Marine Antineoplastic Templates: Clinical Trials (I-III) and Motifs Carried via Antibodies to Target Specific Cancerous Tissues

Aquatic environment is one of the important sources of active agents that own diverse biological properties. Metabolites from these sources are considered as alternate source to meet the mandate for effective medicines. Despite notable developments in cancer managing and/ or treatment in the past years, there remains a vital requirement for innovative agents and/or innovating approaches to treat resistant and solid tumours. However, in the recent era there are new technological innovations in the elucidation of the structures, the semi-synthetic and synthetic approaches of the new antineoplastic compounds. Biological assays enable isolation and clinical evaluation of numerous scaffolds from the marine environment. This review gives a general summary of some anti-cancer agents with a brief description of their mechanisms of action. It sheds a view to the approved drugs, the potent scaffolds that newly modulated as antibody-drug conjugates, and the drug-candidates under clinical phases (I-III) with their status.

Cancer conclusively, is the disease resulted as a reflection of cell's overgrowth and uncontrollable proliferation. It is among the preeminent cause of morbidity and mortality in the world. [1][2] Cancer has risk factors that predispose patients to it and these factors could be either internal or external. Internal factors are those related to patient immunity or genetic mutations. External factors include aging, obesity, smoking, lack of physical activity and exercise and exposure to infecting substances or hazardous substances. 4 Cancer can be more complicated when cancer-cells start invading to the surrounding local environment in the body by metastasis that is responsible for 90% of deaths in cancer patients. [3][4][5] Cancers can be detected early or later and sometimes accidentally with patients being treated or diagnosed for other types of diseases. [6][7][8] The diagnosis of cancer can be carried out based on the symptoms, tumour markers or histopathological tests that can be combined with other approaches like imaging techniques. 9 Cancer management may be approached by one of the three methods such as surgery, radiation and chemotherapy or a combination of two of them. 10 The problems associated with these approaches include the side effects that will affect the normal cells and lead to a decreased patients life quality. 11 Other problem is that the treatment may not be useful as a result of tumour metastasis or drug resistance. 12 Cancer is still a major public health problem that requires establishment of new strategies to discover new drugs that are able to fight it. 1 The drugs obtained from natural origin can be formulated in three structural forms: original, semisynthetic or analogue structures. Recently, the interest has been driven towards marine as a source of natural products mainly due to the structural diversity and availability of multiple scaffolds that can serve in the field of therapeutics as lead compounds, in particular, secondary metabolites that are produced by different aquatic organisms. 1,13 Metabolites produced during the life of organisms are assumed as a defensive mechanism for survival and adaptation with life surroundings. Beside the so many scaffolds reported for anticancer activity, marine-secondary metabolites representing an important addition. In this review, the light is focused on the diverse secondary metabolites, and it will provide an explanation of their value as anticancer compounds. Previously, we reported several frames including natural, semisynthetic, and synthetic anticancer agents. [14][15][16][17][18][19][20] This review is a valuable contribution to look for other structural templates of promising new antineoplastic agents to treat resistant and solid cancers. [21][22][23][24][25][26][27] Aquatic Sources as Anticancer Agent Thousands of secondary metabolites had been isolated from different sea creatures such as plants, macroalgae (seaweed), microalgae, bacteria, actinomycetes, fungi, sponges and soft corals all are viewed in the area of medicinal field. 4 Aquatic plants live in a harsh climate. One of the most recognized plants providing anticancer agents is mangrove that produces bioactive compounds and secondary metabolites like hormones and anticancer agents. 28 Table 1, illustrated the summary of approved anticancer agents and drugs under clinical trials.

Gram-Positive Mycelial Bacteria
Actinomycetes produce metabolites to highest extent that is why they account about 45% of total marine metabolites. The highly potent thicoraline and BE-22179 (Compounds 3, 4, Table 2), representing a large 26-membered polypeptide ring with SS-bond and hydroxylquinolone moieties, and enterochelin (Compound 5, Table 2) as three 2, 3-dihydroxybenzamides in 12-memered cyclic structure, 34 isolated from actinomycin called Micromonospora marina. They are potent cytotoxic compounds that are effective against colon cancer.

Sponges Provided Pentacyclic-22-Membered-Ring Scaffold
These are of high contribution by ~ 30% of all-natural products of marine origin and of important biological activities. 35 The moderately active cytotoxins spongo-thymidine and spongouridine were isolated from Caribbean sponge tethya crypta. 4 Eribulin (Compound 6, Table 3) which is a semisynthetic analogue of halichondrin B (Compound 7, Table 2) of IC50 = 7 uM had been isolated from sponge Halichondria okadai. Compound 6; a perhydro-pentacyclic-structure in a 22-membered-ring in the smaller frame of macrocycle. It provided potent IC50 value of 0.27 nM, was developed as mesylate (methansulfonate) salt. It stopped and emphatically hindered the development rate of microtubules. [36][37][38][39] had been obtained from sponge Halichondria okadai. It also, provided anticancer activity against breast cancer. 39 Eribulin has antimitotic and non-mitotic effects. It hinders microtubule polymerization interfering with microtubule-dynamic-stability as an antimitotic. While the non-mitotic action on tumour biology have moreover been built up. It counts for tumour vasculature remodelling, expanded vascular perfusion, decreased hypoxia, and phenotypic changes. 40 Tubulin displayed antiproliferative action against many diverse human cancer cell lines. Eribulin decreased both the rate and degree of tubulin polymerization, also it affects more than one stage in cell-cycle like G2 and M in lymphoma and pancreatic cancer cells and also it leads to initiation to apoptosis after 8-10 hrs. [40][41] Eribulin has high affinity to microtubules. In non-mitotic mechanism Eribulin has three different mechanisms, one is through its impact on tumor vascular remodelling and perfusion, another is through its effects on EMT (epithelial mesenchymal transition) process, and finally its effects on the relocation, assault and metamorphosis of cancer cells. 42

Fungal Sources Provided Indole-Bearing Pentacyclic Angular Scaffold
Some of fungi secondary metabolites have antioxidant activity toward free radical. Fungal xanthone derivatives produced by several numbers of plant families and fungi, Penicillium raistrickii, many Phomopsis spp. Alkaloids separated from Penicillium spp., found in deep ocean showed antitumor activity. These alkaloids are meleagrin analogues, meleagrin D (Compound 8, Table 2) and diketo-piperazine, roquefortine (Compound 9, Table 2) induced HL-60 cell death or detained the cellular process through G 2 /M phase, and bearing mild cytotoxic activity (against A-549, IC50 of 32.2 and 55.9 µM, respectively. 4,43 .

Soft Corals Provided 14-Membered-Carbocyclic Scaffold
Sarcophyton is widely spread in tropical and sub-tropical oceans. One of the important metabolites produced by soft coral is cembranoids like cembranoid diterpine (Compound 10, Table  2). Cembranoids have a verity of activities, like cytotoxic and anti-inflammatory. It is reported that furano-cembranoids from Nephthea spp, and Sarcophyton cherbonnieri are effective against breast cancer and liver cancer. 1,4 The secondary metabolites are structurally variable and possessing valuable biological activities. 44 The utmost familiar domains are carbohydrates (glycosides, polysaccharides), peptides, alkaloids, polyketides, terpenes, and polyphenols. 1,4 The followings are brief explanations of each class: Polyketides Scaffolds are large groups of secondary metabolites that contain alternating carbonyl and methylene groups, they include macrolides, polyethers, polyols and aromatics. 1 Polyphenols scaffolds, also named polyhydroxyphenols. 45 are natural products that have phenolic skeleton and they are divided into subgroups according to either their structure, function or origin. These subgroups are: phenolic acids, poly phenolic flavonoids, tannins, catechin, anthocyanidins, epigallocatechin, lignin, epigallocatechin gallate (Compound 11, Table 2), 46 and gallic acid (Compound 12, Table 2). [47][48][49] Terpenes scaffolds are secondary metabolites of isoprene units. Terpenes of different scaffolds of different numbers of carbons as C-10, C-15, C-20, C-25, C-30 and C-40 58 have been reported as more powerful antibacterial than being cytoxic. [59][60] Alkaloids from sea sources as nitrogencontaining natural scaffolds are many subclasses such as indole, pyrrole, pyridoacrine, isoquinoline, guanidine or amino-imidazole. [58][59][60] Peptides Scaffolds having many physiological effects in the cells obtained by extraction from sea sources. Peptides are isolated by enzymatic hydrolysis of organism-proteins. 61 Carbohydrates (CH 2 O) n of sea sources are involved in glycoproteins, lipids and with RNA and DNA. Mono-saccharides, di-saccharides and polysaccharides were identified. 47 The most common class of carbohydrates are the polysaccharides. Some of the polysaccharides have potent anticancer effects via interfering with DNA synthesis, induction of apoptosis and other mechanisms of actions. 1,47,61 All of the aforementioned bioactive classes had been obtained from different seasources like: sponges, molluscs, tunicates, corals, ascidians, bacteria, fungi, sea weed. 1,48 They are active against many types of cancer and exhibited anti-cancer activity via cell growth inhibition, disruption of the mitotic spindle, induction of apoptosis and inhibition of invasion or metastasis. 1

Approved Anticancer Marine Drugs
Secondary metabolites were studied broadly throughout the last 30 years for their biological activities. 62 Cytarabine, brentuximab vedotin, eribulin mesylate, and trabectedin are some of the approved anticancer agents. The followings are the approved anticancer agents.

Cytarabine
Food and drug administration (FDA) approved cytarabine in 1969. It is now a synthetic nucleoside anticancer drug that has been originally obtained from sea Cryptotheca crypta sponge. 1 There are other trivial names for cytarabine including chemical name, Ara-C and cytosine arabinoside. 63 Cytarabine (Compound 16 , Table  3) acts in phase S-specific manner to induce cytotoxicity and kill cancer cells through inhibition of DNA polymerase and being incorporated into DNA instead of the normal DNA building blocks. 63 Cytarabine is indicated for the treatment of leukaemia, 1, 63-64 described by the reproduction of immature leukemic blasts and their invasion to the bone marrow. It can be caused by intrinsic (genetic) or extrinsic (inflammation and release of cytokines and chemokines) factors. 66 It is administrated by intravenous infusion, intramuscular, intrathecal or subcutaneous injections. 67 Biologically it must be activated by phosphorylation when it is up-taken by the cells to the active form cytarabine-triphosphate to be able to induce its effect. 63 It is metabolized by being deactivated via deamination into the inactive and non-toxic form uracil-arabinoside and this metabolism decreases its activity 63-64 .
One approach used to solve the issue of deamination and inactivation of cytarabine was to co-administer it with tetrahydro-uridine which is a potent inhibitor of cytidine deaminase so the antitumor activity of cytarabine will increase. 64 Because of its short half-life it is advised to overcome this problem via prolonged infusion of cytarabine in a high-dose therapy which will lead to increased levels in the cerebrospinal fluid where cytidine enzyme is absent and the drug can remain active to induce its effect. [66][67] Targeting of folate receptor by designing the prodrug of cytarabine solved the problem of its high hydrophilicity. This prodrug was assembled ( Figure 1) to form spherical nano-assemblies (bare NAs) that adsorb folic acid-bovine serum albumin conjugate and (NAs/FA-BSA) form folate receptor targeting molecule. The conjugate concentrates in cancer cells that have increased expression of folate receptor therefore the antitumor activity of cytarabine will increase. 68

Trabectedin
Trabectedin (Compound 17, Table 3) is an alkaloid from Ecteinascidia turbinate. It was approved by European medicines agency (EMA) in 2007 as a single medication for the treatment of rare solid tumours of mesenchymal cells. 65 It is the foremost marketed natural medicine for relapsed ovarian cancer. Recently, trabectedin was accepted by FDA in 2015. 69 The results of ten years of use have shown a suitable toxicity proûle, without evidence of accumulative side eûects. However, because of an intensive hepatic metabolism liver dysfunction, predominantly chartered by increased transaminase levels, was reported as frequent side eûect. [66][67][68][69][70][71][72] T r a b e c t e d i n c o l l a b o r a t e w i t h DNA double helix, the structure of the compound triggers a cascade of occasions that restricted with a few translation components. It also causes inflection of cytokines and chemokines by typical cells. 73 The preclinical studies on combination of trabectedin with other anticancer drugs have been currently progressing globally and one of the combinations including (trabectedinpegylated-liposomal doxorubicin) was authorized by the European commission for treating relapsed platinum-sensitive ovarian cancer. 73,4

Antibody drug conjugates (ADC) Brentuximab Vedotin (Adcetris™)
Brentuximab Vedotin (Compound 18, Figure 2) is one of the recent medications successfully endorsed by FDA in 2011 and received a conditional approval from EMA in 2012 for Hodgkin and non-Hodgkin lymphoma treatment. Brentuximab vedotin represents the novel strategy of targeting drugs. It is CD-30 directed antibodydrug conjugate (ADC) containing three major fragments: a chimeric human-murine IgG1 that selectively targets CD30, monomethyl auristatin E; MMAE (Compound 15, Table 2), related to dolastatins such as dolastatin 10 and 15 (Compounds 13 and 14, Table 2). 74-75 MMAE is a fully synthetic analogue of dolastatin 10, isolated Dolabella auricularia. Later scientists isolated dolastatins from cyanobacteria Symploca hydnoides and Lyngbya majuscule which are a part of the sea hare's diet. To date, dolastatin 10 is the most powerful antineoplastic substances known, with an ED 50 in the picomolar range against various cancer-cell lines. Dolastatins in-vivo activity is not adequate for direct application as antineoplastic drug. Phase I and phase II clinical trials of dolastatin 10 and the water-soluble analogue auristatin PE results indicated that they were ineffective due to lack of efficacy and existence of side effects. In this strategy MMAE is linked to a protein that targets CD30 (a cell membrane protein sitting on the surface of Hodgkin's lymphoma cells). This linkage has resulted in highly effective and well tolerated agent, Brentuximab-vedotin. It took about 40 years from the initial bioactive extract to the approved drug. [76][77]

Pinatuzumab Vedotin
Pinatuzumab Vedotin (Compound 19, Figure 2) is another ADC is aimed to act against surface antigens associated with cancer. It is consisting of an anti-microtubule MMAE attached to another protein anti-CD22 antibody through peptide linker that can be cleaved by protease enzyme. CD22 is one of the immunoglobulin superfamily, restricted to and expressed by mature B-lymphocytes. CD22 has a critical role in regulation of B-lymphocytes mediated signalling and it also regulates the function and survival of B-lymphocytes as well as apoptosis. [74][75][76][77][78] The mechanism of action is when Pinatuzumab Vedotin binds to CD22, it gets into the cell then it will start releasing its cytotoxic agent MMAE that will act against microtubules in a way similar to vincristine mechanism (microtubules destabilisation). [74][75][76][77][78] Phase I trials suggested that PV can be given alone or in combination with rituxiamb to treat refractory NHL or CLL. [75][76][77][78] The advantages of using Pinatuzumab Vedotin include site specific drug delivery, increased efficacy and reduced systemic side effects. Subsequently, phase II trials started by i.v. dosage of 1.8 mg/kg, body weight or more given in a cycle of 21 days. [75][76][77][78][79] The toxicities that were recorded with Pinatuzumab Vedotin include non-hematological toxicity (nausea, diarrhea, fatigu), grade-3 neutropenia, thrombocytopenia and peripheral neuropathy. Most side effects can be reduced by extending the cycle length to 28 days and giving growth factors that allow neutrophils to become healthy and return to the normal count more quickly [75][76][77][78][79][80] .

Glembatumumab Vedotin
Glembatumumab Vedotin (Compound 20, Figure 2) is right now in clinical trial phase-2 for treating osteosarcoma, melanoma, and breast cancer. It is an antibody-drug conjugate (ADC) that targets glycoprotein GPNMB (Glycoprotein Nonmetastatic Melanoma Protein B). GPNMB is involved in multiple functions, examples are its anti-inflammatory effect, role in mineralization of bone and differentiation of osteoblasts. It also functions in cellular adhesion and gets localized exactly in plasma membrane [81][82] .
GPNMB is highly expressed in a very wide group of cancers including glioblastoma, astrocytoma, breast cancer, gastric cancer, and lung cancer. GPNMB is a glycoprotein comprises a huge extracellular space with cytoplasmic tail composed of short 53 amino acids. GPNMB expression is regulated by various cytokines, growth factors, microRNAs (miRNAs) and by protein stabilization. GPNMB is involved with varied patterns in a wide range of tissues including bones, skin, liver, and immune system. [83][84][85][86][87] The role of GPNMB in tumour formation is in melanoma, GPNMB is present in both cancer cells and normal cells but its percentage in cancer cell is higher (60%-80%) while in normal cell is (50%). For growth and metastasise, melanoma needs GPNMB, a specific phonotype of GPNMB is needed as knockdown the glycoprotein in some immune-competent mice that have melanoma studies found that no change in their growth, compared to culturing of t-cells with GPNMB. Knockdown causes the T-cell ability of functioning is impaired, it affects the production of inflammatory markers like IL-2 and IFN-ã and lose of ability to detect and kill the target cell. Glembatumumab Vedotin is a monoclonal antibody that mainly targeting GPNMB, in a recent study, the activity of Glembatumumab Vedotin was tested in number of osteosarcomas as a result, 85% of the cells responded to the drug. As shown in (Figure 2) GPNMB has multiple critical functions in cancer cells formation and if inhibited by Glembatumumab Vedotin, cancer can be treated. [88][89][90]

Tisotumab Vedotin
Tisotumab Vedotin (Compound 21, Figure  2) is also an ADC that can be isolated from marine    mollusks and cyanobacteria. It is composed of an anti-microtubule MMAE linked to a monoclonal antibody specific for tissue factor (TF) through a cleavable linker 74 . TF is highly expressed in many cancer types like prostate, pancreatic, lung, cervical, and breast cancers which are associated with metastasis and since it is a cause of poor prognosis of the disease, TF is considered an attractive target for anticancer agents. TF is also called thromboplastin or factor III and it is mainly expressed in three types of cells which are: perivascular cells, fibroblasts and the cells of the smooth muscles in the sub-endothelial walls of blood vessels and normally it plays a role in the extrinsic pathway of coagulation of blood through activation of factor VII. [72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91] In cancer, TF is implicated in angiogenesis (new blood vessels formation), tumor migration and advancement. The way which Tisotumab Vedotin acts by is through binding to TF and incorporation into the cell to release MMAE that then will induce its direct microtubule disrupting effect to kill the cell and its neighbouring cells through a bystander killing effect. Tisotumab Vedotin is currently under phase I clinical trials in a hope to treat different types of cancer like metastatic or relapsed cervical cancer. Cervical cancer is usually treated with firstline therapy consisting of bevacizumab along with paclitaxel and cisplatin or paclitaxel and topotecan but the problem associated with first-line therapy is the low survival and high relapse rates risk so there is a need for out of danger and powerful agent that can enhance the clinical outcomes of treatment. 91 Tisotumab Vedotin was given as i.v. dosage of 2 mg/kg, body weight in a cycle of 3 weeks (to allow normalisation of neutrophils count) and it exhibited a promising antitumor effect in solid tumors especially in case of metastatic or relapsed cervical cancer [91][92] . The most reported side effects were nausea, vomiting, weakness, anaemia, peripheral neuropathy, conjunctivitis, and bleeding from the nose. [91][92][93]

Anticancer Agents in Clinical Trial Phase 1
Phase I clinical trials can be divided into phase I(A) and phase I(B) trials. Dose escalation administration where a single administration of the doses in phase I(A), and multiple or daily administration in phase I(B) were done. [94][95]

Bryostatin 1 (Compound 22)
It is a macrocyclic lactone that is derived from marine bryozoan Bugular neritina and it can also be isolated from other organisms like sponges and tunicates. Bryostatin 1 is in clinical studies (phase I), to determine its activity against different types of cancer and it showed positive results in the management of ovarian cancer, breast cancer, lymphoma 97 and malignant melanoma. 96 There are 20 different bryostatins that have a general molecular formula of C 47 H 68 O 17 61, 96,98 . The mechanism of antineoplastic effect of bryostatin 1 was through binding to protein kinase C enzyme (PKC) and alteration of cellular activity. PKC is a family of serine/threonine specific kinases that have a major role in cell growth and death regulation [99][100] . PKC is activated by calcium or diacyl-glycerol. PKC has a carboxyl terminal catalytic domain and an amino terminal regulatory domain with ATP and phorbol (Compound 23) binding sites respectively. Both of pryostatin 1 and phorbol ester bind to the same binding site in the regulatory domain but the difference is when bryostatin 1 binds, it induces antineoplastic effect while phorbol ester induces effect that promotes tumor formation. [96][97][98][99][100][101] The cells exposed to bryostatin 1 for short period, PKC was activated resulting in three effects which are proliferation suppression, differentiation induction and promotion of apoptosis in cancer cells. Longer term exposure led to down-regulation of PKC and inhibition of its activity. Bcl-2 is implicated in human diffuse large cell lymphoma and its inhibition by bryostatin 1 has a beneficial effect in treatment. [99][100][101][102] The Phase I trials suggested bryostatin 1 as a single agent will not have encouraging effect while it will have better effect when given in combination with other agents like cytarabine or paclitaxel for treating multiple cancer types. In clinical trial phase I, Bryostatin 1 is currently being tested against other diseases like HIV and Alzheimer's disease, its side effects include lethargy, vomiting, and muscular pain. 103 One of the disadvantage of bryostatin 1, is that it is only produced and obtained in small quantities from its marine source and this problem can be resolved using bacterial gene expression to produce more bryostatin 1 that can be applied in biomedical and biotechnological procedures. [96][97][98][99] In phase II the efficacy and toxicity of bryostatin 1 were assessed for patients with advanced colorectal cancer who have had previous 5-fluorouracil therapy. The primary end point was tumor response. All 25 patients had disease progression within four cycles. Myalgia was the most common toxicity. Myelo-suppression was not seen. The compound as a weekly 24-hour continuous infusion lacks single-agent antitumor activity in advanced colorectal cancer. 104

Hemiasterlin and Taltobulin
Hemiasterlin; E7974 (Compound 24, Table 4) and Taltobulin; HTI-286 (Compound 25, Table 4) (a synthetic analogue) are tripeptides isolated from different marine sponges like Hemiasterella minor, Siphonochalina, Cymbastela or Auletta species. E7974 and HTI-286 tested in phase I clinical trials to treat tumors that express high level of p-glycoproteins and become resistant to Paclitaxel and Vincristine. Hemiasterlin works by inducing an antimitotic action that will result in depolymerisation of microtubules and cell cycle arrest at G2/M phase. 74 The in-vitro and in-vivo assessments of HTI-286 were compared with vincristine and paclitaxel and found more potent in cell growth inhibition, proliferation suppression and apoptosis induction. It was not affected by p-glycoprotein and drug efflux pump overexpression especially in case of colon cancer. It also showed a tumor suppression effect in other types of tumor cell lines like malignancy, lung, ovary, and breast cancer. 105 Microtubules are important components of the cell cytoskeleton, and they have an important key aspect in maintaining the shape of the cellular components, internal movement, and cellular division through polymerisation and depolymerisation. They have their role in the formation of the mitotic spindle [106][107] . Microtubules are consisting of subunits called alpha-tubulin and beta-tubulin, microtubules form cylindrical tubes. 74,

LAF389
LAF389 (Compound 26, Table 4) is a synthetic bengamide B analogue. LAF389 in phase I clinical trial was discontinued because it exhibited cardiotoxicity. Bengamide B isolated from Jaspidae sponges 110 . LAF389 possess both antiproliferative and anti-angiogenetic properties. [108][109][110] In addition to preclinical investigations that offer a broad antitumor activity. It is characterized as potent methionine amino-peptidase (MetAP) inhibitor, which inhibits both MetAp1 and 2 directly or indirectly. Inhibition of MetAP will cause cell cycle arrest as MetAP is responsible for DNA repair process, cell transformation, and trade in secretory vesicles and infection. [111][112][113]

Metabolites of Clinical Trial Phase 2
The studies of clinical trial phase II are performed on a group of few hundred patients and designed to determine the optimum dose of a potential drug that will induce therapeutic effect. The examples of some aquatic natural products that are in phase II clinical trials have been mentioned as below.

Tasidotin
Dolastatins are group of linear peptides originally obtained from hare Dolabella auricularia formed by cyanobacteria. Tasidotin and its C-carboxylate (Compounds 27, 28, Table 4) are synthetic third generation dolastatin-15 analogue (Compound 15). One of the major Tasidotin metabolites is C-carboxylate (Compound 28) modifies the dynamic stability of microtubules in a comparable way to Tasidotin but was 10 to 30 times more stronger 117 . The hydrolytic product of Tasidotin has activity on polymerization however, it appears to give less cytotoxic activity. [114][115][116][117][118] Anticancer Metabolites in Clinical Trial Phase 1/2 Discodermolide Discodermolide (Compound 29, Table 4) is a polyketide obtained from sponge Discodermia dissolute. It undergoes phase I/II trials and has showed a good antitumor activity and in the treatment of several cancers like those affecting breast, ovaries, and lungs. Before being known to have a cytotoxic effect, Discodermolide was considered as an immunosuppressant as was shown during in-vitro and in-vivo testing. Discodermolide is a microtubule-stabilizing agent that promotes microtubule polymerization and prevents depolymerization resulting in cell cycle arrest at G2-M phase and inhibition of mitosis, therefore it acts in a way like that of paclitaxel or even more potent. [119][120] So, it is useful as a synergistic agent as well as an alternative option to Paclitaxel for tumors that have mutated beta-tubulin and became paclitaxel-resistant as well as other multidrug resistant (MDR) tumors. The information about structure-activity-relationship (SAR) for semisynthetic approaches to improve pharmacokinetics (PK) properties of discodermolide-analogues were reported. 74,119 Soblidotin Soblidotin; TZT-1027 (Compound 30, Table 4) is a sea-derived peptide of an aquatic origin isolated from marine bacterium Salinispora tropica, it is in phase I and II clinical trials. Soblidotin is a newly developed dolastatin 10 analogue that has multiple activities. 4,[120][121] The main difference between soblidotin and the parent drug dolastatin is the terminal dolaphenine amino-acid residue in dolastatin that is replaced in soblidotin by phenylamine group, however, the two compounds have similar activity inhibiter of polymerization of tubulin via attaching to vinca peptide site. In 2002, Soblidotin started its phase I clinical trial, and is currently in three different phases I, II and III in many companies. Soblidotin tested against lung cancer with a mechanism of tubulin inhibition and vascular disruption. 121 Some researchers have found that it inhibits microtubule polymerization, hinder the microtubule assembly/ disassembly balance via interaction with tubulin. It also has anti-vascular action that destroyed the new tumor vasculature. It stops the cell cycle at two phases G2 and throughout to Bcl-2 phosphorylation followed by activation of caspase-3 pathway that led to apoptosis. [120][121][122]

Anticancer Compounds in Clinical Phase III Trials
Phase III trials representing the full-scale assessment of treatment.

Plitidepsin
Plitidepsin (Compound 31, Table 4), a natural marine cyclic depsipeptide called as (Aplidin®). However, it is accessible now by chemical synthesis. 74 It was fundamentally sequestered from a mediterranean tunicate Aplidium albicans. It is useful for treating breast and lung cancers and is in clinical trial phase III studies. 127 Furthermore, clinical trial (Phase I and II) studies of plitidepsin showed brilliant antitumor property in patients with multiple myeloma, advanced medullary thyroid carcinoma, non-Hodgkin's lymphoma, advanced melanoma and urothelium carcinoma. 128 Plitidepsin aggravate cell cycle arrest dose-dependently and results to apoptosis in cultured cells obtained from solid tumors 127 . These outcomes are linked to the provocation of early oxidative stress by stimulation of Rac1 GTPase and the inhibition of protein phosphatases which in concurrence cause the prolonged activation of c-Jun N-terminal kinase (JNK) and p38 MAPK. 74

Gemcitabine
Gemcitabine (Compound 32, Table 4) is an anticancer nucleoside (fluorinated derivative of cytarabine) metabolic inhibitor which is a synthetic version of pyrimidine nucleoside analog acting as a prodrug in which the atoms on the 22 carbon of deoxycytidine is substituted by fluorine atoms. Gemcitabine is presently in phase II & III clinical studies. 129 It has been used for the management of numerous carcinomas of pancreas, bladder, breast, and lung cancer (non-small cell). It is used as a primary treatment for pancreatic cancer alone and in conjunction with cisplatin for the management of late-stage or metastatic bladder and lung cancer (non-small cell). 74 When transported into the cell, gemcitabine is transformed into the functional form which is difluorodeoxycytidine diphosphate (dFdCDP). Then it is subsequently changed into difluorodeoxycytidine triphosphate (dFdCTP) via deoxycytidine kinase and both forms inhibit the progression of DNA synthesis. 74

Limitations of Aquatic Metabolites and Sources as a Basis of Anticancer Agents
It is confirmed that using the bioactive agents from aquatic sources lead to the invention of new active drugs that made a big difference and would be attractive to most drug inventors worldwide. Nonetheless, taking into consideration some restrictions that may hinder the study of aquatic compounds which is as followings: the low amounts of bioactive agents from the organisms, some inorganic salts in addition to some toxins may be present due to the environment or even the organisms themselves. 1 Hence, an exertion should be done to identify the potential adulterant to make marine extracts suitable with in vitro testing. Numerous diagnostic procedures are now accessible for the identification, and isolation of active components in marine extracts. [130][131] Recommendations to Overcome Supply Problems The controlled aquaculture techniques are the best option to solve the dependency of the variety of chemical compounds (chemo-type) produced by an organism on environmental conditions. These techniques might fend off the issue of exhausting the aquatic resources but also could be a practical option to produce the desired biomass for the scaled-up production needed in a drug discovery pipeline. Furthermore, improvements in chemical synthesis techniques and combinatorial chemistry provided satisfactory solutions for appropriate sourcing. 132 At last, the cellular markers for most of the newly found marine natural products are undiscovered; therefore, various screening techniques along with proteomics and metabolomics should be mingled to blown away the constraints of in-vitro techniques.
A significant concern for aqua-derived drug discovery is the permanent accessibility of adequate quantity of these organisms and their metabolites without causing injury to environment. If obtaining from natural sources cannot be achieved in a sustainable way, alternative approaches exist to solve the issue of marine supply, like:

Biotechnological Cultivation
Most aquatic organisms cannot be easily cultured in an artificial environment, so cocultivation process (mixed fermentation of two organisms or more to make the environment like their environment) is used and it will significantly increase chemical diversity and improve the yield of marine-derived compounds production.

Genetic Engineering
It is the transfer of a compound genetic materials and information to the inside of a host cell to produce the desired compound in a sustainable manner, but the exact genetic information must be known to avoid wrong genes insertion. 114

Synthetic or Semi-Synthetic Adjustments
Both techniques can be used to transform a readily available compound into a final desired product, and these are economic alternatives for total synthesis to have variable structures and enhance the product characteristics. 133

COnCLuSIOn
Secondary metabolites obtained from aquatic sources represent a wonderful area of underexplored but magic weapons that can be used against cancer. The currently available therapeutic approaches have faced limitations on their way for cancer treatment in terms of either side effects or emergence of strains resistant to treatment. Cytarabine, Trabectedin, Eribulin Mesylate and Brentuximab Vedotin are anticancer drugs that have gained the approval from FDA and EMA to be used clinically for cancer treatment. Other compounds are still undergoing different clinical trials phases for their safety and efficacy. Marine is a treasure that must be researched well and exploited. Improvement of chemical synthesis and aquaculture or isolation processes can be utilized to overcome the issues mentioned. Other approaches like biotechnological cultivation, genetic engineering and synthetic or semi-synthetic modifications are to be used to solve supply problems and obtain aqua-derived compounds in high yield to guarantee a sustainable and effective production.


SARS-CoV-2 S Protein Subunit 1 Elicits Ca2+ Influx – Dependent Ca2+ Signals in Pancreatic Stellate Cells and Macrophages In Situ

Abstract The S protein subunit 1 (S1) of SARS-CoV-2 is known to be responsible for the binding of the virus to host cell receptors, but the initial intracellular signalling steps following receptor activation of cells in the exocrine pancreas are unknown. Using an intact live mouse pancreatic lobule preparation, we observed that S1 elicited Ca2+ signals in stellate cells and macrophages, but not in the dominant acinar cells. The Ca2+ signals occurred mostly in the form of repetitive Ca2+ spikes. The probability of observing Ca2+ signals depended on the S1 concentration. The threshold was close to 70 nM, whereas at 600 nM, all cells responded. The SARS-Cov-2 nucleocapsid protein did not elicit any Ca2+ signals in any of the three cell types tested. The S1-induced Ca2+ signals in stellate cells started much faster (122 ± 37s) than those in macrophages (468 ± 68s). Furthermore, the interleukin-18 binding protein (IL-18BP) abolished the responses in macrophages without affecting the Ca2+ signals in stellate cells. The S1-elicited Ca2+ signals were completely dependent on the presence of external Ca2+ and were abolished by a selective inhibitor (CM4620) of Orai1 Ca2+ Release Activated Ca2+ channels. SARS-CoV-2 may contribute to acute pancreatitis, an often fatal inflammatory human disease. The S1-elicited Ca2+ signals we have observed in the pancreatic stellate cells and endogenous macrophages may play an important part in the development of the inflammatory process.

Introduction
SARS-CoV-2 enters cells via receptor-mediated endocytosis 1 , using binding of its S protein subunit to ACE2 receptors thereby allowing endocytic uptake of the virus 2 . SARS-CoV-2 is known to enter a number of cell types in the human body including the respiratory tract 2 , the central nervous system 3  reported cases of acute pancreatitis (AP) 4 . The ACE2 receptor is present in the pancreas 4 , but the mechanism by which SARS-CoV-2 can induce AP as well as the type of cells targeted by the virus are currently unknown.
Pancreatic acinar cells dominate the exocrine pancreas and are largely responsible for the initiation of AP 5 . However, other cell types in this tissue also play important roles, in both physiological and pathophysiological processes, including AP [5][6][7][8][9][10][11] . Our novel method of using isolated lobules of the exocrine pancreas 7,9,10 , allowing preservation of the normal microscopic structure of the pancreatic environment, enables simultaneous recordings of intracellular signals i.e., cytosolic Ca 2+ 6-11 and nitric oxide 11 in several different cell types, including stellate cells 9 , intrinsic nerves 10 and pancreatic macrophages 7 .
Whereas physiological and pathological Ca 2+ signals in pancreatic acinar cells are well understood 5 , their roles in the stellate cells 9 and the macrophages 7 are less clear. There is, however, evidence that Ca 2+ and NO signals in stellate cells amplify the pathological effects in AP [8][9][10][11] . In our recent work 7 we have studied immune cell Ca 2+ signalling in response to stimulation with ATP and identified the responding cells as macrophages by immunohistochemistry with external markers specific for this cell type [12][13][14] . Macrophages are involved in the development of both AP and pancreatic cancer 15 , leading to inflammation and exacerbation of the patients' conditions 16 , but the evidence for this has largely been limited to work on cell cultures or fixed tissue samples of the pancreas. Taking advantage of the pancreatic lobule preparation, we now report for the first time Ca 2+ signals induced by the SARS-CoV-2 S1 protein in pancreatic cells.

Animals
Ethical Approval: All animal studies were ethically reviewed and conducted according to the UK Animals (Scientific Procedures) Act, 1986. All experimental protocols were performed under a Project Licence granted by the UK Home Office and approved by the Animal Care and Ethics Committees at Cardiff School of Biosciences, Cardiff University. Animals were maintained in plastic cages supplied with fresh corn cob bedding, tap water, and commercial pelleted diet.

Ca 2+ Measurements
Pancreatic lobules were loaded with Fluo-4 acetoxymethyl (AM) ester at room temperature as described previously 7 . The lobules were transferred into a flow chamber and perfused with the standard solution alone or containing different chemicals as described in the experimental protocols of the result section. Cells were visualized using a Leica SP5 MPII two-photon confocal microscope, with an x63 1.2NA objective lens. The Fluo-4 excitation wavelength was 488 nm and emission was collected at 500-560 nm with resolution of 256 × 256 pixels and speed of 0.7 frames/s. Images were analysed using Leica Confocal Software (Leica, Mannheim, Germany). Fluorescence signals were plotted as normalized F/F0. ANOVA or Student's t-test were performed for statistical analysis.

Immunostaining in ExVivo Pancreatic Lobules
Immunostaining of live pancreatic lobules was performed as previously described 7 . Mouse F4/80 Alexa Fluor 647-conjugated monoclonal rat antibodies were used to label specific surface proteins of immune cells, usually at the end of Ca 2+ measurement experiments, unless otherwise stated. After blocking with 1% BSA and 10% goat serum containing PBS, the isolated pancreatic lobules were incubated for 1 h at room temperature with the selected antibody. Antibody staining was visualized by exciting Alexa Fluor 647 with 633 nm laser at 10% power and emitted light was collected at 640-700 nm. Conjugated antibody fluorescence was also overlaid with Fluo-4 staining as described in the Ca 2+ measurements section. Lobules were attached to the glass coverslips covered with poly-L-lysin.

Results
As previously reported 7,10 , pancreatic macrophages are typically sensitive to ATP or ADP and display Ca 2+ signals when stimulated by these agents, whereas pancreatic stellate cells are typically insensitive to ATP, but are always responsive to bradykinin (BK), displaying Ca 2+ signals when stimulated by the nona-peptide. Pancreatic acinar cells are always sensitive to acetylcholine and cholecystokinin, but never respond to stimulation with BK. They can occasionally display Ca 2+ signals in response to stimulation with ATP. Nevertheless, ATP-sensitive cells are most likely to be macrophages and this can be verified after the recording of Ca 2+ signals by staining with fluorescently labelled antibodies for macrophages (F4/80) ( Figure 1A-D). In the macrophages, untagged S1 protein (200 nM) elicited either a single Ca 2+ spike ( Figure 1A, 38% of cells tested, n = 17) or a train of such spikes ( Figure 1B, 40% of cells tested, n = 18). Similar results were obtained from the BK-sensitive stellate cells ( Figures 1C, D, n = 23). A lower concentration of S1 (70nM) evoked Ca 2+ signals in a smaller proportion of cells (33% of macrophages and 25% of stellate cells, Figure 1E), whereas a higher concentration of S1 (600nM) induced Ca 2+ signals in every stellate cell and macrophage tested ( Figure 1E). The latency after S1 application, before the first Ca 2+ signal appeared, was much longer in the case of the macrophages than for the stellate cells ( Figure 1F, P < 0.006). The S1-elicited Ca 2+ signals in Figure 1. Pancreatic immune and stellate cells exhibit Ca 2+ signals in response to the addition of SARS-CoV2 S1 protein. A-B. Representative traces demonstrate single and multiple Ca 2+ signals from pancreatic immune cells in response to S1 addition (Pancreatic macrophages (PM), green traces). Lobules were stimulated with 2.5 μM ATP, followed by application of 200 nM S1 (for 20 min). At the end of each experiment 2.5 μM ATP was consecutively added (n = 50). At the end of Ca 2+ measurements, lobules were stained under the microscope with fluorescently labelled antibodies for macrophages (F4/80) and nuclear staining with Hoechst 33342. Images of nuclear staining (Hoechst 33342, Fig 1Aa), calcium dye (Fluo-4, Fig 1Ab) and antibody staining (AlexaFluo 647, F4/80, Fig 1Ac) for the same area as for the trace in Fig 1A (images in Fig 1Aa-c with overlay presented in Fig 1Ad; scale bar 10μm). C-D. Representative traces of single (C) and multiple (D) Ca 2+ responses from pancreatic stellate cells (PSC, red traces) in pancreatic lobules. Lobules were stimulated with 200 nM S1 for 20 min. At the end of each experiment 5 nM and/or 30 nM BK were sequentially added (n = 23). (E) Concentration-dependence of PSCs and PMs responding to S1 stimulation by Ca 2+ signals (red and green data bars, respectively). No responses were observed from acinar cells (PAC, blue data bars). The number of responses was calculated including single and multiple Ca 2+ spikes to different concentrations of S1 (70 nM, 200 nM, 600 nM). (F) Time between application of S1 (200nM) and beginning of elevation of the cytosolic Ca 2+ concentration in PSCs (122 ± 37s) and PMs (468 ± 68s). PSCs responded significantly faster, P < 0.006. (G) Representative responses to S1 in the presence of IL-18 binding protein (IL-18BP, ) in PMs and PSCs. (H) No Ca 2+ signals to S1 were observed in the presence of IL-18BP (600nM) in PMs and PACs (n = 7 and n = 33, respectively) while 63.5% of PSCs did respond (n = 11), similarly to results shown in Fig 1E. macrophages were abolished by the IL-18 binding protein IL-18BP (Fig 1G, H), whereas the responses to S1 in the stellate cells were unchanged (63.5%, n = 11). S1 never elicited any Ca 2+ signals in the acinar cells (n > 100). We have also tested the effect of the S1 receptor binding domain (RBD). As seen in Figure 2A-E, RBD at a concentration of 200 nM, elicited Ca 2+ signals in 69% of the macrophages (n = 16) and 71% of the stellate cells tested (n = 17).
We did not observe any spontaneous (in the absence of S1 or RBD) Ca 2+ signals in any of the stellate cells or macrophages We investigated whether the S1-elicited Ca 2+ signals were dependent on the presence of Ca 2+ in the external solution. In the absence of external Ca 2+ , as seen in Figures 3A and B, S1 did not elicit any change in the cytosolic Ca 2+ concentration in Figure 3. Removal of external Ca 2+ or application of the Orai1 inhibitor CM4620 block S1-induced Ca 2+ signals in pancreatic immune and stellate cells. A-B. Representative traces showing that there were no Ca 2+ signals in response to 200 nM S1 in PMs (n = 11, green trace) and PSCs (n = 9, red trace) in the absence of external Ca 2+ . 25 nM BK and 2.5 μM ATP were added at the end of each experiment. C-D. Representative traces demonstrating the absence of Ca 2+ signals in response to 200 nM S1 in PMs (n = 11, green trace) and PSCs (red trace; only 2 out of the 8 tested cells responded with Ca 2+ signals of reduced amplitude and duration), when treated with the Orai1 CRAC channel inhibitor CM4620 (10 μM). 2.5 μM ATP was added at the end of each experiment. macrophages that were responsive to ATP (n = 11) or in stellate cells (n = 9) that displayed Ca 2+ signals in response to stimulation with BK. In both these cell types, it has previously been shown that an inhibitor of the opening of Orai1 Ca 2+ Release Activated Ca 2+ (CRAC) channels markedly reduces the duration of Ca 2+ signals evoked by ATP or BK 7,9 . As shown in Figure 3C and D, the Orai1 inhibitor CM4620 abolished S1-elicited Ca 2+ signal generation in the macrophages (n = 11) and markedly inhibited or abolished Ca 2+ signals in 75% of the stellate cells tested (n = 8).

Discussion
There is general agreement that ACE2, the SARS-CoV-2 receptor, is expressed in the exocrine pancreas, but little is known about its distribution between the different cell types [17][18][19] . Specifically, there is currently no information on the presence or absence of ACE2 in the pancreatic stellate cells or macrophages. Our finding that S1 and its RBD elicits Ca 2+ signals in both stellate cells and macrophages provides the first evidence for functional receptors, but not necessarily on both cell types. The much longer delay in the response to S1 in the macrophages ( Figure 1F), as compared to the stellate cells, might indicate an indirect action of S1 on the macrophages. This conclusion is strengthened by the experiments (Figure 1G) showing that the IL-18 binding protein IL-18BP abolished S1-elicited Ca 2+ signal generation in the macrophages but not in the stellate cells. The simplest explanation would be that S1-elicited Ca 2+ signals in the stellate cells stimulate IL-18 secretion, which then in turn elicits Ca 2+ signals in the macrophages. Stellate cells are capable of IL-18 production and, in the case of chronic pancreatitis, it has been shown that levels of IL-18 are increased following activation by lipopolysaccharide (LPS) or TGF-ß 20 . Although plausible, and compatible with our data, the hypothesis that the S1-elicited Ca 2+ signals in the macrophages are mediated by IL-18 from the stellate cells needs to be tested further in future experiments. The absence of Ca 2+ signals in the acinar cells in response to S1 does not rule out the existence of receptors on these cells, as receptor activation could elicit effects other than Ca 2+ signals but, so far, there are no data providing evidence for functional receptors on the acinar cells.
At this stage there is no information about the intracellular steps leading to the generation of the Ca 2+ signals, but the fact that these signals, in contrast to those evoked in the same cells by ATP or BK, are completely dependent on the presence of external Ca 2+ and can be inhibited by the Orai1 CRAC channel inhibitor CM4620, suggests a mechanism of generation that is somewhat different from that of the physiological agonists.
Based on our previous studies of Ca 2+ and NO signalling in pancreatic stellate cells 9,10 and Ca 2+ signalling in pancreatic macrophages 7 , we have proposed that AP, although initiated in the acinar cells, progresses to a severe and often fatal disease state through necrotic amplification loops, sustained by excessive Ca 2+ entry through CRAC channels in stellate cells, macrophages and acinar cells. We have specifically provided evidence for a number of interaction pathways between acinar cells and stellate cells and between acinar cells and macrophages 5 . The new data presented here suggest a further pathway, not hitherto recognized, between stellate cells and macrophages in which IL-18-secreted by stellate cells in response to activation of SARS-CoV-2 receptors-acts on macrophages to generate Ca 2+ signals in these cells. IL-18 levels are elevated in AP, and it has previously been suggested that this agent may play a role in acute and chronic pancreatitis together with several other interleukins [21][22][23] .
The S1 and RBD-elicited Ca 2+ signals in macrophages and stellate cells, demonstrated in this study, would be likely to exacerbate an otherwise mild case of AP initiated, for example, by the primary action of bile acids or a combination of ethanol and fatty acids on the acinar cells, and could help to explain the known cases of severe AP associated with SARS-CoV-2 infection 4,24 . Our finding that the S1-and RBD-elicited Ca 2+ signals can be inhibited by an Orai1 CRAC channel blocker, is a further argument in favour of this pharmacological treatment against AP 5,25 .

Funding
The work was supported by grants from the Medical Research Council (UK) (MR/J002771/1 and G19/22/2 to OHP) and Children with Cancer UK grants (17/248 and 19/288 to OVG and JVG).


AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

■ INTRODUCTION
AlphaFold has overcome age-long bottlenecks and forcefully bared the power of artificial intelligence (AI) in biological research. 1−3 AlphaFold has combined numerous deep learning innovations to predict the three-dimensional (3D) structures of proteins at or near experimental scale resolution, inspiring the community (including us) to rethink studies of function, evolution, and disease (e.g., refs 4−13). The sheer volume of the rapidly generated accurate structures argues that new, ambitious, frontier-pushing studies will emerge. It also points to research projects that should be reconsidered. The richness of high quality data that are being compiled in databases (e.g., refs 5 and 14−25) is already strengthening studies that require protein structures, such as mapping binding sites and interactions in signaling pathways, and identification of hot spots, including latent and rare cancer driver mutations. 26−34 The most profound impact will likely be in accelerating and improving production of new medications (e.g., ref 35), and in generating data that can be used toward this vital aim (e.g., refs 5, 17, 18, and 36−39). AI developments and applications 40 may further help foretell whether the signal propagating downstream will be strong enough to reach its genomic target to activate (suppress) gene expression, 41 and predict pathways. 42−49 Altogether, these powerful approaches and the databases that they create revamp and transform traditional and ongoing research involving the use of structures. They also embolden us to step back, rethink, and innovate our projects.
AlphaFold's achievements have been made possible by the protein databank (PDB), currently with a size nearing 200 000 experimentally determined structures. It has been trained on protein chains from the PDB and uses the input sequence to query databases of protein sequences to construct a multiple sequence alignment. 4 However, its striking success has not led us to a deeper mechanistic understanding of exactly how a protein sequence folds, thus not assisting in the folding of a protein from its sequence. Below, we first briefly describe the protein folding problem, 50,51 and strategies to predict protein structures. We describe key conceptual and computational developments and the transformative AlphaFold advances. We outline its strengths and some weaknesses. We emphasize what it has accomplished, and what it has not, and the magnitude of the challenges, underscoring the difference between the theoretical folding problem, which was not solved, 51−57 and practical predictions by incorporating additional evolution information that generally have been. 58− 65 We proceed to AI approaches to the complementary problem of protein−protein interactions (PPIs) by these methods and others, 66−75 with the human−microbiome PPI as a relevant and topical example. 66 AI-powered prediction of human−microbe PPIs can accelerate research into questions such as how microbiota hijack cell signaling and provide drug targets. 76−80 We discuss how AI can reshape drug discovery, for example by amplifying repurposing of FDA-approved drugs, 81−91 an area which is already thriving. AI can also select combinations of drug targets, powerfully guiding and accelerating experiments by providing specific testable hypotheses. Machine learning has already proved its merit in the life and medical sciences. 1,92−98 Coupled with harnessed exascale computing, 99,100 advanced, AI-powered methods are set to revolutionize therapeutic development, providing prioritized drug combinations for the attending physicians.
Finally, we note that AlphaFold, which predicts single ranked structures for a protein sequence, is unable to address directly allosteric mechanisms, which are based on the populations of conformational states in the ensembles. 101−117 Allostery, where the signal propagates dynamically with the shifts in the populations, underlies regulation and thus cell life. 118,119 Due to its higher specificity and consequently lower toxicity, which results from targeting nonconserved allosteric sites, allostery also increasingly features in allosteric drugs. 120−125 Can we then foresee AlphaFold assisting in unraveling the mechanisms of allosteric hotspot mutations and allosteric drug discovery? Indirectly it can and does, even in our hands. The rigid structures that AlphaFold predicts can be submitted to MD simulations that generate such ensembles (Figure 1). At the same time, as we discuss here, other AI-based strategies can assist directly in such efforts, most effectively via accelerating and enhancing MD simulations. Efforts are also likely to persist in exploiting AI toward prediction of allosteric binding sites. Nevertheless, it behooves us to recall that the effectiveness of allosteric sites is determines by both stable interactions at the site, which is something that AI can help with, and initiation of effective allosteric signals, which would be more challenging. Current approaches to predict allosteric binding sites address Figure 1. Current strategy of allosteric drug discovery in computational structural biology employing the AlphaFold program with artificial intelligence (AI)-powered methods (top panel). Experimental instruments, such as X-ray crystallography, cryo-electron microscopy (cryo-EM), and nuclear magnetic resonance (NMR) can resolve protein structures, but often miss the coordinates of highly fluctuating regions in the protein structure. AlphaFold can predict the missing coordinates of these regions. The resulting structure can be subjected to molecular dynamics (MD) simulations that provide conformational dynamics, conformational changes, and folding characteristics of the protein. An example is shown for Src homology region 2-containing protein tyrosine phosphatase 2 (SHP2) (bottom panel). The X-ray structure of SHP2 (PDB ID: 4DGP) misses residues in two flexible regions, which can be predicted by AlphaFold. SHP2 contains two Src homology 2 (SH2) domains (nSH2 and cSH2) and a protein tyrosine phosphatase (PTP) domain. only the former. In that sense, they resemble the characterization of orthosteric sites, except that their scoring is based on statistics of allosteric sites.
Orthosteric drugs block the active site; allosteric drugs alter the population of the active state of the protein, including the active site, through binding at a site far away. 126 We suggested that allosteric drugs can constitute of "anchors" and "drivers" atoms, where the anchor atoms bind to the allosteric pocket, without changing the conformation of the binding site. The interactions of the anchor atoms stabilize ligand binding, resembling protein−ligand binding at the orthosteric site. The binding of driver atoms "pulls" or "pushes" atoms in the protein pocket. This initiates the allosteric signal, which shifts the receptor population from the inactive to the active state. Driver atoms can trigger agonism and antagonism. AlphaFold cannot handle population shifts. AI strategies can but will need to go beyond prediction of stabilizing interactions.
Finally, not surprisingly, prediction of the structures of intrinsically disordered proteins (IDPs) and regions (IDRs) is another problem where AlphFold falls short. Disordered proteins (regions) are characterized by broad and heterogeneous ensembles where the differences in the relative conformational stabilities are small, or even minor and the barriers are low. 127−135 The conformations interconvert, leading to low probabilities of AlphaFold's reliably capturing those most favored, or the conformational distribution. Nevertheless, leveraging, learning, and mining of the conformations can exploit AI. 136−138 AI-powered algorithms, which are fed vast compiled data, and enabled by the emerging massive compute power are propelling a revolution in computational biology ( Figure 1). Unlike quantum computing, in the case of AI and data-driven computing, the technological innovations at the requisite scales are already at hand.

■ PROTEIN FOLDING VERSUS PREDICTION OF PROTEIN STRUCTURE
Protein Folding. The protein folding problem embraces two questions: 51 first, the conceptual question of how a protein's amino acid sequence dictates its 3D atomic structure, and second, how, starting from a single amino acid sequence, to successfully predict the 3D structure, without using information related to other available (homologous, same family) sequences nor structures of any related sequences. Such computational prediction methods are guided by the conceptual notion that this is how the protein folds in nature. Single sequence-based prediction in solution considers forces related to hydrogen bonds, ion pairs, van der Waals attractions, 139 and chiefly water-mediated hydrophobic interactions, with the hydrophobic effect the driving force for protein folding. This formal folding problem emerged six decades ago, alongside the first atomic-resolution protein (globins) structure. The structure led to thermodynamic questions of the balance of interatomic forces that determines the structure of the protein, how the protein can fold so quickly, that is the kinetics of the pathways, and the computational problem of protein structure prediction. The landmark thermodynamic hypothesis of Christian Anfinsen and his colleagues 140,141 stated that the native structure of a protein is its thermodynamically most stable structure, and it is determined only by its amino acid sequence and the conditions it is at, with kinetics playing no role. No other considerations are at play, that is, whether it is synthesized in the lab or on the ribosome or undergoes chaperone assisted folding. The folding paradigm stipulated that unfolded molecules will always spontaneously fold into the same shape; that is, the linear amino acid sequence specifies a protein's folded native state. 58,142−144 Anfinsen's thermodynamic hypothesis emphasized the shape of the energy landscape where the native state is the one with the lowest free energy. 141,145 Computationally, that description posed the problem of prediction of protein structure, forming the basis for approaches that dominated the field for scores of years. If only the sequence matters, along with the physicochemical forces, it should be possible for "good" algorithms to fold it. Assuming that the crystal structure represents the minimum energy state, the "goodness" of the predicted structure can then be assessed by comparison with it. Anfinsen's description combines sampling of alternative conformations, ranking them by energy and identifying the lowest energy state. 51,146−148 Subsequent efforts focused on prediction of secondary structures, although the dominant role of the hydrophobic interactions suggested that secondary structure is an outcome of the 3D structure and its cause. 149,150 The small (5−10 kcal/mol) difference in the stability of the native structures as compared to the denatured states 151 compounded the challenge that predicting methods faced.
Already early on, Cyrus Levinthal conceptualized the key problem facing the protein and the prediction algorithms: 152 the vast time scales for the protein to search the folding space and reach its most stable native state under biological conditions. 58 For prediction algorithms' sampling backbone states, the search space size grows exponentially with chain length, becoming an impossibility. Levinthal argued that there is no need to search this vast space since the energy landscape is funnel-like, rather than flat, and thus can guide sampling toward the biological conformational basin. 51,153 Packed hydrophobic cores optimize their van der Waals (vdW) interactions, restrict torsion angles, and abolish internal "holes", with hydrogen bonds and salt bridges balancing the loss of interactions with water. Harold Scheraga employed physical chemistry to pioneer studies to decipher how amino acid sequences influence the 3D folding pathways, thermodynamics, and biological activity of proteins. Neither AlphaFold nor broadly other protein structure prediction algorithms consider folding pathways. Physical chemistry is accounted for implicitly; in the case of AlphaFold, via AI.
Protein Structure Prediction. Prediction of protein structure can be template-based or template-free, which does not use global similarity to an experimental (protein data bank, PDB) structure. 58 Template-free modeling exploits physicsbased energy functions. Both can exploit machine learning and AI to use data in the PDB. Template-based modeling selects a structural template and uses sequence alignment. Templatefree modeling uses conformational sampling and ranking. It may start with multiple-sequence alignment to related sequences to predict local structural features, which will guide the 3D modeling followed by refining and ranking.
Integrative modeling 154−156 that assembles structures from individual components may suffer from high false-positive rate. Computational integrative approach can combine data from experimental methods, bioinformatics, physics, and statistics for rapid and accurate structure determination of protein complexes. The algorithms can integrate experimental data, such as X-ray crystallography, NMR spectroscopy, 2D and 3D electron microscopy (EM), small-angle X-ray scattering (SAXS), mass spectrometry (MS), hydrogen−deuterium exchange (HDX), mutations, sequence conservation and covariation, and statistical analysis of known structures. Computationally, the algorithms can derive from computer vision, image processing, computational geometry, machine learning, robotics, and graph algorithms. Machine learning has however been used toward protein structure prediction. 157−163 AlphaFold is not the first in being a machine-learning model. Its remarkable success (with scores of near 90 even in the difficult targets in the 2020 Critical Assessment of Protein Structure Prediction, CASP) was influenced by its training not only on all the PDB structures but also on structures it predicted, and it uses the structure and correlation data to predict the pairs of amino acids that are in contact as well as all amino acid pairwise distances. It also ensured that the distances between the amino acids satisfy the triangle inequality, saving time at intermediate steps. 164 To date, AlphaFold illuminates half of the dark human proteins. 10 Still, questions remain, such as which structural states exist for a give protein, and what is the population of each state. Addressing these questions is vital to relate protein structure to function. This is where AlphaFold falls short. However, the models it produces can serve as input to generate ensembles, for example by MD simulations, which, if carried out at sufficiently longtime scales, in parallel, it should be able to produce. Simulations can sample the relevant states, can enumerate possible state combinations (multistate models), and can determine the population sizes for the states.
The Structure−Function Paradigm Overlooked Ensembles and Dynamic Energy Landscapes: AlphaFold Is Attuned but Is Unable to Address Them. The sequence− structure−function dogma was the touchstone of a generation. It dominated molecular biology for decades. It was introduced by physical chemists who explained that biological macromolecules function when they are folded. Thus, to understand how molecules function, one needs to consider their 3D structures, a transformative paradigm that became a tenet of modern biology. Today, it is broadly recognized that rigid molecules cannot perform a function, leading the way to the appreciation that to sustain life, molecular flexibility is a necessity. That however has not fully translated to the understanding of the powerful concept of the energy landscape. 165 That is, that biomolecules are dynamical objects that are always interconverting between a variety of structures with varying energies, 166 and that this is the origin of allosteric mechanisms. 167−169 This notion of flexibility as interconversion between conformations is critical for understanding biological processes and their regulation, such as protein activation as a shift of the ensemble from the inactive to the active state, how allosteric drugs work, cell signaling, and binding mechanisms through conformational selection rather than induced fit. The conceptual evolution from the classic structure−function paradigm to dynamic energy landscapes of biomolecular function and allosteric mechanisms, poses a challenge to AlphaFold's powerful predictions. To understand biological regulation, structure should be linked to function through protein ensembles in terms of populations and relative energies, which is the foundation of allostery (Figure 2). Despite its transformative power and vast broad impact, the AlphaFold predictions are unable to address it directly. It is only through their sampling that this functional aim can be accomplished.
Around their native states, protein landscapes consist of rapidly interconverting conformations. The ensembles are "fuzzy". 170,171 Events associated with their environments and functions, such as changes in pH, interactions with ions, water, and lipids, and binding of small or macromolecules, promote conformational changes. These are frustrated by their local restricted molecular environment. 172 The cooperative, accommodating structural changes shift the ensemble. The shifted, now populated states are frustrated by their current neighboring residues conformations. Binding and catalysis involve making and breaking covalent and noncovalent interactions at the interaction site. These propagate through frustration, influencing the conformational states of the ensemble. The shifts in the ensemble alter the relative stabilities, i.e., the populations of the states, thus influencing the allosteric transitions. Importantly, frustration does not create new conformations; instead, it alters the number of molecules populating it. 173 Frustration is thus a powerf ul tool harnessed by evolution for f unction. 174 Biomolecules must be described statistically, not statically. 166,175 Static descriptions were the norm for decades. Yet, a static description cannot capture function. It cannot describe protein activation from the inactive to the active state upon some activation event, such as binding a hormone, or being covalently modified by a post-translational modification, or the presence of oncogenic driver mutations. It is also unable to describe how high affinity binding to an activator shifts protein molecules to their active state. 176,177 It will further fail when attempting to describe how allosteric "rescue mutations" 178,179 work (albeit not other rescue mutations, e.g., refs 180−183), how allosteric drugs are able to block the active site, and how mutations countering them can be overcome. All these processes which take place in the cell would not have been possible had the protein existed in a single structure or was flipping between only two states, active and inactive. While there is a single conformation that the active enzyme should adopt for productive catalysis, there are multiple ways to inactivate it and thus many inactive states. The notion of a single structure bred the concept of the "lock-and-key" binding mechanism. This view was superseded by the "induced fit" mechanism which considered the presence of only two states, an active and an inactive state. In an induced fit scenario, the ligands bind to the single "open" protein structure and the interaction between a protein and a rigid binding partner induces a conformational change in the protein. 184 In contrast, the conformational selection mechanism 167−169,185 theorizes that the energy surface hosts a very large number of conformations, and the one that fits best is selected, with subsequent minor induced fit optimization, largely by side chains.
AlphaFold exploits AI to make template-free predictions of protein structures from their sequences, equipping biologists with structures with good resolution. The predictions that it yields, like those obtained by homology modeling, are rigid. Flexibility is implicitly captured by the absence of, or low confidence levels of predicted structure for certain regions, as in the case of intrinsically disordered proteins. Thus, computational methods once relegated to the periphery of biology, are now at the forefront, driving "the second molecular biology revolution". AlphaFold can drive breakthroughs in fundamental problems in the life sciences, including precision medicine, with promise to transform research and accelerate drug discovery. It is driven by deep learning innovations, which appear poised to transform MD simulations.

AI and Machine Language in Simulations.
Machine learning for molecular simulations�tools, strategies, and principles�have been reviewed recently. 186 As can be seen from this excellent review, machine learning has already been making a significant impact on the development of approximate methods for complex atomic systems. The innovation in the development and integration of MD simulations with deep learning can reproduce, interpret, predict, and generate data relating to the behavior of biological macromolecules. 187−192 Deep learning methods can help MD simulations excel in their efficiency and scales, with AI bridging between deep learning technologies and simulations. Challenges toward broad usage include smooth connection of AI and MD and automation of workflows. These could popularize novel deep learning tools in MD simulations toward efficiently exploiting both powerful methods. The number of publications in this area has been skyrocketing, emphasizing the recognition of the potency of AI and machine learning in simulation. As an example, MD simulations need to perform extensive sampling of the conformational space that require long time scales. Deep learning involving, e.g., variational autoencoders have been shown to be useful. The learned latent space in the variational autoencoders has been employed to generate unsampled protein conformations, and simulations starting from these conformations accelerated the sampling. 193 In another example, a deep learning framework with mixed classical and machine learning potentials (TorchMD) has been developed for molecular simulations. 188 The review cited above provides additional diverse examples. Deep learning has also been already exploited in structural modeling and design 60,194−196 and analysis 191 and linking these to function. 197 AI and Machine Language in Prediction of Pathogen−Human Host PPIs. AI and deep learning are also being developed and applied to experimental determination and prediction of macromolecular structures, 198 as well as to PPIs. 199,200 Applications of AI approaches to human−microbiome protein−protein interactions have also been reviewed recently. 66 These interactions play important roles in human health and disease. There is a rapid increase of data that microbes, bacteria, and viruses impact human health. They can modulate human signaling and immune response by interacting with the human proteins. To decipher this modulation, it is important to identify the specific interactions, the human host proteins that are involved, and the structure of the complex. Identification of the interactions along with their structural details at atomic resolution permit understanding the mechanisms involved in pathogen survival and assist in drug discovery targeting these interactions. The interactions help the pathogens to elude and bypass the immune defense, with the pathogens hijacking host signaling. Mechanistically, pathogen proteins can have surfaces which resemble those of the host, allowing them to mimic and compete with host protein interactions (Figure 3). They bind to the host protein and rewire its physiological signaling. Data, including structural details, are scarce and large-scale experimental detection is challenging. Efficient and robust computational strategies to predict the interactions is thus vital. We have developed an algorithm and server to predict these human host−microbial PPIs (HMIs) based on their protein structures, which can be experimental or modeled. In large scale applications, AlphaFold can now be used toward this aim. Machine learning permits both the large-scale efficient and generalizable application and addressing the complex dynamics of such relationships that the machine learning algorithms can decipher.
Challenges in machine learning for PPI prediction relate to both data and method. With limited microbial but not human data, microbial sample sizes are small. In sequence-based algorithms, the dimensionality problem can be pronounced, where the difficulty exponentially grows as the feature size increases. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), or autoencoders can be used to embed the samples into lowerdimensional spaces, 201,202 and preprocessing and postprocessing pipelines can be employed for other data. In structurebased methods the problems may relate to the quantity and diversity of the representation. Data relating to host-microbe PPIs with 3D structures are sparse, thus facing a problem in training and evaluating the computational methods. Additional problems involve lack of gold standard test data set. Evaluation metrics are also unclear, the PPI networks are sparse, and more. 66  ■ CONCLUSIONS AI and machine learning are appending projects. They are applied in diverse applications, including biological networks. 204 They impact disease biology, drug discovery, microbiome research, and synthetic biology. They also evolved a machine learning pipeline for molecular complex detection in protein-interaction networks, 205 as well as the relevance of major signaling pathways in cancer survival. 46 Here, we briefly overviewed the immense impact of AlphaFold, and of AI in structural biology, with some examples. We highlighted what AlphaFold can and cannot accomplish and why. Allosteric mechanisms fall into the latter category. Nevertheless, through MD simulations of models that AlphaFold produces, this aim can be accomplished as well. Still, however, even though simulations would address this dynamics problem, at such scales, the cost is prohibitive. A paradigm-shifting machine learning method is needed to model protein dynamics.
AlphaFold and its underlying deep learning innovations have opened up the next frontiers in protein science, 164 including precision medicine. 206 Protein structures connect to cell biology, chemistry, biophysics, and medicine. To date, over 180 000 protein structures are available, open to all researchers across the world in the PDB database. Still, structures of pathogens are not within them, and neither are many others, which are essential to human health. The resource is now there, and with growing computational power, eventually, these will be there as well. Nonetheless, the availability of the structures is insufficient. For us, as biophysicists, the key is what are the significant questions to ask. What should the focus of our research be, such that we do not repeat what was so well done but instead exploit the new capabilities to ask the really important questions. The authors declare no competing financial interest.

Biographies
Ruth Nussinov pioneered the "conformational selection and population shift" (1999) as an alternative to the "induced fit" textbook model to explain the molecular mechanism of recognition and posited that population shift underlies allosteric regulation. She extended this pre-existing ensemble model to catalysis (2000) and oncogenic activation, contributing to extraordinary advancements in understanding structure and function. Nussinov received her Ph.D. in 1977 from Rutgers University and did postdoctoral work in the Structural Chemistry Department of the Weizmann Institute. Subsequently, she was at the Chemistry Department at Berkeley, the Biochemistry Department at Harvard, and a visiting scientist at the NIH. In 1984, she joined the Department of Human Genetics, at the Medical School at Tel Aviv University. In 1985, she accepted a concurrent position at the National Cancer Institute of the NIH, Leidos Biomedical Research, where she is a Senior Principal Scientist and Principal Investigator heading the Computational Structural Biology Section at the NCI. She has authored over 700 scientific papers. She was the Editor-in-Chief of PLoS Computational Biology, is currently the Editor-in-Chief of Current Opinion in Structural Biology, and is Associate Editor and is on the Editorial Boards of a number of journals. She is a frequent speaker in Domestic and International meetings, symposia, and academic institutions, has won multiple awards and has been elected fellow of several societies, and has been a highly cited researcher. Her National Cancer Institute website gives further details. https://ccr.cancer.gov/ruth-nussinov.

Mingzhen Zhang is a computational scientist in Computational
Structural Biology Section at Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute (NCI), Frederick, MD. His research focuses on the activation mechanism of key oncogenic proteins in Ras signaling pathways. He uses computational tools to uncover the detailed highresolution activation mechanisms of lipid kinase, PI3K, and protein kinase, Raf, the main downstream effectors of Ras, and to explore the structural principles for cancer drug discovery.
Yonglan Liu is a postdoctoral researcher in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD. Her research interests include computational structural biology, biophysics, and biochemistry, specifically in cancer, neurodegenerative diseases, and biomaterials by using MD simulation and machine learning algorithms. Currently in NCI, she is studying the structural principles of signaling proteins, such as Bcr, c-Abl, and SHP2, and uncovering their mechanisms of protein−protein interaction, activation, and inhibition, for drug discovery.