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Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets
Kevin Drew ,
* E-mail: kdrew@utexas.edu (KD); cmueller@simonsfoundation.org (CLM); marcotte@icmb.utexas.edu (EMM)
Affiliation Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, United States of America
Christian L. Müller ,
Affiliation Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, United States of America
Richard Bonneau,
Affiliations Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, United States of America, New York University Center for Genomics and Systems Biology, New York University, New York, NY, United States of America
Edward M. Marcotte
http://orcid.org/0000-0001-8808-180X
Kevin Drew,
Christian L. Müller,
Kevin Drew Christian L. Müller Richard Bonneau Edward M. Marcotte
Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method's value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available.
Proteins physically associate into complexes in order to carry out the essential functions of life. Knowing how proteins are physically arranged three dimensionally in these complexes provides clues towards how they work. In principle, the associations between proteins in large-scale proteomics datasets should often reflect direct physical contacts between proteins in each complex. Here, we describe a statistical method to discover which subunits within complexes directly contact each other based on their co-purification behavior in published co-fractionation mass spectrometry datasets. Within our predictions, we recover many known protein-protein contacts, serving to validate our method, as well as unknown contacts that can inform future studies of these complexes. Specifically, we observe confident contacts between subunits within the exocyst and tRNA multi-synthetase complexes, two complexes that have incomplete structural information. Using our method, we further provide structural information for a previously missing subunit of the EKC/KEOPS complex. We anticipate that this method and the associated predictions will help to better inform our understanding of the functions and structures of diverse protein complexes.
Citation: Drew K, Müller CL, Bonneau R, Marcotte EM (2017) Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets. PLoS Comput Biol 13(10): e1005625. https://doi.org/10.1371/journal.pcbi.1005625
Editor: Trey Ideker, University of California, San Diego, UNITED STATES
Received: March 3, 2017; Accepted: June 6, 2017; Published: October 12, 2017
Copyright: © 2017 Drew et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files or hosted on our github repository: https://github.com/marcottelab/direct_contact.
Funding: This work was supported by grants from the National Institutes of Health (https://www.nih.gov) (F32 GM112495 to KD; DP1 GM106408, R01 GM076536, R01DK110520, R01 HD085901, R35 GM122480 to EMM), Army Research Office (http://www.aro.army.mil) (W911NF-12-1-0390 to EMM), Cancer Prevention and Research Institute of Texas (http://www.cprit.state.tx.us) (to EMM), the Simons Foundation (https://www.simonsfoundation.org) (Flatiron Institute, Center for Computational Biology, to CLM and RB), and the Welch foundation (http://www.welch1.org) (F-1515, to EMM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This is a PLOS Computational Biology Methods paper.
Many proteins assemble into large macromolecular complexes with essential cellular functions. The three dimensional arrangement of proteins in a complex is vital to the complex's function and knowledge of this arrangement would be highly valuable in understanding the mechanism of function. Conserved protein complexes are estimated to number in the thousands but the vast majority of these are structurally elusive by traditional structural biology techniques. Advances in proteomics technologies have allowed for the high throughput identification of protein complexes across the tree of life including large-scale affinity purification mass spectrometry (AP-MS) datasets [1–3] as well as high-throughput co-fractionation mass spectrometry (CF-MS) datasets comprising thousands of experiments across human, metazoan and prokaryotes [4–7].
In the CF-MS approach, cellular lysate is biochemically fractionated by multiple, non-denaturing chromatographic methods and then complexes are inferred bioinformatically in a machine-learning framework using correlations of the resulting protein elution profiles as a prominent feature. Although this approach has primarily been used to identify component subunits of complexes, we previously observed that the correlation structure of the protein elution profiles also revealed structural information about the complexes [6]. This allowed for the identification of sub-complexes, which were accurate when compared to known structural models and when compared to known functions. However, correlation did not consistently reveal the directly bound protein pairs that other experiments such as yeast two-hybrid [8, 9] and chemical crosslinking [10–14] can reveal across large portions of the proteome. Other computational approaches have been proposed to identify direct contacts by analyzing co-occurrence of proteins in mass spectrometry experiments but they have only been applied to AP-MS datasets [15].
Protein sub-complexes are valuable in understanding the three dimensional arrangement of proteins in a complex but correlation often convolutes specific physical interactions between proteins with indirect interactions and non-physical relationships. Removal of these spurious interactions from the correlation network is crucial to identifying which specific proteins directly contact each other. A classical statistical approach to remove such interactions can be achieved with graphical models [16]. Graphical models represent the conditional dependence structure of a set of random variables as a graph. Unfortunately, classical statistical methods to estimate graphical models fail in scenarios where the number of variables (e.g., proteins) greatly exceeds the number of samples, such as the case with co-fractionation profiles. However, recent advances in the field of statistical analysis, specifically on the topic of sparse high-dimensional statistical inference, have led to new methods for addressing these underdetermined problems (see, e.g. [17] and references therein). In biology, these methods enabled a number of successful applications of graphical modeling, such as estimating interactions between genes from high-throughput expression profiles [18], predicting contacts between amino acid residues from multiple sequence alignments [19], and inferring associations of microbes from environmental sequencing data [20], respectively.
Here, we apply a graphical model to identify direct protein interactions (Fig 1) from one of the largest proteomic interaction datasets to date consisting of approx. 3,000 published human CF-MS experiments [6]. We make the assumption that conditional dependence is a proxy for direct protein interactions, which is consistent with the biochemical chromatography methods used in CF-MS experiments due to their separation of native complexes and sub-complexes. We evaluated the performance of our predictions in a precision-recall framework on a benchmark of large protein complexes with known molecular structures and observe substantial improvement over correlation alone. We also observe that the ranking of the learned conditional dependencies is insensitive to particular choices of the regularization parameter λ which balances model complexity and model fit. We additionally characterize our method's performance finding better predictions for well-observed complexes and validate our predictions with a whole cell lysate crosslinking dataset where we observe enriched overlap. We therefore believe, in principle, these measures of conditional dependence could also be applied to additional proteomic datasets such as AP-MS as well as used in conjunction with other features of direct protein-protein contacts in supervised machine learning frameworks to further improve predictive performance.
Fig 1. Overview of direct contact prediction between protein complex subunits.
Co-fractionation / mass spectrometry (CF-MS) aims to repeatedly separate mixtures of native protein complexes (True Network) by non-denaturing chromatography. Protein elution profiles are generated by mass spectrometry identification of proteins across all chromatography fractions collected. Correlation between proteins' elution profiles (left side) performs well for identifying the subunit composition of complexes [4, 6, 7], but suffers from indirect associations among proteins that inhibit its ability to identify directly contacting subunits within each complex. We predict direct contacts (right side) by effectively inverting the correlation matrix to discriminate between conditionally dependent and conditionally independent associations, which correspond to direct and indirect protein interactions respectively. Specifically, we incorporate pseudo-counts, scale and transform the correlation matrix, use a sparse graphical model learning framework to compute conditionally dependent partial correlations, followed by StARS stability analysis [29] to re-score the resulting conditional dependency matrix such that each entry corresponds to the frequency with which it is supported by subsample trials. We retain non-zero scores between subunits within each pre-defined human protein complex [32] as our prediction of direct contacts.
We highlight predictions made for the 26S proteasome complex and demonstrate agreement with the true set of contacts. We show new predictions for complexes without known structures, specifically the exocyst and tRNA multi-synthetase complex, to illustrate the utility of our approach. Finally, in our predicted set of directly contacting proteins we show support for direct contact of a recently identified component of the human EKC/KEOPS complex. Our results suggest that our predicted direct protein interaction edges will be a valuable constraint that can be used in structurally modeling the thousands of stable protein complexes in the human proteome inaccessible to current structure determination techniques, as we demonstrate with an improved 3D model of the EKC/KEOPS complex.
Calculation of conditionally dependent protein-protein interactions
In order to identify direct physical interactions between proteins, we first organized a large, published dataset of human CF-MS experiments [6]. CF-MS experiments consist of two steps, the first being to biochemically separate native protein complexes and sub-complexes along a specified gradient (e.g., hydrodynamic radius, charge, etc.) using non-denaturing separation techniques that preserve intact complexes. The second step is to identify and quantify the proteins that elute at each time point, providing a characteristic elution profile for each protein observed. The aim of our approach is to use these elution profiles to reconstruct the physical interaction network of the proteins identified, and specifically find which proteins directly contact each other within complexes.
The dataset comprises d = 15,964 protein elution profiles each consisting of a vector of n = 2,989 protein abundance values. Each protein abundance value is derived from 28 fractionation experiments using multiple, distinct biochemical separation techniques, including ion exchange chromatography, isoelectric focusing and sucrose gradients, analyzing native protein extracts isolated from HeLa cells (17 experiments), HEK293 cells (8), glioma stem cells (2) and neural stem cells (1). Fractionation experiments consist of a series of collected fractions along a biochemical gradient of the applied chromatography method. The number of fractions ranges between ~10 to ~200 per experiment depending on the method. Each fraction of extract is then subjected to proteomic analysis using mass spectrometry producing observed protein abundances. We use the pipeline described in Wan et al. 2015 [6], where the proteomic consensus identification tool, MSBlender [21] is used to identify proteins from mass spectra. For peptide identifications, we use a false discovery rate of < 1%. Missing values that arise when a protein is not identified in a given fraction are set to 0.0. This diverse set of experimental conditions allows for the analysis of a large fraction of the proteome and thorough separation of endogenous complexes. We denote the resulting CF-MS data matrix by . Each column Xi, i = 1,…,n represents relative protein abundance data (compositions) and is normalized to sum up to 1.
We next introduce a sparse graphical model learning framework to infer direct (physical) protein interactions from CF-MS data from the covariation pattern of the protein abundances. Here, the nodes of the graph represent proteins and the edges approximate direct protein contacts. We first note that components of the compositions Xi are not independent due to the unit sum constraint. Thus, higher order statistics, such as covariance matrices of compositional data exhibit negative bias due to closure. To alleviate this shortcoming we borrow a transformation technique from compositional data analysis [22], the so-called centered log-ratio (CLR) transformation. The CLR transformation is defined as , where g(Xi) denotes the geometric mean. This transformation is particularly useful, as it is symmetric and isometric with respect to the original composition. The CLR maps compositional data from the d-dimensional simplex to a (d − 1)-hyperplane in d-dimensional Euclidean space. A pseudo-count of 1 is added to all entries in X to ensure applicability of the transformation. We denote the corresponding covariance matrix by Γ = cov(CLR(X)).
Recent work [23] has shown that, in the sparse high-dimensional setting and under certain technical conditions, the covariance matrix Γ is a good estimator for the covariance matrix Σ ∈ ℝd×d of the unknown absolute abundances. This observation is the basis for the proposed graphical model inference framework. Following [20, 24], we propose to learn a sparse undirected graph representing node-node interactions via the following minimization problem: for all j = 1,…,d where tr denotes the trace operator, ‖∙‖1 denotes the element-wise L1 norm, and λ > 0 is the regularization parameter. Each of the d subproblems is equivalent to fitting a linear regression model with L1 penalization (Lasso) [25] to each protein profile, using the other profiles as predictors. To relax any distributional dependencies of the regression, we also apply a non-paranormal (copula) transform to the data before the linear regression step [12]. To symmetrize the graph, derived from the described node-wise regression (or neighborhood selection) algorithm, the OR rule is applied across all node neighborhoods, i.e., an edge in the protein-protein graph is present if either node i is associated with node j or vice versa. It has been shown in [24] that, under certain conditions, the non-zero entries of this symmetrized adjacency matrix are asymptotically identical to the non-zero elements Θij of the inverse covariance (or precision) matrix Θ = Σ−1. This allows a clear statistical interpretation of the edges in terms of partial correlation coefficients among the nodes [26]. Thus, the procedure is able to remove transitive correlations among nodes by approximately learning the full conditional dependence among all nodes.
Model selection and interaction ranking
One of the key challenges in learning a sparse graphical model from data is the selection of the regularization parameter λ > 0. In the unsupervised setting, several methods have been proposed, including cross validation and information criteria [27, 28]. One state-of-the-art model selection scheme is the Stability Approach to Regularization Selection (StARS) [29]. StARS selects the minimum amount of regularization that results in a graph that is sparse and comprises a stable edge set under random subsampling of the data at a prescribed stability level 1 − β [30, 31]. StARS typically selects N = 20 sub-samples of size and learns a graphical model from each subsample across the entire λ-path (here, 30 values of λ are chosen between 0 and λmax). StARS records for each edge in the empirical frequency of edge presence Pij across the entire λ-path, stored in a list of matrices P(λ) ∈ [0,1]d×d. Standard StARS selects λ where the normalized sum of variances of the Pij in the corresponding P(λ) drops below β = 0.1. It has been shown in [31] that this selection can lead to sub-optimal regularization selection. In the present application, we thus opted for an alternative semi-supervised selection procedure. For all positive edges in the interaction graph, we interpreted the edge frequencies as (protein) contact probabilities and ranked edges in order of decreasing contact probability. We compared these ranked predictions to a benchmark of physically interacting proteins determined from multi-protein complexes with known three-dimensional structures and selected the λ that maximized the area under the precision recall (AUPR) curve. The selected λ corresponds to a more conservative StARS variability threshold of β = 0.005. We also note that, in our application, our introduced edge ranking based on the edge stability was insensitive to the precise selection of λ.
Finally, we filtered our reported direct contact predictions by protein interactions that are present in 896 complexes larger than 4 subunits from the human protein complex map, hu.MAP [32]. This step was to ensure pairs of proteins are present in the same complex thereby increasing the likelihood of direct contact.
All computation was performed in R using the Hotelling package [33] for CLR transformation and the Huge [34] package for graphical modeling.
For comparison purposes, correlation analysis was applied to each pair of protein co-elution profiles in the human CF-MS dataset. Profiles were first normalized by the total number of theoretical tryptic peptides for each protein and then a z-score was calculated for each value in the matrix relative to its corresponding fraction (i.e., column-wise standardization). Pearson correlation coefficients were then calculated for each pair of proteins.
Assembly of a multi-protein complex structural benchmark
In order to evaluate the predictive performance of our direct contact prediction method we assembled a benchmark of 29 large non-redundant protein complexes with known structure (S1 Table). Due to the ease at which direct contacts can be predicted at random for small complexes, we restrict our benchmark to complexes having > 4 unique subunits. Note, subunits from certain complexes may not be sampled in our data or have ambiguous ortholog mapping. We process the reported biological assembly of each complex using the PISA tool [35], which calculates macromolecular interface surface area. All pairs of proteins within each complex with interfacial areas (Å2) > 0.0 were considered physically contacting and marked a true contact, comprising benchmark positive examples. Protein pairs with no interface area were considered not contacting, comprising benchmark negative examples. Note that protein pairs that spanned two complexes (e.g., protein 1 in complex 1 and protein 2 in complex 2) were not considered. For complexes whose structure was determined in an organism other than human, InParanoid [36] was used to identify human orthologs of the structurally solved subunit. If no human ortholog could be found for a given subunit, interactions involving that subunit were not considered. We split the benchmark into two sets, the first (10 complexes) to evaluate λ selection and performance and the second (19 complexes) to evaluate generality of the method. The complete protein pair benchmark is provided in S2 Table.
Overlap enrichment of inter-protein crosslinking dataset
We evaluated the overlap of our direct contact predictions with a set of identified inter-protein crosslink interactions from Liu et al. [10]. Similar to the method described in [32] we collapsed all crosslink interactions to one interaction per pair of proteins. We first generate a random overlap distribution by selecting random pairs of proteins from the crosslinking dataset and calculate the overlap with the direct contact predictions for 1000 repeated trials. We then calculate a z-score for the overlap of the direct contact predictions and the reported crosslinking interactions with regards to random distribution. We repeat the process for determining the enrichment of complexes from hu.MAP and the crosslinking interactions.
Structural modeling of the EKC/KEOPS complex
To construct a structural model of the human EKC/KEOPS complex, we built structural models of human EKC/KEOPS proteins based on available template structures in the Protein Data Bank (PDB) [37] and then aligned those models with existing co-complex structures. Specifically, we used HHPred [38] to build alignments of the query protein and PDB sequences and then used MODELLER [39] to build homology models. Homology models of human proteins were then structurally aligned to the homologous structures in yeast and archeal crystal structures [40–42] using DaliLite [43].
Discovery of conditionally dependent protein interactions
Fig 1 shows a workflow of our direct contact prediction framework. Native complexes represented by the true physical interaction network are biochemically fractionated and their proteins identified using mass spectrometry. In order to find pairwise relationships between proteins in a given CF-MS dataset, prior work has relied on correlation analysis, which effectively reconstructs the subunit composition of complexes (especially when used as features in a supervised machine learning framework, a case we do not consider here), but only partially indicates the direct binding relationships among those subunits [4, 6].
More specifically, using correlation to identify pairwise relationships results in a large fraction of indirect interactions. For example, consider proteins A, B and C, where A directly binds B, B directly binds C, but A does not directly bind C. In this scenario, a network based on correlation would produce a spurious edge between proteins A and C due to the indirect relationship mediated by protein B. To address this issue, the inverse covariance matrix can be calculated, which represents a network of undirected edges between conditionally dependent nodes. With respect to CF-MS data, the nodes represent proteins and the conditional dependence edges represent direct physical contacts.
The construction of this network has many theoretical solutions due to the limited number of samples and vast number of possible interactions, but methods are available to infer the inverse covariance matrix when the resulting network is expected to be sparse. Sparsity is a safe assumption with respect to protein interactions, as estimates of the total number of expected human protein-protein interactions range between 150k – 650k, orders of magnitude less than the roughly 200–300 million possible interactions [44–46].
As described in detail in the Methods, we analyzed a dataset of approx. 3,000 co-fractionation / mass spectrometry experiments [4, 6], and restrict direct contact predictions to known co-complex interactions. Specifically, we use complexes with structures in the PDB for evaluation and a set of 896 protein complexes larger than 4 unique subunits derived from >9000 published mass spectrometry proteomics experiments [1, 3, 4, 6] in hu.MAP [32], for all other predictions. In all, we identified 2,434 potential interactions (S3 Table).
Conditionally dependent interactions are more likely to represent directly contacting subunits within complexes
To evaluate whether our direct contact prediction method accurately identifies true interactions, we compared our predictions to a benchmark of physically interacting proteins determined from multi-protein complexes with known three-dimensional structures (S1 Table), as described in the Methods. Fig 2A plots the precision recall curve of our direct contact prediction method relative to the set of 10 complexes used to select λ. We observed high precision for the most confidently predicted contacts. This performance is in contrast to correlation analysis, also plotted in Fig 2A, which has limited accuracy for high correlation coefficients. Plotting precision-recall curves for the 29 alternative λ values considered during λ selection (Fig 2A, gray curves) confirmed that all predictions made with alternative λ values substantially outperformed correlation alone, demonstrating that this parameter was highly stable with regard to its selected value.
Fig 2. Scoring protein interactions by their conditional dependence accurately recovers direct protein-protein contacts within multi-subunit complexes.
A. We compared the value of the pairwise Pearson correlation coefficients between protein elution profiles (red curve) versus the derived conditionally dependent interactions (i.e., direct contact predictions) (black curve) for their ability to recapitulate true protein contacts in 10 complexes with known 3D structures. High-scoring conditionally dependent interactions were strongly enriched for true contacts, unlike the most highly correlated protein elution profiles. Additionally, we plot precision recall curves for predictions made with alternative λ choices (gray curves) and observe improved performance over correlation alone suggesting performance is robust to the selection of this parameter. The random line (dashed) represents the theoretical baseline for all true positives (TP) divided by the total number of possible subunit pairs (TP:335 / Total:1583) B. Evaluation of conditionally dependent interactions on an additional 19 non-redundant complexes showing consistent performance on a leave out set. Random = (TP:261 / Total:1575). C. Evaluation on combined 29 complexes used in A and B. Direct contact probability thresholds and correlation coefficient thresholds are marked in black and red text, respectively. Random = (TP:596 / Total:3158). D. Distributions of area under the precision recall curve (PR AUC) for the individual 29 complexes showing large variance across complexes but showing direct contacts outperforming correlation and random. Precision = TP/(TP+FP); recall = TP/(TP+FN).
We further evaluated our direct contact predictions on an additional 19 complexes with known structure (Fig 2B) and observe consistent behavior of our method in terms of precision recall. Interestingly, while correlation performs poorly relative to our method including all λ values on the first set of complexes, it performs substantially better on the second benchmark almost equal to our method. The precision recall curve of the combined benchmark with both direct contact probability and correlation threshold markers can be found in Fig 2C.
We next asked if the ability to predict direct contacts was consistent across all complexes or if certain complexes performed better than others. We therefore calculated the area under the precision recall curve (PR AUC) for each individual complex and plotted its distribution in Fig 2D. For our direct contact predictions, we observe a large variance of PR AUC suggesting our method performs well for certain complexes and is limited for others. We still find, however, direct contact predictions outperform correlation analysis and random predictions.
Direct contact predictions are made for well-observed complexes
To further understand what types of complexes for which our method is appropriate, we investigated how much of an impact the amount of experimental observation affected the degree to which high confident direct contact predictions were made. We first calculated the number of nonzero protein abundance measurements (i.e. count of fractions) for each observed protein and then computed the mean count for every complex in the structure benchmark. Fig 3A shows the distribution of the mean counts for complexes that had at least one prediction with a direct contact probability > = 0.5 and those complexes which did not. We observe a difference in the distributions suggesting that complexes that are well sampled in our dataset are more likely to have high confident predictions. It is important to note that several complexes in our benchmark are not well sampled and our method errs on the side of false negatives so as to limit making false predictions. We additionally plot all direct contact predictions in Fig 3B to better understand the relationship between the direct contact probability score and amount of experimental sampling. We see that pairs of proteins that have high confidence predictions are more likely to have been well sampled suggesting that repeated observations of the proteins across many experiments are important. This trend is likely due to our subsampling scoring procedure which is robust to spurious co-elutions from a single experiment.
Fig 3. The direct contact prediction method makes high confident predictions for well-sampled complexes.
A. Distribution of protein subunit sampling for complexes with a max direct contact probability > = 0.5 (blue) and complexes with a max probability < 0.5 (green). Sampling is measured for a complex by averaging the number of fractions for which each protein in the complex is observed (i.e. nonzero fractions). Our method performs better for well-sampled complexes and is limited for poorly sampled ones. B. Distribution of the mean number of nonzero fractions for pairs of proteins predicted by the direct contact method. Pairs of proteins with high probabilities are well sampled compared to those with lower probabilities.
Fig 4 shows the relationship between correlation and direct contact probability for four examples of complexes in our structural benchmark spanning a range of well observed to poorly observed. Two of the complexes, the proteasome and spliceosome have high confidence predictions made by our method, while the other two, mitochondrial ribosome and mitochondrial super-complex have high-ranking correlation analysis predictions but lack high ranking predictions by our method. The proteasome (pdbid: 4CR2) is well observed with an average nonzero fraction count of ~356. Fig 4A shows our method makes many high confident true positive contact predictions for the proteasome (i.e. top 9/10 are correct) while protein pairs with high correlation coefficient have more of a mix of true positive and false positives. The spliceosome (pdbid: 5MQF, Fig 4B) is moderately observed in the dataset with an average nonzero fraction count of ~182 and still shows good relative discrimination between true and false positive contacts (i.e. top 5/10 are correct). Most of the co-fractionation experiments were focused on identifying soluble cytosolic complexes and therefore membrane bound complexes as well as complexes in subcellular compartments have limited coverage. For example, two mitochondrial complexes, the mitochondrial ribosome (pdbid: 4CE4, Fig 4C) and the mitochondrial super-complex (pdbid: 2YBB, Fig 4D) are identified in a limited number of fractions, on average ~42 and ~105 nonzero fractions respectively. Our method makes very few direct contact predictions for both complexes while correlation has a wide distribution of coefficients, many receiving high scores. Interestingly, high correlation coefficients for the mitochondrial ribosome have a high false positive rate (i.e. top 10 are all false positives) while the mitochondrial super-complex performs better with 7 out of the top 10 pairs being true positives. The poor performance on the mitochondrial ribosome by correlation analysis contributes to the substantial dip in performance seen in the precision recall curves (Fig 2A). These examples further demonstrate the ability of the direct contact prediction method to balance true and false positives and to accurately report contacts when sufficient data is available. As more CF-MS experimental datasets are published, we anticipate an improvement in the coverage of moderately to lowly observed complexes.
Fig 4. Relationship between direct contact probability and correlation for four example complexes with known structure.
A. Direct contact predictions for the well-observed proteasome (pdbid: 4CR2) show good discrimination between true (blue circle) and false positives (red x) compared to that of correlation. B. Direct contact predictions for the moderately observed spliceosome complex (pdbid: 5MQF) shows good discrimination between true and false positives but with a limited number of total predictions. C. The direct contact method does not make high confident predictions for the mitochondrial ribosome (pdbid: 4CE4) due to its limited sampling while correlation makes many high ranking false positive predictions. D. Similar to C, the direct contact method does not make predictions for the mitochondrial super-complex (pdbid: 2YBB) due to its limited sampling while correlation makes several high confident true positive predictions.
Conditionally dependent interactions have high overlap with inter protein crosslinks
To assess our direct contact predictions on an independent dataset different from protein structures, we compared to a human cell lysate mass spectrometry crosslinking dataset [10]. The maximum Cα-Cα distance between cross-linked residues for the DSSO cross-linker reagent used is 23.4 Å, making an identified cross-linked subunit pair a reasonable proxy for directly contacting proteins. Since our direct contact predictions are limited to co-complex subunits, we first compare the crosslinking dataset to the set of complexes with which we restricted our predictions. Fig 5 shows that the overlap of complex edges and cross-linked subunits as well as the overlap of our conditionally dependent interactions and cross-linked subunits are both enriched compared to random pairs. Further, we see a much larger enrichment in our conditionally dependent interactions as opposed to complex edges demonstrating the direct contact predictions are highly enriched for physically close and contacting proteins pairs in human cell lysate.
Fig 5. Direct contact predictions have highly enriched overlap with HeLa lysate crosslinking interactions.
We report the enriched overlap of direct contact predictions and crosslinking interactions (z-score = 36, red triangle) relative to a distribution of random pairs of proteins in the crosslinking dataset (blue). Since we restrict our direct contact predictions to co-complex interactions within hu.MAP complexes, we additionally compare to the enriched overlap of co-complex edges and crosslinking interactions (z-score ~24, red circle). This shows direct contact predictions have a highly enriched overlap with crosslinking interactions above expected by co-complex edges alone.
Evaluation of direct contacts within the 26S proteasome
We next highlight our method's ability to identify direct physical contacts among proteins by focusing on a specific protein complex with known structure. The 26S proteasome makes for a clear example of the utility of conditional dependency inference over correlation analysis due to the availability of known three-dimensional structures of this complex [47–50] and the presence of well-defined sub-complexes (e.g., the 20S core and 19S cap). Fig 6A shows the contacts from the known proteasome structure in the upper right portion of the matrix. Interactions are observed amongst the PSMA1 through PSMA7 subunits and PSMB1 through PSMB7 subunits, representing the core, as well as PSMC1 through PSMC6 and PSMD1 through PSMD14 subunits, representing the cap. Notably, not all subunits of the core contact each other, and there are relatively few contacts made between core and cap subunits. These known contacts can be compared with the case shown in the lower left portion of the matrix in Fig 6A, which plots correlation scores from fractionation profiles. While the correlation data exhibit a clear block structure with respect to the core and cap, they do not exhibit the more detailed structure observed in the true contact matrix.
Fig 6. Prediction of direct contacts between subunits of the 26S proteasome.
A. Matrix of true contacts (upper right, derived from PDB entry 4CR2 [47]) and correlation coefficients (lower left) for the 26S proteasome. Correlation identifies general sub-complex structure but fails to discriminate between direct and indirect interactions (6 out of top 10 predictions correct). B. Matrix of true contacts (upper right) and direct contact predictions (lower left) for the 26S proteasome. The direct contact method identifies many true contacts while strongly reducing the number of false positive predictions (9 out of top 10 predictions correct).
The conditionally dependent interactions for these same data are plotted in the lower left portion of the matrix in Fig 6B, representing the method's estimate of directly contacting subunits. In contrast to the full block structure exhibited by the raw correlations, the direct contact predictions capture finer details of the true contact matrix. Notably, many of the spurious indirect contacts predicted by the correlation matrix are successfully eliminated. For example, the core subunit PSMA6 does not directly contact PSMA1, PSMA7 or PSMB1-5, but does directly contact PSMA2-5 and PSMB6-7. This binding specificity is at least partly captured by the direct contact predictions, but is completely missed by the correlation analysis. Specifically, our method predicts no direct contacts between PSMA6 and PSMA7 or PSMB1-3 subunits, while correlation analysis produces high correlation coefficients for all core subunits. This example exposes the inability of correlation to identify specific direct physical contacts amongst indirect contacts and demonstrates the capacity to remove spurious contacts based on identification of conditional independence.
Several high confidence false positive predictions are biologically meaningful
We looked further into cases were we predicted high confidence direct contacts that were labeled as incorrect based on structure data. We noticed an incorrect but high confidence direct contact prediction between two subunits of the spliceosome, SNRPD2 and SNRPD3 (direct contact prob = 1.0). The electron microscopy structure of the spliceosome (pdbid: 5MQF) shows these two subunits within ~17 Å of each other and between the two subunits is an RNA molecule. CF-MS is primarily a proteomics technique and does not observe other molecules such as RNA. We therefore expect to have a degree of error with respect to complexes with structural RNA present, as CF-MS will not show co-elution profiles that discriminate RNA—protein sub-assemblies. We do believe that when these data do become available, the direct contact prediction method is robust enough to identify direct contacts between RNA and protein molecules. Thus, in this case, the high confidence prediction points to a close biological relationship between the two subunits.
Additionally, we predict a high confidence direct contact (direct contact prob = 0.95) between two subunits of the eIF3 complex, specifically eIF3e and eIF3h. The C-termini of these subunits participate in an octameric helical bundle at the center of the complex but do not directly contact in the structure used for evaluation (pdbid 5A5T) [51]. In contrast, another structure of eIF3 (pdbid: 3J8B) [52] does have eIF3e and eIF3h directly contacting in the helical bundle. Both structures have limited resolution and are not considered atomic-models suggesting that our data can inform in this discrepancy between models.
Prediction of direct contacts within human protein complexes with unknown structures
The prediction of direct contacts gives an opportunity to characterize the structural architecture of complexes that do not yet have a solved structure. The exocyst complex, for example, is a hetero-octamer involved in tethering vesicles to the plasma membrane and is not well understood at the molecular level [53]. Recent studies by Heider et al. [54] and Picco et al. [55] have attempted to resolve the yeast exocyst subunit connectivity map using co-purification and nanometer precision fluorescence microscopy, respectively. Interestingly, Heider and colleagues identified two sub-complexes, sub-complex I consisting of Sec3/EXOC1 (denoting yeast/human orthologs), Sec5/EXOC2, Sec6/EXOC3, Sec8/EXOC4 and sub-complex II consisting of Sec15/EXOC6, Sec10/EXOC5, Exo84/EXOC8 and Exo70/EXOC7. Our direct contact predictions, plotted in Fig 7A, support the presence of these two sub-complexes in addition to identifying inter-sub-complex contacts between EXOC4—EXOC7, EXOC4—EXOC5, and EXOC3—EXOC8. These contacts along with the highly confident direct contact predicted between EXOC3—EXOC4 (also supported by the Heider et al. data) suggests that EXOC3 and EXCO4 form the core subunits of sub-complex I and serve as a bridge to sub-complex II. Likewise, the direct contacts predicted between EXOC5, EXOC7 and EXOC8 suggest they form the core of sub-complex II and are reciprocally responsible for the bridge between sub-complexes. In comparison to the correlation network shown in Fig 7B we observe a much denser network with fewer discriminating edges that help to identify the sub-complexes. We also see a range of correlation coefficients that, empirically, have lower precision then their corresponding direct contact probabilities when evaluated on our combined structural benchmark (Fig 2C). For instance, the interaction EXOC3-EXOC4 has a direct contact probability of 0.85 which is estimated to have a physical contact precision of ~70% while the corresponding correlation coefficient of 0.8 has an empirical precision of ~45%. This example illustrates the ability of our method to predict high confident physical interactions that discriminate from other indirect interactions.
Fig 7. Prediction of direct contacts within human protein complexes of unknown structure.
Direct contacts and correlation coefficients were calculated between 8 members of the human exocyst complex (A: direct contacts, B: correlation) and 10 members of the tRNA multi-synthetase complex (C: direct contacts, D: correlation). Contact predictions are visualized here by drawing each direct contact prediction or correlation prediction as an edge connecting the relevant subunits. Each predicted direct contact is associated with its prediction score, which indicates the stability support for that interaction. In both complexes, certain direct interactions are strongly supported, suggesting key contacts formed in the three-dimensional organization of these complexes, neither of which has yet been resolved. The comparison between direct contact predictions and correlation predictions indicates that the graphical model removes edges considered conditionally independent from the direct contact network providing high confidence predictions. For A and B, colors (blue and green) represent known sub-complexes from Heider et al. [54]. For C and D, red represents structural subunits and purple represents synthetases.
A second large complex that has thus far eluded structural characterization is the multi-aminoacyl-tRNA synthetase (also known as MARS) complex, which is composed of 9 synthetases and 3 structural subunits (AIMP1/p43, AIMP2/p38, and EEF1E1/p18/AIMP3) and is estimated to be 1 to 1.5 MDa in size [56]. Individual synthetases within the MARS complex are responsible for covalently attaching specific amino acids to their respective tRNAs and are essential for life. However, the function of the conserved supra-molecular assembly remains unclear. Structural studies, although limited, have identified a few trends in terms of overall architecture of the MARS complex [57], including the presence of two sub-complexes mediated by a core AIMP2/p38 subunit. As shown in Fig 7C, the direct contact predictions clearly establish AIMP2 as central to the architecture of the MARS complex, and strongly link the two larger structural subunits, AIMP1 with AIMP2. Yeast two-hybrid further supports the AIMP1 and AIMP2 interaction as well as the AIMP2 –DARS interaction and AIMP2 –KARS interaction [8]. Additionally, we see strong interactions between the isoleucyl tRNA synthetase IARS and other members of the complex, including the LARS subunit which is supported by mass spectrometry crosslinking data [11]. This suggests that IARS, in addition to AIMP1 and AIMP2, plays a central role in the physical organization of the MARS complex.
We further compare the direct contact network to the correlation network for the MARS complex (Fig 7D). Like the correlation network for the exocyst complex described above, the correlation network for the MARS complex is substantially denser, with many more edges of similar coefficients connecting subunits. Interestingly, we find high correlation edges between subunits DARS and MARS, which do not have an edge in the direct contact network (Fig 7C). Since our method attempts to remove spurious conditionally independent edges, this suggests that the correlation coefficient observed between the DARS and MARS subunits can be explained by their mutual interaction with IARS. We see a similar pattern of a high correlation edge absent in the direct contact network including MARS-RARS, MARS-QARS, QARS-RARS, LARS-DARS as well as others. Many of these subunits also interact with the IARS subunit, again suggesting it is the central organizing subunit of the complex. This example demonstrates the utility of direct contact predictions to potentially remove spurious edges from a physical interaction graph.
Conditional dependency supports a 3D structural model of the human EKC/KEOPS complex
We observed multiple conditionally dependent interactions among a conserved human multi-protein complex with a recently discovered missing subunit. The Endopeptidase-like and Kinase associated to transcribed Chromatin (EKC)/Kinase, Endopeptidase and Other Proteins of small Size (KEOPS) complex is a highly conserved protein complex known to introduce an essential modification to tRNAs across the tree of life [58–60]. The N6-threonylcarbamoyladenosine (t6A) modification is required for normal cell growth and accurate protein translation in bacteria, archaea, and eukaryotes. While the bacterial and lower eukaryotic components of the EKC/KEOPS complex are known, some of the human subunits are substantially diverged and have only recently been discovered [61, 62].
In yeast, the complex consists of five proteins, visualized in Fig 8A: the atypical TP53 receptor kinase/ATPase (Bud32), the Kinase-Associated Endopeptidase (Kae1), and three small proteins, Cgi121, Pcc1, and Gon7 [58, 60]]. Clear orthologs of four of these occur in humans and have previously been confirmed to participate in the EKC/KEOPS complex: TP53RK (the ortholog of Bud32, known to partially complement a Bud32 mutant [63]), TPRKB (the ortholog of Cgi121), LAGE3 (the ortholog of Pcc1), and OSGEP (the ortholog of Kae1). The yeast Gon7 has generally been thought to be fungi-specific [59, 61], and has no clear mammalian ortholog in major ortholog databases [36, 64].
Fig 8. Identification of direct contacts between C14ORF142 and the human EKC/KEOPS tRNA modification complex.
A. Highly conserved across both prokaryotes and eukaryotes, the EKC/KEOPS complex is thought to consist of 5 proteins, visualized here for the yeast complex based on a combination of modeling and crystal structures (PDB entries 4WX8, 5JMV, 3EN9 and 4WW5). B. Conditionally dependent interactions among subunits of the human protein complex highlight specific contacts that match closely to those observed between the yeast proteins, and additionally implicate the human protein C14ORF142 as directly bound to OSGEP and LAGE3. C. Using the yeast structure as a homology template, a 3D model was built of the 4 known human subunits and C14ORF142, taking advantage of C14ORF142's distant similarity to the yeast Gon7 subunit.
We found that the conditionally dependent interactions (plotted in Fig 8B) strongly supported direct binding of human TP53RK with TPRKB, consistent with expectation from the yeast and archeal crystal structures [40, 41]. Direct binding was also indicated between LAGE3 and OSGEP, again consistent with structural data from archeal homologues [42]. We next observed strong evidence supporting direct binding of OSGEP and LAGE3 with human protein, C14ORF142. Using profile-profile matching, we observe distant but significant homology between C14ORF142 and Gon7 (16% sequence identity and probability score of 92.0), as measured by HHpred [38], which identified Gon7 as the top hit for C14ORF142 from the full non-redundant (reduced to 70% identity) PDB database. This distant sequence similarity strongly supported the observed conditionally dependent protein-protein interactions and suggested that C14ORF142 was indeed likely to substitute for Gon7 within the human complex. Recently, C14ORF142 has been identified as the likely Gon7 ortholog by co-purification with known EKC/KEOPS members [62]. Additionally, the EKC/KEOPS complex was reconstituted in vitro and GST-C14ORF142 was shown to bind directly to the OSGEP-LAGE3 sub-complex validating our prediction.
Taking advantage of our predicted direct contacts of C14ORF142 with OSGEP and LAGE3, we constructed a 3D model of the human EKC/KEOPS complex by homology modeling the human proteins onto their yeast orthologs of known 3D structure, including modeling C14ORF142 on the known Gon7 structure (Fig 8C). The resulting 3D model accounts for most of the OSGEP, TP53RK, and TPRKB amino acid sequences, but leaves the C-terminal region of C14ORF142 and the N-terminal region of LAGE3 unmodeled, pointing to additional aspects of this complex still yet to be described. Importantly, the model faithfully recapitulates the known functional and interaction data from the literature, the direct contact predictions from the co-fractionation / mass spectrometry datasets, and the newly recognized C14ORF142/Gon7 structural homology, and thus serves to integrate a large body of data into a single model to help guide future mechanistic studies of this ancient human protein complex.
Knowledge of the three dimensional architecture of a protein complex is highly beneficial to understanding its mechanistic function, but thousands of complexes have thus far proved elusive to traditional structural biology techniques. We present an orthogonal approach in determining aspects of the three dimensional architecture of complexes by analyzing large scale CF-MS datasets. Using our method, we predicted thousands of direct contacts between complex subunits. We expect this resource can be used as a valuable constraint for structurally modeling the many stable protein complexes in the human proteome using available modeling tools [65, 66]. The method should easily extend to new organisms as additional large-scale CF-MS datasets become available. Code and input elution profiles file can be found at https://github.com/marcottelab/direct_contact.
S1 Table. PDB entries of multi-protein complexes in protein contact benchmark.
All complexes selected had 1) a PDB entry and 2) > 4 subunits. True = pairs of proteins with >0.0 Å2 interface surface area.
https://doi.org/10.1371/journal.pcbi.1005625.s001
(XLS)
S2 Table. Full protein contact benchmark.
Complete list of contacting and non-contacting protein pairs derived from PDB entries in S1 Table.
S3 Table. Direct contact predictions.
List of direct contact predictions with probability score. Predictions were restricted to only pairs that are co-complex in hu.MAP [32].
S1 File. Structural model of human EKC/KEOPS complex.
PDB formatted file consisting of the structural homology models of TPRKB, TP53RK, OSGEP, LAGE3 and C14ORF142 interacting in complex.
(PDB)
Conceptualization: KD CLM EMM.
Data curation: KD.
Formal analysis: KD CLM.
Funding acquisition: KD CLM RB EMM.
Investigation: KD CLM EMM.
Methodology: KD CLM.
Project administration: KD CLM RB EMM.
Resources: RB EMM.
Software: KD.
Supervision: RB EMM.
Validation: KD EMM.
Visualization: KD EMM.
Writing – original draft: KD CLM EMM.
Writing – review & editing: KD CLM RB EMM.
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Protein interactions
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Protein complexes
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Protein structure prediction
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Cross-linking
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Protein structure comparison
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Proteasomes
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text:
Q: How to do error handling in a REST API with Express middleware routing Assume we have a REST API with the following route:
app.post("/accounts/:accountId", isAuthorized, isSelf, updateAccount);

Within this scenario, a lot can go wrong. The user could be unauthorized because he is not logged in, or because he is trying to update an account which is not his, or he is missing a required property value. Using middleware you can extract certain validation, so it can be used across multiple routes.
The situation
I am creating a POST to this route, but I do not sent an authorization header. The isAuthorized, successfully captures the error, and instead of calling the next function, it will use a res.send(403) or something. The isSelf and updateAccount function would do something similair.
The problem
I need to manually follow a pattern across all the functions to return a similar error handling. Which is - as a developer - pretty error prone. Since it's fairly easy to mess up conventions when you would need to manage it across all kind of files.
The question
What is the simplest way to manage errors?
Please note that all the middleware functions are custom functions, so they can be written to follow a generalized approach.

A: A little late to the game but, this is how i handle errors in my express based API projects.
There are mainly 2 reasons error can happen.

*

*User try to access a route which is not defined.

*Something can go wrong when user is interacting with database. Which will directly throw an error.
For the first type of error, this is how i manage it.
app.use((req, res, next) => {
  const error = new Error('Not Found!');
  error.status = 404;
  next(error);
});

*

*When you declare this middle ware after you have defined all the routes, and when user try to request an invalid route it will go through this middle ware.

*I am creating a new error object using the default Error object in NodeJs. Then i am assigning a status and passing it to a next main error handler.
Second type of errors. which can be caused by database related errors or validation errors etc.
This is how i handle those.
app.use((error, req, res, next) => {
  res.status(error.status || 500);
  res.json({
    error: {
      message: error.message,
    },
  });
});

*

*If the error is the not found error which is coming from the previous handler, then make an object and send the error details.

*In database interaction if something goes wrong it directly throws an error, then this middle ware will catch it and assign it a status and take the message from thrown error and make it into an object and return it as json.
This is the most convenient way i found to handle errors.
I learned this from here.

A: I would create functions that contain "test"s that you often run.
Example:
function test1(req, res){
    if (typeof req.body.value != undefined){
        return true;
    } else {
        return false;
    }
}

A: You have to define your last middleware to be the error handling function like this:
app.use((err, req, res, next) => {
    res.status(err.status).json(err);
});

And in your route you can do:
app.get('/',() => {
    var err = {};
    err.message = 'Not found'
    err.status = 404;
    next(err)
})

A: i am also new to node and i also face this kind of situation but i found a way i insert an function after performing any query which has two paramiter first is "err" which contain any error and second paramiter is "data" which contain data which you will get after performing query.
function(err, data){
  if(err){
    console.log(err)
  }
  console.log(data)
}

in my opinion it is the simplest but effective way of error handling in rest API

A: What i mostly do in my nodejs express server is:
if(err) {
  res.json({error:err})
  throw new Error(err)
}
else {
 do other stuff
}

I am not sure if it is the best practice or not but what i know is it will send the err response as well as it will print error stack trace in the console for easier debugging.

redpajama_set_name: RedPajamaStackExchange


text:
Vieira Reaches Milestone at Kern's October Classic
The entire staff and ownership group of the Spears Srl Southwest Tour Series would like to send out our congratulations to Carlos E. Vieira!! The 2020 season has been a challenge, due to the Covid-19 concerns in the state of California, but there have been several milestones set during the season. One landmark, that will be achieved at the October Classic at Kern County Raceway, is start #100 for long-time series supporter Carlos Vieira. The Livingston, CA driver will make that momentous start on October 25th, after beginning that journey on April 26th, 2008 at Irwindale Speedway. He will join Jacob Gomes and Scott Sanchez as the only drivers to achieve that mark.
"It's hard to believe that this will be my 100th start" Vieira explained. "It's even harder to believe that I am still looking for my first win in this series. I have won Late Model and Super Late Model races, but I have not been able to get it done in this series yet. We have come close many times, but luck has not been on my side in those situations. Either way, I have been having a blast. Besides having fun, like I talk about in my new book "Knockin' Doorz Down", racing has been good for me. It helps keep me out of trouble, and focused on the important things in life. Wins or no wins, I keep coming back because it's something that I need and want to do for myself."
Vieira continued, showing both his gratitude for his supporters, and his positivity looking forward, "I want to thank everyone that has helped and supported me over the years. Maybe this 100th start will be that win that I have been chasing, but if it's not, life is still good, and I'm still blessed."
Despite not having achieved his goal of winning in the series, he has accumulated 15 top-five and 48 top-10 efforts. The latter is good for sixth all-time. Vieira has also become one of the most consistent competitors over the years, having only failed to see the checkered flag twice over the last three seasons.
As he heads to Kern County Raceway this weekend, Vieira is heading to track at which he has had success. He has three top-five's with a career best second place run, in 2016. Can he improve that one additional position and bring home his first win? It would certainly make a memorable 100th start in his 51 FIFTY LTM, Allied Packaging Camry.
The "JM ENVIRONMENTAL, INC. Wild West Shootout Round #3 – Boogie Man 127 presented by KHI Management Inc." will pay $5,027 to win, thanks to the Winner's Bonus from FLUIDYNE High Performance. $1,000 will be paid out to the leader on lap 27 from JM Environmental. $500 will be paid at the halfway mark, thanks to Kendall Peters with AKM Maintenance. The Performance Friction Brakes Zero Drag Fast Time Award will also payout another $500 with help from JM Environmental. The SPEARS Southwest Tour Series will be joined by the SPEARS Modified Series, ARCA Menards Pro Series West, Sigma Performance Services Pro Late Models and Super Stocks. The Pro Late Models and Super Stocks will compete on both Saturday and Sunday, with 60 and 40 lap features, respectively.
For those unable to attend, the event will be shown live at www.SpearsRacing.TV. The Spears Southwest Tour Series will be featured on the live broadcast, along with the Spears Modifieds, Pro Late Models and the Super Stocks. Saturday's broadcast will begin at approximately 3:30 pm (PST) and capture the final practice sessions for the Spears Modifieds and Spears SRL Southwest Tour Series. The Super Stock and Sigma Performance Services Pro Late Model Features will follow. Sunday will capture the Spears SRL Southwest Tour, Spears Modified, Sigma Performance Services Pro Late Model and Super Stock Feature events.
The event will also be shown on MAVTV at a date to be announced later. It will be a part of the 2020 "Southwest Short Track Racing" series, which will air every other week on Sundays at 11:00 am and 2:00 pm (PST).

redpajama_set_name: RedPajamaCommonCrawl


A nanocrystalline iron-rich telluride, Fe5Te4, was prepared mechanochemically using ball milling procedures in an inert atmosphere, starting from FexTe powder mixtures with x = 1.0, 1.25 and 1.5, with x = 1.25 leading to the pure phase. Its crystal structure has a tetragonal (I4/m) symmetry and shows an anomalously short Fe–Fe interaction of 2.52 Å. The microstructure of the nanocrystalline material was deeply studied by synchrotron X-ray total scattering techniques and the Debye scattering equation (DSE) method. A DSE-based microstrain model, integrated with structure, size and morphology information, indicates the presence of highly strained nanoparticles, with strain more preferentially accumulated in the ab-plane than along the c-axis of the tetragonal structure. Magnetic and Mössbauer spectroscopic characterization indicates that the samples present a spin glass state below the freezing temperature (ca. 150 K) and a magnetic behavior dependent on the applied field, showing ferromagnetic-like loops and a superparamagnetic-like increase of magnetization up to the maximum applied field of 90 kOe.


Nelson P. Miller, Associate Dean for the Grand Rapids Campus and Professor of Law, offers this tongue-in-cheek retort to the calls for reducing the scope of legal education.
National media reported that President Obama joined a chorus of law school critics a while back by suggesting that to save on tuition costs, law students should graduate in just two rather than three years. Law graduates can learn on the job at law firms, the argument goes. Those senior law partners love teaching new associates the professional ropes. And clients love paying those new associates for work that they cannot yet do, haven't you heard?
The argument makes so much sense that one has to ask, why stop at law schools?
Most agree that healthcare is too scarce and expensive in part because of a doctor shortage. Medical students graduate with much larger debt loads than law students. Why not license medical students after just two years? A little on-the-job training never hurt anyone, especially in a surgical suite. That's why they have malpractice insurance, isn't it? They give you a free do-over, right? The receptionist can probably tell you if that lump is cancer anyway. Or you can use the internet for self-diagnosis. Didn't you just read that patients are out-diagnosing their doctors now by two to one?
Commercial airline pilots are in great demand due largely to increases in consumer air travel in China and other Asian nations. Commercial pilots undergo years of rigorous training and certification before qualified to fly. Why not certify commercial pilots after a third less training? On-the-job training can work there, too. The junior pilot who almost landed the flight from China in San Francisco Bay didn't kill anyone, did he? Think of the debts those new pilots would avoid, cutting their training by a third. They could buy that condo in Aspen years earlier.
And then we have engineers—the kind that ensure that bridges and buildings stand. Have you seen how expensive engineering school is lately? Everyone knows that we have far too few students entering the science, technology, engineering, and mathematics (STEM) fields. Those years of professional school are not only expensive but discouraging. Why not cut the schooling of engineers by a third? Urban renovation knocks down buildings these days within a decade or two anyway. They no longer need to last. Think instead of the stress those engineers would avoid having to pay for all that extra educational debt.
Accountants don't need as much professional training as they get either, now do they? A few mistakes on your taxes won't hurt you that much. Haven't you heard that the IRS has a new consumer-protection function to control its over-zealous auditors? Most tax debts are not dischargeable in bankruptcy, but the IRS will work with you. And when you try to sell your business with your books a mess, buyers don't care. They know that they can always sue you for misrepresentation.
Nurses, social workers, emergency medical technicians—they can all get by just fine with one third less education. And they, too, will have lots less educational debt and lots less stress paying back that debt. On-the-job training can work in every profession, can't it?
Our real concern should not be for the training of those who enter these critical professions where lives, families, businesses, and finances are on the line. You don't need a lawyer to draft a proper will. Let your life's fortune go where it pleases. You won't be around anyway. That business you sold after a lifetime's work building it up? The buyer shouldn't have to pay for it when your lawyer screwed up the paperwork. Your ex-spouse wants the kids, home, and retirement accounts? Be generous. Don't expect your lawyer to know what's fair or how to get it.
No, our real concern should be for the education of hairdressers. Let's keep our heads on straight, here, after all. Who cares how much educational debt your hairdresser incurred when it's your hair about which we're talking. Not just hairdressers, either. Plumbers, too. Definitely plumbers. And pedicurists. And chefs. Let's keep our priorities straight here. Protection of the public is paramount. Have you had an overcooked steak lately? We should not stand for that.
At Cooley, students actually can graduate in two years, and at substantial savings, by enrolling in Cooley's accelerated full-time program. But they go full time, all year long, and they take the full 90-credit curriculum taught by the same outstanding faculty as any other Cooley student.
And while we're at it, let's reduce enrollment in all graduate schools by one-third. Think of all the money that could be diverted from Federal student loans to road repairs?


I'm sooo in love with my new Mid rise jeans from Chicnova!!! Just love the effect they give with my vintage denim shirt, don't you think so? I have never bought ombre clothing, and I must say this jeans are just so original! I think they will be a recurrent piece in my wardrobe, as well as my new Prada bag present from my boyfriend 🙂 I was looking for a red bag for a long time and finally decided to pick this one, like it or not?
And for today I have started the day with some home made pancakes, and unfortunatelly won't go to the beach as it is cloudy but maybe a good film at the cinme is not a bad idea! Loooove summer!!
Estoy taaaaaaaan enamorada de mis nuevos Mid rise Jeans de Chicnova!! Me encanta el efecto que hacen junto a mi camiseta tejana vintage, verdad? Nunca me he comprado nada de efecto ombre, y he de decir que estos jeans son super originales! Creo que serán una pieza recurrente en mi armari, igual que mi nuevo bolso de Prada, regalo de mi novio 🙂 Estaba buscando desde hace tiempo un bolso rojo y al final me decidí por este, os gusta?
Hoy me he despertado con un desayuno de Pancakes hechos en casa, y es una lástima que no podré ir a la playa pues está bastante nublado, pero estaba pensando de ir a ver una peli al cine! Me encantaaaaaaaaa el verano!!
I like your jeans! 🙂 And the bag, oh my, the bag! The bag of my dreams, no joke!!
Great denim total look! We love touch of red!
Love these ombre jeans, they look gorgeous on you! The sandals are quite unique too!


Delargo DT Infant font family, 4 styles from $ by DTP Types. Delargo DT Infant Demi Italic. Buying choices. Album ▽. Delargo DT Infant Demi font by DTP Types, from $ Information about the font Delargo DT Infant Demi and where to buy it.
Delargo DT Infant Demi Font - Download Delargo DT Infant Demi font. for PSO - Bitstream Vera Sans Bold, DejaVu Sans Condensed Bold, Prima Sans Bold. Font Name: Delargo DT Infant Demi. Publisher: MyFonts. License Information: Commercial. Foundry Reference: Delargo DT Infant - Delargo DT Infant is a sans-serif font design, published by DTP Types Limited.
Delargo DT Infant,. Delargo DT Infant is a sans-serif font design, published by DTP Types Limited. Delargo DT Infant. Delargo DT Infant4 Delargo DT Infant Demi, Delargo DT Infant Demi Italic, Delargo DT Infant Italic, Delargo DT Infant Regular. supplier. 'Find My Font' is an easy to use application for identifying fonts in digital images.


text:
Federal Open Source Repositories
================================

Live version available at:
-------------------------

http://federal-repositories.herokuapp.com/  (you'll have to do a one-time authentication against your github account)

_Note that the original version at http://gsa.github.com/federal-open-source-repos/ is still live but now hits the API rate limits of GitHub for unathenticated users.  This is a good problem! It's because there's such growth in government GitHub usage._

**Also note sister projects** at www.govcode.org and http://ada.mbecker.cc/USAxGITHUB.

Contributing to the Project
---------------------------

1. Fork the project using the "fork" button in the top-right corner
1. Create a new branch
1. Make changes
1. Commit, push
1. Visit your repository's page to submit a pull request

*Note: By contributing to this project, you agree to license your work under the same terms as those that govern this project's distribution. See license.txt for more information.*

redpajama_set_name: RedPajamaGithub


Public & Private Grand Canyon Tours, Hikes, and Backpacking Trips from Flagstaff, Williams, Tusayan, Grand Canyon Village, Sedona, Phoenix. Private tours of the Southwest.
NEW HALF-DAY TOURS! - Don't have all day to spend on a tour? Do the next best thing — spend a half day with us! These incredible 3 to 4 hour sightseeing tours pick you up at the Backcountry Information Center right inside the Park in a climate controlled vehicle that seats up to 10 guests. Our touring vans and SUVs are fully enclosed and have all leather customized interiors that guarantee comfort and high visibility. AC to keep cool! No wind, no need for a blanket on chilly days, and best of all -- no bugs in your teeth! Prices for Adults start at just $125!
Day hikes for all experience levels. Want to sightsee and get below the rim? No problem! Looking for an all-day hike where you learn about the history, geology, and peoples of the Canyon? We have you covered. Have you been hiking all your life and want to take it to the limit? Lets do it! All you need are sturdy shoes and a good hat because every hike includes transportation, lunch, snacks, beverages, water, and gear.
Let one of our tour planners create a one of a kind experience you will treasure for the rest of your life. From a one day guided Grand Canyon tour to a week-long Southwest adventure, we can do it all. Don't let planning your next vacation be stressful, let All-Star handle the heavy lifting and relax knowing you are in expert hands.
Want to see the stars from the bottom of the Grand Canyon? Let All-Star take you there. Our guides have decades of experience in the Canyon, that's why we have the best itineraries in the business. Join a group trip or let us design an adventure just for you. Great food, great equipment, awesome guides, how can you go wrong? Call us today.
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All-Star Grand Canyon Tours Inc. offers luxury, eco-friendly, full service Private Grand Canyon and Four Corners guided tours; as well as the best Daily Grand Canyon Tours from Flagstaff, Williams, Tusayan and Grand Canyon Village. We are located in beautiful Flagstaff, Arizona and operate in the Grand Canyon and Four Corners region of the Southwestern, United States (otherwise known as the "Grand Circle"). Of our many services, we provide Custom Private Tours with genuine unique itineraries and experiences; we are your Southwestern Concierge. Our Private Tours showcase the Southwest from driving, boating, chartered and scenic air tours to Luxury Base Camps, camping, horseback riding, fly fishing, float trips and family picnics. For the more adventurous mind, Private Day Hikes, Private Backpacking, helicopter tours, river rafting, skiing and snowboarding and mountain biking are also available.
Our group Daily Grand Canyon Tours are an affordable,fun option for experiencing the Grand Canyon in one day. They include comfortable round trip transportation from your hotel to the canyon. No need to drive to a meeting point, we pick up at all hotels in Flagstaff, Williams, Tusayan and Grand Canyon Village. We have small group sizes, so you will receive the personal attention you deserve. We include a delicious picnic lunch and snacks and drinks all day; everything is taken care of, so you can effortlessly enjoy the day.
For the more adventuresome individuals, our group Daily Grand Canyon Hikes are also a fantastic way to experience Grand Canyon. We offer 3 different hiking tours, our Beginner Hike with Tour, Intermediate Day Hike, and Advanced Day Hike. They also include round trip hotel transportation, and are fully outfitted will all necessary gear and supplies.
Our mission at All-Star Grand Canyon Tours, is to use our expertise in the Four Corners Region to help you find the best of the abundance of activities and experiences that the Southwest has to offer; your Southwestern Concierge. Any of our services can focus on numerous places within the Grand Circle; Grand Canyon National Park, Zion National Park, Bryce Canyon National Park, Sedona, Jerome, Lake Powell, Antelope Canyon, Canyon De Chelly National Monument, Monument Valley, the Painted Desert, the Petrified Forest National Park, the Hopi and Navajo Reservations and world famous Route 66, just to name a few. Our services are available to individuals, families, youth groups, corporations and groups of all sizes. You'll find we are the best of the West!


text:
BeforeTheConcert.com launching this Spring/Summer
Site News | By Bryan Kremkau on Feb 2nd, 2012
I've got an exciting announcement to make! No, I'm not closing down ReadJunk.com. I'm pleased to announce a new website I'll be working on this Spring/Summer called BeforeTheConcert.com. Before The Concert will be a web video series that will focus on bands & musicians before their concerts.
There are a lot of music sessions websites out there; a lot of good ones but too many indie rock bands honestly. The series will aim at getting a wider range of genres & styles represented. I want to try to do something different than just bands playing music sessions in random settings. There will be that of course, but I also want to focus on the behind the scenes stuff. How bands prepare, what do they do during their downtime; plus it would nice to get some stories from the bands or people I'm filming. So in a way, this will be documentary shorts but mixed with music sessions, interviews, goofing off and anything else bands will let me film. I'd also like to include a section of some bloopers and outtakes of me shooting because frankly, I'm going to mess up a bunch of times since this will be a new venture for me.
Right now, I have a huge list of bands I need to ask to participate but if you're interested, be sure to hit me up at bryan@readjunk.com and we'll figure something out. As of right now, I don't really have anyone else helping out with the series but I'm hoping to find 1 or 2 more people in the NYC area that will want to help out with filming, especially with audio portion. I don't really know too much about the audio side of things so it would be nice to get some advice on that.
I don't have a time frame of when this site will launch but I'm hoping to launch by Spring/beginning of Summer. In the meantime, you can follow BTC on Twitter and Facebook on the latest happenings. You can also check out some videos I've done on Vimeo as well.
Before The Concert, BeforeTheConcert.com, behind the scenes, BTC, doc, documentary, documentary shorts, film, interviews, music documentary, music sessions, music videos, video
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ReadJunk Podcast – Chris Malone (The Pandemics)
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Pick It Up – Ska in the 90s World Premiere Is April 26th


Had she stopped there I would have humbly received her acknowledgement and we might have moved on to the desert in the same spirit of conviviality we had begun. It was when she smiled, drew a breath and said, "Yet -- " that I knew all had not been said that needed saying from her perspective.
Read the rest at Acton.org.
The Rome Reports news service has put together some video and text based on Acton's Dec. 2 conference in Rome, Italy, "Ethics, Aging, and the Coming Healthcare Challenge" Acton has also created a special web page where you can download the speeches and presentations from the event.
When read for its own sake, Centesimus Annus emerges as an uncompromising rejection of collectivism in its Marxist, communist, socialist, and even welfare-statist manifestations. While the encyclical allows for a certain amount of intervention by the state in such areas as wage levels, social security, unemployment insurance, and the like (always according to subsidiarity and only for the sake of the common good),Centesimus Annus also expresses repeated concern for observing the principle of subsidiarity and warns against the effects of intervention on both the economic prosperity of a nation and the dignity and rights of each person.