Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.

Revitalizing Cancer Treatment: Exploring the Role of Drug Repurposing

Cancer persists as a global challenge necessitating continual innovation in treatment strategies. Despite significant advancements in comprehending the disease, cancer remains a leading cause of mortality worldwide, exerting substantial economic burdens on healthcare systems and societies. The emergence of drug resistance further complicates therapeutic efficacy, underscoring the urgent need for alternative approaches. Drug repurposing, characterized by the utilization of existing drugs for novel clinical applications, emerges as a promising avenue for addressing these challenges. Repurposed drugs, comprising FDA-approved (in other disease indications), generic, off-patent, and failed medications, offer distinct advantages including established safety profiles, cost-effectiveness, and expedited development timelines compared to novel drug discovery processes. Various methodologies, such as knowledge-based analyses, drug-centric strategies, and computational approaches, play pivotal roles in identifying potential candidates for repurposing. However, despite the promise of repurposed drugs, drug repositioning confronts formidable obstacles. Patenting issues, financial constraints associated with conducting extensive clinical trials, and the necessity for combination therapies to overcome the limitations of monotherapy pose significant challenges. This review provides an in-depth exploration of drug repurposing, covering a diverse array of approaches including experimental, re-engineering protein, nanotechnology, and computational methods. Each of these avenues presents distinct opportunities and obstacles in the pursuit of identifying novel clinical uses for established drugs. By examining the multifaceted landscape of drug repurposing, this review aims to offer comprehensive insights into its potential to transform cancer therapeutics.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

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.

Antimicrobial, antioxidant and cytotoxic activities of the leaf and stem extracts of Carissa bispinosa used for dental health care

BACKGROUND

Carissa bispinosa (L.) Desf. ex Brenan is one of the plants used traditionally to treat oral infections. However, there is limited data validating its therapeutic properties and photochemistry. The aim of this study was to investigate the protective efficacy of the leaf and stem extracts of C. bispinosa against oral infections.

METHODS

The phenolic and tannin contents were measured using Folin-Ciocalteau method after extracting with different solvents. The minimum inhibitory concentrations (MIC) of the extracts were assessed using the microdilution method against fungal (Candida albicans and Candida glabrata) and bacterial (Streptococcus pyogenes, Staphylococcus aureus and Enterococcus faecalis) strains. The 2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing power (FRP) models were utilised to assess the antioxidant potential of the extracts. Cytotoxicity of the leaf acetone extract was evaluated using the methylthiazol tetrazolium assay.

RESULTS

The methanol leaf extract had the highest phenolic content (113.20 mg TAE/g), whereas hexane extract displayed the highest tannin composition of 22.98 mg GAE/g. The acetone stem extract had the highest phenolic content (338 mg TAE/g) and the stem extract yielded the highest total tannin content (49.87 mg GAE/g). The methanol leaf extract demonstrated the lowest MIC value (0.31 mg/mL), whereas the stem ethanol extract had the least MIC value of 0.31 mg/mL. The stem methanol extract had the best DPPH free radical scavenging activity (IC50, 72 µg/mL) whereas the stem ethanol extract displayed maximum FRP with absorbance of 1.916. The leaf acetone extract had minimum cytotoxicity with the lethal concentration (LC50) of 0.63 mg/mL.

CONCLUSIONS

The results obtained in this study validated the protective effect of C. bispinosa against oral infections.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Structure-based computational design of novel covalent binders for the treatment of sickle cell disease

The quest in finding an everlasting panacea to the pernicious impact of sickle cell disease (SCD) in the society hit a turn of success since the recent discovery of a small molecule reversible covalent inhibitor, Voxelotor. A drug that primarily promotes the stability of oxygenated hemoglobin and inhibit the polymerization of HbS by enhancing hemoglobin's affinity for oxygen has opened a new frontier in drug discovery and development. Despite eminent efforts made to reproduce small molecules with better therapeutic targets, none has been successful. To this end, we employed the use of structure-based computational techniques with emphasis on the electrophilic warhead group of Voxelotor to harness novel covalent binders that could elicit better therapeutic response against HbS. The PubChem database and DataWarrior software were used to design random molecules using Voxelotor's electrophilic functionality. Following the compilation of these chemical entities, a high-throughput covalent docking-based virtual screening campaign was conducted which revealed three (Compound_166, Compound_2301, and Compound_2335) putative druglike candidates with higher baseline energy value compared to the standard drug. Subsequently, in silico ADMET profiling was carried out to evaluate their pharmacokinetics and pharmacodynamics properties, and their stability was evaluated for 1 μs (1 μs) using molecular dynamics simulation. Finally, to prioritize these compounds for further development in drug discovery, MM/PBSA calculations was employed to evaluate their molecular interactions and solvation energy within the HbS protein. Despite the admirable druglike and stability properties of these compounds, further experimental validations are required to establish their preclinical relevance for drug development.

Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation

Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group-protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.

Identification of EGFR inhibitors as potential agents for cancer therapy: pharmacophore-based modeling, molecular docking, and molecular dynamics investigations

CONTEXT

As a member of a large family of proteins that together regulate various aspects of cell growth and development, the epidermal growth factor receptor (EGFR) is a validated target for the development of new drugs. Herein, we compiled a library of 62 compounds from the PubChem database with similar pharmacophores as osimertinib, which to our knowledge represents the only drug capable of overcoming EGFR-T790M-mutated NSCLC until date. Subsequently, we launched a docking-based virtual screening campaign against the EGFR kinase with the compiled chemical entities. The virtual screen identified 3 hit candidates (CID_126667097, CID_137660592, and CID_137659061) with lower binding energy/higher affinity (- 8.7 kcal/mol, - 8.6 kcal/mol, and - 8.5 kcal/mol, respectively) than the standard osimertinib (- 8.4 kcal/mol). Molecular dynamics metrics such as RMSD, RMSF, ROG, and intermolecular H-bond were used to substantiate the stability of the promising drug candidates at the binding pocket of EGFR after 100,000 ps production run. Overall, our molecular modeling study portrayed CID_126667097, CID_137660592, and CID_137659061 as lead-like drug candidates that may be further developed for the treatment of EGFR-associated cancer disease.

METHODS

Molecular docking was conducted with Autodock Vina. A total of 62 compounds were compiled for the docking screen, which were then downloaded in SMILE format and converted to Protein Data Bank (PDB) format using the Openbabel online server. Finally, Gromacs 2022.3 was used to perform MD simulation to substantiate the stability of the hit candidates.

bioTCIs: Middle-to-Macro Biomolecular Targeted Covalent Inhibitors Possessing Both Semi-Permanent Drug Action and Stringent Target Specificity as Potential Antibody Replacements

Monoclonal antibody therapies targeting immuno-modulatory targets such as checkpoint proteins, chemokines, and cytokines have made significant impact in several areas, including cancer, inflammatory disease, and infection. However, antibodies are complex biologics with well-known limitations, including high cost for development and production, immunogenicity, a limited shelf-life because of aggregation, denaturation, and fragmentation of the large protein. Drug modalities such as peptides and nucleic acid aptamers showing high-affinity and highly selective interaction with the target protein have been proposed alternatives to therapeutic antibodies. The fundamental limitation of short in vivo half-life has prevented the wide acceptance of these alternatives. Covalent drugs, also known as targeted covalent inhibitors (TCIs), form permanent bonds to target proteins and, in theory, eternally exert the drug action, circumventing the pharmacokinetic limitation of other antibody alternatives. The TCI drug platform, too, has been slow in gaining acceptance because of its potential prolonged side-effect from off-target covalent binding. To avoid the potential risks of irreversible adverse drug effects from off-target conjugation, the TCI modality is broadening from the conventional small molecules to larger biomolecules possessing desirable properties (e.g., hydrolysis resistance, drug-action reversal, unique pharmacokinetics, stringent target specificity, and inhibition of protein-protein interactions). Here, we review the historical development of the TCI made of bio-oligomers/polymers (i.e., peptide-, protein-, or nucleic-acid-type) obtained by rational design and combinatorial screening. The structural optimization of the reactive warheads and incorporation into the targeted biomolecules enabling a highly selective covalent interaction between the TCI and the target protein is discussed. Through this review, we hope to highlight the middle to macro-molecular TCI platform as a realistic replacement for the antibody.

Computational exploration of molecular flexibility and interaction of meropenem analogs with the active site of oxacillinase-23 in Acinetobacter baumannii

Background: Carbapenem-resistant Acinetobacter baumannii is an opportunistic pathogen responsible for nosocomial infections and is one of the biggest global threats according to the World Health Organization (WHO), particularly causing substantial morbidity and mortality. Objectives: This study aimed at using computational approaches to screen meropenem and its analogs against OXA-23-positive Acinetobacter baumannii, analyzing the correlations between kinetic and phenotypic characteristics. Methods: A total of 5,450 compounds were screened using virtual screening workflow (HTVS, Glide-SP, and Glide-XP) to identify the best compounds based on their binding energy and interactions against OXA-23 and OXA-27 as they had phenotypic data available. Molecular dynamics simulation and density functional theory (DFT) studies were performed from the outcome of molecular docking analysis. Results: During simulations, meropenem and its analogs exhibited high-level stable interactions with Ser79, Ser126, Thr217, Trp219, and Arg259 of OXA-23. Meropenem displayed a CovDock energy of about -3.5 and -1.9 kcal mol^-1 against OXA-23 and OXA-27, respectively. Among the 5,450 compounds, Pubchem_10645796, Pubchem_25224737, and ChEMBL_14 recorded CovDock energy between -6.0 and -9.0 kcal mol^-1. Moreover, the infra-red (IR) spectrophotometric analysis revealed C=O and C-N atoms showing bands at 1,800 and 1,125 cm^-1, respectively. These observed data are in congruence with the experimental observations. Conclusion: The identified compounds showed good agreement with the spectrophotometric analysis using DFT methods. In the earlier studies, meropenem's MIC value was 32 μg mL^-1 in OXA-23-positive isolate A2265 compared to the MIC of 1 μg mL^-1 in Δbla _OXA-23 A2265. Comparing the CovDock energy and hydrogen-bonding interactions, the predicted results are in good agreement with the experimental data reported earlier. Our results highlight the importance of OXA-23 molecular docking studies and their compliance with the phenotypic results. It will help further in developing newer antibiotics for treating severe infections associated with carbapenem-resistant A. baumannii.