PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins

Species-specific metabolism of triphenyl phosphate and its mono-hydroxylated product by human and rat CYP2E1 and the carp ortholog

Organophosphorus flame retardants (PFRs) are a class of flame retardants and environmental pollutants with various biological effects. Recentstudies have evidenced activation of some PFRs by human CYP enzymes (including CYP2E1) for genotoxic effects. However, the activity of CYPs in fish species toward PFR metabolism remains unclear. This study was aimed on comparing the metabolism of triphenyl phosphate (TPHP) and 4-OH-TPHP in human, rat, and common carp, and the involvement of human CYP2E1 and its orthologs in the metabolism, by using fomepizole (4-MP, CYP2E1 inhibitor) as a modulator, in silico molecular docking and dynamics analyses. The rate of TPHP metabolism was apparently faster with human and rat, microsomes than with fish microsomes, the major metabolites were phosphodiester and hydroxylated phosphate, with 30-80 % of TPHP forming unidentified metabolites in the system of each species. 4-OH-TPHP was readily metabolized by both human and rat microsomes, whereas it was hardly metabolized in carp assays. Meanwhile, with 4-MP the transformation of TPHP to 4-OH-TPHP was enhanced in the human/rat systems while suppressed in the carp system. Moreover, the formation of unidentified metabolites in human and rat systems was mostly inhibited by 4-MP. Through molecular dynamics analysis TPHP and its primary metabolites showed high affinity for human and rat CYP2E1, as well as the carp ortholog (CYP2G1-like enzyme), however, the 4-OH-TPHP bond to the latter was too far from the heme to permit a biochemical reaction. This study suggests that the metabolism/activation of TPHP might be favored in mammals rather than carp, a fish species.

A Tripeptide (Ser-Arg-Pro, SRP) from Sipunculus nudus L. Improves Cadmium-Induced Acute Kidney Injury by Targeting the MAPK, Inflammatory, and Apoptosis Pathways in Mice

Cadmium (Cd) is a toxic heavy metal that causes nephrosis, including acute kidney injury. To prevent and treat acute kidney injury (AKI) following Cd exposure, a tripeptide, Ser-Arg-Pro (SRP), from Sipunculus nudus L. was employed, and its potential efficacy in AKI was assessed. Oral administration of SRP significantly alleviated Cd-induced kidney damage, leading to improved renal function and the attenuation of structural abnormalities. A network pharmacology analysis revealed the potential of SRP in renal protection by targeting various pathways, including mitogen-activated protein kinase (MAPK) signaling, inflammatory response, and apoptosis pathways. Mechanistic studies indicated that SRP achieves renal protection by inhibiting the activation of MAPK pathways (phosphorylation of p38, p56, ERK, and JNK) in the oxidative stress cascade, suppressing inflammatory responses (iNOS, Arg1, Cox2, TNF-α, IL-1β, and IL-6), and restoring altered apoptosis factors (caspase-9, caspase-3, Bax, and Bcl-2). Hence, SRP has the potential to be used as a therapeutic agent for the treatment of Cd-induced nephrotoxicity.

Structural basis of p53 inactivation by cavity-creating cancer mutations and its implications for the development of mutant p53 reactivators

The cavity-creating p53 cancer mutation Y220C is an ideal paradigm for developing small-molecule drugs based on protein stabilization. Here, we have systematically analyzed the structural and stability effects of all oncogenic Tyr-to-Cys mutations (Y126C, Y163C, Y205C, Y220C, Y234C, and Y236C) in the p53 DNA-binding domain (DBD). They were all highly destabilizing, drastically lowering the melting temperature of the protein by 8-17 °C. In contrast, two non-cancerous mutations, Y103C and Y107C, had only a moderate effect on protein stability. Differential stabilization of the mutants upon treatment with the anticancer agent arsenic trioxide and stibogluconate revealed an interesting proximity effect. Crystallographic studies complemented by MD simulations showed that two of the mutations, Y234C and Y236C, create internal cavities of different size and shape, whereas the others induce unique surface lesions. The mutation-induced pockets in the Y126C and Y205C mutant were, however, relatively small compared with that of the already druggable Y220C mutant. Intriguingly, our structural studies suggest a pronounced plasticity of the mutation-induced pocket in the frequently occurring Y163C mutant, which may be exploited for the development of small-molecule stabilizers. We point out general principles for reactivating thermolabile cancer mutants and highlight special cases where mutant-specific drugs are needed for the pharmacological rescue of p53 function in tumors.

Repurposing of drug molecules from FDA database against Hepatitis C virus E2 protein using ensemble docking approach

Hepatitis C virus, a member of the Flaviviridae family and genus Hepacivirus, is an enveloped, positively single stranded RNA virus. Its surface consists of a heterodimer of E1 and E2 proteins which play a crucial role in receptor binding and membrane fusion. In this study we have used in silico virtual screening by utilizing ensemble docking on the approved drugs. These drugs can bind with high efficiency to the 36 prominent conformations of the CD81 binding site clustered from a total of 3 µs MD simulation data on the E2 protein. We started with 9213 compounds from the FDA list of drugs and progressively came down to 5 compounds which have been seen to bind with very high efficiency to not only all the conformations but also the two predicted druggable pockets that encompass the CD81 binding site. MM/PBSA binding energy calculations also point to the highly stable interaction of the compounds to the E2 protein. This study may in future broaden the arsenal of therapeutics for use against HCV infection and lead to more effective care against the virus.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Identifying Potential Ligand Binding Sites on Glycogen Synthase Kinase 3 Using Atomistic Cosolvent Simulations

Glycogen synthase kinase 3 β (GSK3β) is a serine/threonine kinase that phosphorylates several protein substrates in crucial cell signaling pathways. Owing to its therapeutic importance, there is a need to develop GSK3β inhibitors with high specificity and potency. One approach is to find small molecules that can allosterically bind to the GSK3β protein surface. We have employed fully atomistic mixed-solvent molecular dynamics (MixMD) simulations to identify three plausible allosteric sites on GSK3β that can facilitate the search for allosteric inhibitors. Our MixMD simulations narrow down the allosteric sites to precise regions on the GSK3β surface, thereby improving upon the previous predictions of the locations of these sites.

Impact of PmrB mutations on clinical Klebsiella pneumoniae with variable colistin-susceptibilities: Structural insights and potent therapeutic solutions

Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections continue to impose high morbidity threats to hospitalized patients worldwide, limiting therapeutic options to last-resort antibiotics like colistin. However, the dynamic genomic landscape of colistin-resistant K. pneumoniae (COLR-Kp) invoked ardent exploration of underlying molecular signatures for therapeutic propositions/designs. We unveiled the structural impact of the widespread and emerging PmrB mutations involved in colistin resistance (COLR) in K. pneumoniae. In the present study, clinical isolates of K. pneumoniae expressed variable susceptibilities to colistin (>0.5 μg/mL for resistant and ≤0.25 μg/mL for susceptible) despite mutations such as T157P, G207D and T246A. The protein sequences extracted from in-house sequenced genomes were used to model mutant PmrB proteins and analyze the underlying structural alterations. The mutations were contrasted based on molecular dynamics simulation trajectories, free-energy landscapes and structural flexibility profiles. The altered backbone flexibilities can be an essential factor for mutant selection by COLR K. pneumoniae and can provide clues to deal with emerging mutants. Furthermore, PmrB having high druggability confidence (>0.99), was explored as a potential target for 1396 virtually screened FDA-approved drug candidates. Among the top-10 compounds (scores >70), amphotericin B was found to be potential candidate with high affinity (Binding energy <-8 kcal/mol) and stable interactions (RMSF <0.7 Å) against PmrB druggable pockets, despite the mutations, which encourages future adjunct therapeutic research against COLR-Kp.

Comparative Analysis of Conformational Dynamics and Systematic Characterization of Cryptic Pockets in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 Spike Complexes with the ACE2 Host Receptor: Confluence of Binding and Structural Plasticity in Mediating Networks of Conserved Allosteric Sites

In the current study, we explore coarse-grained simulations and atomistic molecular dynamics together with binding energetics scanning and cryptic pocket detection in a comparative examination of conformational landscapes and systematic characterization of allosteric binding sites in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 spike full-length trimer complexes with the host receptor ACE2. Microsecond simulations, Markov state models and mutational scanning of binding energies of the SARS-CoV-2 BA.2 and BA.2.75 receptor binding domain complexes revealed the increased thermodynamic stabilization of the BA.2.75 variant and significant dynamic differences between these Omicron variants. Molecular simulations of the SARS-CoV-2 Omicron spike full-length trimer complexes with the ACE2 receptor complemented atomistic studies and enabled an in-depth analysis of mutational and binding effects on conformational dynamic and functional adaptability of the Omicron variants. Despite considerable structural similarities, Omicron variants BA.2, BA.2.75 and XBB.1 can induce unique conformational dynamic signatures and specific distributions of the conformational states. Using conformational ensembles of the SARS-CoV-2 Omicron spike trimer complexes with ACE2, we conducted a comprehensive cryptic pocket screening to examine the role of Omicron mutations and ACE2 binding on the distribution and functional mechanisms of the emerging allosteric binding sites. This analysis captured all experimentally known allosteric sites and discovered networks of inter-connected and functionally relevant allosteric sites that are governed by variant-sensitive conformational adaptability of the SARS-CoV-2 spike structures. The results detailed how ACE2 binding and Omicron mutations in the BA.2, BA.2.75 and XBB.1 spike complexes modulate the distribution of conserved and druggable allosteric pockets harboring functionally important regions. The results are significant for understanding the functional roles of druggable cryptic pockets that can be used for allostery-mediated therapeutic intervention targeting conformational states of the Omicron variants.

Finding Lead Compounds for Dengue Antivirals from a Collection of Old Drugs through In Silico Target Prediction and Subsequent In Vitro Validation

Dengue virus (DENV) infection is one of the most widely spread flavivirus infections. Despite the fatality it could cause, no antiviral treatment is currently available to treat the disease. Hence, this study aimed to repurpose old drugs as novel DENV NS3 inhibitors. Ligand-based (L-B) and proteochemometric (PCM) prediction models were built using 62,354 bioactivity data to screen for potential NS3 inhibitors. Selected drugs were then subjected to the foci forming unit reduction assay (FFURA) and protease inhibition assay. Finally, molecular docking was performed to validate these results. The in silico studies revealed that both models performed well in the internal and external validations. However, the L-B model showed better accuracy in the external validation in terms of its sensitivity (0.671). In the in vitro validation, all drugs (zileuton, trimethadione, and linalool) were able to moderately inhibit the viral activities at the highest concentration tested. Zileuton showed comparable results with linalool when tested at 2 mM against the DENV NS3 protease, with a reduction of protease activity at 17.89 and 18.42%, respectively. Two new compounds were also proposed through the combination of the selected drugs, which are ziltri (zilueton + trimethadione) and zilool (zileuton + linalool). The molecular docking study confirms the in vitro observations where all drugs and proposed compounds were able to achieve binding affinity ≥ -4.1 kcal/mol, with ziltri showing the highest affinity at -7.7 kcal/mol, surpassing the control, panduratin A. The occupation of both S1 and S2 subpockets of NS2B-NS3 may be essential and a reason for the lower binding energy shown by the proposed compounds compared to the screened drugs. Based on the results, this study provided five potential new lead compounds (ziltri, zilool, zileuton, linalool, and trimethadione) for DENV that could be modified further.

Peptide ligands for the affinity purification of adeno-associated viruses from HEK 293 cell lysates

Adeno-associated viruses (AAVs) are the vector of choice for delivering gene therapies that can cure inherited and acquired diseases. Clinical research on various AAV serotypes significantly increased in recent years alongside regulatory approvals of AAV-based therapies. The current AAV purification platform hinges on the capture step, for which several affinity resins are commercially available. These adsorbents rely on protein ligands-typically camelid antibodies-that provide high binding capacity and selectivity, but suffer from low biochemical stability and high cost, and impose harsh elution conditions (pH < 3) that can harm the transduction activity of recovered AAVs. Addressing these challenges, this study introduces peptide ligands that selectively capture AAVs and release them under mild conditions (pH = 6.0). The peptide sequences were identified by screening a focused library and modeled in silico against AAV serotypes 2 and 9 (AAV2 and AAV9) to select candidate ligands that target homologous sites at the interface of the VP1-VP2 and VP2-VP3 virion proteins with mild binding strength (KD  ~ 10-5 -10- 6  M). Selected peptides were conjugated to Toyopearl resin and evaluated via binding studies against AAV2 and AAV9, demonstrating the ability to target both serotypes with values of dynamic binding capacity (DBC10%  > 1013 vp/mL of resin) and product yields (~50%-80%) on par with commercial adsorbents. The peptide-based adsorbents were finally utilized to purify AAV2 from a HEK 293 cell lysate, affording high recovery (50%-80%), 80- to 400-fold reduction of host cell proteins (HCPs), and high transduction activity (up to 80%) of the purified viruses.

Sour Tamarind Is More Antihypertensive than the Sweeter One, as Evidenced by In Vivo Biochemical Indexes, Ligand-Protein Interactions, Multitarget Interactions, and Molecular Dynamic Simulation

This research investigated the antihypertensive effects of tamarind products and compared their potentials based on an animal model's data verified by molecular docking, multitarget interactions, and dynamic simulation assays. GC-MS-characterized tamarind products were administered to cholesterol-induced hypertensive albino rat models. The two-week-intervened animals were dissected to collect their serum and organs and respectively subjected to analyses of their hypertension-linked markers and tissue architectures. The lead biometabolites of tamarinds interacted with eight target receptors in the molecular docking and dynamic simulation studies and with multitarget in the network pharmacological analyses. The results show that the serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), C-reactive protein (CRP), troponin I, and lipid profiles were maximally reinstated by the phenolic-enriched ripened sour tamarind extract compared to the sweet one, but the seed extracts had a smaller influence. Among the tamarind's biometabolites, ϒ-sitosterol was found to be the best ligand to interact with the guanylate cyclase receptor, displaying the best drug-likeliness with the highest binding energy, -9.3 Kcal. A multitargeted interaction-based degree algorithm and a phylogenetic tree of pathways showed that the NR3C1, REN, PPARG, and CYP11B1 hub genes were consistently modulated by ϒ-sitosterol to reduce hypertension and related risk factors. The dynamic simulation study showed that the P-RMSD values of ϒ-sitosterol-guanylate cyclase were stable between 75.00 and 100.00 ns at the binding pocket. The findings demonstrate that ripened sour tamarind extract may be a prospective antihypertensive nutraceutical or supplement target affirmed through advanced preclinical and clinical studies.

Epithelial-mesenchymal transition-related gene signature for prognosis of lung squamous cell carcinoma

Epithelial-mesenchymal transition (EMT) is associated with tumor invasion and progression, and is regulated by DNA methylation. A prognostic signature of lung squamous cell carcinoma (LUSC) with EMT-related gene data has not yet been established. In our study, we constructed a co-expression network using differentially expressed genes (DEGs) obtained from The Cancer Genome Atlas (TCGA) to identify hub genes. We conducted a correlation analysis between the differentially methylated hub genes and differentially expressed EMT-related genes to screen EMT-related differentially methylated genes (ERDMGs). Functional enrichment was performed to annotate the ERDMGs. The least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses were performed to build a survival prognosis prediction model. Additionally, druggability analysis was performed to predict the potential drug targets of ERDMGs. We screened 11 ERDMGs that were enriched in cell adhesion molecules and other signaling pathways. Finally, we constructed a 4-ERDMG model, which showed good ability to predict survival prognosis in the training and validation sets. The model could serve as an independent predictive factor for patients with LUSC. Additionally, our druggability analysis predicted that CC chemokine ligand 23 (CCL23) and Hepatocyte nuclear factor 1b (HNF1B) may be the underlying drug targets of LUSC. We established a new risk score (RS) system as a prognostic indicator to predict the outcome of patients with LUSC, which will help in the improvement of treatment strategies.

Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event

Ras GTPase is an enzyme that catalyzes the hydrolysis of guanosine triphosphate (GTP) and plays an important role in controlling crucial cellular signaling pathways. However, this enzyme has always been believed to be undruggable due to its strong binding affinity with its native substrate GTP. To understand the potential origin of high GTPase/GTP recognition, here we reconstruct the complete process of GTP binding to Ras GTPase via building Markov state models (MSMs) using a 0.1 ms long all-atom molecular dynamics (MD) simulation. The kinetic network model, derived from the MSM, identifies multiple pathways of GTP en route to its binding pocket. While the substrate stalls onto a set of non-native metastable GTPase/GTP encounter complexes, the MSM accurately discovers the native pose of GTP at its designated catalytic site in crystallographic precision. However, the series of events exhibit signatures of conformational plasticity in which the protein remains trapped in multiple non-native conformations even when GTP has already located itself in its native binding site. The investigation demonstrates mechanistic relays pertaining to simultaneous fluctuations of switch 1 and switch 2 residues which remain most instrumental in maneuvering the GTP-binding process. Scanning of the crystallographic database reveals close resemblance between observed non-native GTP binding poses and precedent crystal structures of substrate-bound GTPase, suggesting potential roles of these binding-competent intermediates in allosteric regulation of the recognition process.

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology

Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.

Identifying promising druggable binding sites and their flexibility to target the receptor-binding domain of SARS-CoV-2 spike protein

The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for viral infection. The interaction of its receptor-binding domain (RBD) with the human angiotensin-converting enzyme 2 (ACE2) protein is required for the virus to enter the host cell. We identified RBD binding sites to block its function with inhibitors by combining the protein structural flexibility with machine learning analysis. Molecular dynamics simulations were performed on unbound or ACE2-bound RBD conformations. Pockets estimation, tracking and druggability prediction were performed on a large sample of simulated RBD conformations. Recurrent druggable binding sites and their key residues were identified by clustering pockets based on their residue similarity. This protocol successfully identified three druggable sites and their key residues, aiming to target with inhibitors for preventing ACE2 interaction. One site features key residues for direct ACE2 interaction, highlighted using energetic computations, but can be affected by several mutations of the variants of concern. Two highly druggable sites, located between the spike protein monomers interface are promising. One weakly impacted by only one Omicron mutation, could contribute to stabilizing the spike protein in its closed state. The other, currently not affected by mutations, could avoid the activation of the spike protein trimer.

Sub-pocket-focused designing of tacrine derivatives as potential acetylcholinesterase inhibitors

Human acetylcholinesterase (hAChE) has a potential role in the management of acetylcholine, one of the neurotransmitters that modulate the overall activity of cholinergic system, AChE inhibitors have a greater impact in the therapeutics. Though the atomic structure of hAChE has been extensively studied, the precise active site geometry upon binding to different ligands are yet to be explored. In the present study, an extensive structural analysis of our recently reported hAChE-tacrine complex has carried out and revealed the presence of two prominent sub-pockets located at the vicinity of the hAChE active site. Structural bioinformatics assisted studies designed 132 putative sub-pockets focused tacrine derivatives (SPFTDs), their molecular docking, free energy estimations revealed that they are stronger than tacrine in terms of binding affinity. Our in vitro studies also supported the in silico findings, all these SPFTDs are having better potencies than tacrine. Cytotoxic nature of these SPFTDs on HepG2 and Neuro-2a cell lines, diminishes the possibilities for future in vivo studies. However, the identification of these sub pockets and the SPFTDs paved a new way to the future drug discovery especially since AChE is one of the promising and approved drug targets in treatment of AD drug discovery.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence-based self-supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence-based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue-binding weights from the well-trained network using the gradient-weighted class activation mapping (Grad-Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule-binding sites. Thus, our work provides a highly effective sequence-based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule-binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.

Druggable sites identification in Streptococcus mutans VicRK system evaluated by catechols

Dental caries is a global public health problem, being the most common non-communicable disease. Streptococcus mutans, the causative agent of human cariogenic dental biofilms, produce glycosyltransferases (Gtfs) whose gene expression is modulated by the VicRK system, which makes them a promising target for dental biofilm inhibitor developments. Bioinformatics have playing a significant role in drug discovery programs mainly in novel hit identification. In this study, potential inhibitors against the S. mutans VicK system have been identified through Structure-based Virtual Screening performed between the VicK druggable sites followed byMolecular Dynamic simulations (MD) with binding affinity analysis by MM-PBSA approach. First, VicK protein was downloaded from PDB, and druggability analyses were performed by PockDrug and FTMap servers describing three interaction sites (S1, S2, and S3) that covered the most important domains for stability and activity. Next, a catechol virtual screening (n = 383) was performed on AutoDock4.2, and better-docked catechols showed strong binding affinity interaction through hydrogen bonding, hydrophobic interactions, and π-stacking with VicK auto kinase and phosphatase activity sites. Ligand efficiency indexes were also calculated (LE, LELP, LLE, and BEI) and showed optimal values. Furthermore, a 200 ns MD simulation run showed stability (RMSD and RMSF) and a high number of hydrogen bonds into peltatoside and maritimein, the two best VicK complexes. These results supported that catechols could potentially inhibit exopolysaccharides synthesis and be used in the biofilm management of new anti-cariogenic and antimicrobial agents.

Computer-aided genomic data analysis of drug-resistant Neisseria gonorrhoeae for the Identification of alternative therapeutic targets

Neisseria gonorrhoeae is an emerging multidrug resistance pathogen that causes sexually transmitted infections in men and women. The N. gonorrhoeae has demonstrated an emerging antimicrobial resistance against reported antibiotics, hence fetching the attention of researchers to address this problem. The present in-silico study aimed to find putative novel drug and vaccine targets against N. gonorrhoeae infection by the application of bioinformatics approaches. Core genes set of 69 N. gonorrhoeae strains was acquired from complete genome sequences. The essential and non-homologous metabolic pathway proteins of N. gonorrhoeae were identified. Moreover, different bioinformatics databases were used for the downstream analysis. The DrugBank database scanning identified 12 novel drug targets in the prioritized list. They were preferred as drug targets against this bacterium. A viable vaccine is unavailable so far against N. gonorrhoeae infection. In the current study, two outer-membrane proteins were prioritized as vaccine candidates via reverse vaccinology approach. The top lead B and T-cells overlapped epitopes were utilized to generate a chimeric vaccine construct combined with immune-modulating adjuvants, linkers, and PADRE sequences. The top ranked prioritized vaccine construct (V7) showed stable molecular interaction with human immune cell receptors as inferred during the molecular docking and MD simulation analyses. Considerable response for immune cells was interpreted by in-silico immune studies. Additional tentative validation is required to ensure the effectiveness of the prioritized vaccine construct against N. gonorrhoeae infection. The identified proteins can be used for further rational drug and vaccine designing to develop potential therapeutic entities against the multi-drug resistant N. gonorrhoeae.

Computational analysis of natural product B-Raf inhibitors

B-Raf protein is a serine-threonine kinase and an important signal transduction molecule of the MAPK signaling pathway that mediates signals from RAS to MEK, ultimately promoting various essential cellular functions. The B-Raf kinase domain is divided into two subdomains: a small N-terminal lobe and a large C-terminal lobe, with a deep catalytic cleft between them. The N-terminal lobe contains a phosphate-binding loop (P-loop) and nucleotide-binding pocket, while the C-terminal lobe binds the protein substrates and contains the catalytic loop. The ligand pharmacophore was generated by using 17 different natural products and the receptor pharmacophore was generated by using protein structures. The reported natural product B-Raf inhibitors were analyzed according to the pharmacophore analysis (HipHop fit), virtual screening tools by Lipinski's rule of five. Thirteen out of seventeen molecules share the best ligand based pharmacophoric model (HipHop_5). The best receptor based pharmacophoric model came as AADHR. The compounds were docked against the B-Raf receptors (PDB ID: 3OG7, 4XV2, 5C9C). The compound DHSilB with cDOCKER interaction energy of -62.7 kcal/mol, -83.3 kcal/mol, -73.6 kcal/mol as well as the compound DHSilA with cDOCKER interaction energy of -63.9 kcal/mol, -63.2 kcal/mol, -74.7 kcal/mol showed satisfactory interaction with the respective receptors. Finally, the MD simulation was run for 100 ns for the top docked compounds DHSilA and DHSilB with the B-Raf proteins (PDB ID: 3OG7, 4XV2 and 5C9C). After the MD simulation run for 100 ns, the ligand 2,3-dehydrosilybin A (DHSilA) was found to be more stable in terms of the trajectories of RMSD, RMSF, Rg and H-bonds.

Exploring new antiviral targets for influenza and COVID-19: Mapping promising hot spots in viral RNA polymerases

Influenza and COVID-19 are infectious respiratory diseases that represent a major concern to public health with social and economic impact worldwide, for which the available therapeutic options are not satisfactory. The RdRp has a central role in viral replication and thus represents a major target for the development of antiviral approaches. In this study, we focused on Influenza A virus PB1 polymerase protein and the betacoronaviruses nsp12 polymerase protein, considering their functional and structural similarities. We have performed conservation and druggability analysis to map conserved druggable regions, that may have functional or structural importance in these proteins. We disclosed the most promising and new targeting regions for the discovery of new potential polymerase inhibitors. Conserved druggable regions of putative interaction with favipiravir and molnupiravir were also mapped. We have also compared and integrated the current findings with previous research.

Natural Compounds of Lasia spinosa (L.) Stem Potentiate Antidiabetic Actions by Regulating Diabetes and Diabetes-Related Biochemical and Cellular Indexes

Natural biometabolites of plants have been reported to be useful in chronic diseases including diabetes and associated complications. This research is aimed to investigate how the biometabolites of Lasia spinosa methanol stem (MEXLS) extract ameliorative diabetes and diabetes-related complications. MEXLS was examined for in vitro antioxidant and in vivo antidiabetic effects in a streptozotocin-induced diabetes model, and its chemical profiling was done by gas chromatography-mass spectrometry analysis. The results were verified by histopathological examination and in silico ligand-receptor interaction of characterized natural biometabolites with antidiabetic receptor proteins AMPK (PDB ID: 4CFH); PPARγ (PDB ID: 3G9E); and mammalian α-amylase center (PDB ID: 1PPI). The MEXLS was found to show a remarkable α-amylase inhibition (47.45%), strong antioxidant action, and significant (p < 0.05) decrease in blood glucose level, serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), low-density lipoprotein (LDL), urea, uric acid, creatinine, total cholesterol, triglyceride (TG), liver glycogen, creatinine kinase (CK-MB), and lactate dehydrogenase (LDH) and increase in serum insulin, glucose tolerance, and high-density lipoprotein (HDL). Rat’s pancreas and kidney tissues were found to be partially recovered in histopathological analyses. Methyl α-d-galactopyranoside displayed the highest binding affinity with AMPK (docking score, −5.764), PPARγ (docking score, −5.218), and 1PPI (docking score, −5.615) receptors. Data suggest that the MEXLS may be an exciting source to potentiate antidiabetic activities affirming a cell-line study.

Identification of BIK as an unfavorable prognostic marker and novel therapeutic target in microsatellite stable colorectal cancer harboring KRAS mutations

KRAS mutations lead to persistent activation of multiple downstream effectors that drive the cancer phenotype. Approximately 30%-50% of colorectal cancer (CRC) patients harbor KRAS mutations, which confer more aggressive tumor biology and shorter overall survival (OS), especially in microsatellite stable (MSS) metastatic CRC. Given that KRAS mutant protein has been proven difficult to target directly, identifying genes that function closely with KRAS and targeting these genes seems to be a promising therapeutic strategy for KRAS-mutated MSS CRC. Here, KRAS function-sensitive genes were identified by assessing the correlation between gene dependency scores from CRISPR knockout screens and KRAS mRNA expression in KRAS-mutated MSS CRC cell lines in the Cancer Cell Line Encyclopedia (CCLE) database. If the correlation coefficient was ≥ 0.6, the gene was considered a KRAS function-sensitive gene. Then KRAS function-sensitive genes related to prognosis were screened out in The Cancer Genome Atlas (TCGA) cohort, and the prognostic value was validated in the Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was performed to investigate the potential mechanisms. PockDrug-Server was used to predict the druggability of candidate genes. The results showed that in 20 KRAS-mutated MSS CRC cell lines, 13 genes were identified as KRAS function-sensitive genes. Of these 13 genes, only BIK expression was significantly associated with progression-free survival (PFS) and OS, and the BIK-high patients had significantly poorer PFS (HR=3.18, P=0.020) and OS (HR=4.74, P=0.030) than the BIK-low patients. Multivariate Cox regression analysis revealed high BIK expression as an independent predictor for poorer prognosis in KRAS-mutated MSS CRC. The prognostic value of BIK was also successfully validated in a GEO cohort. The results of ssGSEA showed that the BIK-high group was more prone to strong metastasis activity than the BIK-low group. Pocket druggability prediction analysis presented that BIK had three druggable pockets, and their druggability scores were above 0.8. These findings suggested that BIK is a promising prognostic marker and therapeutic target in KRAS-mutated MSS CRC.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Multi-epitope chimeric vaccine designing and novel drug targets prioritization against multi-drug resistant Staphylococcus pseudintermedius

Biofilm synthesizing multi-drug resistant Staphylococcus pseudintermedius bacteria has been recognized as the human infectious agent. It has been detected in the diseases of skin, ear, and postoperative infections. Its infections are becoming a major health problem due to its multi-drug resistance capabilities. However, no commercial vaccine for the treatment of its infections is currently available in the market. Here we employed the subtractive proteomics and reverse vaccinology approach to determine the potential novel drug and vaccine targets against S. pseudintermedius infections in humans. After screening the core-proteome of the 39 complete genomes of S. pseudintermedius, 2 metabolic pathways dependent and 34 independent proteins were determined as novel potential drug targets. Two proteins were found and used as potential candidates for designing the chimeric vaccine constructs. Depending on the properties such as antigenicity, toxicity and solubility, multi-epitope based vaccines constructs were designed. For immunogenicity enhancement, different specific sequences like linkers, PADRE sequences and molecular adjuvants were added. Molecular docking and molecular dynamic simulation analyses were performed to evaluate the prioritized vaccine construct’s interactions with human immune cells HLA and TLR4. Finally, the cloning and expression ability of the vaccine construct was determined in the bacterial cloning system and human body immune response was predicted through immune simulation analysis. In conclusion, this study proposed the potential drug and vaccine targets and also designed a chimera vaccine to be tested and validated against infectious S. pseudintermedius species.

Probabilistic Pocket Druggability Prediction via One-Class Learning

The choice of target pocket is a key step in a drug discovery campaign. This step can be supported by in silico druggability prediction. In the literature, druggability prediction is often approached as a two-class classification task that distinguishes between druggable and non-druggable (or less druggable) pockets (or voxels). Apart from obvious cases, however, the non-druggable class is conceptually ambiguous. This is because any pocket (or target) is only non-druggable until a drug is found for it. It is therefore more appropriate to adopt a one-class approach, which uses only unambiguous information, namely, druggable pockets. Here, we propose using the import vector domain description (IVDD) algorithm to support this task. IVDD is a one-class probabilistic kernel machine that we previously introduced. To feed the algorithm, we use customized DrugPred descriptors computed via NanoShaper. Our results demonstrate the feasibility and effectiveness of the approach. In particular, we can remove or mitigate biases chiefly due to the labeling.

Virus-host interaction network analysis in Colorectal cancer identifies core virus network signature and small molecules

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Systematic analysis of virus-host networks identified key pathways in CRC.

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Core virus-CRC network revealed the growth pathway regulated by viruses.

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Short linear motif analysis identified druggable regions in virus proteins.

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Virtual screening revealed key anti-viral molecules against viral proteins.

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Molecular dynamics simulations showed the effect of anti-viral molecules.

Colorectal cancer (CRC) is a significant contributor to cancer-related deaths caused by an unhealthy lifestyle. Multiple studies reveal that viruses are involved in colorectal tumorigenesis. The viruses such as Human Cytomegalovirus (HCMV), Human papillomaviruses (HPV16 & HPV18), and John Cunningham virus (JCV) are known to cause colorectal cancer. The molecular mechanisms of cancer genesis and maintenance shared by these viruses remain unclear. We analysed the virus-host networks and connected them with colorectal cancer proteome datasets and extracted the core shared interactions in the virus-host CRC network. Our network topology analysis identified prominent virus proteins RL6 (HCMV), VE6 (HPV16 and HPV18), and Large T antigen (JCV). Sequence analysis uncovered short linear motifs (SLiMs) in each viral target. We used these targets to identify the antiviral drugs through a structure-based virtual screening approach. This analysis highlighted that temsavir, pimodivir, famotine, and bictegravir bind to each virus protein target, respectively. We also assessed the effect of drug binding using molecular dynamic simulations, which shed light on the modulatory effect of drug molecules on SLiM regions in viral targets. Hence, our systematic screening of virus-host networks revealed viral targets, which could be crucial for cancer therapy.

Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design

Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Is there a common allosteric binding site for G-protein coupled receptors?

Targeting the allosteric sites on G-protein coupled receptors (GPCRs) for drug discovery is attracting increased interest. Given a GPCR target, identifying the allosteric binding sites in it remains a challenge. Previous works from our and other labs suggest the intracellular region below the middle of the transmembrane (TM) domain that spatially overlaps with the G-protein binding site could contain a common allosteric site for all GPCRs. We performed several bioinformatics analyses on this site for more than 100 representative human GPCR structures. Results of the studies confirmed that the proposed region contains an allosteric site that is druggable for 89% of the GPCRs and is not 100% identical between a GPCR and its most similar homolog for 94% of the GPCRs. The physico-chemical properties and amino acid composition of this site vary among and within GPCR classes. Since this proposed region occupies the space existing in all GPCRs of known structure, it could represent a common host of an allosteric site for all GPCRs that can be targeted for structure-based allosteric drug design.

Incredible affinity of Kattosh with PPAR-γ receptors attenuates STZ-induced pancreas and kidney lesions evidenced in chemicobiological interactions

Since ancient times, plants have been used as green bioresources to ensure a healthier life by recovering from different diseases. Kattosh (Lasia spinosa L. Thwaites) is a local plant with various traditional uses, especially for arthritis, constipation and coughs. This research investigated the effect of Kattosh stem extract (LSES) on streptozotocin-induced damage to the pancreas, kidney, and liver using in vitro, in vivo and in silico methods. In vitro phytochemical, antioxidative and anti-inflammatory effects of LSES were accomplished by established methods followed by antidiabetic actions in in vivo randomized controlled intervention in STZ-induced animal models for four weeks. In an in silico study, LSES phytocompounds interacted with antidiabetic receptors of peroxisome proliferator-activated receptor-gamma (PPAR, PDB ID: 3G9E), AMP-activated protein kinase (AMPK, PDB ID: 4CFH) and α-amylase enzyme (PDB ID: 1PPI) to verify the in vivo results. In addition, LSES showed promising in vitro antioxidative and anti-inflammatory effects. In contrast, it showed a decrease in weekly blood glucose level, normalized lipid profile, ameliorated liver and cardiac markers, managed serum AST and ALT levels, and increased glucose tolerance ability in the animal model study. Restoration of pancreatic and kidney damage was reflected by improving histopathological images. In ligand-receptor interaction, ethyl α-d-glucopyranoside of Kattosh showed the highest affinity for the α-amylase enzyme, PPAR, and AMPK receptors. Results demonstrate that the affinity of Kattosh phytocompounds potentially attenuates pancreatic and kidney lesions and could be approached as an alternative antidiabetic source with further clarification.

Identification of Natural Products as SENP2 Inhibitors for Targeted Therapy in Heart Failure

Aims: Sentrin-specific protease -2 (SENP2) is involved in deSUMOylation. Increased deSUMOylation in murine hearts by SENP2 upregulation resulted in cardiac dysfunction and congenital heart defects. Natural compounds via regulating cell proliferation and survival, induce cell cycle cessation, cell death, apoptosis, and producing reactive oxygen species and various enzyme systems cause disease prevention. Then, natural compounds can be suitable inhibitors and since SENP2 is a protein involved in heart disease, so our aim was inhibition of SENP2 by natural products for heart disease treatment. Material and methods: Molecular docking and molecular dynamics simulation of natural products i.e. Gallic acid (GA), Caffeic acid (CA), Thymoquinone (TQ), Betanin, Betanidin, Fisetin, and Ebselen were done to evaluate the SENP2 inhibitory effect of these natural products. The toxicity of compounds was also predicted. Results: The results showed that Betanin constituted a stable complex with SENP2 active site as it revealed low RMSD, high binding energy, and hydrogen bonds. Further, as compared to Ebselen, Betanin demonstrated low toxicity, formed a stable complex with SENP2 via four to seven hydrogen bonds, and constituted more stable MD plots. Therefore, depending upon the outcomes presented herein, Betanin significantly inhibited SENP2 and hence may be considered as a suitable natural compound for the treatment of heart failure. Further clinical trials must be conducted to validate its use as a potential SENP2 inhibitor.

Bovine serum albumin with gallic acid: Molecular modeling and physicochemical profiling

Introduction. Gallic acid is a biologically active natural compound with strong antioxidant properties. Gallic acid is highly soluble and stable. It is known to increase the thermal stability of protein. However, its bioavailability is low, but interaction with proteins can solve this problem. Bovine serum albumin can bind various ligands, including polyphenols. The resulting complex of gallic acid and bovine serum albumin can become a promising functional food additive. Study objects and methods. This research featured in silico molecular modeling of gallic acid and bovine serum albumin using the HyperChem program. The methods of infrared spectrometry, potentiometry, and sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) made it possible to describe the physicochemical profile of the complex. Results and discussion. The molecular modeling confirmed that hydrophobic interactions were responsible for the chemical bond between gallic acid and bovine serum albumin. The SDS-PAGE test showed that the protein molecule remained intact. The reducing properties of the complex grew as the concentration of gallic acid increased. At 100 mg/L of gallic acid, the reducing properties were 7.8 ± 1.3 mg/L equivalent of gallic acid. At 200 and 300 mg/L, the values reached 15.90 ± 2.65 and 23.30 ± 5.05 mg/L, respectively. The IR spectrometry revealed a significant difference between the samples with different concentrations of gallic acid. Conclusion. The research managed to predict the properties of the complex of bovine serum albumin and gallic acid during its formation. The resulting complex had the highest reducing properties at 0.69 g of bovine serum albumin and 300 mg of gallic acid. The obtained parameters can be used in the food industry to develop new food additives.

Computational Identification of Possible Allosteric Sites and Modulators of the SARS-CoV-2 Main Protease

In this study, we target the main protease (M^pro) of the SARS-CoV-2 virus as it is a crucial enzyme for viral replication. Herein, we report three plausible allosteric sites on M^pro that can expand structure-based drug discovery efforts for new M^pro inhibitors. To find these sites, we used mixed-solvent molecular dynamics (MixMD) simulations, an efficient computational protocol that finds binding hotspots through mapping the surface of unbound proteins with 5% cosolvents in water. We have used normal mode analysis to support our claim of allosteric control for these sites. Further, we have performed virtual screening against the sites with 361 hits from M^pro screenings available through the National Center for Advancing Translational Sciences (NCATS). We have identified the NCATS inhibitors that bind to the remote sites better than the active site of M^pro, and we propose these molecules may be allosteric regulators of the system. After identifying our sites, new X-ray crystal structures were released that show fragment molecules in the sites we found, supporting the notion that these sites are accurate and druggable.

Most Probable Druggable Pockets in Mutant p53-Arg175His Clusters Extracted from Gaussian Accelerated Molecular Dynamics Simulations

p53, a tumor suppressor protein, is essential for preventing cancer development. Enhancing our understanding of the human p53 function and its modifications in carcinogenesis will aid in developing more highly effective strategies for cancer prevention and treatment. In this study, we have modeled five human p53 forms, namely, inactive, distal-active, proximal-active, distal-Arg175His mutant, and proximal-Arg175His mutant forms. These forms have been investigated using Gaussian accelerated molecular dynamics (GaMD) simulations in OPC water model at physiological temperature and pH. Our observations, obtained throughout [Formula: see text] of production run, are in good agreement with the relevant results in the classical molecular dynamics (MD) studies. Therefore, GaMD method is more economic and efficient method than the classical MD method for studying biomolecular systems. The featured dynamics of the five human p53-DBD forms include noticeable conformational changes of L1 and [Formula: see text]-[Formula: see text] loops as well as [Formula: see text]-[Formula: see text] and [Formula: see text]-[Formula: see text] turns. We have identified two clusters that represent two distinct conformational states in each p53-DBD form. The free-energy profiles of these clusters demonstrate the flexibility of the protein to undergo a conformational transition between the two clusters. We have predicted two out of seven possible druggability pockets on the clusters of the Arg175His forms. These two druggability pockets are near the mutation site and are expected to be actual pockets, which will be helpful for the compound clinical progression studies.

Montelukast is a dual-purpose inhibitor of SARS-CoV-2 infection and virus-induced IL-6 expression identified by structure-based drug repurposing

Drug-repurposing has been instrumental to identify drugs preventing SARS-CoV-2 replication or attenuating the disease course of COVID-19. Here, we identify through structure-based drug-repurposing a dual-purpose inhibitor of SARS-CoV-2 infection and of IL-6 production by immune cells. We created a computational structure model of the receptor binding domain (RBD) of the SARS-CoV-2 spike 1 protein, and used this model for insilico screening against a library of 6171 molecularly defined binding-sites from drug molecules. Molecular dynamics simulation of candidate molecules with high RBD binding-scores in docking analysis predicted montelukast, an antagonist of the cysteinyl-leukotriene-receptor, to disturb the RBD structure, and infection experiments demonstrated inhibition of SARS-CoV-2 infection, although montelukast binding was outside the ACE2-binding site. Molecular dynamics simulation of SARS-CoV-2 variant RBDs correctly predicted interference of montelukast with infection by the beta but not the more infectious alpha variant. With distinct binding sites for RBD and the leukotriene receptor, montelukast also prevented SARS-CoV-2-induced IL-6 release from immune cells. The inhibition of SARS-CoV-2 infection through a molecule binding distal to the ACE-binding site of the RBD points towards an allosteric mechanism that is not conserved in the more infectious alpha and delta SARS-CoV-2 variants.

Elucidation of SARS-Cov-2 Budding Mechanisms through Molecular Dynamics Simulations of M and E Protein Complexes

SARS-CoV-2 and other coronaviruses pose major threats to global health, yet computational efforts to understand them have largely overlooked the process of budding, a key part of the coronavirus life cycle. When expressed together, coronavirus M and E proteins are sufficient to facilitate budding into the ER-Golgi intermediate compartment (ERGIC). To help elucidate budding, we ran atomistic molecular dynamics (MD) simulations using the Feig laboratory's refined structural models of the SARS-CoV-2 M protein dimer and E protein pentamer. Our MD simulations consisted of M protein dimers and E protein pentamers in patches of membrane. By examining where these proteins induced membrane curvature in silico, we obtained insights around how the budding process may occur. Multiple M protein dimers acted together to induce global membrane curvature through protein-lipid interactions while E protein pentamers kept the membrane planar. These results could eventually help guide development of antiviral therapeutics that inhibit coronavirus budding.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

In Silico Characterization and Virtual Screening of GntR/HutC Family Transcriptional Regulator MoyR: A Potential Monooxygenase Regulator in Mycobacterium tuberculosis

Simple Summary

In an era where the world faces new diseases and pathogens, another emerging challenge is neglected pathogens becoming more notorious. Transcriptional regulators play a vital role in the pathogenesis and survival of these pathogens. Hence, characterizing transcriptional regulators, either in vitro or in silico, is of great importance. Here, we present the first structural characterization of a GntR/HutC regulator in Mycobacterium tuberculosis via in silico methods. We have suggested its possible role and potential as a drug target as well as identified possible drug leads that can be used for further improvements.

Abstract

Mycobacterium tuberculosis is a well-known pathogen due to the emergence of drug resistance associated with it, where transcriptional regulators play a key role in infection, colonization and persistence. The genome of M. tuberculosis encodes many transcriptional regulators, and here we report an in-depth in silico characterization of a GntR regulator: MoyR, a possible monooxygenase regulator. Homology modelling provided a reliable structure for MoyR, showing homology with a HutC regulator DasR from Streptomyces coelicolor. In silico physicochemical analysis revealed that MoyR is a cytoplasmic protein with higher thermal stability and higher pI. Four highly probable binding pockets were determined in MoyR and the druggability was higher in the orthosteric binding site consisting of three conserved critical residues: TYR179, ARG223 and GLU234. Two highly conserved leucine residues were identified in the effector-binding region of MoyR and other HutC homologues, suggesting that these two residues can be crucial for structure stability and oligomerization. Virtual screening of drug leads resulted in four drug-like compounds with greater affinity to MoyR with potential inhibitory effects for MoyR. Our findings support that this regulator protein can be valuable as a therapeutic target that can be used for developing drug leads.

Comparative study of the interaction of ivermectin with proteins of interest associated with SARS-CoV-2: A computational and biophysical approach

The SARS-CoV-2 pandemic has accelerated the study of existing drugs. The mixture of homologs called ivermectin (avermectin-B1a [HB1a] + avermectin-B1b [HB1b]) has shown antiviral activity against SARS-CoV-2 in vitro. However, there are few reports on the behavior of each homolog. We investigated the interaction of each homolog with promising targets of interest associated with SARS-CoV-2 infection from a biophysical and computational-chemistry perspective using docking and molecular dynamics. We observed a differential behavior for each homolog, with an affinity of HB1b for viral structures, and of HB1a for host structures considered. The induced disturbances were differential and influenced by the hydrophobicity of each homolog and of the binding pockets. We present the first comparative analysis of the potential theoretical inhibitory effect of both avermectins on biomolecules associated with COVID-19, and suggest that ivermectin through its homologs, has a multiobjective behavior.

Epiphytic Acampe ochracea orchid relieves paracetamol-induced hepatotoxicity by inhibiting oxidative stress and upregulating antioxidant genes in in vivo and virtual screening

Orchids are basically ornamental, and biological functions are seldom evaluated. This research investigated the effects of Acampe ochracea methanol extract (AOME) in ameliorating the paracetamol (PCM) induced liver injury in Wistar albino rats, evaluating its phytochemical status through UPLC-qTOF-MS analysis. With molecular docking and network pharmacology, virtual screening verified the inevitable interactions between the UPLC-qTOF-MS-characterized compounds and hepatoprotective drug receptors. The AOME has explicit a dose-dependent decrease of liver enzymes acid phosphatase (ACP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), total bilirubin, as well as an increase of serum total protein and antioxidant enzymes catalase (CAT), superoxide dismutase (SOD), glutathione reductase (GSH) with a virtual normalization (p < 0.05-p < 0.001) and the values were almost equivalent to the reference drug silymarin. After pretreatment with AOME, PCM-induced malondialdehyde (MDA) levels were considerably decreased (p < 0.001). Histopathological examinations corroborated the functional and biochemical findings. The AOME upregulated the genes involved in antioxidative (CAT, SOD, β-actin, PON1, and PFK1) and hepatoprotective mechanisms in PCM intoxicated rats. An array of 103 compounds has been identified from AOME through UPLC-qTOF-MS analysis. The detected compounds were substantially related to the targets of several liver proteins and antioxidative enzymes, according to an in silico study. Virtual prediction by SwissADME and admetSAR showed that AOME has drug-like, non-toxic, and potential pharmacological activities in hepatic damage. Furthermore, VEGFA, CYP19A1, MAPK14, ESR1, and PPARG genes interact with target compounds impacting the significant biological actions to recover PCM-induced liver damage.

Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3CLpro

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received global attention due to the serious threat it poses to public health. Since the outbreak in December 2019, millions of people have been affected and its rapid global spread has led to an upsurge in the search for treatment. To discover hit compounds that can be used alone or in combination with repositioned drugs, we first analyzed the pharmacokinetic and toxicological properties of natural products from Brazil's semiarid region. After, we analyzed the site prediction and druggability of the SARS-CoV-2 main protease (Mpro), followed by docking and molecular dynamics simulation. The best SARS-CoV-2 Mpro complexes revealed that other sites were accessed, confirming that our approach could be employed as a suitable starting protocol for ligand prioritization, reinforcing the importance of catalytic cysteine-histidine residues and providing new structural data that could increase the antiviral development mainly against SARS-CoV-2. Here, we selected 10 molecules that could be in vitro assayed in response to COVID-19. Two compounds (b01 and b02) suggest a better potential for interaction with SARS-CoV-2 Mpro and could be further studied.

Molecular Insight into the Effect of a Single-Nucleotide Polymorphic Variation on the Structure and Dynamics of Methionine Synthase Reductase and Its Association with Neural Tube Defects

Neural tube defects (NTDs) are among the common and severe congenital malformations in neonates. According to a WHO report, nearly three lakh babies are affected per year worldwide by NTDs. Most studies revealed that folate deficiency is the key element to promote NTD with other oligogenic and multifactorial elements. This folate is metabolized by the FOCM (folate one-carbon metabolism) pathway. The most important step in the FOCM pathway is the conversion of methionine to homocysteine, which is guided by the enzyme MTRR. Several single-nucleotide polymorphisms (SNPs) in the MTRR gene are strongly associated with the progression of NTD. A nonsynonymous allelic variant (rs1532268) of the protein leads to a missense mutation at the 202nd position from serine to leucine (S202L) and is associated with a higher disease prevalence in different populations. In our study, this SNP indicates a 2-fold increase in the risk of disease progression (p-value of 0.03; OR 2.76; 95% CI 1.08-7.11). Here, extensive molecular dynamics simulations and interaction network analysis reveal that the change of 202nd serine to leucine alters the structures of the FAD and NAD binding domains, which restricts the ligand binding. The S202L variation alters the functional dynamics that might impede the electron transport chain along the NADP(H)→ FAD→ FMN pathway and hamper phosphorylation by kinases like GSK-3 and CaM-II during the posttranscriptional modification of the protein. The present study provides functional insights into the effect of the genetic variations of the MTRR gene on the NTD disease pathogenesis.

Gaussian accelerated molecular dynamics (GaMD): principles and applications

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

Central and peripheral pain intervention by Ophiorrhizarugosa leaves: Potential underlying mechanisms and insight into the role of pain modulators

ETHNOPHARMACOLOGICAL RELEVANCE

Ophiorrhiza rugosa var. prostrata is a traditional medicinal plant used by the indigenous and local tribes (Chakma, Marma and Tanchangya) of Bangladesh for the management of chest pain, body ache, and earache. However, the knowledge of anti-nociceptive and anti-inflammatory potentials of this plant is scarce.

AIM OF THE STUDY

Therefore, we scrutinized the anti-nociceptive and anti-inflammatory properties of O. rugosa leaves along with its possible mechanism(s) of action using chemical and heat-induced pain models.

METHODS AND MATERIALS

O. rugosa was extracted using 100% ethanol (EEOR) followed by exploring phytochemicals and assessing acute toxicity. To determine anti-nociceptive potentials, chemical-induced (acetic acid and formalin) and heat-induced (hot plate and tail immersion) nociceptive models were followed. To investigate the possible involvement of opioid receptors during formalin, hot plate, and tail immersion tests, naltrexone was administered whereas methylene blue and glibenclamide were used to explore cGMP involvement and ATP-sensitive K⁺ channel pathways, respectively. Moreover, the anti-inflammatory potential was assessed using the carrageenan-induced paw edema test model. Motor behaviours of EEOR were assessed by the open-field test. Finally, bioactive constituents (identified by GC-MS) from O. rugosa were subjected to molecular docking and ADME/t analysis to evaluate its potency and safety.

RESULTS

During chemical-induced and heat-induced pain models, EEOR exhibited significant and effective nociception suppression at all experimental doses (200 and 400 mg/kg). Also, the administration of naltrexone corroborated the association of opioid receptors with the anti-nociceptive activity by EEOR. Similarly, cGMP and ATP-sensitive K⁺ channel pathways were also found to be involved in the anti-nociceptive mechanism. Furthermore, significant and dose-dependent inhibition of inflammation induced by carrageenan was recorded for EEOR. Both doses of EEOR did not affect the animal's locomotor capacity in the open-field test. Besides, in silico test identified the key compounds (loliolide, harman, squalene, vitamin E, and gamma-sitosterol) that inhibited some particular receptors regarding pain and inflammation.

CONCLUSION

This research exposes central and peripheral pain intervention as well as anti-inflammatory activity of O. rugosa. Also, the identified compounds from this plant support its activities by effectively inhibiting anti-nociceptive and anti-inflammatory receptors. Overall, these outcomes valorize the ethnomedicinal efficacy of O. rugosa in managing various painful conditions.

Analysing the essential proteins set of Plasmodium falciparum PF3D7 for novel drug targets identification against malaria

BACKGROUND

Plasmodium falciparum is an obligate intracellular parasite of humans that causes malaria. Falciparum malaria is a major public health threat to human life responsible for high mortality. Currently, the risk of multi-drug resistance of P. falciparum is rapidly increasing. There is a need to address new anti-malarial therapeutics strategies to combat the drug-resistance threat.

METHODS

The P. falciparum essential proteins were retrieved from the recently published studies. These proteins were initially scanned against human host and its gut microbiome proteome sets by comparative proteomics analyses. The human host non-homologs essential proteins of P. falciparum were additionally analysed for druggability potential via in silico methods to possibly identify novel therapeutic targets. Finally, the PfAp4AH target was prioritized for pharmacophore modelling based virtual screening and molecular docking analyses to identify potent inhibitors from drug-like compounds databases.

RESULTS

The analyses identified six P. falciparum essential and human host non-homolog proteins that follow the key druggability features. These druggable targets have not been catalogued so far in the Drugbank repository. These prioritized proteins seem novel and promising drug targets against P. falciparum due to their key protein-protein interactions features in pathogen-specific biological pathways and to hold appropriate drug-like molecule binding pockets. The pharmacophore features based virtual screening of Pharmit resource predicted a lead compound i.e. MolPort-045-917-542 as a promising inhibitor of PfAp4AH among prioritized targets.

CONCLUSION

The prioritized protein targets may worthy to test in malarial drug discovery programme to overcome the anti-malarial resistance issues. The in-vitro and in-vivo studies might be promising for additional validation of these prioritized lists of drug targets against malaria.

Chlamydia trachomatis core genome data mining for promising novel drug targets and chimeric vaccine candidates identification

Chlamydia trachomatis is involved in most sexually transmitted diseases. The species has emerged as a major public health threat due to its multidrug-resistant capabilities, and new therapeutic target inferences have become indispensable to combat its pathogenesis. However, no commercial vaccine is yet available to treat the C. trachomatis infection. In this study, we used the publicly available complete genome sequences of C. trachomatis and performed comparative proteomics and reverse vaccinology analyses to explore novel drug and vaccine targets against this devastating pathogen. We identified 713 core proteins from 71 C. trachomatis complete genome sequences and prioritized them based on their cellular essentiality, virulence, and available antibiotic resistance. The analyses led to the identification of 16 pathogen-specific proteins with no resolved 3D structures, though holding significant druggable potential. The sequences of the three shortlisted candidates' membrane proteins were used for designing vaccine constructs. The antigenicity, toxicity, and solubility profile-based lead epitopes were prioritized for multi-epitope-based vaccine constructs in combination with specific linkers, PADRE sequences, and molecular adjuvants for immunogenicity enhancement. The molecular-level interactions of the prioritized vaccine construct with human immune cells HLA and TLR4/MD were validated by molecular docking and molecular dynamic simulation analyses. Furthermore, the cloning and expression potential of the lead vaccine construct was predicted in the E. coli cloning vector system. Additional testing and experimental validation of these multi-epitope constructs appear promising against C. trachomatis-mediated infection.