
Hasegawa, M., Nishigaki, N., Washio, Y., Kano, K., Harris, P. A., Sato, H., et al. (2007). Discovery of novel benzimidazoles as potent inhibitors of TIE-2 and VEGFR-2 tyrosine kinase receptors. J. Med. Chem. 50, 4453–4470. doi: 10.1021/jm0611051
Hendrickson, J. B., Grier, D. L., and Toczko, A. G. (1985). A logic-based program for synthesis design. J. Am. Chem. Soc. 107, 5228–5238. doi: 10.1021/ja00304a033
Hendrickson, J. B., and Toczko, A. G. (1988). Synthesis design logic and the SYNGEN (synthesis generation) program. Pure Appl. Chem. 60, 1563–1572. doi: 10.1351/pac198860111563
Hess, B., Kutzner, C., van der Spoel, D., and Lindahl, E. (2008). GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 4, 435–447. doi: 10.1021/ct700301q
Hodous, B. L., Geuns-Meyer, S. D., Hughes, P. E., Albrecht, B. K., Bellon, S., Bready, J., et al. (2007). Evolution of a highly selective and potent 2-(pyridin-2-yl)-1,3,5-triazine Tie-2 kinase inhibitor. J. Med. Chem. 50, 611–626. doi: 10.1021/jm061107l
Hopkins, A. L., Groom, C. R., and Alex, A. (2004). Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9, 430–431. doi: 10.1016/S1359-6446(04)03069-7
Huang, H., Bhat, A., Woodnutt, G., and Lappe, R. (2010). Targeting the ANGPT-TIE2 pathway in malignancy. Nat. Rev. Cancer 10, 575–585. doi: 10.1038/nrc2894
Ihlenfeldt, W.-D., and Gasteiger, J. (1996). Computer-assisted planning of organic syntheses: the second generation of programs. Angew. Chem. Int. Ed. 34, 2613–2615.
Imre, G., Kalszi, A., Jkli, I., and Farkas, Ö. (2006). "Advanced automatic generation of 3d molecular structures," in 1st European Chemistry Congress (Budapest).
Ivanov, I., Heydeck, D., Hofheinz, K., Roffeis, J., O'Donnell, V. B., Kuhn, H., et al. (2010). Molecular enzymology of lipoxygenases. Arch. Biochem. Biophys. 503, 161–174. doi: 10.1016/j.abb.2010.08.016
Law, J., Zsoldos, Z., Simon, A., Reid, D., Liu, Y., Khew, S. Y., et al. (2009). Route Designer: a retrosynthetic analysis tool utilizing automated retrosynthetic rule generation. J. Chem. Inf. Model. 49, 593–602. doi: 10.1021/ci800228y
Lin, F. Y., and Tseng, Y. J. (2011). Structure-based fragment hopping for lead optimization using predocked fragment database. J. Chem. Inf. Model. 51, 1703–1715. doi: 10.1021/ci200136j
Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3–26. doi: 10.1016/S0169-409X(00)00129-0
Mata, P., Gillet, V. J., Johnson, A. P., Lampreia, J., Myatt, G. J., Sike, S., et al. (1995). Sprout: 3d structure generation using templates. J. Chem. Inf. Model. 15, 479–493. doi: 10.1021/ci00025a016
Oliner, J., Min, H., Leal, J., Yu, D., Rao, S., You, E., et al. (2004). Suppression of angiogenesis and tumor growth by selective inhibition of angiopoietin-2. Cancer Cell 6, 507–516. doi: 10.1016/j.ccr.2004.09.030
Oostenbrink, C., Soares, T. A., van der Vegt, N. F., and van Gunsteren, W. F. (2005). Validation of the 53A6 GROMOS force field. Eur. Biophys. J. 34, 273–284. doi: 10.1007/s00249-004-0448-6
Orita, M., Ohno, K., and Niimi, T. (2009). Two "golden ratio" indices in fragment-based drug discovery. Drug Discov. Today 14, 321–328. doi: 10.1016/j.drudis.2008.10.006
Saxty, G., Woodhead, S. J., Berdini, V., Davies, T. G., Verdonk, M. L., Wyatt, P. G., et al. (2007). Identification of inhibitors of protein kinase B using fragment-based lead discovery. J. Med. Chem. 50, 2293–2296. doi: 10.1021/jm070091b
Schwarz, K., Walther, M., Anton, M., Gerth, C., Feussner, I., and Kuhn, H. (2001). Structural basis for lipoxygenase specificity : conversion of the human leukocyte 5-lipoxygenase to a 15-lipoxygenating enzyme species by site-directed mutagenesis. J. Biol. Chem. 276, 773–779. doi: 10.1074/jbc.M005114200
Shaffer, D. N., and Mansmann, P. T. (1997). Leukotriene inhibition and advances in the treatment of asthma: a pharmacological review. Pediatr. Allergy Immunol. 11, 171–179. doi: 10.1089/pai.1997.11.171
Socorro, I. M., and Goodman, J. M. (2006). The ROBIA program for predicting organic reactivity. J. Chem. Inf. Model. 46, 606–614. doi: 10.1021/ci050379e
Steele, V. E., Holmes, C. A., Hawk, E. T., Kopelovich, L., Lubet, R. A., Crowell, J. A., et al. (1999). Lipoxygenase inhibitors as potential cancer chemopreventives. Cancer Epidem. Biomar. 8, 467–483.
Trott, O., and Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461. doi: 10.1002/jcc.21334
Woods, J. W., Evans, J. F., Ethier, D., Scott, S., Vickers, P. J., Hearn, L., et al. (1993). 5-lipoxygenase and 5-lipoxygenase-activating protein are localized in the nuclear envelope of activated human leukocytes. J. Exp. Med. 178, 1935–1946. doi: 10.1084/jem.178.6.1935
Yu, Q. (2005). The dynamic roles of angiopoietins in tumor angiogenesis. Future Oncol. 1, 475–484. doi: 10.2217/14796694.1.4.475
Keywords: fragment-based, lead optimization, structure-based drug design, computer-assisted synthesis, human 5-lipoxygenase, tie-2 kinase
Citation: Lin F-Y, Esposito EX and Tseng YJ (2018) LeadOp+R: Structure-Based Lead Optimization With Synthetic Accessibility. Front. Pharmacol. 9:96. doi: 10.3389/fphar.2018.00096
Received: 28 September 2017; Accepted: 29 January 2018;
Leonardo G. Ferreira, University of São Paulo, Brazil
Alain Couvineau, Institut National de la Santé et de la Recherche Médicale (INSERM), France
Yingxia Li, Fudan University, China
Copyright © 2018 Lin, Esposito and Tseng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yufeng J. Tseng, yjtseng@csie.ntu.edu.tw
This article is part of the Research Topic
Chemoinformatics Approaches to Structure- and Ligand-Based Drug Design


text:
Paskenta Elections To Settle Dispute
Article Excerpts:
http://www.modbee.com Warring factions in Corning casino tribe agree to election to settle dispute
BY STEPHEN MAGAGNINI
After weeks of armed standoffs, cyber attacks, allegations of multimillion-dollar theft and legal battles over who controls the tribe's $100 million-a-year casino, warring factions of the Paskenta band of Nomlaki Indians in Tehama County have agreed to a binding general election to settle their dispute.
A federal judge in Sacramento signed off Monday on the landmark agreement, which came after a marathon three-day mediation that included 12 consecutive hours of negotiations at one stretch, said Andy Freeman, who is recognized as tribal chair by both factions. "The mediation went well. It gave us the opportunity to talk to each other and see both sides," Freeman said. "The bottom line is the whole tribe needs to talk."
The dispute centered on allegations by Freeman and other council members that Tribal Economic Development Director John Crosby, a former FBI agent, misappropriated $1.4 million from tribal accounts to pay for his house, swimming pool, basketball court and remodeling work, along with several cars and $209,000 worth of gold. Tribal Treasurer Leslie Lohse, one of the ousted council members, was accused of embezzling money and misusing the tribe's jet to attend her son Kyle's professional baseball games.
Freeman produced canceled checks to support his allegations of wrongdoing. Crosby and Lohse, in turn, supplied employment contracts indicating they were each granted $5 million lines of credit "to either be paid forgiven or extended" after 2020 if they negotiated a new gambling compact with the state. Both said they've acted in the tribe's best interests.
Read Full Article Here -->http://www.modbee.com Warring factions in Corning casino tribe agree to election to settle dispute
Leslie Lohse is Chair of the California Tribal Business Alliance (CTBA). Robert Smith is CTBA's Treasurer. Pala is one of the Three Tribes that make up the California Tribal Business Alliance along with Paskenta and Viejas.
Labels: Andy Freeman, John Crosby, Leslie Lohse, Paskenta, Paskenta Band of Nomlaki Indians, Rolling Hills Casino


text:
The Time Dubai Airport Found a $150,000 Diamond Ring in the Trash and Returned It
Michael Y. Park
It's not unusual to find loose change or even a lost cell phone in the bathroom on a plane after a flight, but it's hard to top a $150,000 diamond ring in the trash.
That's exactly what the police at Dubai Airport say they found in one of the airport's most memorable lost-and-found cases, and the story even gets a happy ending, because the woman who lost the ring got it back.
The unlucky (or incredibly lucky) passenger had come off an Emirates plane from Bangkok (BKK) and was due to catch a flight to Nice (NCE) on March 20, 2014, when she realized her ring was missing. As a business-class passengers, one of the amenities she received was a small pouch. She decided to put the ring in it temporarily, but accidentally left it behind when she disembarked.
"This kind of pouch usually isn't handed to the lost-and-found office because it belongs to Emirates," an airport police spokesman said in an email. "The cleaner on the plane disposed of the pouch in the garbage, thinking it was of no use anymore."
By the time the passenger realized what she'd left in the discarded pouch, the ring was already in a trash bin somewhere.
"She had lost hope and didn't think she would ever have her ring back," police said.
But as soon as she filed a claim with lost and found, they contacted the cleaning department and police. Meanwhile, the passenger had to leave for her connecting flight to France immediately.
"Dubai police staff went to the plane, then to the garbage area, and started searching with the help of the cleaners and their supervisor," the police spokesman said.
They found the pouch and the ring and ran over to the police checkpoint nearest the gate with the flight to Nice. They stopped the passenger and gave her back her ring before she caught her plane to Europe.
"She was very happy and amazed," the spokesman said.
Dubai airport has returned other memorable items to their owners through the years, he added.
"Due to the huge number of passengers traveling through Dubai Airport, people often lose their belongings, varying from expensive items to cheap ones to those that carry sentimental value," he said.
Other pricey finds reunited with owners include: 10 ounces of gold, 334,000 UAE dirhams in cash (about $91,000), and an even more expensive bespoke diamond ring by celebrity jeweler Lorraine Schwartz worth $500,000.
Featured image (of Meghan Markle's wedding ring) by Chris Jackson/Getty Images.
Michael Y. Park is a senior contributing editor who's written for The New York Times, Reuters, Bon Appetit and others. For TPG, he's written about Air Force One, immigration and security, airline food and more.


This popular spot in Eugene overlooks one of the most serene stretches of the Willamette River. Tiered indoor seating and an outdoor deck provide a panoramic view. Enjoy McMenamins' handcrafted ales, wines and spirits along with classic Northwest pub fare.


Invensys is to sell its Lambda power supply business to TDK of Japan for £134 million. The proceeds from the sale will be use to reducing the liabilities of the Invensys Group.
The Lambda division consists of Lambda <?xml:namespace prefix = st1 ns = "urn:schemas-microsoft-com:office:smarttags" />USA, Lambda Europe, Densei-Lambda and and conducts business in 13 countries around the world. The division has bases in the US, Canada, the UK, Germany, France, Italy and Sweden. Densei-Lambda operates in Japan, Israel, China, Malaysia, Singapore and Thailand. In the year ending March 31 2005, the division had sales of £229 million and operating profit of £11 million.
The deal is subject to customary regulatory approvals and is expected to be completed on or before 30 September 2005.
"This disposal is a further step in the Group's previously stated plans for the divestiture of non-core businesses," said Ulf Henriksson, Chief Executive Designate of Invensys.
Last month, Invensys signed an agreement to sell the majority of its Advanced Building Systems operations in Europe and the Middle East to Schneider Electric of France for £81 million.