Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease

Molecular modeling and simulation approaches to characterize potential molecular targets for burdock inulin to instigate protection against autoimmune diseases

In the current study, we utilized molecular modeling and simulation approaches to define putative potential molecular targets for Burdock Inulin, including inflammatory proteins such as iNOS, COX-2, TNF-alpha, IL-6, and IL-1β. Molecular docking results revealed potential interactions and good binding affinity for these targets; however, IL-1β, COX-2, and iNOS were identified as the best targets for Inulin. Molecular simulation-based stability assessment demonstrated that inulin could primarily target iNOS and may also supplementarily target COX-2 and IL-1β during DSS-induced colitis to reduce the role of these inflammatory mechanisms. Furthermore, residual flexibility, hydrogen bonding, and structural packing were reported with uniform trajectories, showing no significant perturbation throughout the simulation. The protein motions within the simulation trajectories were clustered using principal component analysis (PCA). The IL-1β-Inulin complex, approximately 70% of the total motion was attributed to the first three eigenvectors, while the remaining motion was contributed by the remaining eigenvectors. In contrast, for the COX2-Inulin complex, 75% of the total motion was attributed to the eigenvectors. Furthermore, in the iNOS-Inulin complex, the first three eigenvectors contributed to 60% of the total motion. Furthermore, the iNOS-Inulin complex contributed 60% to the total motion through the first three eigenvectors. To explore thermodynamically favorable changes upon mutation, motion mode analysis was carried out. The Free Energy Landscape (FEL) results demonstrated that the IL-1β-Inulin achieved a single conformation with the lowest energy, while COX2-Inulin and iNOS-Inulin exhibited two lowest-energy conformations each. IL-1β-Inulin and COX2-Inulin displayed total binding free energies of - 27.76 kcal/mol and - 37.78 kcal/mol, respectively, while iNOS-Inulin demonstrated the best binding free energy results at - 45.89 kcal/mol. This indicates a stronger pharmacological potential of iNOS than the other two complexes. Thus, further experiments are needed to use inulin to target iNOS and reduce DSS-induced colitis and other autoimmune diseases.

Abrogation of ORF8-IRF3 binding interface with Carbon nanotube derivatives to rescue the host immune system against SARS-CoV-2 by using molecular screening and simulation approaches

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to over six million deaths worldwide. In human immune system, the type 1 interferon (IFN) pathway plays a crucial role in fighting viral infections. However, the ORF8 protein of the virus evade the immune system by interacting with IRF3, hindering its nuclear translocation and consequently downregulate the type I IFN signaling pathway. To block the binding of ORF8-IRF3 and inhibit viral pathogenesis a quick discovery of an inhibitor molecule is needed. Therefore, in the present study, the interface between the ORF8 and IRF3 was targeted on a high-affinity carbon nanotube by using computational tools. After analysis of 62 carbon nanotubes by multiple docking with the induced fit model, the top five compounds with high docking scores of - 7.94 kcal/mol, - 7.92 kcal/mol, - 7.28 kcal/mol, - 7.19 kcal/mol and - 7.09 kcal/mol (top hit1-5) were found to have inhibitory activity against the ORF8-IRF3 complex. Molecular dynamics analysis of the complexes revealed the high compactness of residues, stable binding, and strong hydrogen binding network among the ORF8-nanotubes complexes. Moreover, the total binding free energy for top hit1-5 was calculated to be - 43.21 ± 0.90 kcal/mol, - 41.17 ± 0.99 kcal/mol, - 48.85 ± 0.62 kcal/mol, - 43.49 ± 0.77 kcal/mol, and - 31.18 ± 0.78 kcal/mol respectively. These results strongly suggest that the identified top five nanotubes (hit1-5) possess significant potential for advancing and exploring innovative drug therapies. This underscores their suitability for subsequent in vivo and in vitro experiments, marking them as promising candidates worthy of further investigation.

Computational screening of natural products to identify potential inhibitors for human neuropilin-1 (NRP1) receptor to abrogate the binding of SARS-CoV-2 and host cell

Recently, a new variant B.1.1.529 or Omicron variant and its sub-variants (BA2.75, BA.5) of SARS-CoV-2 (Severe acute respiratory virus 2) have been reported with a larger number of mutations in the spike protein and particularly in the RBD (receptor-binding domain). The omicron (B.1.1.529) variant has aggravated the pandemic situation further and needs more analysis for therapeutic development. Keeping in view the urgency of the required data, the current study used molecular modeling and simulation-based methods to target the NRP1 (Neuropilin 1) protein to halt the entry into the host cell. Employing a molecular screening approach to screen the North-East African natural compounds database (NEANCDB) revealed Subereamine B with a docking score of -8.44 kcal/mol, Zinolol with the docking score of -8.05 while Subereamine A with a docking score of -7.88 kcal/mol as the best hits against NRP1. Molecular simulation-based further validation revealed stable dynamics, good structural packing, and dynamic residues flexibility index. Moreover, hydrogen bonding fraction analysis demonstrated the interactions remained sustained during the simulation. Furthermore, the total binding free energy for Subereamine B was -44.24 ±0.91 kcal/mol, for Zinolol -34.32 ±0.40 kcal/mol while for Subereamine A the TBE was calculated to be -41.78 ± 0.36 kcal/mol respectively. This shows that the two arginine-based alkaloids, i.e. Subereamine B and Subereamine A could inhibit the NRP1 more strongly than Zinolol. In conclusion, this study provides a basis for the development of novel drugs against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Pharmacotherapeutic Potential of Natural Products to Target the SARS-CoV-2 PLpro Using Molecular Screening and Simulation Approaches

Because of the essential role of PLpro in the regulation of replication and dysregulation of the host immune sensing, it is considered a therapeutic target for novel drug development. To reduce the risk of immune evasion and vaccine effectiveness, small molecular therapeutics are the best complementary approach. Hence, we used a structure-based drug-designing approach to identify potential small molecular inhibitors for PLpro of SARS-CoV-2. Initial scoring and re-scoring of the best hits revealed that three compounds NPC320891 (2,2-Dihydroxyindene-1,3-Dione), NPC474594 (Isonarciclasine), and NPC474595 (7-Deoxyisonarciclasine) exhibit higher docking scores than the control GRL0617. Investigation of the binding modes revealed that alongside the essential contacts, i.e., Asp164, Glu167, Tyr264, and Gln269, these molecules also target Lys157 and Tyr268 residues in the active site. Moreover, molecular simulation demonstrated that the reported top hits also possess stable dynamics and structural packing. Furthermore, the residues' flexibility revealed that all the complexes demonstrated higher flexibility in the regions 120-140, 160-180, and 205-215. The 120-140 and 160-180 lie in the finger region of PLpro, which may open/close during the simulation to cover the active site and push the ligand inside. In addition, the total binding free energy was reported to be - 32.65 ± 0.17 kcal/mol for the GRL0617-PLpro, for the NPC320891-PLpro complex, the TBE was - 35.58 ± 0.14 kcal/mol, for the NPC474594-PLpro, the TBE was - 43.72 ± 0.22 kcal/mol, while for NPC474595-PLpro complex, the TBE was calculated to be - 41.61 ± 0.20 kcal/mol, respectively. Clustering of the protein's motion and FEL further revealed that in NPC474594 and NPC474595 complexes, the drug was seen to have moved inside the binding cavity along with the loop in the palm region harboring the catalytic triad, thus justifying the higher binding of these two molecules particularly. In conclusion, the overall results reflect favorable binding of the identified hits strongly than the control drug, thus demanding in vitro and in vivo validation for clinical purposes.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

Exploring the Traditional Chinese Medicine (TCM) database chemical space to target I7L protease from monkeypox virus using molecular screening and simulation approaches

In the current study, we used molecular screening and simulation approaches to target I7L protease from monkeypox virus (mpox) from the Traditional Chinese Medicines (TCM) database. Using molecular screening, only four hits TCM27763, TCM33057, TCM34450 and TCM31564 demonstrated better pharmacological potential than TTP6171 (control). Binding of these molecules targeted Trp168, Asn171, Arg196, Cys237, Ser240, Trp242, Glu325, Ser326, and Cys328 residues and may affect the function of I7L protease in in vitro assay. Moreover, molecular simulation revealed stable dynamics, tighter structural packing and less flexible behaviour for all the complexes. We further reported that the average hydrogen bonds in TCM27763, TCM33057, TCM34450 and TCM31564I7L complexes remained higher than the control drug. Finally, the BF energy results revealed -62.60 ± 0.65 for the controlI7L complex, for the TCM27763I7L complex -71.92 ± 0.70 kcal/mol, for the TCM33057I7L complex the BF energy was -70.94 ± 0.70 kcal/mol, for the TCM34450I7L the BF energy was -69.94 ± 0.85 kcal/mol while for the TCM31564I7L complex the BF energy was calculated to be -69.16 ± 0.80 kcal/mol. Although, we used stateoftheart computational methods, these are theoretical insights that need further experimental validation.

Structure-based design of promising natural products to inhibit thymidylate kinase from Monkeypox virus and validation using free energy calculations

Monkeypox (MPXV) is a globally growing public health concern with 80,328 active cases and 53 deaths have been reported. No specific vaccine or drug is available for the treatment of MPXV. Hence, the current study also employed structure-based drug designing, molecular simulation, and free energy calculation methods to identify potential hit molecules against the TMPK of MPXV, which is a replicatory protein that helps the virus to replicate its DNA and increase the number of DNAs in the host cell. The 3D structure of TMPK was modeled with AlphaFold and screening of multiple natural products libraries (4,71,470 compounds) identified TCM26463, TCM2079, and TCM29893 from traditional Chinese medicines database (TCM), SANC00240, SANC00984, and SANC00986 South African natural compounds database (SANCDB), NPC474409, NPC278434 and NPC158847 from NPASS (natural product activity and species source database) while CNP0404204, CNP0262936, and CNP0289137 were shortlisted from coconut database (collection of open natural products) as the best hits. These compounds interact with the key active site residues through hydrogen bonds, salt bridges, and pie-pie interactions. The structural dynamics and binding free energy results further revealed that these compounds possess stable dynamics with excellent binding free energy scores. Moreover, the dissociation constant (K_D) and bioactivity analysis revealed stronger activity of these compounds exhibit stronger biological activity against MPXV and may inhibit it in in vitro conditions. All the results demonstrated that the designed novel compounds possess stronger inhibitory activity than the control complex (TPD-TMPK) from the vaccinia virus. The current study is the first to design small molecule inhibitors for the replication protein of MPXV which may help in controlling the current epidemic and also overcome the challenge of vaccine evasion.

Structural insights into the effect of mutations in the spike protein of SARS-CoV-2 on the binding with human furin protein

The SARS COV-2 and its variants are spreading around the world at an alarming speed, due to its higher transmissibility and the conformational changes caused by mutations. The resulting COVID-19 pandemic has imposed severe health consequences on human health. Several countries of the world including Pakistan have studied its genome extensively and provided productive findings. In the current study, the mCSM, DynaMut2, and I-Mutant servers were used to analyze the effect of identified mutations on the structural stability of spike protein however, the molecular docking and simulations approaches were used to evaluate the dynamics of the bonding network between the wild-type and mutant spike proteins with furin. We addressed the mutational modifications that have occurred in the spike protein of SARS-COV-2 that were found in 215 Pakistani's isolates of COVID-19 patients to study the influence of mutations on the stability of the protein and its interaction with the host cell. We found 7 single amino acid substitute mutations in various domains that reside in spike protein. The H49Y, N74K, G181V, and G446V were found in the S1 domain while the D614A, V622F, and Q677H mutations were found in the central helices of the spike protein. Based on the observation, G181V, G446V, D614A, and V622F mutants were found highly destabilizing and responsible for structural perturbation. Protein-protein docking and molecular simulation analysis with that of furin have predicted that all the mutants enhanced the binding efficiency however, the V622F mutant has greatly altered the binding capacity which is further verified by the K_D value (7.1 E^-14) and therefore may enhance the spike protein cleavage by Furin and increase the rate of infectivity by SARS-CoV-2. On the other hand, the total binding energy for each complex was calculated which revealed -50.57 kcal/mol for the wild type, for G181V -52.69 kcal/mol, for G446V -56.44 kcal/mol, for D614A -59.78 kcal/mol while for V622F the TBE was calculated to be -85.84 kcal/mol. Overall, the current finding shows that these mutations have increased the binding of Furin for spike protein and shows that D614A and V622F have significant effects on the binding and infectivity.

Discovery of Isojacareubin as a covalent inhibitor of SARS-CoV-2 main protease using structural and experimental approaches

The ongoing pandemic with the emergence of immune evasion potential and, particularly, the current omicron subvariants intensified the situation further. Although vaccines are available, the immune evasion capabilities of the recent variants demand further efficient therapeutic choices to control the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Hence, considering the necessity of the small molecule inhibitor, we target the main protease (3CLpro), which is an appealing target for the development of antiviral drugs against SARS-CoV-2. High-throughput molecular in silico screening of South African natural compounds database reported Isojacareubin and Glabranin as the potential inhibitors for the main protease. The calculated docking scores were reported to be -8.47 and -8.03 kcal/mol, respectively. Moreover, the structural dynamic assessment reported that Isojacareubin in complex with 3CLpro exhibit a more stable dynamic behavior than Glabranin. Inhibition assay indicated that Isojacareubin could inhibit SARS-CoV-2 3CLpro in a time- and dose-dependent manner, with half maximal inhibitory concentration values of 16.00 ± 1.35 μM (60 min incubation). Next, the covalent binding sites of Isojacareubin on SARS-CoV-2 3CLpro was identified by biomass spectrometry, which reported that Isojacareubin can covalently bind to thiols or Cysteine through Michael addition. To evaluate the inactivation potency of Isojacareubin, the inactivation kinetics was further investigated. The inactivation kinetic curves were plotted according to various concentrations with gradient-ascending incubation times. The KI value of Isojacareubin was determined as 30.71 μM, whereas the Kinact value was calculated as 0.054 min-1 . These results suggest that Isojacareubin is a covalent inhibitor of SARS-CoV-2 3CLpro .

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Identification of SARS-CoV-2 RNA dependent RNA polymerase inhibitors using pharmacophore modelling, molecular docking and molecular dynamics simulation approaches

The RNA-dependent RNA polymerase (RdRp) is one of the crucial enzymes in severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) catalysing the replication of RNA, therefore acts as a potential target for antiviral drug design. The fixation of a ligand in the active site of RdRp may alter the SARS-CoV-2 life cycle. Present work aimed at identifying novel inhibitors of the SARS-CoV-2 RdRp enzyme by performing pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation (MDS). Initially, the pharmacophore model of SARS-CoV-2 RdRp was constructed and the resulting model was used to screen compounds from ChEMBL, ZINC and PubChem databases. During the investigation, 180 compounds were screened using the above model and subjected to molecular docking with RdRp. Two compounds viz. ChEMBL1276156 and PubChem135548348 showed a strong binding affinity with RdRp than its standard inhibitor, Remdesivir. Toxicity prediction of these two compounds reveals their non-toxic nature. These compounds were further subjected to MDS for 100 ns to check their stability after binding with RdRp. The MDS of RdRp-ChEMBL1276156 and RdRp-PubChem135548348 complexes show enhanced stability in comparison to the RdRp-Remdesivir complex. The average interaction energy calculated after 100 ns of MDS was -146.56 and -172.68 KJ mol^-1 for RdRp-CHEMBL1276156 complex and RdRp-PubChem135548348 complex, respectively, while -59.90 KJ mol^-1 for RdRp-Remdesivir complex. The current investigation reveals that these two compounds are potent inhibitors of SARS-CoV-2 RdRp and they could be tested in the experimental condition to evaluate their efficacy against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Discovery of natural products to block SARS-CoV-2 S-protein interaction with Neuropilin-1 receptor: A molecular dynamics simulation approach

Neuropilin-1 (NRP1) is a widely expressed cell surface receptor protein characterized by its pleiotropic function. Recent reports highlighted NRP1 as an additional entry point of the SARS-CoV-2 virus, enhancing viral infectivity by interacting with the S-protein of SARS-CoV-2. The ubiquitous distribution and mechanism of action of NRP1 enable the SARS-CoV-2 virus to attack multiple organs in the body simultaneously. Therefore, blocking NRP1 is a potential therapeutic approach against SARS-CoV-2 infection. The current study screened the South African natural compounds database (SANCDB) for molecules that can disrupt the SARS-CoV-2 S protein-NRP1 interaction as a potential antiviral target for SARS-CoV-2 cellular entry. Following excessive screening and validation analysis 3-O-Methylquercetin and Esculetin were identified as potential compounds to disrupt the S-protein-NRP1 interaction. Furthermore, to understand the conformational stability and dynamic features between NRP1 interaction with the selected natural products, we performed 200 ns molecular dynamics (MD) simulations. In addition, molecular mechanics-generalized Born surface area (MM/GBSA) was utilized to calculate the free binding energies of the natural products interacting with NRP1. 3-O-methylquercetin showed an inhibitory effect with binding energies ΔG of -25.52 ± 0.04 kcal/mol to NRP1, indicating the possible disruption of the NRP1-S-protein interaction. Our analysis demonstrated that 3-O-methylquercetin presents a potential antiviral compound against SARS-CoV-2 infectivity. These results set the path for future functional in-vitro and in-vivo studies in SARS-CoV-2 research.

Investigation of the binding and dynamic features of A.30 variant revealed higher binding of RBD for hACE2 and escapes the neutralizing antibody: A molecular simulation approach

With the emergence of Delta and Omicron variants, many other important variants of SARS-CoV-2, which cause Coronavirus disease-2019, including A.30, are reported to increase the concern created by the global pandemic. The A.30 variant, reported in Tanzania and other countries, harbors spike gene mutations that help this strain to bind more robustly and to escape neutralizing antibodies. The present study uses molecular modelling and simulation-based approaches to investigate the key features of this strain that result in greater infectivity. The protein-protein docking results for the spike protein demonstrated that additional interactions, particularly two salt-bridges formed by the mutated residue Lys484, increase binding affinity, while the loss of key residues at the N terminal domain (NTD) result in a change to binding conformation with monoclonal antibodies, thus escaping their neutralizing effects. Moreover, we deeply studied the atomic features of these binding complexes through molecular simulation, which revealed differential dynamics when compared to wild type. Analysis of the binding free energy using MM/GBSA revealed that the total binding free energy (TBE) for the wild type receptor-binding domain (RBD) complex was -58.25 kcal/mol in contrast to the A.30 RBD complex, which reported -65.59 kcal/mol. The higher TBE for the A.30 RBD complex signifies a more robust interaction between A.30 variant RBD with ACE2 than the wild type, allowing the variant to bind and spread more promptly. The BFE for the wild type NTD complex was calculated to be -65.76 kcal/mol, while the A.30 NTD complex was estimated to be -49.35 kcal/mol. This shows the impact of the reported substitutions and deletions in the NTD of A.30 variant, which consequently reduce the binding of mAb, allowing it to evade the immune response of the host. The reported results will aid the development of cross-protective drugs against SARS-CoV-2 and its variants.

Deep learning tools for advancing drug discovery and development

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug-target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

Blocking key mutated hotspot residues in the RBD of the omicron variant (B.1.1.529) with medicinal compounds to disrupt the RBD-hACE2 complex using molecular screening and simulation approaches

A new variant of SARS-CoV-2 known as the omicron variant (B.1.1.529) reported in South Africa with 30 mutations in the whole spike protein, among which 15 mutations are in the receptor-binding domain, is continuously spreading exponentially around the world. The omicron variant is reported to be highly contagious with antibody-escaping activity. The emergence of antibody-escaping variants is alarming, and thus the quick discovery of small molecule inhibitors is needed. Hence, the current study uses computational drug screening and molecular dynamics simulation approaches (replicated) to identify novel drugs that can inhibit the binding of the receptor-binding domain (RBD) with hACE2. Screening of the North African, East African and North-East African medicinal compound databases by employing a multi-step screening approach revealed four compounds, namely (-)-pipoxide (C1), 2-(p-hydroxybenzyl) benzofuran-6-ol (C2), 1-(4-hydroxy-3-methoxyphenyl)-2-{4-[(E)-3-hydroxy-1-propenyl]-2-methoxyphenoxy}-1,3-propanediol (C3), and Rhein (C4), with excellent anti-viral properties against the RBD of the omicron variant. Investigation of the dynamics demonstrates stable behavior, good residue flexibility profiles, and structural compactness. Validation of the top hits using computational bioactivity analysis, binding free energy calculations and dissociation constant (K D) analysis also indicated the anti-viral properties of these compounds. In conclusion, this study will help in the design and discovery of novel drug therapeutics, which may be used against the emerging omicron variant of SARS-CoV-2.

The Omicron (B.1.1.529) variant of SARS-CoV-2 binds to the hACE2 receptor more strongly and escapes the antibody response: Insights from structural and simulation data

As SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) continues to inflict chaos globally, a new variant officially known as B.1.1.529 was reported in South Africa and was found to harbor 30 mutations in the spike protein. It is too early to speculate on transmission and hospitalizations. Hence, more analyses are required, particularly to connect the genomic patterns to the phenotypic attributes to reveal the binding differences and antibody response for this variant, which can then be used for therapeutic interventions. Given the urgency of the required analysis and data on the B.1.1.529 variant, we have performed a detailed investigation to provide an understanding of the impact of these novel mutations on the structure, function, and binding of RBD to hACE2 and mAb to the NTD of the spike protein. The differences in the binding pattern between the wild type and B.1.1.529 variant complexes revealed that the key substitutions Asn417, Ser446, Arg493, and Arg498 in the B.1.1.529 RBD caused additional interactions with hACE2 and the loss of key residues in the B.1.1.529 NTD resulted in decreased interactions with three CDR regions (1-3) in the mAb. Further investigation revealed that B.1.1.529 displayed a stable dynamic that follows a global stability trend. In addition, the dissociation constant (K_D), hydrogen bonding analysis, and binding free energy calculations further validated the findings. Hydrogen bonding analysis demonstrated that significant hydrogen bonding reprogramming took place, which revealed key differences in the binding. The total binding free energy using MM/GBSA and MM/PBSA further validated the docking results and demonstrated significant variations in the binding. This study is the first to provide a basis for the higher infectivity of the new SARS-CoV-2 variants and provides a strong impetus for the development of novel drugs against them.

Targeting the RBD of Omicron Variant (B.1.1.529) with Medicinal Phytocompounds to Abrogate the Binding of Spike Glycoprotein with the hACE2 Using Computational Molecular Search and Simulation Approach

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to inflict chaos globally. The emergence of a novel Omicron variant (B.1.1.529) in South Africa harbors 30 mutations in the spike protein. The variant is distinguished from other variants of concern (VOCs) with an increased (15) number of mutations in the receptor-binding domain (RBD) and suggests higher chances of causing reinfections. Initial reports also claimed that this variant escapes all the neutralizing antibodies, thus demanding a novel strategy against it. Thus, in this study, we performed a computational molecular screening against the RBD of the Omicron (B.1.1.529) variant and assessed the binding affinity of potent drugs against the RBD. The multi-steps screening of the South African Natural Compounds Database (SANCDB) revealed four medicinal compounds as excellent (potential) anti-viral agents against the Omicron variant, namely SANC00944, SANC01032, SANC00992, and SANC00317. The simulation analysis of these compounds in complex with the RBD demonstrated stable dynamics and structural compactness. Moreover, the residual flexibility analysis revealed that the flexibility of three loops required for interaction with hACE2 has been reduced by the binding of these drugs. The post-simulation validation of these compounds such as binding free energy, in silico bioactivity, and dissociation constant prediction validated the anti-viral potency of these compounds. The total binding free energy (TBFE) for the SANC01032-RBD complex was reported to be -46.54 kcal/mol; for the SANC01032-RBD complex, the TBFE was -41.88 kcal/mol; for the SANC00992-RBD complex the TBFE was -29.05 kcal/mol, while for the SANC00317-RBD complex the TBFE was -31.03 kcal/mol. The results showed the inhibition potential of these compounds by targeting the RBD. In conclusion, this study will help in the design and discovery of novel drug therapeutics, which may be used against the emerging Omicron variant of SARS-CoV-2.

Abrogation of SARS-CoV-2 interaction with host (NRP1) neuropilin-1 receptor through high-affinity marine natural compounds to curtail the infectivity: A structural-dynamics data

The evolution of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants around the globe has made the coronavirus disease 2019 (COVID-19) pandemic more worrisome, pressuring the health care system and resulting in an increased mortality rate. Recent studies recognized neuropilin-1 (NRP1) as a key facilitator in the invasion of the new SARS-CoV-2 into the host cell. Therefore, it is considered an imperative drug target for the treatment of COVID-19. Hence, a thorough analysis was needed to understand the impact and to guide new therapeutics development. In this study, we used structural and biomolecular simulation techniques to identify novel marine natural products which could block this receptor and stop the virus entry. We discovered that the binding affinity of CMNPD10175, CMNPD10017, CMNPD10114, CMNPD10115, CMNPD10020. CMNPD10018, CMNPD10153, CMNPD10149 CMNPD10464 and CMNPD10019 were substantial during the virtual screening (VS). We further explored these compounds by analyzing their absorption, distribution, metabolism, and excretion and toxicity (ADMET) properties and structural-dynamics features. Free energy calculations further established that all the compounds exhibit stronger binding energy for NRP1. Consequently, we hypothesized that these compounds might be the best lead candidates for therapeutic interventions hindering virus binding to the host cell. This study provides a strong impetus to develop novel drugs against the SARS-CoV-2 by targeting NRP1.

Drug similarity and structure-based screening of medicinal compounds to target macrodomain-I from SARS-CoV-2 to rescue the host immune system: a molecular dynamics study

The outbreak of the recent coronavirus (SARS-CoV-2), which causes a severe pneumonia infection, first identified in Wuhan, China, imposes significant risks to public health. Around the world, researchers are continuously trying to identify small molecule inhibitors or vaccine candidates by targeting different drug targets. The SARs-CoV-2 macrodomain-I, which helps in viral replication and hijacking the host immune system, is also a potential drug target. Hence, this study targeted viral macrodomain-I by using drug similarity, virtual screening, docking and re-docking approaches. A total of 64,043 compounds were screened, and potential hits were identified based on the docking score and interactions with the key residues. The top six hits were subjected to molecular dynamics simulation and Free energy calculations and repeated three times each. The per-residue energy decomposition analysis reported that these compounds significantly interact with Asp22, Ala38, Asn40, Val44, Phe144, Gly46, Gly47, Leu127, Ser128, Gly130, Ile131, Phe132 and Ala155 which are the critical active site residues. Here, we also used ADPr as a positive control to compare our results. Our results suggest that our identified hits by using such a complicated computational pipeline could inhibit the SARs-CoV-2 by targeting the macrodomain-1. We strongly recommend the experimental testing of these compounds, which could rescue the host immune system and could help to contain the disease caused by SARs-CoV-2.Communicated by Ramaswamy H. Sarma.

Rescuing the Host Immune System by Targeting the Immune Evasion Complex ORF8-IRF3 in SARS-CoV-2 Infection with Natural Products Using Molecular Modeling Approaches

The perennial emergence of SARS-CoV-2 and its new variants causing upper respiratory complexities since December 2019 has aggravated the pandemic situation around the world. SARS-CoV-2 encodes several proteins among which ORF8 is a novel factor that is unique to SARS-CoV-2 only and is reported to help the virus in disease severity and immune evasion. ORF8-IRF3 complex induces endoplasmic reticulum stress, thus helps in the evasion of immune response. Consequently, targeting the ORF8-IRF3 complex is considered as a prime target for the discovery of novel drugs against SARS-CoV-2. In this regard, computational methods are of great interest to fast track the identification and development of novel drugs. Virtual screening of South African Natural Compounds Database (SANCDB), followed by docking and molecular dynamics (MD) simulation analysis, were performed to determine novel natural compounds. Computational molecular search and rescoring of the SANCDB database followed by induced-fit docking (IFD) protocol identified Quercetin 3-O-(6″-galloyl)-beta-D-galactopyranoside (SANC00850), Tribuloside (SANC01050), and Rutin (SANC00867) are the best scoring compounds. Structural-dynamic properties assessment revealed that these three compounds have stable dynamics, compactness, and a higher number of hydrogen bonds. For validation, we used MM/GBSA, in silico bioactivity estimation and dissociation constant (KD) approaches, which revealed that these compounds are the more potent inhibitors of the ORF8-IRF3 complex and would rescue the host immune system potentially. These compounds need further in vitro and in vivo validations to be used as therapeutics against SARS-CoV-2 to rescue the host immune system during COVID-19 infection.

Computational Evaluation of Abrogation of HBx-Bcl-xL Complex with High-Affinity Carbon Nanotubes (Fullerene) to Halt the Hepatitis B Virus Replication

Hepatitis B virus (HBV) is the world's most prevalent chronic viral infection. More than 350 million individuals are chronic carriers of the virus, with an estimated 2 billion infected persons. For instance, the role of HBx protein in attachment and infection is very obvious and consequently deemed as an important druggable target. Targeting the interface and discovering novel drugs greatly advanced the field of therapeutics development. Therefore, in the current study, HBx to Bcl-xL is abrogated on high-affinity carbon nanotubes using computational structural biology tools. Our analysis revealed that among the total 62 carbon fullerenes, only 13 compounds exhibited inhibitory activity against HBx, which was further confirmed through IFD-based rescoring. Structural dynamics investigation revealed stable binding, compactness, and hydrogen bonds reprogramming. Moreover, the binding free energy calculation results revealed that the top hits1-4 possess the total binding energy of -54.36 kcal/mol (hit1), -50.81 kcal/mol (hit2), -47.09 kcal/mol (hit3), and -45.59 kcal/mol for hit4. In addition, the predicted KD values and bioactivity scores further validated the inhibitory potential of these top hits. The identified compounds need further in vitro and in vivo validation to aid the treatment process of HBV.

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.

Virtual screening-driven drug discovery of SARS-CoV2 enzyme inhibitors targeting viral attachment, replication, post-translational modification and host immunity evasion infection mechanisms

The novel coronavirus SARS-CoV2, the causative agent of the pandemic disease COVID-19, emerged in December 2019 forcing lockdown of communities in many countries. The absence of specific drugs and vaccines, the rapid transmission of the virus, and the increasing number of deaths worldwide necessitated the discovery of new substances for anti-COVID-19 drug development. With the aid of bioinformatics and computational modelling, ninety seven antiviral secondary metabolites from fungi were docked onto five SARS-CoV2 enzymes involved in viral attachment, replication, post-translational modification, and host immunity evasion infection mechanisms followed by molecular dynamics simulation and in silico ADMET prediction (absorption, distribution, metabolism, excretion and toxicity) of the hit compounds. Thus, three fumiquinazoline alkaloids scedapin C (15), quinadoline B (19) and norquinadoline A (20), the polyketide isochaetochromin D1 (8), and the terpenoid 11a-dehydroxyisoterreulactone A (11) exhibited high binding affinities on the target proteins, papain-like protease (PLpro), chymotrypsin-like protease (3CLpro), RNA-directed RNA polymerase (RdRp), non-structural protein 15 (nsp15), and the spike binding domain to GRP78. Molecular dynamics simulation was performed to optimize the interaction and investigate the stability of the top-scoring ligands in complex with the five target proteins. All tested complexes were found to have dynamic stability. Of the five top-scoring metabolites, quinadoline B (19) was predicted to confer favorable ADMET values, high gastrointestinal absorptive probability and poor blood-brain barrier crossing capacities.Communicated by Ramaswamy H. Sarma.

Combined drug repurposing and virtual screening strategies with molecular dynamics simulation identified potent inhibitors for SARS-CoV-2 main protease (3CLpro)

The current coronavirus (SARS-COV-2) pandemic and phenomenal spread to every nook and cranny of the world has raised major apprehensions about the modern public health care system. So far as a result of this epidemic, 4,434,653 confirmed cases and 302,169 deaths are reported. The growing infection rate and death toll demand the use of all possible approaches to design novel drugs and vaccines to curb this disease. In this study, we combined drugs repurposing and virtual drug screening strategies to target 3CLpro, which has an essential role in viral maturation and replication. A total of 31 FDA approved anti-HIV drugs, and Traditional Chinese medicines (TCM) database were screened to find potential inhibitors. As a result, Saquinavir, and five drugs (TCM5280805, TCM5280445, TCM5280343, TCM5280863, and TCM5458190) from the TCM database were found as promising hits. Furthermore, results from molecular dynamics simulation and total binding free energy revealed that Saquinavir and TCM5280805 target the catalytic dyad (His41 and Cys145) and possess stable dynamics behavior. Thus, we suggest that these compounds should be tested experimentally against the SARS-COV-2 as Saquinavir has been reported to inhibit HIV protease experimentally. Considering the intensity of coronavirus dissemination, the present research is in line with the idea of discovering the latest inhibitors against the coronavirus essential pathways to accelerate the drug development cycle.Communicated by Ramaswamy H. Sarma.

Insights Into Mutations Induced Conformational Changes and Rearrangement of Fe2+ Ion in pncA Gene of Mycobacterium tuberculosis to Decipher the Mechanism of Resistance to Pyrazinamide

Pyrazinamide (PZA) is the first-line drug commonly used in treating Mycobacterium tuberculosis (Mtb) infections and reduces treatment time by 33%. This prodrug is activated and converted to an active form, Pyrazinoic acid (POA), by Pyrazinamidase (PZase) enzyme. Mtb resistance to PZA is the outcome of mutations frequently reported in pncA, rpsA, and panD genes. Among the mentioned genes, pncA mutations contribute to 72-99% of the total resistance to PZA. Thus, considering the vital importance of this gene in PZA resistance, its frequent mutations (D49N, Y64S, W68G, and F94A) were investigated through in-depth computational techniques to put conclusions that might be useful for new scaffolds design or structure optimization to improve the efficacy of the available drugs. Mutants and wild type PZase were used in extensive and long-run molecular dynamics simulations in triplicate to disclose the resistance mechanism induced by the above-mentioned point mutations. Our analysis suggests that these mutations alter the internal dynamics of PZase and hinder the correct orientation of PZA to the enzyme. Consequently, the PZA has a low binding energy score with the mutants compared with the wild type PZase. These mutations were also reported to affect the binding of Fe2+ ion and its coordinated residues. Conformational dynamics also revealed that β-strand two is flipped, which is significant in Fe2+ binding. MM-GBSA analysis confirmed that these mutations significantly decreased the binding of PZA. In conclusion, these mutations cause conformation alterations and deformities that lead to PZA resistance.

Potential Cancer- and Alzheimer's Disease-Targeting Phosphodiesterase Inhibitors from Uvaria alba: Insights from In Vitro and Consensus Virtual Screening

Inhibition of the major cyclic adenosine monophosphate-metabolizing enzyme PDE4 has shown potential for the discovery of drugs for cancer, inflammation, and neurodegenerative disorders such as Alzheimer's disease. As a springboard to explore new anti-cancer and anti-Alzheimer's chemical prototypes from rare Annonaceae species, the present study evaluated anti-PDE4B along with antiproliferative and anti-cholinesterase activities of the extracts of the Philippine endemic species Uvaria alba using in vitro assays and framed the resulting biological significance through computational binding and reactivity-based experiments. Thus, the PDE4 B2B-inhibiting dichloromethane sub-extract (UaD) of U. alba elicited antiproliferative activity against chronic myelogenous leukemia (K-562) and cytostatic effects against human cervical cancer (HeLa). The extract also profoundly inhibited acetylcholinesterase (AChE), an enzyme involved in the progression of neurodegenerative diseases. Chemical profiling analysis of the bioactive extract identified 18 putative secondary metabolites. Molecular docking and molecular dynamics simulations showed strong free energy binding mechanisms and dynamic stability at 50-ns simulations in the catalytic domains of PDE4 B2B, ubiquitin-specific peptidase 14, and Kelch-like ECH-associated protein 1 (KEAP-1 Kelch domain) for the benzylated dihydroflavone dichamanetin (16), and of an AChE and KEAP-1 BTB domain for 3-(3,4-dihydroxybenzyl)-3',4',6-trihydroxy-2,4-dimethoxychalcone (8) and grandifloracin (15), respectively. Density functional theory calculations to demonstrate Michael addition reaction of the most electrophilic metabolite and kinetically stable grandifloracin (15) with Cys151 of the KEAP-1 BTB domain illustrated favorable formation of a β-addition adduct. The top-ranked compounds also conferred favorable in silico pharmacokinetic properties.

Antibacterial and COX-2 Inhibitory Tetrahydrobisbenzylisoquinoline Alkaloids from the Philippine Medicinal Plant Phaeanthus ophthalmicus

Phaeanthus ophthalmicus (Roxb. ex G.Don) J.Sinclair (previously known as P. ebracteolatus (Presl) Merr) is a Philippine medicinal plant occurring as evergreen shrub in the lowland forests of Luzon islands. It is used traditionally by Filipinos to treat bacterial conjunctivitis, ulcer and wound infections. Based on previous investigations where cyclooxygenase-2 (COX-2) functions as immune-linked factor in infectious sensitivities to bacterial pathogens by triggering pro-inflammatory immune-associated reactions, we investigated the antimicrobial and COX inhibitory activities of the extracts and tetrahydrobisbenzylisoquinoline alkaloids of P. ophthalmicus in vitro and in silico to validate its ethnomedicinal uses. Thus, the dichloromethane-methanol (DCM-MeOH) crude extract and alkaloid extracts exhibiting antibacterial activities against drug-resistant bacterial strains such as methicillin-resistance Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), Klebsiella pneumoniae + CRE and Pseudomonas aeruginosa + MBL afforded (+)-tetrandrine (1) and (+)-limacusine (2) as the major biologically active tetrahydrobisbenzylisoquinoline alkaloidal constituents after purification. Both tetrahydrobisbenzylisoquinoline alkaloids 1 and 2 showed broad spectrum antibacterial activity with strongest inhibition against the Gram-negative bacteria MβL-Pseudomonas aeruginosa Klebsiella pneumoniae + CRE. Interestingly, the alkaloid limacusine (2) showed selective inhibition against ovine COX-2 in vitro. These results were ascertained by molecular docking and molecular dynamics simulation experiments where alkaloid 2 showed strong affinity in the catalytic sites of Gram-negative bacterial enzymes P. aeruginosa elastase and K. pneumoniae KPC-2 carbapenemase (enzymes involved in infectivity mechanisms), and of ovine COX-2. Overall, our study provides credence on the ethnomedicinal use of the Philippine medicinal plant P. ophthalmicus as traditional plant-based adjuvant to treat bacterial conjunctivitis and other related infections. The antibacterial activities and selective COX-2 inhibition observed for limacusine (2) point to its role as the biologically active constituent of P. ophthalmicus. A limited number of drugs with COX-2 inhibitory properties like celecoxib also confer antibacterial activity. Thus, tetrahydrobisbenzyl alkaloids, especially 2, are promising pharmaceutical inspirations for developing treatments of bacterial/inflammation-related infections.

Targeting the N-terminal domain of the RNA-binding protein of the SARS-CoV-2 with high affinity natural compounds to abrogate the protein-RNA interaction: a molecular dynamics study

The emergence of COVID-19 took the world by shock in December 2019, starting from Wuhan, China and swiftly spreading across the globe. The number of COVID-19 cases continues to rise which is a global burden on the health care system worldwide. Efforts are continuing to come up with a solution either to develop a small molecular inhibitor or vaccine, but still no success. In the fight against SARS-CoV-2, targeting a different protein of the SARS-CoV-2 is the need of the hour to impede and relinquish the current pandemic. Therefore, in this study, computational modelling and simulation approaches are used to target the N-terminal domain of the phosphor-nucleoprotein (RNA binding protein), which is primarily responsible for binding and packing the viral genome to get ribonucleoprotein complex (RNP). Our multi-step drug screening approach shortlisted potential drugs. These top hits were confirmed by re-docking which revealed that the interacting molecules block the key residues i.e. Thr57, His59, Ser105, Arg107, and Arg177 and thus ultimately block the NTD from RNA recognition. Furthermore, the activity of the top four hits was also confirmed by using molecular dynamics simulation and free energy calculation. Our analysis suggests that these top hits possess strong inhibitory properties and should be tested experimentally. In conclusion, we hope these top hits would abrogate the binding of RNA and the NTD of the SARS-CoV-2, which might be helpful to combat COVID-19.Communicated by Ramaswamy H. Sarma.

Phylogenetic Analysis and Structural Perspectives of RNA-Dependent RNA-Polymerase Inhibition from SARs-CoV-2 with Natural Products

Abstract

Most recently, an outbreak of severe pneumonia caused by the infection of SARS-CoV-2, a novel coronavirus first identified in Wuhan, China, imposes serious threats to public health. Upon infecting host cells, coronaviruses assemble a multi-subunit RNA-synthesis complex of viral non-structural proteins (nsp) responsible for the replication and transcription of the viral genome. Therefore, the role and inhibition of nsp12 are indispensable. A cryo-EM structure of RdRp from SARs-CoV-2 was used to identify novel drugs from Northern South African medicinal compounds database (NANPDB) by using computational virtual screening and molecular docking approaches. Considering Remdesivir as the control, 42 compounds were shortlisted to have docking score better than Remdesivir. The top 5 hits were validated by using molecular dynamics simulation approach and free energy calculations possess strong inhibitory properties than the Remdesivir. Thus, this study paved a way for designing novel drugs by decoding the architecture of an important enzyme and its inhibition with compounds from natural resources. This disclosing of necessary knowledge regarding the screening and the identification of top hits could help to design effective therapeutic candidates against the coronaviruses and design robust preventive measurements.

Graphic abstract