Advances in Docking

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Recent advances in chemometric modelling of inhibitors against SARS-CoV-2

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great harm to all countries worldwide. This disease can be prevented by vaccination and managed using various treatment methods, including injections, oral medications, or aerosol therapies. However, the selection of suitable compounds for the research and development of anti-SARS-CoV-2 drugs is a daunting task because of the vast databases of available compounds. The traditional process of drug research and development is time-consuming, labour-intensive, and costly. The application of chemometrics can significantly expedite drug R&D. This is particularly necessary and important for drug development against pandemic public emergency diseases, such as COVID-19. Through various chemometric techniques, such as quantitative structure-activity relationship (QSAR) modelling, molecular docking, and molecular dynamics (MD) simulations, compounds with inhibitory activity against SARS-CoV-2 can be quickly screened, allowing researchers to focus on the few prioritised candidates. In addition, the ADMET properties of the screened candidate compounds should be further explored to promote the successful discovery of anti-SARS-CoV-2 drugs. In this case, considerable time and economic costs can be saved while minimising the need for extensive animal experiments, in line with the 3R principles. This paper focuses on recent advances in chemometric modelling studies of COVID-19-related inhibitors, highlights current limitations, and outlines potential future directions for development.

Introducing the Automated Ligand Searcher

The Automated Ligand Searcher (ALISE) is designed as an automated computational drug discovery tool. To approximate the binding free energy of ligands to a receptor, ALISE includes a three-stage workflow, with each stage involving an increasingly sophisticated computational method: molecular docking, molecular dynamics, and free energy perturbation, respectively. To narrow the number of potential ligands, poorly performing ligands are gradually segregated out. The performance and usability of ALISE are benchmarked for a case study containing known active ligands and decoys for the HIV protease. The example illustrates that ALISE filters the decoys successfully and demonstrates that the automation, comprehensiveness, and user-friendliness of the software make it a valuable tool for improved and faster drug development workflows.

Target-ligand binding affinity from single point enthalpy calculation and elemental composition

Reliable target-ligand binding thermodynamics data are essential for successful drug design and molecular engineering projects. Besides experimental methods, a number of theoretical approaches have been introduced for the generation of binding thermodynamics data. However, available approaches often neglect electronic effects or explicit water molecules influencing target-ligand interactions. To handle electronic effects within a reasonable time frame, we introduce a fast calculator QMH-L using a single target-ligand complex structure pre-optimized at the molecular mechanics level. QMH-L is composed of the semi-empirical quantum mechanics calculation of binding enthalpy with predicted explicit water molecules at the complex interface, and a simple descriptor based on the elemental composition of the ligand. QMH-L estimates the target-ligand binding free energy with a root mean square error (RMSE) of 0.94 kcal mol-1. The calculations also provide binding enthalpy values and they were compared with experimental binding thermodynamics data collected from the most reliable isothermal titration calorimetry studies of systems including various protein targets and challenging, large peptide ligands with a molecular weight of up to 2-3 thousand. The single point enthalpy calculations of QMH-L require modest computational resources and are based on short runs with open source and/or free software like Gromacs, Mopac, MobyWat, and Fragmenter. QMH-L can be applied for fast, automated scoring of drug candidates during a virtual screen, enthalpic engineering of new ligands or thermodynamic explanation of complex interactions.

DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein-Ligand Binding Affinity Prediction

Accurate prediction of the protein-ligand binding affinity (PLBA) with an affordable cost is one of the ultimate goals in the field of structure-based drug design (SBDD), as well as a great challenge in the computational and theoretical chemistry. Herein, we have systematically addressed the complicated solvation and desolvation effects on the PLBA brought by the difference of the explicit water in the protein cavity before and after ligands bind to the protein-binding site. Based on the new solvation model, a nonfitting method at the first-principles level for the PLBA prediction was developed by taking the bridging and displaced water (BDW) molecules into account simultaneously. The newly developed method, DOX_BDW, was validated against a total of 765 noncovalent and covalent protein-ligand binding pairs, including the CASF2016 core set, Cov_2022 covalent binding testing set, and six testing sets for the hit and lead compound optimization (HLO) simulation. In all of the testing sets, the DOX_BDW method was able to produce PLBA predictions that were strongly correlated with the corresponding experimental data (R = 0.66-0.85). The overall performance of DOX_BDW is better than the current empirical scoring functions that are heavily parameterized. DOX_BDW is particularly outstanding for the covalent binding situation, implying the need for considering an electronic structure in covalent drug design. Furthermore, the method is especially recommended to be used in the HLO scenario of SBDD, where hundreds of similar derivatives need to be screened and refined. The computational cost of DOX_BDW is affordable, and its accuracy is remarkable.

Insight into the Potential Mechanisms of Endocrine Disruption by Dietary Phytoestrogens in the Context of the Etiopathogenesis of Endometriosis

Phytoestrogens (PEs) are estrogen-like nonsteroidal compounds derived from plants (e.g., nuts, seeds, fruits, and vegetables) and fungi that are structurally similar to 17β-estradiol. PEs bind to all types of estrogen receptors, including ERα and ERβ receptors, nuclear receptors, and a membrane-bound estrogen receptor known as the G protein-coupled estrogen receptor (GPER). As endocrine-disrupting chemicals (EDCs) with pro- or antiestrogenic properties, PEs can potentially disrupt the hormonal regulation of homeostasis, resulting in developmental and reproductive abnormalities. However, a lack of PEs in the diet does not result in the development of deficiency symptoms. To properly assess the benefits and risks associated with the use of a PE-rich diet, it is necessary to distinguish between endocrine disruption (endocrine-mediated adverse effects) and nonspecific effects on the endocrine system. Endometriosis is an estrogen-dependent disease of unknown etiopathogenesis, in which tissue similar to the lining of the uterus (the endometrium) grows outside of the uterus with subsequent complications being manifested as a result of local inflammatory reactions. Endometriosis affects 10-15% of women of reproductive age and is associated with chronic pelvic pain, dysmenorrhea, dyspareunia, and infertility. In this review, the endocrine-disruptive actions of PEs are reviewed in the context of endometriosis to determine whether a PE-rich diet has a positive or negative effect on the risk and course of endometriosis.

Characterization of a Novel Milk-Clotting Aspartic Protease from Penicillium sp. and Structural Explanation for its High Milk-Clotting Index

A novel milk-clotting enzyme isolated from Penicillium sp. ACCC 39790 (PsMCE) was prepared by heterologous expression. The recombinant PsMCE had an apparent molecular mass of 45 kDa and exhibited maximum casein hydrolysis activity at pH 4.0 and 50 °C. The PsMCE activity was enhanced by calcium ions and strongly inhibited by pepstatin A. Through hydrolysis pattern and cleavage site analyses, the milk-clotting activity of PsMCE was related to its specific hydrolysis between Phe₁₀₅ and Met₁₀₆ in the κ-casein proteins. The structural basis of PsMCE was characterized using homology modeling, molecular docking, and interactional analysis. The P1' region of PsMCE is critical for its selective binding to the hydrolytic site in κ-casein, and the hydrophobic forces play a decisive role in the specific cleavage of Phe₁₀₅ and Met₁₀₆. These interactional analyses between PsMCE and the ligand peptide clarified the fundamentals of its high milk-clotting index (MCI). PsMCE could be applied in cheese making due to its thermolability and high MCI value as a potential milk-clotting enzyme.

Computational Approaches to the Rational Design of Tubulin-Targeting Agents

Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.

Structural evolution of Delta lineage of SARS-CoV-2

One of the main obstacles in prevention and treatment of COVID-19 is the rapid evolution of the SARS-CoV-2 Spike protein. Given that Spike is the main target of common treatments of COVID-19, mutations occurring at this virulent factor can affect the effectiveness of treatments. The B.1.617.2 lineage of SARS-CoV-2, being characterized by many Spike mutations inside and outside of its receptor-binding domain (RBD), shows high infectivity and relative resistance to existing cures. Here, utilizing a wide range of computational biology approaches, such as immunoinformatics, molecular dynamics (MD), analysis of intrinsically disordered regions (IDRs), protein-protein interaction analyses, residue scanning, and free energy calculations, we examine the structural and biological attributes of the B.1.617.2 Spike protein. Furthermore, the antibody design protocol of Rosetta was implemented for evaluation the stability and affinity improvement of the Bamlanivimab (LY-CoV55) antibody, which is not capable of interactions with the B.1.617.2 Spike. We observed that the detected mutations in the Spike of the B1.617.2 variant of concern can cause extensive structural changes compatible with the described variation in immunogenicity, secondary and tertiary structure, oligomerization potency, Furin cleavability, and drug targetability. Compared to the Spike of Wuhan lineage, the B.1.617.2 Spike is more stable and binds to the Angiotensin-converting enzyme 2 (ACE2) with higher affinity.

Qi Gong Wan ameliorates adipocyte hypertrophy and inflammation in adipose tissue in a PCOS mouse model through the Nrf2/HO-1/Cyp1b1 pathway: Integrating network pharmacology and experimental validation in vivo

ETHNOPHARMACOLOGICAL RELEVANCE

Initially recorded in Yifang Jijie (an ancient Chinese text), Qi Gong Wan (QGW) is used to treat obese women with infertility. QGW can help promote follicular development and maturation, regulate the balance of serum hormones between testosterone and estradiol, enhance endometrial receptivity, improve waist circumference, and ameliorate insulin resistance. It contains eight herbs: Pinellia ternata (Thunb.) Makino (Banxia), Citrus maxima (Burm.) (Juhong), Poria cocos (Schw.) Wolf. (Fuling), Atractylodes macrocephala Koidz (Baizhu), Cyperus rotundus L. (Xiangfu), Conioselinum anthriscoides 'Chuanxiong' (Chuanxiong), Massa Medicata Fermentata (Shenqu), and Glycyrrhiza uralensis Fisch. ex DC. (Gancao). However, the underlying mechanism of how QGW affects women with PCOS remains unclear.

AIM OF THE STUDY

QGW has been widely used to treat PCOS patients with obesity clinically. This study was designed to identify its chemical and pharmacological properties.

MATERIALS AND METHODS

Network pharmacology was used to predict the active compounds, potential targets, and pathways of QGW. Female C57BL/6J mice were injected with letrozole and fed a high-fat diet to establish a PCOS-insulin resistance (PCOS-IR) model. Body weight, estrous cycles, ovarian pathology, and serum insulin resistance were measured. qRT-PCR was used to examine the inflammation-related and steroid hormone biosynthesis-related mRNA expression in adipose tissue. Western blotting was used to determine the protein levels of Nrf2, HO-1, and Cyp1b1 in adipose tissue. Molecular docking was used to reveal the key chemical compounds of QGW.

RESULTS

Network pharmacology revealed a total of 91 active ingredients in QGW that were associated with 167 targets. QGW could potentially treat PCOS-IR via nitrogen metabolism, steroid hormone biosynthesis, and ovarian steroidogenesis pathways. In the PCOS-IR mouse model, we found that QGW decreased the mean diameter of adipocytes and the total adipocyte area. Furthermore, QGW was found to significantly lower the expression of inflammation-related genes including Tnfɑ and C4a/b and the steroid hormone biosynthesis-related gene Cyp1b1. QGW showed a tendency to improve cystic follicles, fasting insulin, and HOMA-IR index in the PCOS-IR mouse model. Combining these findings with the results of KEGG analysis, we conclude that QGW promotes the Nrf2/HO-1/Cyp1b1 pathway to protect adipose tissue under conditions of PCOS. Molecular docking revealed that rutin, nicotiflorin, and baicalein may be the key chemical compounds of QGW through which it improves adipocyte hypertrophy and inflammation.

CONCLUSIONS

QGW improved adipocyte hypertrophy and inflammation in the PCOS-IR mouse model by activating the Nrf2/HO-1/Cyp1b1 pathway to protect adipose tissue. Our work thus provides a new research avenue for the study of traditional Chinese medicine in the treatment of PCOS.

Molecular Modeling Insights into the Structure and Behavior of Integrins: A Review

Integrins are heterodimeric glycoproteins crucial to the physiology and pathology of many biological functions. As adhesion molecules, they mediate immune cell trafficking, migration, and immunological synapse formation during inflammation and cancer. The recognition of the vital roles of integrins in various diseases revealed their therapeutic potential. Despite the great effort in the last thirty years, up to now, only seven integrin-based drugs have entered the market. Recent progress in deciphering integrin functions, signaling, and interactions with ligands, along with advancement in rational drug design strategies, provide an opportunity to exploit their therapeutic potential and discover novel agents. This review will discuss the molecular modeling methods used in determining integrins' dynamic properties and in providing information toward understanding their properties and function at the atomic level. Then, we will survey the relevant contributions and the current understanding of integrin structure, activation, the binding of essential ligands, and the role of molecular modeling methods in the rational design of antagonists. We will emphasize the role played by molecular modeling methods in progress in these areas and the designing of integrin antagonists.

In Silico Investigation of the Human GTP Cyclohydrolase 1 Enzyme Reveals the Potential of Drug Repurposing Approaches towards the Discovery of Effective BH4 Therapeutics

The GTP cyclohydrolase 1 enzyme (GTPCH1) is the rate-limiting enzyme of the tetrahydrobiopterin (BH₄) biosynthetic pathway. Physiologically, BH₄ plays a crucial role as an essential cofactor for the production of catecholamine neurotransmitters, including epinephrine, norepinephrine and dopamine, as well as the gaseous signaling molecule, nitric oxide. Pathological levels of the cofactor have been reported in a number of disease states, such as inflammatory conditions, neuropathic pain and cancer. Targeting the GTPCH1 enzyme has great potential in the management of a number of disease pathologies associated with dysregulated BH₄ physiology. This study is an in silico investigation of the human GTPCH1 enzyme using virtual screening and molecular dynamic simulation to identify molecules that can be repurposed to therapeutically target the enzyme. A three-tier molecular docking protocol was employed in the virtual screening of a comprehensive library of over 7000 approved medications and nutraceuticals in order to identify hit compounds capable of binding to the GTPCH1 binding pocket with the highest affinity. Hit compounds were further verified by molecular dynamic simulation studies to provide a detailed insight regarding the stability and nature of the binding interaction. In this study, we identify a number of drugs and natural compounds with recognized anti-inflammatory, analgesic and cytotoxic effects, including the aminosalicylate olsalazine, the antiepileptic phenytoin catechol, and the phlorotannins phlorofucofuroeckol and eckol. Our results suggest that the therapeutic and clinical effects of hit compounds may be partially attributed to the inhibition of the GTPCH1 enzyme. Notably, this study offers an understanding of the off-target effects of a number of compounds and advocates the potential role of aminosalicylates in the regulation of BH₄ production in inflammatory disease states. It highlights an in silico drug repurposing approach to identify a potential means of safely targeting the BH₄ biosynthetic pathway using established therapeutic agents.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

New Chemicals Suppressing SARS-CoV-2 Replication in Cell Culture

Candidates to being inhibitors of the main protease (Mpro) of SARS-CoV-2 were selected from the database of Voronezh State University using molecular modeling. The database contained approximately 19,000 compounds represented by more than 41,000 ligand conformers. These ligands were docked into Mpro using the SOL docking program. For one thousand ligands with best values of the SOL score, the protein–ligand binding enthalpy was calculated by the PM7 quantum-chemical method with the COSMO solvent model. Using the SOL score and the calculated protein–ligand binding enthalpies, eighteen compounds were selected for the experiments. Several of these inhibitors suppressed the replication of the coronavirus in cell culture, and we used the best three among them in the search for chemical analogs. Selection among analogs using the same procedure followed by experiments led to identification of seven inhibitors of the SARS-CoV-2 replication in cell culture with EC50 values at the micromolar level. The identified inhibitors belong to three chemical classes. The three inhibitors, 4,4-dimethyldithioquinoline derivatives, inhibit SARS-CoV-2 replication in Vero E6 cell culture just as effectively as the best published non-covalent inhibitors, and show low cytotoxicity. These results open up a possibility to develop antiviral drugs against the SARS-CoV-2 coronavirus.

TAIGET: A small-molecule target identification and annotation web server

Background: Accurate target identification of small molecules and downstream target annotation are important in pharmaceutical research and drug development.

Methods: We present TAIGET, a friendly and easy to operate graphical web interface, which consists of a docking module based on AutoDock Vina and LeDock, a target screen module based on a Bayesian–Gaussian mixture model (BGMM), and a target annotation module derived from >14,000 cancer-related literature works.

Results: TAIGET produces binding poses by selecting ≤5 proteins at a time from the UniProt ID-PDB network and submitting ≤3 ligands at a time with the SMILES format. Once the identification process of binding poses is complete, TAIGET then screens potential targets based on the BGMM. In addition, three medical experts and 10 medical students curated associations among drugs, genes, gene regulation, cancer outcome phenotype, 2,170 cancer cell types, and 73 cancer types from the PubMed literature, with the aim to construct a target annotation module. A target-related PPI network can be visualized by an interactive interface.

Conclusion: This online tool significantly lowers the entry barrier of virtual identification of targets for users who are not experts in the technical aspects of virtual drug discovery. The web server is available free of charge at http://www.taiget.cn/.

Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel

(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.

Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations

Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.

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/.

Identifying the Novel Inhibitors Against the Mycolic Acid Biosynthesis Pathway Target "mtFabH" of Mycobacterium tuberculosis

Mycolic acids are the key constituents of mycobacterial cell wall, which protect the bacteria from antibiotic susceptibility, helping to subvert and escape from the host immune system. Thus, the enzymes involved in regulating and biosynthesis of mycolic acids can be explored as potential drug targets to kill Mycobacterium tuberculosis (Mtb). Herein, Kyoto Encyclopedia of Genes and Genomes is used to understand the fatty acid metabolism signaling pathway and integrative computational approach to identify the novel lead molecules against the mtFabH (β-ketoacyl-acyl carrier protein synthase III), the key regulatory enzyme of the mycolic acid pathway. The structure-based virtual screening of antimycobacterial compounds from ChEMBL library against mtFabH results in the selection of 10 lead molecules. Molecular binding and drug-likeness properties of lead molecules compared with mtFabH inhibitor suggest that only two compounds, ChEMBL414848 (C1) and ChEMBL363794 (C2), may be explored as potential lead molecules. However, the spatial stability and binding free energy estimation of thiolactomycin (TLM) and compounds C1 and C2 with mtFabH using molecular dynamics simulation, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) indicate the better activity of C2 (ΔG = -14.18 kcal/mol) as compared with TLM (ΔG = -9.21 kcal/mol) and C1 (ΔG = -13.50 kcal/mol). Thus, compound C1 may be explored as promising drug candidate for the structure-based drug designing of mtFabH inhibitors in the therapy of Mtb.

Larvicidal and histopathological efficacy of cinnamic acid analogues: a novel strategy to reduce the dengue vector competence

Background: A novel strategy such as conjugation of amino, Schiff's bases, and thiadiazole moieties to the cinnamic acid nucleus has been adopted in this study to discover new molecules that target the dengue envelope protein (DENVE). Aim: Among the different domains of dengue virus envelope protein (PDB ID 1OKE), we have selected a ligand-binding domain for our structure-based drug design. The designed compounds have also been docked against DENVE protein. Methodology: Based on the in silico results and synthetic feasibility, three different schemes were used to synthesize twenty-three novel cinnamic acid derivatives. Sci-finder ascertained their novelty. The synthesized derivatives were consistent with their assigned spectra. The compounds were further evaluated for their larvicidal activity and histopathological analysis. Multiple linear regression analysis was performed to derive the QSAR model, which was further evaluated internally and externally for the prediction of activity. Results and discussion: Four compounds, namely CA 2, CA 14, ACA 4, and CATD 2, effectively showed larvicidal activity after 24, 48, and 72 h exposure; particularly, compound CA2 showed potent larvicidal activity with LC₅₀ of 82.15 μg ml^-1, 65.34 μg ml^-1, and 38.68 μg ml^-1, respectively, whereas intermittent stages, causes of abscess in the gut, and siphon regions were observed through histopathological studies. Conclusion: Our study identified some novel chemical scaffolds as effective DENVE inhibitors with efficacious anticipated pharmacokinetic profiles, which can be modified further.

Computational Prediction of the Binding Pose of Metal-Binding Pharmacophores

Computational modeling of inhibitors for metalloenzymes in virtual drug development campaigns has proven challenging. To overcome this limitation, a technique for predicting the binding pose of metal-binding pharmacophores (MBPs) is presented. Using a combination of density functional theory (DFT) calculations and docking using a genetic algorithm, inhibitor binding was evaluated in silico and compared with inhibitor-enzyme cocrystal structures. The predicted binding poses were found to be consistent with the cocrystal structures. The computational strategy presented represents a useful tool for predicting metalloenzyme-MBP interactions.

Computational Methods for the Interaction between Cyclodextrins and Natural Compounds: Technology, Benefits, Limitations, and Trends

Cyclodextrins (CDs) have a hollow structure with a hydrophobic interior and hydrophilic exterior. Forming inclusion complexes with CDs will maximize the bioavailability of natural compounds and enable active components to be processed into functional foods, medicines, additives, and so forth. However, experimental methods cannot explain CD-guest binding at the atomic level. Different models have been recently developed to simulate the interaction between CDs and guests to study the binding conformation and analyze noncovalent forces. This review paper summarizes modeling methods of CD-natural compound complexes. The methods include quantitative structure-activity relationships, molecular docking, molecular dynamics simulations, and quantum-chemical calculations. The applications of these methods to enhance the solubility and bioactivities of guest molecules, assist material transportation, and promote compound extraction are also discussed. The purpose of this review is to explore interaction mechanisms of CDs and guests and to help expand new applications of CDs.

Quantum-Chemical Quasi-Docking for Molecular Dynamics Calculations

The quantum quasi-docking procedure is used to compare the docking accuracies of two quantum-chemical semiempirical methods, namely, PM6-D3H4X and PM7. Quantum quasi-docking is an approximation to quantum docking. In quantum docking, it is necessary to search directly for the global minimum of the energy of the protein-ligand complex calculated by the quantum-chemical method. In quantum quasi-docking, firstly, we look for a wide spectrum of low-energy minima, calculated using the MMFF94 force field, and secondly, we recalculate the energies of all these minima using the quantum-chemical method, and among these recalculated energies we determine the lowest energy and the corresponding ligand position. Both PM6-D3H4X and PM7 are novel methods that describe well-dispersion interactions, hydrogen and halogen bonds. The PM6-D3H4X and PM7 methods are used with the COSMO implicit solvent model as it is implemented in the MOPAC program. The comparison is made for 25 high quality protein-ligand complexes. Firstly, the docking positioning accuracies have been compared, and we demonstrated that PM7+COSMO provides better positioning accuracy than PM6-D3H4X. Secondly, we found that PM7+COSMO demonstrates a much higher correlation between the calculated and measured protein-ligand binding enthalpies than PM6-D3H4X. For future quantum docking PM7+COSMO is preferable, but the COSMO model must be improved.

Uses and Abuses of the Atomic Displacement Parameters in Structural Biology

B-factors determined with X-ray crystallographic analyses are commonly used to estimate the flexibility degree of atoms, residues, and molecular moieties in biological macromolecules. In this chapter, the most recent studies and applications of B-factors in protein engineering and structural biology are briefly summarized. Particular emphasis is given to the limitations in using B-factors, in order to prevent inappropriate applications. It is eventually predicted that future applications will involve anisotropically refined B-factors, deep learning, and data produced by cryo-EM.

Machine-learning methods for ligand-protein molecular docking

Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains use AI, including molecular simulation for drug discovery. In this review, we provide an overview of ligand-protein molecular docking and how machine learning (ML), especially deep learning (DL), a subset of ML, is transforming the field by tackling the associated challenges.

Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

A Computational Perspective on Molecular Recognition by Galectins

This article presents an overview of recent computational studies dedicated to the analysis of binding between galectins and small-molecule ligands. We first present a summary of the most popular simulation techniques adopted for calculating binding poses and binding energies, and then discuss relevant examples reported in the literature for the three main classes of galectins (dimeric, tandem and chimera). We show that simulation of galectin-ligand interactions is a mature field which has proven invaluable for completing and unraveling experimental observations. Future perspectives to further improve the accuracy and cost-effectiveness of existing computational approaches will involve the development of new schemes to account for solvation and entropy effects, which represent the main current limitations to the accuracy of computational results.

Structural Analysis of a Novel Aspartic-Type Endopeptidase from Moringa oleifera Seeds and Its Milk-Clotting Properties

A novel aspartic-type endopeptidase was previously obtained from Moringa oleifera seeds; however, its specific milk-clotting properties have remained unclear. Here, we used various biophysical and molecular simulation approaches for characterizing the structure and function of the aspartic-type endopeptidase. The endopeptidase was preferentially active toward κ-casein (CN) and hydrolyzed it more than calf rennet; however, its ability to hydrolyze α-CN and β-CN was weaker than that of calf rennet. The endopeptidase cleaved κ-CN at Gln135-Asp136 and generated a 15 588.18 Da peptide with 135 amino acids. We further simulated the docking complex of the endopeptidase and κ-CN and found out that they possibly combined with each other via hydrogen bonds. The flocculation reaction between the endopeptidase and κ-CN indicated that milk coagulation occurred within 60 min. Overall, our observations suggest that the aspartic-type endopeptidase can be a potential rennet alternative for cheese making.

Recent Advances in the Development of Thalidomide-Related Compounds as Anticancer Drugs

INTRODUCTION

Thalidomide is an old well-known drug that was first used as morning sickness relief in pregnant women before being withdrawn from the market due to its severe side effects on normal fetal development, However, over the last few decades, the interest in this old drug has been renewed because of its efficacy in several important disorders for instance, multiple myeloma, breast cancer, and HIV-related diseases due to its antiangiogenic and immunomodulatory properties. Unfortunately, even in these cases, many aftereffects as deep vein thrombosis, peripheral neuropathy, constipation, somnolence, pyrexia, pain, and teratogenicity have been reported, showing the requirement of careful and monitored use. For this reason, research efforts are geared toward the synthesis and optimization of new thalidomide analogues lacking in toxic effects to erase these limits and improve the pharmacological profile.

AIMS

This review aims to examine the state-of-the-art concerning the current studies on thalidomide and its analogues towards cancer diseases (with few hints regarding the antimicrobial activity), focusing the attention on the possible mechanisms of action involved and the lack of toxicity.

CONCLUSION

In the light of the collected data, thalidomide analogues and their ongoing optimization could lead, in the future, to the realization of a promising therapeutic alternative for cancer-fighting.

[Development of antiviral drugs based on inhibitors of the SARS-COV-2 main protease]

Docking and quantum-chemical methods have been used for screening of drug-like compounds from the own database of the Voronezh State University to find inhibitors the SARS-CoV-2 main protease, an important enzyme of the coronavirus responsible for the COVID-19 pandemic. Using the SOL program more than 42000 3D molecular structures were docked into the active site of the main protease, and more than 1000 ligands with most negative values of the SOL score were selected for further processing. For all these top ligands, the protein-ligand binding enthalpy has been calculated using the PM7 semiempirical quantum-chemical method with the COSMO implicit solvent model. 20 ligands with the most negative SOL scores and the most negative binding enthalpies have been selected for further experimental testing. The latter has been made by measurements of the inhibitory activity against the main protease and suppression of SARS-CoV-2 replication in a cell culture. The inhibitory activity \of the compounds was determined using a synthetic fluorescently labeled peptide substrate including the proteolysis site of the main protease. The antiviral activity was tested against SARS-CoV-2 virus in the Vero cell culture. Eight compounds showed inhibitory activity against the main protease of SARS-CoV-2 in the submicromolar and micromolar ranges of the IC50 values. Three compounds suppressed coronavirus replication in the cell culture at the micromolar range of EC50 values and had low cytotoxicity. The found chemically diverse inhibitors can be used for optimization in order to obtain a leader compound, the basis of new direct-acting antiviral drugs against the SARS-CoV-2 coronavirus.

Deep Learning in Virtual Screening: Recent Applications and Developments

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Critical assessment of AI in drug discovery

Introduction: Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as language translation and computer vision are now being applied in drug discovery. AI has enabled multiple aspects of drug discovery including the analysis of high content screening data, and the design and synthesis of new molecules.Areas covered: This perspective provides an overview of the application of AI in several areas relevant to drug discovery including property prediction, molecule generation, image analysis, and organic synthesis planning.Expert opinion: While a variety of machine learning methods are now being routinely used to predict biological activity and ADME properties, methods of representing molecules continue to evolve. Molecule generation methods are relatively new and unproven but hold the potential to access new, unexplored areas of chemical space. The application of AI in drug discovery will continue to benefit from dedicated research, as well as AI developments in other fields. With this pairing algorithmic advancements and high-quality data, the impact of AI in drug discovery will continue to grow in the coming years.

Coupling Monte Carlo, Variational Implicit Solvation, and Binary Level-Set for Simulations of Biomolecular Binding

We develop a hybrid approach that combines the Monte Carlo (MC) method, a variational implicit-solvent model (VISM), and a binary level-set method for the simulation of biomolecular binding in an aqueous solvent. The solvation free energy for the biomolecular complex is estimated by minimizing the VISM free-energy functional of all possible solute-solvent interfaces that are used as dielectric boundaries. This functional consists of the solute volumetric, solute-solvent interfacial, solute-solvent van der Waals interaction, and electrostatic free energy. A technique of shifting the dielectric boundary is used to accurately predict the electrostatic part of the solvation free energy. Minimizing such a functional in each MC move is made possible by our new and fast binary level-set method. This method is based on the approximation of surface area by the convolution of an indicator function with a compactly supported kernel and is implemented by simple flips of numerical grid cells locally around the solute-solvent interface. We apply our approach to the p53-MDM2 system for which the two molecules are approximated by rigid bodies. Our efficient approach captures some of the poses before the final bound state. All-atom molecular dynamics simulations with most of such poses quickly reach the final bound state. Our work is a new step toward realistic simulations of biomolecular interactions. With further improvement of coarse graining and MC sampling, and combined with other models, our hybrid approach can be used to study the free-energy landscape and kinetic pathways of ligand binding to proteins.

Molecular Docking Analysis of Flavonoid Compounds with Matrix Metalloproteinase- 8 for the Identification of Potential Effective Inhibitors

Background:

Matrix metalloproteinase-8 (MMP-8) participates in the degradation of different types of collagens in the extracellular matrix and basement membrane. Up-regulation of the MMP-8 has been demonstrated in many disorders including cancer development, tooth caries, periodontal/ peri-implant soft and hard tissue degeneration, and acute/chronic inflammation. Therefore, MMP-8 has become an encouraging target for therapeutic procedures for scientists. We carried out a molecular docking approach to study the binding affinity of 29 flavonoids, as drug candidates, with the MMP-8. Pharmacokinetic and toxicological properties of the compounds were also studied. Moreover, it was attempted to identify the most important amino acids participating in ligand binding based on the degree of each of the amino acids in the ligand-amino acid interaction network for MMP-8.

Methods:

Three-dimensional structure of the protein was gained from the RCSB database (PDB ID: 4QKZ). AutoDock version 4.0 and Cytoscape 3.7.2 were used for molecular docking and network analysis, respectively. Notably, the inhibitor of the protein in the crystalline structure of the 4QKZ was considered as a control test. Pharmacokinetic and toxicological features of compounds were predicted using bioinformatics web tools. Post-docking analyses were performed using BIOVIA Discovery Studio Visualizer version 19.1.0.18287.

Results and Discussions:

According to results, 24 of the studied compounds were considered to be top potential inhibitors for MMP-8 based on their salient estimated free energy of binding and inhibition constant as compared with the control test: Apigenin-7-glucoside, nicotiflorin, luteolin, glabridin, taxifolin, apigenin, licochalcone A, quercetin, isorhamnetin, myricetin, herbacetin, kaemferol, epicatechin, chrysin, amentoflavone, rutin, orientin, epiafzelechin, quercetin-3- rhamnoside, formononetin, isoliquiritigenin, vitexin, catechine, and isoquercitrin. Moreover, His- 197 was found to be the most important amino acid involved in the ligand binding for the enzyme.

Conclusion:

The results of the current study could be used in the prevention and therapeutic procedures of a number of disorders such as cancer progression and invasion, oral diseases, and acute/chronic inflammation. Although, in vitro and in vivo tests are inevitable in the future.

Discovering Potential RNA Dependent RNA Polymerase Inhibitors as Prospective Drugs Against COVID-19: An in silico Approach

COVID-19, caused by Severe Acute Respiratory Syndrome Corona Virus 2, is declared a Global Pandemic by WHO in early 2020. In the present situation, though more than 180 vaccine candidates with some already approved for emergency use, are currently in development against SARS-CoV-2, their safety and efficacy data is still in a very preliminary stage to recognize them as a new treatment, which demands an utmost emergency for the development of an alternative anti-COVID-19 drug sine qua non for a COVID-19 free world. Since RNA-dependent RNA polymerase (RdRp) is an essential protein involved in replicating the virus, it can be held as a potential drug target. We were keen to explore the plant-based product against RdRp and analyze its inhibitory potential to treat COVID-19. A unique collection of 248 plant compounds were selected based on their antiviral activity published in previous literature and were subjected to molecular docking analysis against the catalytic sub-unit of RdRp. The docking study was followed by a pharmacokinetics analysis and molecular dynamics simulation study of the selected best-docked compounds. Tellimagrandin I, SaikosaponinB2, Hesperidin and (-)-Epigallocatechin Gallate were the most prominent ones that showed strong binding affinity toward RdRp. All the compounds mentioned showed satisfactory pharmacokinetics properties and remained stabilized at their respective binding sites during the Molecular dynamics simulation. Additionally, we calculated the free-binding energy/the binding properties of RdRp-ligand complexes with the connection of MM/GBSA. Interestingly, we observe that SaikosaponinB2 gives the best binding affinity (∆Gbinding = -42.43 kcal/mol) in the MM/GBSA assay. Whereas, least activity is observed for Hesperidin (∆Gbinding = -22.72 kcal/mol). Overall our study unveiled the feasibility of the SaikosaponinB2 to serve as potential molecules for developing an effective therapy against COVID-19 by inhibiting one of its most crucial replication proteins, RdRp.

Chemical composition and pharmacological mechanism of ephedra-glycyrrhiza drug pair against coronavirus disease 2019 (COVID-19)

Traditional Chinese medicine (TCM) had demonstrated effectiveness in the prevention and control of COVID-19. Statistics showed that Ephedra and Glycyrrhiza were frequently used in the treatment of COVID-19. We hypothesized that the Ephedra-Glycyrrhiza drug pair is a potential choice for the treatment of COVID-19. Here, 112 active compounds were identified from Ephedra-Glycyrrhiza via network pharmacology approach. Ephedra-Glycyrrhiza pair enrichment analysis demonstrated that these compounds might participate in the cAMP, PI3K-Akt, JAK-STAT and chemokine signaling pathways, which had a high correlation with respiratory, nervous, blood circulation and digestive system-related diseases. Pathway analysis between Ephedra-Glycyrrhiza and COVID-19 showed that the key targets were TNF-α, IL2, FOS, ALB, and PTGS2. They might control PI3K-Akt signaling pathway to exert immune regulation, organ protection and antiviral effects. Molecular docking results showed that the active compounds from the Ephedra-Glycyrrhiza pair bound well to COVID-19 related targets, including the main protease (Mpro, also called 3CLpro), the spike protein (S protein), and the angiotensin-converting enzyme 2 (ACE2). The Molecular dynamics simulation was analyzed for the stability and flexibility of the complex. In conclusion, our study elucidated the potential pharmacological mechanism of Ephedra-Glycyrrhiza in the treatment of COVID-19 through multiple targets and pathways.

Supercomputing, Docking and Quantum Mechanics in Quest for Inhibitors of Papain-like Protease of SARS-CoV-2

Lomonosov-2 supercomputer is used to search for new organic compounds that can suppress the replication of the SARS-CoV-2 coronavirus. The latter is responsible for the COVID-19 pandemic. Docking and a quantum-chemical semiempirical atomistic modeling method are used to find inhibitors of the SARS-CoV-2 papain-like protease, which is one of the key coronavirus enzymes responsible for its replication. The atomistic model of the papain-like protease of this coronavirus is based on the high-resolution structure deposited in the Protein Data Bank. The SOL docking program has been used for virtual screening of more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40000$$\end{document} low molecular weight molecules (ligands). Ligands with the highest protein-ligand binding energy, selected using the docking results, were subjected to quantum-chemical calculations. The latters are performed by the PM7 semiempirical method with the COSMO implicit solvent model using the MOPAC program. The enthalpy of protein-ligand binding is calculated for the best position of the ligand in the protein. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$19$$\end{document} ligands were selected for experimental in vitro testing as candidates for papain-like protease inhibitors base on docking and quantum-chemical results. In case of experimental confirmation, these compounds may become the basis for direct-acting antiviral drugs for the SARS-CoV-2 coronavirus.

Targeting a conserved pocket (n-octyl-β-D-glucoside) on the dengue virus envelope protein by small bioactive molecule inhibitors

Dengue virus enters the cell by receptor-mediated endocytosis followed by a viral envelope (DENVE) protein-mediated membrane fusion. A small detergent molecule n-octyl-β-D-glucoside (βOG) occupies the hydrophobic pocket which is located in the hinge region plays a major role in the rearrangement. It has been reported that mutations occurred in this binding pocket lead to the alterations of pH threshold for fusion. In addition to this event, the protonation of histidine residues present in the hydrophobic pocket would also impart the conformational change of the E protein evidence this pocket as a promising target. The present study identified novel cinnamic acid analogs as significant blockers of the hydrophobic pocket through molecular modeling studies against DENVE. A library of seventy-two analogs of cinnamic acid was undertaken for the discovery process of DENV inhibitors. A Molecular docking study was used to analyze the binding mechanism between these compounds and DENV followed by ADMET prediction. Binding energies were predicted by the MMGBSA study. The Molecular dynamic simulation was utilized to confirm the stability of potential compound binding. The compounds CA and SCA derivatives have been tested against HSV-1 & 2 viruses. From the computational results, the compounds CA1, CA2, SCA 60, SCA 57, SCA 37, SCA 58, and SCA 14 exhibited favorable interaction energy. The compounds have in-vitro antiviral activity; the results clearly indicate that the compounds showed the activity against both the viruses (HSV-1 & HSV-2). Our study provides valuable information on the discovery of small molecules DENVE inhibitors.Communicated by Ramaswamy H. Sarma.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

High-Throughput Docking Using Quantum Mechanical Scoring

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

Synthesis, Docking, and In Vitro Anticoagulant Activity Assay of Hybrid Derivatives of Pyrrolo[3,2,1-ij]Quinolin-2(1H)-one as New Inhibitors of Factor Xa and Factor XIa

Coagulation factor Xa and factor XIa are proven to be convenient and crucial protein targets for treatment for thrombotic disorders and thereby their inhibitors can serve as effective anticoagulant drugs. In the present work, we focused on the structure–activity relationships of derivatives of pyrrolo[3,2,1-ij]quinolin-2(1H)-one and an evaluation of their activity against factor Xa and factor XIa. For this, docking-guided synthesis of nine compounds based on pyrrolo[3,2,1-ij]quinolin-2(1H)-one was carried out. For the synthesis of new hybrid hydropyrrolo[3,2,1-ij]quinolin-2(1H)-one derivatives, we used convenient structural modification of both the tetrahydro- and dihydroquinoline moiety by varying the substituents at the C^6,8,9 positions. In vitro testing revealed that four derivatives were able to inhibit both coagulation factors and three compounds were selective factor XIa inhibitors. An IC₅₀ value of 3.68 μM for was found for the best factor Xa inhibitor and 2 μM for the best factor XIa inhibitor.