In Silico Insights into the Arsenic Binding Mechanism Deploying Application of Computational Biology-Based Toolsets.

An assortment of environmental matrices includes arsenic (As) in its different oxidation states, which is often linked to concerns that pose a threat to public health worldwide. The current difficulty lies in addressing toxicological concerns and achieving sustained detoxification of As. Multiple conventional degradation methods are accessible; however, they are indeed labor-intensive, expensive, and reliant on prolonged laboratory evaluations. Molecular interaction and atomic level degradation mechanisms for enzyme-As exploration are, however, underexplored in those approaches. A feasible approach in this case for tackling this accompanying concern of As might be to cope with undertaking multivalent computational methodologies and tools. This work aimed to provide molecular-level insight into the enzyme-aided As degradation mechanism. AutoDock Vina, CABS-flex 2.0, and Desmond high-performance molecular dynamics simulation (MDS) were utilized in the current investigation to simulate multivalent molecular processes on two protein sets: arsenate reductase (ArsC) and laccase (LAC) corresponding arsenate (ART) and arsenite (AST), which served as model ligands to comprehend binding, conformational, and energy attributes. The structural configurations of both proteins exhibited variability in flexibility and structure framework within the range of 3.5-4.5 Å. The LAC-ART complex exhibited the lowest calculated binding affinity, measuring -5.82 ± 0.01 kcal/mol. Meanwhile, active site residues ILE-200 and HIS-206 were demonstrated to engage in H-bonding with the ART ligand. In contrast to ArsC, the ligand binding affinity of this bound complex was considerably greater. Additional validation of docked complexes was carried out by deploying Desmond MDS of 100 ns to capture protein and ligand conformation behavior. The system achieved stability during the 100 ns simulation run, as confirmed by the average P-L RMSD, which was ∼1 Å. As a preliminary test of the enzyme's ability to catalyze As species, corresponding computational insights might be advantageous for bridging gaps and regulatory consideration.

In silico trials to design potent inhibitors against matrilysin (MMP-7).

The study deals with structure-based rational drug design against the chief zinc-rely endopeptidase called matrilysin (MMP-7) that is involved in inflammatory and metastasis process of several carcinomas. Hyperactivated matrilysin of human was targeted, because of its hydrolytic actions on extracellular matrix (ECM) protein components constitutes fibrillar collagens, gelatins, fibronectins and it also activates zymogen forms of vital matrix metalloproteinases (gelatinase A-MMP-2 and B-MMP-9) responsible for ECM destruction in many cancers. In the present work, e-pharmacophores were generated for the respective five co-crystal structures of human matrilysin by mapping ligand's pharmacophoric features. During the lead-optimization campaign, the five e-pharmacophores-based shape screening against an in-house library of >21 million compounds created a dataset of 5000 structural analogs. The subsequent three different docking strategies, including rigid-receptor docking, quantum-polarized-ligand docking, induced-fit docking and free energy binding calculations resulted four leads as novel and potent MMP-7 binders. These four leads were observed with good pharmacological features and good receiver operating characteristics curve metrics (ROC: 0.93) in post-docking evaluations against five existing co-crystal inhibitors and 1000 decoy molecules with MMP-7. Moreover, stability and dynamics behavior of matrilysin-lead1 complex and matrilysin-cocrystal ligand (TQJ) complex were analyzed in natural physiological milieu of 1000 ns or 1 µs molecular dynamics simulations. Lead1-MMP-7 complex was found with an average Cα root-mean-square deviation (RMSD) of 2.35 Å, average ligand root-mean-square fluctuations (RMSF) of 0.66 Å and the strong metallic interactions with E220, a key residue for proteolytic action thereby hinders ECM proteolysis that in turn can halt metastatic cancerous condition.Communicated by Ramaswamy H. Sarma.

Hierarchical-Clustering, Scaffold-Mining Exercises and Dynamics Simulations for Effectual Inhibitors Against Lipid-A Biosynthesis of Helicobacter pylori.

INTRODUCTION: Treatment failures of standard regimens and new strains egression are due to the augmented drug resistance conundrum. These confounding factors now became the drug designers spotlight to implement therapeutics against Helicobacter pylori strains and to safeguard infected victims with devoid of adverse drug reactions. Thereby, to navigate the chemical space for medicine, paramount vital drug target opting considerations should be imperative. The study is therefore aimed to develop potent therapeutic variants against an insightful extrapolative, common target LpxC as a follow-up to previous studies. METHODS: We explored the relationships between existing inhibitors and novel leads at the scaffold level in an appropriate conformational plasticity for lead-optimization campaign. Hierarchical-clustering and shape-based screening against an in-house library of > 21 million compounds resulted in panel of 11,000 compounds. Rigid-receptor docking through virtual screening cascade, quantum-polarized-ligand, induced-fit dockings, post-docking processes and system stability assessments were performed. RESULTS: After docking experiments, an enrichment performance unveiled seven ranked actives better binding efficiencies with Zinc-binding potency than substrate and in-actives (decoy-set) with ROC (1.0) and area under accumulation curve (0.90) metrics. Physics-based membrane permeability accompanied ADME/T predictions and long-range dynamic simulations of 250 ns chemical time have depicted good passive diffusion with no toxicity of leads and sustained consistency of lead1-LpxC in the physiological milieu respectively. CONCLUSIONS: In the study, as these static outcomes obtained from this approach competed with the substrate and existing ligands in binding affinity estimations as well as positively correlated from different aspects of predictions, which could facilitate promiscuous new chemical entities against H. pylori.

Integration of core hopping, quantum-mechanics, molecular mechanics coupled binding-energy estimations and dynamic simulations for fragment-based novel therapeutic scaffolds against Helicobacter pylori strains.

The cascade of complications by Helicobacter pylori including extra-gastric and peptic ulcers to gastric cancer imposes a salient cause of cancer death globally. Adverse drug reactions and burgeoned genetically diverse resistant strains create a big barrier in the treatment, thereby demanding novel proof-of-concept ligands and breakthrough medicines. Hence, as a follow-up of the previous proteomics study against 53 H. pylori strains, KdsB was identified as a vital conserved-target enzyme. Herein, the rational therapeutic-design strategies exploiting for such a hidden cryptic inhibitor were utilized in lead-optimization campaigns through shape screening, the powerful scaffold-hopping, rigid-receptor, quantum-polarized ligand and induced-fit docking techniques coupled with estimating molecular-mechanics energies (ΔGbind) through generalized-Born and surface-area-continuum solvation. Variable-dielectric-Surface-Generalized Born, a novel energy model and physics-based corrections for bond-interactions and ADME/Tox predictions led to yield improved eight therapeutic chemical entities with positive synthesizability scores (0-1). Long-range molecular dynamics (300 ns) simulations revealed stability of leads. Significant computational findings with better competitive binding-strengths than experimental ligands could pave the best choice for selecting better leads as it warrants and filter false-positives based on the consensus of scaffolds interactions and suggesting that designed novel class of KdsB-antagonist molecules may dysfunction the target and stimulate new insights for developing effectual medical interventions.

In silico probing exercises, bioactive-conformational and dynamic simulations strategies for designing and promoting selective therapeutics against Helicobacter pylori strains.

A myriad of drug-resistant strains of Helicobacter pylori and adverse drug-reactions create a big-barrier in the treatment, thereby demanding novel proof-of-concept inhibitors and breakthrough medicines. Hence, an affinity-centric protocol was devised to implement scaffold-design for 3-dehydroquinate dehydratase-II (AroQ) as a follow-up of our study against beaucoup strains. Herein, the study focuses on preferred the attractive-target methodically due to its salient features include conserving, essential and specific for H. pylori, not present in humans and gut-flora. Structural refinement, energy minimization and optimization of the developed best-model were employed with confirming active site residues around substrate. Published AroQ-inhibitors and substrate were utilized to probe an in-house library of molecules. The prepared dataset was allowed to lead-optimization campaign includes rigid-receptor docking through high-throughput virtual, standard-precision, extra-precision screening filters, quantum-polarized-ligand (quantum mechanical and molecular mechanical (QM/MM)) and induced-fit docking experiments. Convergence threshold (0.05) and Truncated Newton Conjugate Gradient (TNCG) were set in ConfGen's algorithm to produce high-quality bioactive conformations by thoroughly narrowing the conformational space accessible to the leads. ADME/Tox predictions and long-range molecular dynamics simulations were executed after post-docking evaluations. The approach provided seven ranked compounds with better scoring functions, bioactive-conformers and pharmacokinetics profiles than published ligands and substrate. Simulations revealed more consistency of lead1-AroQ complex throughout chemical time than controls in the formulated physiological milieu. The study outcomes showing the good competitive binding propensity for active-tunnel over the substrate and previous ligands, thereby these leads could be ideal for proposing as selective cutting-edge inhibitors to target AroQ specific for H. pylori strains.

Epitope-driven common subunit vaccine design against H. pylori strains.

The developing potent vaccine is a pre-emptive strategy to tackle drug abuses and maladies of multidrug-resistant Helicobacter pylori strains. Ongoing vaccine studies are being conducted, however, development is in its infancy as ineffective vaccine targets might be. So, the linear perspective may indicate the need for potent subunit vaccine variants. Here, surface-exposed membrane proteins out of 826 common proteins of 53 H. pylori strains were chosen for analysis, as a follow-up to previous studies; these proteins are responsible for antigenicity to elicit the immune response. Antigenic determinant regions on prognostic targets were evaluated in the successive peptide screening using experimental T-cell epitope positive control and optimized with eminent immunoinformatics algorithms. In the milieu of docking, an ensemble of 2200 multiple conformers of complexes of modeled peptide and human leukocyte antigen- antigenD Related Beta-chain (HLA-DRB) was generated. Prioritized physics-based Molecular Mechanics-Generalized Born Surface Area approach coupled with bond length monitoring paved the improvement of prediction accuracy with binding potency estimations. ΔGbind free energy, interaction patterns, enrichment factor contributions and root-mean-square deviation predictions evidenced the existence of better binding affinities of four novel peptides hits with predominant allotype HLA-DR alleles than co-crystal controls. Moreover, conformational plasticity and stability assessments of the better ranked complex epitope-2 (86-FRRNPNINV-94) - HLA-DRB5*0101 formulated in dynamic simulations of 10,416 trajectories depicted stable interaction profile that correlated with docking endpoints. Thus, the proposed novel vaccine cocktails of the study would be ideal candidates and provide new insights for T-cell driven subunit vaccine design against H. pylori strains Communicated by Ramaswamy H. Sarma.

An in silico study: Novel targets for potential drug and vaccine design against drug resistant H. pylori.

Gastric cancer risk and adverse ramifications by augmented multi-drug resistance (MDR) of Helicobacter pylori are alarming serious health concern. Combating through available drugs is a difficult task due to lack of appropriate common targets against genetically diverse strains. To improve efficacy, the effective targets should be identified and critically assessed. In the present study, we aim to predict the potential novel targets against H. pylori strains by employing computer aided approach. The genomic dataset of 53 H. pylori strains was comparatively processed and eventually predicted 826 'conserved gene products'. Further, we performed subtractive genomic approach in search of promising crucial targets through the combination of in silico analyses. Codon adaptation index (CAI) value calculation and literature surveys were also done in order to find highly expressed gene products with novelty. Consequently, four enzymes and three membrane proteins were prioritized as new therapeutic and vaccine targets respectively which found to have more interactors in network with high-confidence score, druggability, antigenicity and molecular weight <110 kDa. Therefore, our results underpin the importance of new targets may counteract with false-positive/negatives and facilitate appropriate potential targets for a new insight of reliable therapeutic development.