Software for molecular docking: a review

Effective Protein-Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation

Virtual screening is a commonly used process to search for feasible drug candidates from a huge number of compounds during the early stages of drug design. As the compound database continues to expand to billions of entries or more, there remains an urgent need to accelerate the process of docking calculations. Reuse of calculation results is a possible way to accelerate the process. In this study, we first propose yet another virtual screening-oriented docking strategy by combining three factors, namely, compound decomposition, simplified fragment grid storing k-best scores, and flexibility consideration with pregenerated conformers. Candidate compounds contain many common fragments (chemical substructures). Thus, the calculation results of these common fragments can be reused among them. As a proof-of-concept of the aforementioned strategies, we also conducted the development of REstretto, a tool that implements the three factors to enable the reuse of calculation results. We demonstrated that the speed and accuracy of REstretto were comparable to those of AutoDock Vina, a well-known free docking tool. The implementation of REstretto has much room for further performance improvement, and therefore, the results show the feasibility of the strategy. The code is available under an MIT license at https://github.com/akiyamalab/restretto.

MDO: A Computational Protocol for Prediction of Flexible Enzyme-Ligand Binding Mode

AIM

Developing a method for use in computer aided drug design Background: Predicting the structure of enzyme-ligand binding mode is essential for understanding the properties, functions, and mechanisms of the bio-complex, but is rather difficult due to the enormous sampling space involved.

OBJECTIVE

Accurate prediction of enzyme-ligand binding mode conformation.

METHOD

A new computational protocol, MDO, is proposed for finding the structure of ligand binding pose. MDO consists of sampling enzyme sidechain conformations via molecular dynamics simulation of enzyme-ligand system and clustering of the enzyme configurations, sampling ligand binding poses via molecular docking and clustering of the ligand conformations, and the optimal ligand binding pose prediction via geometry optimization and ranking by the ONIOM method. MDO is tested on 15 enzyme-ligand complexes with known accurate structures.

RESULTS

The success rate of MDO predictions, with RMSD < 2 Å, is 67%, substantially higher than the 40% success rate of conventional methods. The MDO success rate can be increased to 83% if the ONIOM calculations are applied only for the starting poses with ligands inside the binding cavities.

CONCLUSION

The MDO protocol provides high quality enzyme-ligand binding mode prediction with reasonable computational cost. The MDO protocol is recommended for use in the structure-based drug design.

Discovery of genistein derivatives as potential SARS-CoV-2 main protease inhibitors by virtual screening, molecular dynamics simulations and ADMET analysis

Background: Due to the constant mutation of virus and the lack of specific therapeutic drugs, the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still poses a huge threat to the health of people, especially those with underlying diseases. Therefore, drug discovery against the SARS-CoV-2 remains of great significance.

Methods: With the main protease of virus as the inhibitor target, 9,614 genistein derivatives were virtually screened by LeDock and AutoDock Vina, and the top 20 compounds with highest normalized scores were obtained. Molecular dynamics simulations were carried out for studying interactions between these 20 compounds and the target protein. The drug-like properties, activity, and ADMET of these compounds were also evaluated by DruLiTo software or online server.

Results: Twenty compounds, including compound 11, were screened by normalized molecular docking, which could bind to the target through multiple non-bonding interactions. Molecular dynamics simulation results showed that compounds 2, 4, 5, 11, 13, 14, 17, and 18 had the best binding force with the target protein of SARS-CoV-2, and the absolute values of binding free energies all exceeded 50 kJ/mol. The drug-likeness properties indicated that a variety of compounds including compound 11 were worthy of further study. The results of bioactivity score prediction found that compounds 11 and 12 had high inhibitory activities against protease, which indicated that these two compounds had the potential to be further developed as COVID-19 inhibitors. Finally, compound 11 showed excellent predictive ADMET properties including high absorption and low toxicity.

Conclusion: These in silico work results show that the preferred compound 11 (ZINC000111282222), which exhibited strong binding to SARS-CoV-2 main protease, acceptable drug-like properties, protease inhibitory activity and ADMET properties, has great promise for further research as a potential therapeutic agent against COVID-19.

Harmine and 7,8-dihydroxyflavone synergistically suitable for amyotrophic lateral sclerosis management: An in silico study

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease characterized by progressive degeneration of both upper and lower motor neurons, resulting in paralysis and eventually leads to death from respiratory failure typically within 3 to 5 years of symptom onset. The aim of this work was to predict the pharmacokinetics and identify unique protein targets that are associated with potential anti-ALS phytochemicals and FDA-approved drugs, by in silico approaches.

Materials and methods: Standard computational tools (webserver and software) were used, and the methods used are clustering analysis, pharmacokinetics and molecular target predictions, and molecular docking simulation.

Results and discussion: The results show that riluzole, β-asarone, cryptotanshinone, harmine and 7,8-dihydroxyflavone have similar pharmacokinetics properties. Riluzole and harmine show 95% probability of target on norepinephrine transporter. Huperzine-A and cryptotanshinone show 100% probability of target on acetylcholinesterase. 7,8-dihydroxyflavone shows 35% probability of target on several carbonic anhydrases, 40% probability of target on CYP19A1, and 100% probability of target on inhibitor of nuclear factor kappa B kinase beta subunit and neurotrophic tyrosine kinase receptor type 2, respectively. Harmine also shows 95% probability of target on dual specificity tyrosine-phosphorylation-regulated kinases, threonine-protein kinases (haspin and PIM3), adrenergic receptors, cyclin-dependent kinases (CDK5 and CDK9), monoamine oxidase A, casein kinase I delta, serotonin receptors, dual specificity protein kinases (CLK1, CLK2, and CLK4), and nischarin, respectively. Also, the results of gene expression network show possible involvement of CDK1, CDK2, CDK4, ERK1, ERK2 and MAPK14 signaling pathways. This study shows that riluzole and harmine have closely similar physicochemical and pharmacokinetics properties as well as molecular targets, such as norepinephrine transporter (SLC6A2). Harmine, huperzine-A and cryptotanshinone could modulate acetylcholinesterase (AChE), which is involved in ALS-pathogenesis. The impact of 7,8-dihydroxyflavone on several carbonic anhydrases (CA) I, II, VII, IX, XII, and XIV, as well as CYP19A1, could help in remediating the respiratory failure associated with ALS.

Conclusion: Overall, harmine is found to be superior to riluzole, and the combination of harmine with 7,8-dihydroxyflavone can provide more effective treatment for ALS than the current regime. Further work is needed to validate the predicted therapeutic targets of harmine identified in this study on ALS model or clinical trials, using in silico, in vitro and in vivo techniques.

Graphical abstract:

Novel [1,2,4]-Triazolo[3,4-b]-[1,3,4]thiadizoles as Potent Pyruvate Kinase Inhibitors for Fungal Control

To discover novel target-based fungicidal candidates, a molecular design model was established with a three-dimensional (3D) structure of Rhizoctonia solani pyruvate kinase (RsPK) simulated with the AlphaFold 2 and YZK-C22 as a fungicidal lead. A series of novel [1,2,4]triazolo[3,4-b][1,3,4]thiadiazole derivatives were rationally designed, synthesized, evaluated for their fungicidal performance, and validated for their mode of action. The in vitro bioassays with R. solani indicated that compounds 5g, 5o, and 5z with an EC₅₀ value ranging from 1.01 to 1.54 μg/mL displayed higher fungicidal activity than the positive control YZK-C22 with its EC₅₀ of 3.14 μg/mL. Especially, 5o exhibited high potency and a broad spectrum against Alternaria solani, Botrytis cinerea, Cercospora arachidicola, Physalospora piricola, R. solani, and Sclerotinia sclerotiorum with its EC₅₀ value falling between 1.54 and 13.10 μg/mL. Like all positive controls, 5g, 5o, and 5z showed excellent in vivo growth inhibition against Pseudoperonospora cubensis at 200 μg/mL. Even though the PK enzymatic inhibition assay showed that 5o was approximately 2.6 times less active than YZK-C22 (IC₅₀: 29.14 vs 11.15 μg/mL, respectively), the similar fluorescence quenching patterns of RsPK by 5o and YZK-C22, and the docking results of interactions between RsPK and 5o or YZK-C22 implied that they might share the similar binding site in the RsPK active pocket. Our studies suggested that 5o could be used as a potent fungicidal lead for further optimization. The results of comparative molecular field analysis (CoMFA) provided a direction for further molecular design.

Anti-quorum sensing evaluation of methyleugenol, the principal bioactive component, from the Melaleuca bracteata leaf oil

Quorum sensing (QS) is a cell-to-cell communication in bacteria that couples gene expression through the accumulation of signaling molecules, which finally induce the production of several virulence factors and modulate bacterial behaviors. Plants have evolved an array of quorum sensing inhibitors (QSIs) to inhibit the pathogens, of which aromatic compounds are widely recognized. The essential oil of Melaleuca bracteata was found to exhibit anti-quorum sensing activity, and its principal bioactive component, methyleugenol (ME), had been isolated in our previous study. Here, ME interfered effectively with the QS-regulated processes of toxin secretion in Chomobacterium violaceum ATCC31532, resulting in strong inhibition of QS genes, cviR, cviI, vioA-E, hmsHNR, lasA-B, pilE1-3, and hcnABC, leading to impaired virulence, including violacein production, biofilm biomass, and swarming motility. The accumulation of the signal molecule (N-hexanoyl-DL-homoserine lactone, C6-HSL) in C. violaceum declined upon treatment with ME, suggesting an inhibition effect on the C6-HSL production, and the ME was also capable of degrading the C6-HSL in vitro assay. Molecular docking technique and the consumption change of exogenous C6-HSL in C. violaceum CV026 revealed the anti-QS mechanism of ME consisted of inhibition of C6-HSL production, potentially via interaction with CviR and/or CviI protein. Collectively, the isolated ME, the principal active components of M. bracteata EO, exhibited a wide range of inhibition processes targeting C. violaceum QS system, which supports the potential anti-pathogenic use of M. bracteata EO and ME for treatment of pathogen contamination caused by bacterial pathogens.

Identification and validation of novel microtubule suppressors with an imidazopyridine scaffold through structure-based virtual screening and docking

Targeting the colchicine binding site of α/β tubulin microtubules can lead to suppression of microtubule dynamics, cell cycle arrest and apoptosis. Therefore, the development of microtubule (MT) inhibitors is considered a promising route to anticancer agents. Our approach to identify novel scaffolds as MT inhibitors depends on a 3D-structure-based pharmacophore approach and docking using three programs MOE, Autodock and BUDE (Bristol University Docking Engine) to screen a library of virtual compounds. From this work we identified the compound 7-(3-hydroxy-4-methoxy-phenyl)-3-(3-trifluoromethyl-phenyl)-6,7-dihydro-3H-imidazo[4,5-b]pyridin-5-ol (6) as a novel inhibitor scaffold. This compound inhibited several types of cancer cell proliferation at low micromolar concentrations with low toxicity. Compound 6 caused cell cycle arrest in the G2/M phase and blocked tubulin polymerization at low micromolar concentration (IC₅₀ = 6.1 ±0.1 μM), inducing apoptosis via activation of caspase 9, increasing the level of the pro-apoptotic protein Bax and decreasing the level of the anti-apoptotic protein Bcl2. In summary, our approach identified a lead compound with potential antimitotic and antiproliferative activity.

Interacting binding insights and conformational consequences of the differential activity of cannabidiol with two endocannabinoid-activated G-protein-coupled receptors

Cannabidiol (CBD), the major non-psychoactive phytocannabinoid present in the plant Cannabis sativa, has displayed beneficial pharmacological effects in the treatment of several neurological disorders including, epilepsy, Parkinson’s disease, and Alzheimer’s disease. In particular, CBD is able to modulate different receptors in the endocannabinoid system, some of which belong to the family of G-protein-coupled receptors (GPCRs). Notably, while CBD is able to antagonize some GPCRs in the endocannabinoid system, it also seems to activate others. The details of this dual contrasting functional feature of CBD, that is, displaying antagonistic and (possible) agonistic ligand properties in related receptors, remain unknown. Here, using computational methods, we investigate the interacting determinants of CBD in two closely related endocannabinoid-activated GPCRs, the G-protein-coupled receptor 55 (GPR55) and the cannabinoid type 1 receptor (CB₁). While in the former, CBD has been demonstrated to function as an antagonist, the way by which CBD modulates the CB₁ receptor remains unclear. Namely, CBD has been suggested to directly trigger receptor’s activation, stabilize CB₁ inactive conformations or function as an allosteric modulator. From microsecond-length unbiased molecular dynamics simulations, we found that the presence of the CBD ligand in the GPR55 receptor elicit conformational changes associated with antagonist-bound GPCRs. In contrast, when the GPR55 receptor is simulated in complex with the selective agonist ML186, agonist-like conformations are sampled. These results are in agreement with the proposed modulatory function of each ligand, showing that the computational techniques utilized to characterize the GPR55 complexes correctly differentiate the agonist-bound and antagonist-bound systems. Prompted by these results, we investigated the role of the CBD compound on the CB₁ receptor using similar computational approaches. The all-atom MD simulations reveal that CBD induces conformational changes linked with agonist-bound GPCRs. To contextualize the results we looked into the CB₁ receptor in complex with a well-established antagonist. In contrast to the CBD/CB₁ complex, when the CB₁ receptor is simulated in complex with the ligand antagonist AM251, inactive conformations are explored, showing that the computational techniques utilized to characterize the CB₁ complexes correctly differentiate the agonist-bound and antagonist-bound systems. In addition, our results suggest a previously unknown sodium-binding site located in the extracellular domain of the CB₁ receptor. From our detailed characterization, we found particular interacting loci in the binding sites of the GPR55 and the CB₁ receptors that seem to be responsible for the differential functional features of CBD. Our work will pave the way for understanding the CBD pharmacology at a molecular level and aid in harnessing its potential therapeutic use.

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.

Assessment of melatonin-alpha adrenergic receptor complexes by molecular docking analysis

The pineal melatonin (N-acetyl-5-methoxytryptamine) is a molecule associated in a way or another with probably all physiological systems, aiming to fulfil its functional integrative roles in central nervous system activity, sleep and wakefulness cycles, energy metabolism and thermoregulation, immune, reproductive, endocrine, cardiovascular, respiratory and excretory systems. Within this context, the present study aimed to assess in silico the formation of complexes between ligand melatonin and other potential receptor proteins by molecular docking analyses. The main steps established in this experimental procedure were: a) search and selection of the 3D structure of the melatonin from DrugBank; b) search and selection of 3D structures of other target receptor proteins using STRING, protein BLAST and database PDB; and c) formation of the complexes between melatonin and receptors selected using AutoDock4.0 server by molecular docking analyses. High reliability score and significant similarity were only identified between type 1B melatonin and alpha-2A adrenergic receptor. Thus, molecular docking assays were carried out using ligand melatonin and crystallographic structures of the alpha-2A adrenergic receptor coupled to an antagonist (ID PDB 6kux) and a partial agonist (ID PDB 6kuy) available in the database PDB. Binding energy values of -6.79 and -6.98 kcal/mol and structural stability by non-covalent intermolecular interactions were predicted during the formation of complexes between melatonin and alpha-2A adrenergic receptor 6kux and 6kuy, respectively. In this way, the findings described in current study may indicate strong interactions between melatonin and adrenoceptors, suggesting its possible partial agonist effect on the activation of the alfa-2A adrenergic receptor.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

Potential Molecular Mechanism of Guishao Pingchan Recipe in the Treatment of Parkinson’s Disease Based on Network Pharmacology and Molecular Docking

Objective: To investigate the potential mechanism of Guishao Pingchan Recipe (GPR) against Parkinson's disease (PD) based on network pharmacology and molecular docking. Methods: The main components of GPR were collected based on TCMSP database, Batman-TCM database, Chinese Pharmacopoeia, and Literatures. The potential therapeutic targets of PD were predicted by Drug Bank Database and Gene Cards database. Cytoscape 3.8.2 software was used to construct herb–component–target network. Then, String database was used to construct a PPI network, and DAVID database was used for gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation of targets function. Core components of GPR and hub targets were imported into AutoDock Vina for molecular docking verification and results were visualized by Pymol. Results: 13 candidate components were selected and 288 corresponding targets of GPR for treating PD were obtained. The GO enrichment analysis mainly involved 135 cell components, 187 molecular functions, and 1753 biological processes. Moreover, KEGG pathway enrichment analysis mainly involved 200 signaling pathways. Molecular docking simulation indicated a good binding ability of components and targets. Conclusion: Based on network pharmacology and molecular docking, we found that sitosterol, 4-Cholesten-3-one and stigmasterol in GPR could combine with MAPK3, APP, VEGFA, and CXCR4 and involved in the cAMP, PI3K/Akt, Rap1 signaling pathways. It is suggested that GPR may have therapeutic effects on PD through multi-component, multi-target, and multi-pathway and predict the relevant mechanism of the anti-PD effect of GPR.

Molecular insights into the interaction of CAG trinucleotide RNA repeats with nucleolin and its implication in polyglutamine diseases

Polyglutamine (polyQ) diseases are a type of inherited neurodegenerative disorders caused by cytosine-adenine-guanine (CAG) trinucleotide expansion within the coding region of the disease-associated genes. We previously demonstrated that a pathogenic interaction between expanded CAG RNA and the nucleolin (NCL) protein triggers the nucleolar stress and neuronal cell death in polyQ diseases. However, mechanisms behind the molecular interaction remain unknown. Here, we report a 1.45 Å crystal structure of the r(CAG)5 oligo that comprises a full A'-form helical turn with widened grooves. Based on this structure, we simulated a model of r(CAG)5 RNA complexed with the RNA recognition motif 2 (RRM2) of NCL and identified NCL residues that are critical for its binding to CAG RNA. Combined with in vitro and in vivo site-directed mutagenesis studies, our model reveals that CAG RNA binds to NCL sites that are not important for other cellular functions like gene expression and rRNA synthesis regulation, indicating that toxic CAG RNA interferes with NCL functions by sequestering it. Accordingly, an NCL mutant that is aberrant in CAG RNA-binding could rescue RNA-induced cytotoxicity effectively. Taken together, our study provides new molecular insights into the pathogenic mechanism of polyQ diseases mediated by NCL-CAG RNA interaction.

Design and selection of peptides to block the SARS-CoV-2 receptor binding domain by molecular docking

The novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is currently one of the most contagious viruses in existence and the cause of the worst pandemic in this century, COVID-19. SARS-CoV-2 infection begins with the recognition of the cellular receptor angiotensin converting enzyme-2 by its spike glycoprotein receptor-binding domain (RBD). Thus, the use of small peptides to neutralize the infective mechanism of SARS-CoV-2 through the RBD is an interesting strategy. The binding ability of 104 peptides (University of Nebraska Medical Center's Antimicrobial Peptide Database) to the RBD was assessed using molecular docking. Based on the molecular docking results, peptides with great affinity to the RBD were selected. The most common amino acids involved in the recognition of the RBD were identified to design novel peptides based on the number of hydrogen bonds that were formed. At physiological pH, these peptides are almost neutral and soluble in aqueous media. Interestingly, several peptides showed the capability to bind to the active surface area of the RBD of the Wuhan strain, as well as to the RBD of the Delta variant and other SARS-Cov-2 variants. Therefore, these peptides have promising potential in the treatment of the COVID-19 disease caused by different variants of SARS-CoV-2. This research work will be focused on the molecular docking of peptides by molecular dynamics, in addition to an analysis of the possible interaction of these peptides with physiological proteins. This methodology could be extended to design peptides that are active against other viruses.

Combinatorial delivery of Ribociclib and Green tea extract mediated nanostructured lipid carrier for oral delivery for the treatment of breast cancer synchronizing in silico, in vitro, and in vivo studies

Purpose: The current research investigated the development and evaluation of dual drug-loaded nanostructure lipidic carriers (NLCs) of green tea extract and Ribociclib.Method: In silico study were performed to determine the effectiveness of combinational approach. The prepared NLCs were subjected to in vitro drug release, lipolysis, haemolysis and cell line studies to assess their in vivo prospect.Results: In silico study was done to get docking score of EGCG (-8.98) close to Ribociclib (-10.78) in CDK-4 receptors. The prepared NLCs exhibited particle size (175.80 ± 3.51 nm); PDI (0.571 ± 0.012); and %EE [RBO (80.91 ± 1.66%) and GTE 75.98 ± 2.35%)] respectively. MCF-7 cell lines were used to evaluate the MTT assay, cellular uptake and antioxidant (ROS and SOD) of prepared NLCs. In vitro drug release showed the controlled release up to 72 h. In vitro lipolysis and in vitro haemolysis studies showed the availability of drugs at absorption sites and the greater in vivo prospects of NLCs respectively. Pharmacokinetic study revealed a 3.63-fold and 1.53-fold increment in RBO and GTE bioavailability in female Wistar rats respectively.Conclusion: The prominent potential of green tea extract and RBO-loaded NLCs in enhancing their therapeutic efficacy for better treatment of breast cancer.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Computational Methods in Cooperation with Experimental Approaches to Design Protein Tyrosine Phosphatase 1B Inhibitors in Type 2 Diabetes Drug Design: A Review of the Achievements of This Century

Protein tyrosine phosphatase 1B (PTP1B) dephosphorylates phosphotyrosine residues and is an important regulator of several signaling pathways, such as insulin, leptin, and the ErbB signaling network, among others. Therefore, this enzyme is considered an attractive target to design new drugs against type 2 diabetes, obesity, and cancer. To date, a wide variety of PTP1B inhibitors that have been developed by experimental and computational approaches. In this review, we summarize the achievements with respect to PTP1B inhibitors discovered by applying computer-assisted drug design methodologies (virtual screening, molecular docking, pharmacophore modeling, and quantitative structure–activity relationships (QSAR)) as the principal strategy, in cooperation with experimental approaches, covering articles published from the beginning of the century until the time this review was submitted, with a focus on studies conducted with the aim of discovering new drugs against type 2 diabetes. This review encourages the use of computational techniques and includes helpful information that increases the knowledge generated to date about PTP1B inhibition, with a positive impact on the route toward obtaining a new drug against type 2 diabetes with PTP1B as a molecular target.

A new approach used in docking study for predicting the combination drug efficacy in EML4-ALK target of NSCLC

Combination drug treatments are usually used in many diseases, including cancers and AIDS. This treatment strategy is known as one of the cornerstone in therapies, which potentially reduces drug toxicity and drug resistance and also enhances therapeutic efficacy. Before using a drug in treatment, several experimental studies are done in vivo and in vitro to ensure the drug's efficacy. In such experimental studies, the drug's efficacy is evaluated with the help of drug dose ratio. In the combination drug experimental studies, the efficacy of the drugs is quantified with the Combination Index (CI) value and then interpreted by various terminologies like synergy, additive, and antagonism. Several computational models have now been invented for the speedy identification of combination drug efficacy. Unfortunately, none of these models have predicted the atomic level interaction of the combination drug with the target protein. This type of intermolecular interaction can be identified with the help of docking software. In the proposed work, we try to identify the intermolecular interaction and efficacy of the combination drug Crzizotinib and Temozolomide in the target of EML4-ALK in NSCLC by in silico study. The result of the study was evaluated with drug properties and Complex Energy (CE) of the docked complex rather than using docking score and binding energy. From this study, we could understand that first, Crizotinib and then after the Temozolomide drug binded on the EML4-ALK protein complex, showed very least CE and also identified that the combination of Crizotinib and Temozolomide drug are more effective in NSCLC.Communicated by Ramaswamy H. Sarma.

Impact of Antibiotics as Waste, Physical, Chemical, and Enzymatical Degradation: Use of Laccases

The first traces of Tetracycline (TE) were detected in human skeletons from Sudan and Egypt, finding that it may be related to the diet of the time, the use of some dyes, and the use of soils loaded with microorganisms, such as Streptomyces spp., among other microorganisms capable of producing antibiotics. However, most people only recognise authors dating between 1904 and 1940, such as Ehrlich, Domagk, and Fleming. Antibiotics are the therapeutic option for countless infections treatment; unfortunately, they are the second most common group of drugs in wastewaters worldwide due to failures in industrial waste treatments (pharmaceutics, hospitals, senior residences) and their irrational use in humans and animals. The main antibiotics problem lies in delivered and non-prescribed human use, use in livestock as growth promoters, and crop cultivation as biocides (regulated activities that have not complied in some places). This practice has led to the toxicity of the environment as antibiotics generate eutrophication, water pollution, nutrient imbalance, and press antibiotic resistance. In addition, the removal of antibiotics is not a required process in global wastewater treatment standards. This review aims to raise awareness of the negative impact of antibiotics as residues and physical, chemical, and biological treatments for their degradation. We discuss the high cost of physical and chemical treatments, the risk of using chemicals that worsen the situation, and the fact that each antibiotic class can be transformed differently with each of these treatments and generate new compounds that could be more toxic than the original ones; also, we discuss the use of enzymes for antibiotic degradation, with emphasis on laccases.

FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis

Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting targets, especially the targets with weaker drug binding affinity. In this work, we proposed an integrated pipeline, FL-DTD, to predict the drug interacting targets of novel compounds in a tissue-specific manner. This method was built based on a hypothesis that cells under a status of homeostasis would take responses to drug perturbation by activating feedback loops. Therefore, the drug interacting targets can be predicted by analyzing the network responses after drug perturbation. We evaluated this method using the expression data of estrogen stimulation, gene manipulation and drug perturbation and validated its good performance to identify the annotated drug targets. Using STAT3 as a target protein, we applied this method to drug perturbation data of 500 natural compounds and predicted five compounds with STAT3 interacting activities. Experimental assay validated the STAT3-interacting activities of four compounds. Overall, our evaluation suggests that FL-DTD predicts the drug interacting targets with good accuracy and can be used for drug target discovery.

Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni

Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.

In Silico Study: Combination of α-Mangostin and Chitosan Conjugated with Trastuzumab against Human Epidermal Growth Factor Receptor 2

Breast cancer is a type of cancer with the highest prevalence worldwide. Almost 10–30% of breast cancer cases are diagnosed as positive for HER2 (human epidermal growth factor receptor 2). The currently available treatment methods still exhibit many shortcomings such as a high incidence of side effects and treatment failure due to resistance. This in silico study aims to simulate α-mangostin and chitosan combination conjugated to trastuzumab formulation against HER2 as an effort to improve breast cancer patient therapy. This molecular docking simulation was done through using PatchDock Server. The materials used including the two-dimensional structure of α-mangostin, chitosan, and sodium tripolyphosphate from the PubChem database; trastuzumab FASTA sequence from the DrugBank database; and HER2 structure obtained from a crystal complex with PDB ID: 1N8Z. The results indicated that the particle of α-mangostin and chitosan combinations interacted mostly with the crystallizable fragment (Fc region) of trastuzumab in the conjugation process. The conjugation of trastuzumab to the particle of a combination of α-mangostin and chitosan resulted in the greatest increase in the binding score of the smallest-sized particles (50 Å) with an increase in the score of 3828 and also gave the most similar mode of interaction with trastuzumab. However, the conjugation of trastuzumab eliminated the similarity of the mode of interaction and increased the value of atomic contact energy. Thus, a cominbation of α-mangostin and chitosan conjugated to a trastuzumab formulation was predicted can increase the effectiveness of breast cancer therapy at a relatively small particle size but with the consequence of decreasing atomic contact energy.

Multi-Objective Drug Design Based on Graph-Fragment Molecular Representation and Deep Evolutionary Learning

Drug discovery is a challenging process with a huge molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities.

The emerging potential of interactive virtual reality in drug discovery

INTRODUCTION

The potential of virtual reality (VR) to contribute to drug design and development has been recognized for many years. A recent advance is to use VR not only to visualize and interact with molecules, but also to interact with molecular dynamics simulations 'on the fly' (interactive molecular dynamics in VR, IMD-VR), which is useful for flexible docking and examining binding processes and conformational changes.

AREAS COVERED

The authors use the term 'interactive VR' to refer to software where interactivity is an inherent part of the user VR experience e.g. in making structural modifications or interacting with a physically rigorous molecular dynamics (MD) simulation, as opposed to using VR controllers to rotate and translate the molecule for enhanced visualization. Here, they describe these methods and their application to problems relevant to drug discovery, highlighting the possibilities that they offer in this arena.

EXPERT OPINION

The ease of viewing and manipulating molecular structures and dynamics, using accessible VR hardware, and the ability to modify structures on the fly (e.g. adding or deleting atoms) - and for groups of researchers to work together in the same virtual environment - makes modern interactive VR a valuable tool to add to the armory of drug design and development methods.

Withasomniferol C, a new potential SARS-CoV-2 main protease inhibitor from the Withania somnifera plant proposed by in silico approaches

Exploring potent herbal medicine candidates is a promising strategy for combating a pandemic in the present global health crisis. In Ayurveda (a traditional medicine system in India), Withania somnifera (WS) is one of the most important herbs and it has been used for millennia as Rasayana (a type of juice) for its wide-ranging health benefits. WS phytocompounds display a broad spectrum of biological activities (such as antioxidant, anticancer and antimicrobial) modulate detoxifying enzymes, and enhance immunity. Inspired by the numerous biological actions of WS phytocompounds, the present investigation explored the potential of the WS phytocompounds against the SARS-CoV-2 main protease (3CL^pro). We selected 11 specific withanolide compounds, such as withaphysalin, withasomniferol, and withafastuosin, through manual literature curation against 3CL^pro. A molecular similarity analysis showed their similarity with compounds that have an established inhibitory activity against the SARS-CoV-2. In silico molecular docking and molecular dynamics simulations elucidated withasomniferol C (WS11) as a potential candidate against SARS-CoV-2 3CL^pro. Additionally, the present work also presents a new method of validating docking poses using the AlteQ method.

Bioactive Peptides: An Understanding from Current Screening Methodology

Bioactive peptides with high potency against numerous human disorders have been regarded as a promising therapy in disease control. These peptides could be released from various dietary protein sources through hydrolysis processing using physical conditions, chemical agents, microbial fermentation, or enzymatic digestions. Considering the diversity of the original proteins and the complexity of the multiple structural peptides that existed in the hydrolysis mixture, the screening of bioactive peptides will be a challenge task. Well-organized and well-designed methods are necessarily required to enhance the efficiency of studying the potential peptides. This article, hence, provides an overview of bioactive peptides with an emphasis on the current strategy used for screening and characterization methods. Moreover, the understanding of the biological activities of peptides, mechanism inhibitions, and the interaction of the complex of peptide–enzyme is commonly evaluated using specific in vitro assays and molecular docking analysis.

Risk and molecular mechanisms for boscalid resistance in Penicillium digitatum

The succinate dehydrogenase inhibitor (SDHI) fungicide boscalid is an excellent broad-spectrum fungicide but has not been registered in China to control Penicillium digitatum, the causal agent of green mold of citrus. The present study evaluated the risk and molecular mechanisms for boscalid resistance in P. digitatum. Resistance induction with four arbitrarily selected sensitive isolates of P. digitatum by ultraviolet (UV) irradiation on conidia plated on boscalid-amended potato dextrose agar (PDA) and consecutive growing on boscalid-amended PDA produced five highly resistant isolates with EC₅₀ values greater than 1000 μg/mL and two resistant isolates with EC₅₀ lower than 200 μg/mL. Boscalid resistance of the five mutants with EC₅₀ values above 1000 μg/mL was stable after successive transfers on PDA for 16 generations. However, for the other two mutants with EC₅₀ lower than 200 μg/mL, the EC₅₀ values decreased significantly after successive transfers. There was significant cross-resistance between boscalid and carboxin (r = 0.925, P < 0.001), but no significant cross-resistance was detected between boscalid and fludioxonil (r = 0.533，P = 0.095) or between boscalid and prochloraz (r = -0.543，P = 0.088). The seven resistant mutants varied greatly in the mycelia growth, sporulation, pathogenicity, and sensitivities to exogenous stresses including NaCl, salicylhydroxamic acid (SHAM), and H₂O₂. Alignment of the deduced amino acid sequence showed that there was no point mutation in the target enzyme succinate dehydrogenase (Sdh) subunits SdhA, SdhC, or SdhD in each of the seven resistant mutants, and the mutation of a conserved histidine residue to tyrosine (H243Y) in the subunit SdhB (i.e., iron‑sulfur protein) occurred in only three highly resistant isolates. Molecular docking indicated that mutation H243Y could not prevent the binding of boscalid into the quinone-binding site of SDH in the presence of the heme moiety. However, for SDH without the heme moiety, boscalid could bind into a deeper site with a much higher affinity, and the mutation H243Y spatially blocked the docking of boscalid into the deeper site. This may be the molecular mechanism for boscalid resistance caused by SdhB-H243Y mutation.

Novel potent azetidine-based compounds irreversibly inhibit Stat3 activation and induce antitumor response against human breast tumor growth in vivo

Signal transducer and activator of transcription (Stat)3 is a valid anticancer therapeutic target. We have discovered a highly potent chemotype that amplifies the Stat3-inhibitory activity of lead compounds to levels previously unseen. The azetidine-based compounds, including H172 (9f) and H182, irreversibly bind to Stat3 and selectively inhibit Stat3 activity (IC₅₀ 0.38-0.98 μM) over Stat1 or Stat5 (IC₅₀ > 15.8 μM) in vitro. Mass spectrometry detected the Stat3 cysteine peptides covalently bound to the azetidine compounds, and the key residues, Cys426 and Cys468, essential for the high potency inhibition, were confirmed by site-directed mutagenesis. In triple-negative breast cancer (TNBC) models, treatment with the azetidine compounds inhibited constitutive and ligand-induced Stat3 signaling, and induced loss of viable cells and tumor cell death, compared to no effect on the induction of Janus kinase (JAK)2, Src, epidermal growth factor receptor (EGFR), and other proteins, or weak effects on cells that do not harbor aberrantly-active Stat3. H120 (8e) and H182 as a single agent inhibited growth of TNBC xenografts, and H278 (hydrochloric acid salt of H182) in combination with radiation completely blocked mouse TNBC growth and improved survival in syngeneic models. We identify potent azetidine-based, selective, irreversible Stat3 inhibitors that inhibit TNBC growth in vivo.

Computational Modeling of Supramolecular Metallo-organic Cages-Challenges and Opportunities

Self-assembled metallo-organic cages have emerged as promising biomimetic platforms that can encapsulate whole substrates akin to an enzyme active site. Extensive experimental work has enabled access to a variety of structures, with a few notable examples showing catalytic behavior. However, computational investigations of metallo-organic cages are scarce, not least due to the challenges associated with their modeling and the lack of accurate and efficient protocols to evaluate these systems. In this review, we discuss key molecular principles governing the design of functional metallo-organic cages, from the assembly of building blocks through binding and catalysis. For each of these processes, computational protocols will be reviewed, considering their inherent strengths and weaknesses. We will demonstrate that while each approach may have its own specific pitfalls, they can be a powerful tool for rationalizing experimental observables and to guide synthetic efforts. To illustrate this point, we present several examples where modeling has helped to elucidate fundamental principles behind molecular recognition and reactivity. We highlight the importance of combining computational and experimental efforts to speed up supramolecular catalyst design while reducing time and resources.

fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation

Predicting the native or near-native binding pose of a small molecule within a protein binding pocket is an extremely important task in structure-based drug design, especially in the hit-to-lead and lead optimization phases. In this study, fastDRH, a free and open accessed web server, was developed to predict and analyze protein-ligand complex structures. In fastDRH server, AutoDock Vina and AutoDock-GPU docking engines, structure-truncated MM/PB(GB)SA free energy calculation procedures and multiple poses based per-residue energy decomposition analysis were well integrated into a user-friendly and multifunctional online platform. Benefit from the modular architecture, users can flexibly use one or more of three features, including molecular docking, docking pose rescoring and hotspot residue prediction, to obtain the key information clearly based on a result analysis panel supported by 3Dmol.js and Apache ECharts. In terms of protein-ligand binding mode prediction, the integrated structure-truncated MM/PB(GB)SA rescoring procedures exhibit a success rate of >80% in benchmark, which is much better than the AutoDock Vina (~70%). For hotspot residue identification, our multiple poses based per-residue energy decomposition analysis strategy is a more reliable solution than the one using only a single pose, and the performance of our solution has been experimentally validated in several drug discovery projects. To summarize, the fastDRH server is a useful tool for predicting the ligand binding mode and the hotspot residue of protein for ligand binding. The fastDRH server is accessible free of charge at http://cadd.zju.edu.cn/fastdrh/.

Modern venomics-Current insights, novel methods, and future perspectives in biological and applied animal venom research

Venoms have evolved >100 times in all major animal groups, and their components, known as toxins, have been fine-tuned over millions of years into highly effective biochemical weapons. There are many outstanding questions on the evolution of toxin arsenals, such as how venom genes originate, how venom contributes to the fitness of venomous species, and which modifications at the genomic, transcriptomic, and protein level drive their evolution. These questions have received particularly little attention outside of snakes, cone snails, spiders, and scorpions. Venom compounds have further become a source of inspiration for translational research using their diverse bioactivities for various applications. We highlight here recent advances and new strategies in modern venomics and discuss how recent technological innovations and multi-omic methods dramatically improve research on venomous animals. The study of genomes and their modifications through CRISPR and knockdown technologies will increase our understanding of how toxins evolve and which functions they have in the different ontogenetic stages during the development of venomous animals. Mass spectrometry imaging combined with spatial transcriptomics, in situ hybridization techniques, and modern computer tomography gives us further insights into the spatial distribution of toxins in the venom system and the function of the venom apparatus. All these evolutionary and biological insights contribute to more efficiently identify venom compounds, which can then be synthesized or produced in adapted expression systems to test their bioactivity. Finally, we critically discuss recent agrochemical, pharmaceutical, therapeutic, and diagnostic (so-called translational) aspects of venoms from which humans benefit.

Indexing Ultrafast Shape-Based Descriptors in MongoDB to Identify TLR4 Pathway Agonists

A method is presented for an ultrafast shape-based search workflow for the screening of large compound collections, i.e., those of vendors. The three-dimensional shape of a molecule dictates its biological activity by enabling the molecule to fit into binding pockets of proteins. Quite often, distinctly different chemical compounds that have similar shapes can bind in a similar way. OpenEye pioneered an algorithm for comparing shapes of molecules by overlaying them in a computer and measuring differences between a query molecule and a target molecule. Overlaying shapes is a computationally intensive process and represents a bottleneck in searching for similar molecules. More recent publications describe alternative methods of overlaying molecules, which are accomplished by comparing shape-based descriptors. These methods were implemented in the Open Drug Discovery Toolkit (ODDT) package. We utilized a combination of open-source software packages like ODDT and RDkit to implement a workflow for ultrafast conformer generation and matching that does not require storing precomputed conformers on the file system or in memory. Moreover, the generated descriptors could be optionally stored in MongoDB for performing searches in the future. To speed up the search, we created a set of indexes from the transformed shape-based descriptors. We are in the process of calculating descriptors for multiple vendors, including Enamine's "REAL" collection of 1.2 billion compounds. Currently, the shape similarity search on more than 70 million compounds takes less than 8 s! We exemplified our methodology with the screen of compounds that can act as putative TLR4 agonists. The search was based on a literature-known small-molecule TLR4 agonist series. In due course, we identified compounds with novel structural motifs that were active in mouse and human TLR4 reporter cell lines.

Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution

Steered molecular dynamics (SMD) simulation is a powerful method in computer-aided drug design as it can be used to access the relative binding affinity with high precision but with low computational cost. The success of SMD depends on the choice of the direction along which the ligand is pulled from the receptor-binding site. In most simulations, the unidirectional pathway was used, but in some cases, this choice resulted in the ligand colliding with the complex surface of the exit tunnel. To overcome this difficulty, several variants of SMD with multidirectional pulling have been proposed, but they are not completely devoid of disadvantages. Here, we have proposed to determine the direction of pulling with a simple scoring function that minimizes the receptor–ligand interaction, and an optimization algorithm called differential evolution is used for energy minimization. The effectiveness of our protocol was demonstrated by finding expulsion pathways of Huperzine A and camphor from the binding site of Torpedo California acetylcholinesterase and P450cam proteins, respectively, and comparing them with the previous results obtained using memetic sampling and random acceleration molecular dynamics. In addition, by applying this protocol to a set of ligands bound with LSD1 (lysine specific demethylase 1), we obtained a much higher correlation between the work of pulling force and experimental data on the inhibition constant IC50 compared to that obtained using the unidirectional approach based on minimal steric hindrance.

Recent Advances in Utilizing Omics Approach to Identify the Bioactive Peptides and Ripening Metabolism in Plant-based Food

Abstract:

Bioactive peptides with potential health benefits and metabolic functionality have been identified from plant-based food. The aim of this perspective is to report the recent progress in the research of plant-derived bioactive peptides using the combination of omics technologies and bioinformatics tools. Studies examining bioactive peptides with identified amino acid sequences and well-characterized biological functionalities are highlighted. Various software, webtools and workflows for analyzing and interpreting the biological data acquired from different omics approaches are discussed. The emerging evidence from the integration of proteomics and metabolomics data with advanced laboratory analytical methods supports more potential applications in the envisioned development of nutraceutical and therapeutic products. Notwithstanding, much works are mandatory to resolve those lied-ahead challenges before realizing the proposed applications of plant peptides.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

Drug Repurposing for COVID-19: A Review and a Novel Strategy to Identify New Targets and Potential Drug Candidates

In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) was first identified in the province of Wuhan, China. Since then, there have been over 400 million confirmed cases and 5.8 million deaths by COVID-19 reported worldwide. The urgent need for therapies against SARS-CoV-2 led researchers to use drug repurposing approaches. This strategy allows the reduction in risks, time, and costs associated with drug development. In many cases, a repurposed drug can enter directly to preclinical testing and clinical trials, thus accelerating the whole drug discovery process. In this work, we will give a general overview of the main developments in COVID-19 treatment, focusing on the contribution of the drug repurposing paradigm to find effective drugs against this disease. Finally, we will present our findings using a new drug repurposing strategy that identified 11 compounds that may be potentially effective against COVID-19. To our knowledge, seven of these drugs have never been tested against SARS-CoV-2 and are potential candidates for in vitro and in vivo studies to evaluate their effectiveness in COVID-19 treatment.

Structure-activity relationships of hydrophobic alkyl acrylamides as tissue transglutaminase inhibitors

Tissue transglutaminase (TG2) is a multifunctional protein that plays biological roles based on its ability to catalyse protein cross-linking and to function as a non-canonical G-protein known as Ghα. The non-regulated activity of TG2 has been implicated in fibrosis, celiac disease and the survival of cancer stem cells, underpinning the therapeutic potential of cell permeable small molecule inhibitors of TG2. In the current study, we designed a small library of inhibitors to explore the importance of a terminal hydrophobic moiety, as well as the length of the tether to the irreversible acrylamide warhead. Subsequent kinetic evaluation using an in vitro activity assay provided values for the k _inact and K _I parameters for each of these irreversible inhibitors. The resulting structure-activity relationship (SAR) clearly indicated the affinity conferred by dansyl and adamantyl moieties, as well as the efficiency provided by the shortest warhead tether. We also provide the first direct evidence of the capability of these inhibitors to suppress the GTP binding ability of TG2, at least partially. However, it is intriguing to note that the SAR trends observed herein are opposite to those predicted by molecular modelling - namely that longer tether groups should improve binding affinity by allowing for deeper insertion of the hydrophobic moiety into a hydrophobic pocket on the enzyme. This discrepancy leads us to question whether the existing crystallographic structures of TG2 are appropriate for docking non-peptidic inhibitors. In the absence of a more relevant crystallographic structure, the data from rigorous kinetic studies, such as those provided herein, are critically important for the development of future small molecule TG2 inhibitors.

A Novel Cleavage Pattern of Complement C5 Induced by Chlamydia trachomatis Infection via the Chlamydial Protease CPAF

Urogenital Chlamydia trachomatis infection is one of the most common bacterial sexually transmitted diseases globally. Untreated C. trachomatis infections can ascend to the upper genital tract and establish a series of severe complications. Previous studies using C3^−/− and C5^−/− mice models demonstrated that C3-independent activation of C5 occurred during C. trachomatis infection. However, the mechanism of how chlamydial infection activates C5 in the absence of C3 has yet to be elucidated. To delineate interactions between C5 and chlamydial infection, cleavage products in a co-incubation system containing purified human C5 and C. trachomatis-HeLa229 cell lysates were analyzed, and a novel cleavage pattern of C5 activation induced by C. trachomatis infection was identified. C5 was cleaved efficiently at the previously unidentified site K970, but was cleaved poorly at site R751. C5b was modified to C5b_Ct, which later formed C5b_Ct-9, which had enhanced lytic ability compared with C5b-9. The chlamydial serine protease CPAF contributed to C3-independent C5 activation during C. trachomatis infection. Nafamostat mesylate, a serine protease inhibitor with a good safety profile, had a strong inhibitory effect on C5 activation induced by chlamydial infection. These discoveries reveal the mechanism of C3-independent C5 activation induced by chlamydial infection, and furthermore provide a potential therapeutic target and drug for preventing tubal fibrosis caused by chlamydial infection.

Characterization and molecular docking study of cathepsin L inhibitory peptides (SnuCalCpIs) from Calotropis procera R. Br

Propeptides, released from the autocatalytic activation of its zymogen, are potential inhibitors against proteases involved in cancer cell invasion and migration. Our research team previously obtained novel propeptides (SnuCalCpIs) from transcriptome analysis of the medicinal plant Calotropis procera R. Br. and reported them as promising candidates for cancer therapeutics due to their cathepsin L inhibition activity. In the present study, inhibitory activity among SnuCalCpIs was compared with inhibition efficiency and verified by in silico molecular docking analysis. Only SnuCalCpI03 and SnuCalCpI15, expressed in Escherichia coli, showed inhibitory activity against cathepsin L as competitive inhibitors, and the half-maximal inhibitory concentrations (IC₅₀) values of 2.1 nM and 1.6 nM, respectively. They were stable below 70 °C, maintaining more than 90% inhibitory activity over a wide range of pH (2.0-10.0), except at the isoelectric point (pI). The template-based docking simulation models showed that SnuCalCpI02, SnuCalCpI12, and SnuCalCpI16 could not interact with the substrate-binding cleft of cathepsin L even though they possessed the same conserved domain. In contrast, SnuCalCpI03 and SnuCalCpI15 interacted with cathepsin L along the propeptide binding loop and substrate-binding cleft, resulting in obstruction of substrate access to the active site.

Computational Analysis and Biological Activities of Oxyresveratrol Analogues, the Putative Cyclooxygenase-2 Inhibitors

Polyphenols are a large family of naturally occurring phytochemicals. Herein, oxyresveratrol was isolated from ethanolic crude extracts of Artocarpus lacucha Buch.-Ham., and chemically modified to derive its lipophilic analogues. Biological screening assays showed their inhibitory potency against cyclooxygenase-2 (COX-2) with very low cytotoxicity to the MRC-5 normal cell lines. At the catalytic site of COX-2, docking protocols with ChemPLP, GoldScore and AutoDock scoring functions were carried out to reveal hydrogen bonding interactions with key polar contacts and hydrophobic pi-interactions. For more accurate binding energetics, COX-2/ligand complexes at the binding region were computed in vacuo and implicit aqueous solvation using M06-2X density functional with 6-31G+(d,p) basis set. Our computational results confirmed that dihydrooxyresveratrol (4) is the putative inhibitor of human COX-2 with the highest inhibitory activity (IC₅₀ of 11.50 ± 1.54 µM) among studied non-fluorinated analogues for further lead optimization. Selective substitution of fluorine provides a stronger binding affinity; however, lowering the cytotoxicity of a fluorinated analogue to a normal cell is challenging. The consensus among biological activities, ChemPLP docking score and the binding energies computed at the quantum mechanical level is obviously helpful for identification of oxyresveratrol analogues as a putative anti-inflammatory agent.

Investigation of Cu metal nanoparticles with different morphologies to inhibit SARS-CoV-2 main protease and spike glycoprotein using Molecular Docking and Dynamics Simulation

Nowadays, considering the spread of the coronavirus as a global threat, scientific research on this virus through simulation has been increasing. In this study, effect of Cu nanocluster on prevention and control of disease transmission was examined using molecular docking and molecular dynamics simulation studies on the SARS-CoV-2 main protease and spike glycoprotein. The cytotoxicity of different shapes of copper NPs and resonance changes of their surface plasmons on inactivation of the coronavirus was examined in order to control replication of coronavirus through copper NPs, active site of protease and spike glycoprotein. The simulations results showed that interactions of SARS-CoV-2 main protease and spike glycoprotein target and cylindrical and conical copper NPs ligands were more efficient than spherical copper NPs.

Identification of natural inhibitors against prime targets of SARS-CoV-2 using molecular docking, molecular dynamics simulation and MM-PBSA approaches

The recently emerged COVID-19 has been declared a pandemic by the World Health Organization as to date; no therapeutic drug/vaccine is available for the treatment. Due to the lack of time and the urgency to contain the pandemic, computational screening appears to be the best tool to find a therapeutic solution. Accumulated evidence suggests that many phyto-compounds possess anti-viral activity. Therefore, we identified possible phyto-compounds that could be developed and used for COVID-19 treatment. In particular, molecular docking was used to prioritize the possible active phyto-compounds against two key targets namely RNA dependent RNA polymerase (RdRp) and main protease (Mpro) of SARS-CoV-2. In this study, an antiviral drug- Remdesivir (RdRp inhibitor) and Darunavir (Mpro inhibitor) are used as reference drugs. This study revealed that phyto-molecules- Mulberroside-A/C/E/F, Emblicanin A, Nimbolide, and Punigluconin showed high binding affinity against RdRp while Andrographolides, Mulberrosides, Anolignans, Chebulic acid, Mimusopic acid, and Punigluconin showed better binding affinity against Mpro as compared with the reference drug. Furthermore, ADME profiles validated the drug-likeness properties of prioritized phyto-compounds. Besides, to assess the stability, MD simulations studies were performed along with reference inhibitors for Mpro (Darunavir) and RdRp (Remdesivir). Binding free energy calculations (MM-PBSA) revealed the estimated value (ΔG) of Mpro_Darunavir; Mpro_Mulberroside E; RdRp_Remdesivir and RdRp_Emblicanin A were -111.62 ± 6.788, -141.443 ± 9.313, 30.782 ± 5.85 and -89.424 ± 3.130 kJmol-1, respectively. Taken together, the study revealed the potential of these phyto-compounds as inhibitors of RdRp and Mpro inhibitor that could be further validated against SARS-CoV-2 for clinical benefits.Communicated by Ramaswamy H. Sarma.