Elucidating structural variability in p53 conformers using combinatorial refinement strategies and molecular dynamics.

Low molecular weight proteins and protein assemblies can now be investigated using cryo-electron microscopy (EM) as a complement to traditional structural biology techniques. It is important, however, to not lose sight of the dynamic information inherent in macromolecules that give rise to their exquisite functionality. As computational methods continue to advance the field of biomedical imaging, so must strategies to resolve the minute details of disease-related entities. Here, we employed combinatorial modeling approaches to assess flexible properties among low molecular weight proteins (~100 kDa or less). Through a blend of rigid body refinement and simulated annealing, we determined new hidden conformations for wild type p53 monomer and dimer forms. Structures for both states converged to yield new conformers, each revealing good stereochemistry and dynamic information about the protein. Based on these insights, we identified fluid parts of p53 that complement the stable central core of the protein responsible for engaging DNA. Molecular dynamics simulations corroborated the modeling results and helped pinpoint the more flexible residues in wild type p53. Overall, the new computational methods may be used to shed light on other small protein features in a vast ensemble of structural data that cannot be easily delineated by other algorithms.

Structural and functional effects of the L84S mutant in the SARS-COV-2 ORF8 dimer based on microsecond molecular dynamics study.

The L84S mutation has been observed frequently in the ORF8 protein of SARS-CoV-2, which is an accessory protein involved in various important functions such as virus propagation, pathogenesis, and evading the immune response. However, the specific effects of this mutation on the dimeric structure of ORF8 and its impacts on interactions with host components and immune responses are not well understood. In this study, we performed one microsecond molecular dynamics (MD) simulation and analyzed the dimeric behavior of the L84S and L84A mutants in comparison to the native protein. The MD simulations revealed that both mutations caused changes in the conformation of the ORF8 dimer, influenced protein folding mechanisms, and affected the overall structural stability. In particular, the 73YIDI76 motif has found to be significantly affected by the L84S mutation, leading to structural flexibility in the region connecting the C-terminal ß4 and ß5 strands. This flexibility might be responsible for virus immune modulation.  The free energy landscape (FEL) and principle component analysis (PCA) have also supported our investigation. Overall, the L84S and L84A mutations affect the ORF8 dimeric interfaces by reducing the frequency of protein-protein interacting residues (Arg52, Lys53, Arg98, Ile104, Arg115, Val117, Asp119, Phe120, and Ile121) in the ORF8 dimer.  Our findings provide detail insights for further research in designing structure-based therapeutics against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Molecular dynamics-based insight of VEGFR-2 kinase domain: a combined study of pharmacophore modeling and molecular docking and dynamics.

BACKGROUND: Inhibition of vascular endothelial growth factor receptor 2 (VEGFR-2) tyrosine kinase by small molecules has become a promising target in the treatment of cancer. OBJECTIVE: In this study, we approached pharmacophore modeling coupled with a structure-based virtual screening workflow to identify the potent inhibitors. METHODS: The top selected hit compounds have been rescored using the MM/GBSA approach. To understand the molecular reactivity, electronic properties, and stability of those inhibitors, we have employed density functional theory and molecular dynamics. Following that, the best 21 hit compounds have been further post-processed with a Quantum ligand partial charge-based rescoring process and further validated by implementing molecular dynamics simulation. RESULTS: The ten hit compounds have been hypothesized and considered as potent inhibitors of VEGFR-2 tyrosine kinase. This study also signifies the contribution of QM-based ligand partial charge, which is more accurate in predicting reliable free binding energy and filtering large ligand libraries to hit optimization, rather than assigning those of the force field-based method. From the binding pattern analysis of all the complexes, amino acids, such as Glu885, Cys919, Cys1045, Thr916, Thr919, and Asp1046, were found to have comprehensive interaction with the hit compounds. CONCLUSION: Hence, this could prove to be useful as a potential inhibition site of the VEGFR-2 tyrosine kinase domain for future researchers. Moreover, this study also emphasizes the conformational changes upon ATP binding, based on either the receptor's rigidity or flexibility.

Cysteine focused covalent inhibitors against the main protease of SARS-CoV-2.

In viral replication and transcription, the main protease (Mpro) of SARS-CoV-2 plays an important role and appears to be a vital target for drug design. In Mpro, there is a Cys-His catalytic dyad, and ligands that interact with the Cys145 assumed to be an effective approach to inhibit the Mpro. In this study, approximately 1400 cysteine-focused ligands were screened to identify the best candidates that can act as potent inhibitors against Mpro. Our results show that the selected ligands strongly interact with the key Cys145 and His41 residues. Covalent docking was performed for the selected candidates containing the acrylonitrile group, which can form a covalent bond with Cys145. All atoms molecular dynamics (MD) simulation was performed on the selected four inhibitors including L1, L2, L3 and L4 to validate the docking interactions. Our results were also compared with a control ligand, α-ketoamide (11r). Principal component analysis on structural and energy data obtained from the MD trajectories shows that L1, L3, L4 and α-ketoamide (11r) have structural similarity with the apo-form of the Mpro. Quantitative structure-activity relationship method was employed for pattern recognition of the best ligands, which discloses that ligands containing acrylonitrile and amide warheads can show better performance. ADMET analysis displays that our selected candidates appear to be safer inhibitors. Our combined studies suggest that the best cysteine focused ligands can help to design an effective lead drug for COVID-19 treatment. Communicated by Ramaswamy H. Sarma.

Designing potent inhibitors against the multidrug resistance P-glycoprotein.

The multidrug transporter P-glycoprotein is an ATP binding cassette (ABC) exporter responsible for resistance to tumor cells during chemotherapy. This study was designed with computational approaches aimed at identifying the best potent inhibitors of P-glycoprotein. Although many compounds have been suggested to inhibit P-glycoprotein, however, their information on bioavailability, selectivity, ADMET properties, and molecular interactions has not been revealed. Molecular docking, ADMET analysis, molecular dynamics, Principal component analysis (PCA), and binding free energy calculations were performed. Two compounds D1 and D2 showed the best docking score against P-glycoprotein and both compounds have 4-thiazolidinone derivatives containing indolin-3 one moiety are novel anti-tumor compounds. ADMET calculation analysis predicted D1 and D2 to have acceptable pharmacokinetic properties. The MD simulation discloses that D1-P-glycoprotein and D2-P-glycoprotein complexes are in stable conformation as apo-form. Hydrophobic amino acid such as phenylalanine plays significant on the interactions of inhibitors. Principal component analysis shows that both complexes are relatively similar variables as apo-form except planarity and Columbo energy profile. In addition, Quantitative Structural Activity Relationship (QSAR) of the ligand candidates were subjected to the principal component analysis (PCA) for pattern recognition. Partial-least-square (PLS) regression analysis was further utilized to model drug candidates' QSAR for subsequent prediction of the binding energy of validated drug candidates. PCA revealed groupings of the drug candidates based on the similarity or differences in drug candidates QSAR. Moreover, the developed PLS regression accurately predicted the values of the binding energy of drug candidates, with low residual error of prediction.Communicated by Ramaswamy H. Sarma.

Repurposing fusion inhibitor peptide against SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving. Although several vaccines were approved, this pandemic is still a major threat to public life. Till date, no established therapies are available against SARS-CoV-2. Peptide inhibitors hold great promise for this viral pathogen due to their efficacy, safety, and specificity. In this study, seventeen antiviral peptides which were known to inhibit SARS-CoV-1 are collected and computationally screened against heptad repeat 1 (HR1) of the SARS-CoV-2 spike protein (S2). Out of 17 peptides, Fp13 and Fp14 showed better binding affinity toward HR1 compared to a control peptide EK1 (a modified pan-coronavirus fusion inhibitor) in molecular docking. To explore the time-dependent interactions of the fusion peptide with HR1, molecular dynamics simulation was performed incorporating lipid membrane. During 100 ns MD simulation, structural and energy parameters of Fp13-HR1 and Fp14-HR1 complexes demonstrated lower fluctuations compared to the control EK1-HR1 complex. Furthermore, principal component analysis and free energy landscape study revealed that these two peptides (Fp13 and Fp14) strongly bind to the HR1 with higher affinity than that of control EK1. Tyr917, Asn919, Gln926, lys933, and Gln949 residues in HR1 protein were found to be crucial residues for peptide interaction. Notably, Fp13, Fp14 showed reasonably better binding free energy and hydrogen bond contribution than that of EK1. Taken together, Fp13 and Fp14 peptides may be highly specific for HR1 which can potentially prevent the formation of the fusion core and could be further developed as therapeutics for treatment or prophylaxis of SARS-CoV-2 infection.

A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2.

The main protease of SARS-CoV-2 is one of the important targets to design and develop antiviral drugs. In this study, we have selected 40 antiviral phytochemicals to find out the best candidates which can act as potent inhibitors against the main protease. Molecular docking is performed using AutoDock Vina and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the main protease. The selected candidates strongly interact with the key Cys145 and His41 residues. To validate the docking interactions, 100 ns molecular dynamics (MD) simulations on the five top-ranked inhibitors including hypericin, cyanidin 3-glucoside, baicalin, glabridin, and α-ketoamide-11r are performed. Principal component analysis (PCA) on the MD simulation discloses that baicalin, cyanidin 3-glucoside, and α-ketoamide-11r have structural similarity with the apo-form of the main protease. These findings are also strongly supported by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) investigations. PCA is also used to find out the quantitative structure-activity relationship (QSAR) for pattern recognition of the best ligands. Multiple linear regression (MLR) of QSAR reveals the R2 value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Investigating the binding affinity, interaction, and structure-activity-relationship of 76 prescription antiviral drugs targeting RdRp and Mpro of SARS-CoV-2.

SARS-CoV-2 virus outbreak poses a major threat to humans worldwide due to its highly contagious nature. In this study, molecular docking, molecular dynamics, and structure-activity relationship are employed to assess the binding affinity and interaction of 76 prescription drugs against RNA dependent RNA polymerase (RdRp) and Main Protease (Mpro) of SARS-CoV-2. The RNA-dependent RNA polymerase is a vital enzyme of coronavirus replication/transcription complex whereas the main protease acts on the proteolysis of replicase polyproteins. Among 76 prescription antiviral drugs, four drugs (Raltegravir, Simeprevir, Cobicistat, and Daclatasvir) that are previously used for human immunodeficiency virus (HIV), hepatitis C virus (HCV), Ebola, and Marburg virus show higher binding energy and strong interaction with active sites of the receptor proteins. To explore the dynamic nature of the interaction, 100 ns molecular dynamics (MD) simulation is performed on the selected protein-drug complexes and apo-protein. Binding free energy of the selected drugs is performed by MM/PBSA. Besides docking and dynamics, partial least square (PLS) regression method is applied for the quantitative structure activity relationship to generate and predict the binding energy for drugs. PLS regression satisfactorily predicts the binding energy of the effective antiviral drugs compared to binding energy achieved from molecular docking with a precision of 85%. This study highly recommends researchers to screen these potential drugs in vitro and in vivo against SARS-CoV-2 for further validation of utility.

Exploring the potent inhibitors and binding modes of phospholipase A2 through in silico investigation.

Snake venom of Naja naja comprises of several types of enzymes, and among them, water-soluble proteolytic enzyme, phospholipase A2 (PLA2), is noteworthy for its numerous adverse effects, such as cytotoxicity, cardiotoxicity, hemolytic, anti-coagulant, and hypotensive effects, including being highly potent as a neurotoxin. Limited anti-venom therapy (with their lower efficacy) has attracted considerable pharmacological interest to develop potent inhibitors of PLA2. Thus, 34 experimentally proven and diverse synthetic inhibitors of PLA2 were screened primarily on the basis of Glide extra precision docking and MM-GBSA rescoring function. Then, ten potential hits were subjected to induced fit docking, in which top three potential inhibitors were considered, and those were found to interact with Ca2+, disulfide binding site, and phosphatidylcholine activation sites, thereby, possibly disrupting the catalytic activity of Ca2+ as well as the inflammatory functions of PLA2. These compounds showed positive remarks on various physiochemical properties and pharmacologically relevant descriptors. Gap energy and thermodynamic properties were investigated by employing density functional theory for all compounds to understand their chemical reactivity and thermodynamic stability. Molecular dynamics simulation was performed for 100 ns in order to evaluate the stability and binding modes of docked complexes, and the energy of binding was calculated through MM-PBSA analysis. On the whole, the proposed compounds could be used for targeted inhibition. Communicated by Ramaswamy H. Sarma.

Prediction of Deleterious Non-synonymous SNPs of Human STK11 Gene by Combining Algorithms, Molecular Docking, and Molecular Dynamics Simulation.

Serine-threonine kinase11 (STK11) is a tumor suppressor gene which plays a key role in regulating cell growth and apoptosis. It is widely known as a multitasking kinase and engaged in cell polarity, cell cycle arrest, chromatin remodeling, energy metabolism, and Wnt signaling. The substitutions of single amino acids in highly conserved regions of the STK11 protein are associated with Peutz-Jeghers syndrome (PJS), which is an autosomal dominant inherited disorder. The abnormal function of the STK11 protein is still not well understood. In this study, we classified disease susceptible single nucleotide polymorphisms (SNPs) in STK11 by using different computational algorithms. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking and molecular dynamics analysis. Our results show that W239R and W308C variants are likely to be highly deleterious mutations found in the catalytic kinase domain, which may destabilize structure and disrupt the activation of the STK11 protein as well as reduce its catalytic efficiency. The W239R mutant is likely to have a greater impact on destabilizing the protein structure compared to the W308C mutant. In conclusion, these mutants can help to further realize the large pool of disease susceptibilities linked with catalytic kinase domain activation of STK11 and assist to develop an effective drug for associated diseases.

Quantum chemical calculation and binding modes of H1R; a combined study of molecular docking and DFT for suggesting therapeutically potent H1R antagonist.

Histamine-1 receptor (H1R) belongs to the family of rhodopsin-like G-protein-coupled receptors expressed in cells that mediates allergies and other pathophysiological diseases. For alleviation of allergic symptoms, H1R antagonists are therapeutic drugs; of which the most frequently prescribed are second generation drugs, such as; Cetirizine, Loratadine, Hydroxyzine, Desloratadine, Bepotastine, Acrivastine and Rupatadine. To understand their potency, binding affinity and interaction; we have employed molecular docking and quantum chemical study such as; Induced-fit docking and calculation of quantum chemical descriptors. This study also introduces the binding site characterization of H1R, with its known antagonists and Curcumin (our proposed alternative H1R antagonist); useful for future drug target site. The interactive binding site residues of H1R are found to be; Lys-191, Tyr-108, Asp-107, Tyr-100, Lys-179, Lys-191, Thr-194, Trp-428, Phe-432, Tyr-458, Hie-450, with most of these shown to be inhibited by naturally-occurring compound curcumin. Amongst the FDA approved drugs, Hydroxyzine showed best ligand binding affinity, calculated as - 141.491 kcal/mol and naturally occurring compound, Curcumin showed binding affinity of - 87.046 kcal/mol. The known antagonists of H1R has been used for hypothesizing curcumin as naturally occurring lead compound for the target using accurate molecular docking simulation study. Though the pharmacological action of known inhibitor is already established, they could differ from their reactivity, which we have also focused in our study for predicting drug reactivity.

Assessment of structurally and functionally high-risk nsSNPs impacts on human bone morphogenetic protein receptor type IA (BMPR1A) by computational approach.

BMPR1A (BMP type 1 receptor) is a transmembrane cell-surface receptor also known as ALK3 (activin-like kinases-3) encodes for a type I serine/threonine kinase receptor and a member of the transforming growth-factor ß-receptor (TGF-ß) super family. The BMPR1A has a significant interaction with BMP-2 for protein activity and also has a low affinity with growth and differentiation factor 5 (GDF5); positively regulates chondrocyte differentiation. The genetic variations can alter the structure and function of the BMPR1A gene that causes several diseases such as juvenile polyposis syndrome or hereditary cancer-predisposing syndrome. The current study was carried out to identify potential deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in BMPR1A by implementing different computational algorithms such as SIFT, PolyPhen2, SNAP2, PROVEAN, PhD-SNP, SNPs&GO, nsSNPAnalyzer, and P-Mut. From 205 nsSNPs in BMPR1A, 7 nsSNPs (C76Y, C124R, C124Y, C376Y, R443C, R480W, and W487R) were predicted as deleterious in 8 prediction algorithms. The Consurf analysis showed that selected 7 nsSNPs were present in the highly conserved regions. Molecular dynamics simulation analysis also performed to explore conformational changes in the variant structure with respect to its native structure. According to the MDS result, all variants flexibility and rigidity were unbalanced, which may alter the structural and functional behavior of the native protein. Although, three nsSNPs i.e., C124R, C376Y, and R443C have already been reported in patients associated with JPS, but their structural and functional molecular studies remain uncharacterized. Therefore, the findings of this study can provide a better understanding of uncharacterized nsSNPS and to find their association with disease susceptibility and also facilitate to the researchers for designing or developing the target dependent drugs.