Structural insight into conformational dynamics of non-active site mutations in KasA: A Mycobacterium tuberculosis target protein

Structure-based virtual screening of natural compounds against wild and mutant (R1155K, A1156T and D1168A) NS3-4A protease of Hepatitis C virus

NS3-4A, a serine protease, is a primary target for drug development against Hepatitis C Virus (HCV). However, the effectiveness of potent next-generation protease inhibitors is limited by the emergence of mutations and resulting drug resistance. To address this, in this study a structure-based drug design approach is employed to screen a large library of 7320 natural compounds against both wild-type and mutant variants of NS3-4A protease. Telaprevir, a widely used protease inhibitor, was recruited as the control drug. The top 10 compounds with favorable binding affinities underwent drug-likeness evaluation. Based on ADMET studies, complexes of NP_024762 and NP_006776 were selected for molecular dynamic simulations. Principal component analysis (PCA) was employed to explore the conformational space and protein dynamics of the protein-ligand complex using a Free Energy Landscape (FEL) approach. The cosine values obtained from FEL analysis ranged from 0 to 1, and eigenvectors with cosine values below 0.2 were chosen for further analysis. To forecast binding free energies and evaluate energy contributions per residue, the MM-PBSA method was employed. The results highlighted the crucial role of amino acids in the catalytic domain for the binding of the protease with phytochemicals. Stable associations between the top compounds and the target protease were confirmed by the formation of hydrogen bonds in the binding pocket involving residues: His1057, Gly1137, Ser1139, and Ala1157. These findings suggest the potential of these compounds for further validation through biological evaluation.Communicated by Ramaswamy H. Sarma.

Marine Compound-Carpatamide D as a Potential Inhibitor Against TOP2A and Its Mutant D1021Y in Colorectal Cancer: Insights from DFT, MEP and Molecular Dynamics Simulation

Colorectal cancer (CRC) ranks as the third most prevalent cancer globally, hence there is an urgent need for new and effective therapeutic options. DNA topoisomerase 2A (TOP2A) plays a crucial role in the cell cycle and is involved in CRC progression, making it essential to identify structural and functional relevant alterations. Among the 24 mutations, our findings indicated that mutation D1021Y has the most deleterious effect on the TOP2A protein. Based on virtual screening of 31,561 compounds, we identified three lead candidates: 17683 (nigrospoxydon C), 28461 (carpatamide D), and 28853 (6'-O-acetyl-isohomaarbutin), which showed promising inhibitory effect against TOP2A and its mutant form. These compounds were assessed for their stability using density functional theory (DFT) analysis, where carpatamide D possessed the least energy gap of 4.398 eV showing its high reactivity among all. Further, molecular docking also shows the carpatamide D as the top candidate, which exhibited favourable docking energy against the TOP2A wild type (- 7.47 kcal/mol) and with D1021Y mutant (- 7.62 kcal/mol) as compared to reference compound PK1, which showed - 6.11 kcal/mol TOP2A wild type and - 6.24 kcal/mol against mutant type. The molecular dynamics simulation was performed to analyse the dynamics and stability of complex, which revealed TOP2A_28641 and D1021Y_28641 complexes to be stable with least root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF). Molecular mechanics/Poisson-Boltzmann surface area calculations indicated that TOP2A_28641 and D1021Y_28641 complexes exhibited the lowest binding energy of - 23.55 kcal/mol and - 25.03 kcal/mol, respectively. Our findings suggest carpatamide D as a promising lead compound for the TOP2A_D1021Y targeted cancer therapies, which needs further experimental validation.

Unveiling therapeutic biomarkers and druggable targets in ALS: An integrative microarray analysis, molecular docking, and structural dynamic studies

Amyotrophic lateral sclerosis (ALS), commonly known as Lou Gehrig's disease, is a debilitating neurodegenerative disorder characterized by the progressive degeneration of nerve cells in the brain and spinal cord. Despite extensive research, its precise etiology remains elusive, and early diagnosis is challenging due to the absence of specific tests. This study aimed to identify potential blood-based biomarkers for early ALS detection and monitoring using datasets from whole blood samples (GSE112680) and oligodendrocytes, astrocytes, and fibroblasts (GSE87385) obtained from the NCBI-GEO repository. Through bioinformatics analysis, including protein-protein interactions and molecular pathway analyses, we identified differentially expressed genes (DEGs) associated with ALS. Notably, ALS2, ADH7, ALDH8A1, ALDH3B1, ABHD2, ABHD17B, ABHD12, ABHD13, PGAM2, AURKB, ANAPC11, VAPA, UNC45B, and TNNT2 emerged as top-ranked DEGs, implicated in drug metabolism, protein depalmytilation, and the AKT/mTOR signaling pathways. Among these, AurKB established as a potential therapeutic biomarker with relevance to various neurological conditions. Consequently, AurKB was selected for identifying potential therapeutic molecules and utilized for in silico structural characterization studies. Exploration of the IMPATT database led to the discovery of a lead compound similar to Fostamatinib, currently used for AurKB. Initial molecular docking and MMGBSA-based binding energy analysis were followed by molecular dynamics simulation (MDS) and free energy landscape (FEL) analysis to validate the ligand's binding efficacy and understand dynamic processes within the biological system. The identified potential biomarkers and lead molecule provide novel insights into the correlation between blood cell transcripts and ALS pathology, paving the way for blood-based diagnostic tools for early ALS detection and ongoing disease monitoring.

Mechanistic insights into the conformational changes and alterations in residual communications due to the mutations in the pncA Gene of Mycobacterium tuberculosis: A computational perspective for effective therapeutic solutions

Due to its emerging resistance to first-line anti-TB medications, tuberculosis (TB) is one of the most contagious illness in the world. According to reports, the effectiveness of treating TB is severely impacted by drug resistance, notably resistance caused by mutations in the pncA gene-encoded pyrazinamidase (PZase) to the front-line drug pyrazinamide (PZA). The present study focused on investigating the resistance mechanism caused by the mutations D12N, T47A, and H137R to better understand the structural and molecular events responsible for the resistance acquired by the pncA gene of Mycobacterium tuberculosis (MTB) at the structural level. Bioinformatics analysis predicted that all three mutations were deleterious and located near the active centre of the pncA, affecting its functional activity. Furthermore, molecular dynamics simulation (MDS) results established that mutations significantly reduced the structural stability and caused the rearrangement of FE2+ in the active centre of pncA. Moreover, essential dynamics analysis, including principal component analysis (PCA) and free energy landscape (FEL), concluded variations in the protein motion and decreased conformational space in the mutants. Additionally, the mutations potentially impacted the network topologies and altered the residual communications in the network. The complex simulation study results established the significant movement of the flap region from the active centre of mutant complexes, further supporting the flap region's significance in developing resistance to the PZA drug. This study advances our knowledge of the primary cause of the mechanism of PZA resistance and the structural dynamics of pncA mutants, which will help us to design new and potent chemical scaffolds to treat drug-resistant TB (DR-TB).

Unveiling the intricacies of allosteric regulation in aspartate kinase from the Wolbachia endosymbiont of Brugia Malayi: Mechanistic and therapeutic insights

Aspartate kinase (AK), an enzyme from the Wolbachia endosymbiont of Brugia malayi (WBm), plays a pivotal role in the bacterial cell wall and amino acid biosynthesis, rendering it an attractive candidate for therapeutic intervention. Allosteric inhibition of aspartate kinase is a prevalent mode of regulation across microorganisms and plants, often modulated by end products such as lysine, threonine, methionine, or meso-diaminopimelate. The intricate and diverse nature of microbial allosteric regulation underscores the need for rigorous investigation. This study employs a combined experimental and computational approach to decipher the allosteric regulation of WBmAK. Molecular Dynamics (MD) simulations elucidate that ATP (cofactor) and ASP (substrate) binding induce a closed conformation, promoting enzymatic activity. In contrast, the binding of lysine (allosteric inhibitor) leads to enzyme inactivation and an open conformation. The enzymatic assay demonstrates the optimal activity of WBmAK at 28 °C and a pH of 8.0. Notably, the allosteric inhibition study highlights lysine as a more potent inhibitor compared to threonine. Importantly, this investigation sheds light on the allosteric mechanism governing WBmAK and imparts novel insights into structure-based drug discovery, paving the way for the development of effective inhibitors against filarial pathogens.

Computational identification and analysis of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene: a structural and functional impact

Cytochrome P450 oxidoreductase (POR) protein is essential for steroidogenesis, and POR gene mutations are frequently associated with P450 Oxidoreductase Deficiency (PORD), a disorder of hormone production. To our knowledge, no previous attempt has been made to identify and analyze the deleterious/pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene through an extensive computational approach. Computational algorithms and tools were employed to identify, characterize, and validate the pathogenic SNPs associated with certain diseases. To begin with, all the high-confidence SNPs were collected, and their structural and functional impacts on the protein structures were explored. The results of various in silico analyses affirm that the A287P and R457H variants of POR could destabilize the interactions between the amino acids and the hydrogen bond networks, resulting in functional deviations of POR. The literature study further confirms that the pathogenic mutations (A287P and R457H) are associated with the onset of PORD. Molecular dynamics simulations (MDS) and essential dynamics (ED) studies characterized the structural consequences of prioritized deleterious mutations, representing the structural destabilization that might disrupt POR biological function. The identified deleterious mutations at the cofactor's binding domains might interfere with the essential interactions between the protein and cofactors, thus inhibiting POR catalytic activity. The consolidated insights from the computational analyses can be used to predict potential deleterious mutants and understand the disease's pathological basis and the molecular mechanism of drug metabolism for the application of personalized medication. HIGHLIGHTSNADPH cytochrome P450 oxidoreductase (POR) mutations are associated with a broad spectrum of human diseasesIdentified and analyzed the most deleterious nsSNPs of POR through the sequence and structure-based prediction toolsInvestigated the structural and functional impacts of the most significant mutations (A287P and R457H) associated with PORDMolecular dynamics and PCA-based FEL analysis were utilized to probe the mutation-induced structural alterations in PORCommunicated by Ramaswamy H. Sarma.

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.

Multilayer precision-based screening of potential inhibitors targeting Mycobacterium tuberculosis acetate kinase using in silico approaches

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major threat to global health, with the emergence of multi-drug and extensively drug-resistant strains posing a serious challenge. Thereby, understanding the molecular basis of MTB virulence and disease pathogenesis is critical for developing effective therapeutic strategies. Targeting proteins involved in central metabolism has been recognized as a promising therapeutic approach to combat MTB. In this regard, the enzyme AckA of the acetate metabolic pathway which produces acetate from acetyl phosphate, is an important drug target for various pathogenic organisms. Therefore, this study aimed to identify potential AckA inhibitors through in silico methods, including molecular modeling, molecular dynamics simulation (MDS), and high-throughput virtual screening (HTVS) followed by ADMETox, MMGBSA, Density Functional Theory (DFT) calculations. HTVS of one million compounds from the ZINC database against AckA resulted in the top five hits (ZINC82048449, ZINC1219737510, ZINC1771921358, ZINC119699567, and ZINC1427100376) with better binding affinity and optimal binding free energy. MDS studies on complexes revealed that key residues, Asn195, Asp266, Phe267, Gly314, and Asn318 played a significant role in stable interactions of the top-ranked compounds to AckA. These outcomes provide insights into the optimal binding of the leads to inhibit the acetate pathway and aid in the rational design of novel therapeutic agents. Thus, the identified leads may act as promising compounds for targeting AckA and may serve as a potential therapeutic modality for treating TB. Our findings offer valuable insights into the inhibition of the acetate pathway, while also serving as a blueprint for rational drug design. The identified leads hold promise as compelling compounds for targeting AckA, thereby offering a potential therapeutic avenue for tackling TB. Thus, our study uncovers a pathway toward promising TB therapeutics by elucidating AckA inhibitors. By leveraging in silico methodologies, potent compounds that hold the potential to thwart AckA's role in MTB's acetate pathway have been unveiled. This breakthrough fosters optimism in the quest for novel and effective TB treatments, addressing a global health challenge with renewed vigor.

AI-Based Homology Modelling of Fatty Acid Transport Protein 1 Using AlphaFold: Structural Elucidation and Molecular Dynamics Exploration

Fatty acid transport protein 1 (FATP1) is an integral transmembrane protein that is involved in facilitating the translocation of long-chain fatty acids (LCFA) across the plasma membrane, thereby orchestrating the importation of LCFA into the cell. FATP1 also functions as an acyl-CoA ligase, catalyzing the ATP-dependent formation of fatty acyl-CoA using LCFA and VLCFA (very-long-chain fatty acids) as substrates. It is expressed in various types of tissues and is involved in the regulation of crucial signalling pathways, thus playing a vital role in numerous physiological and pathological conditions. Structural insight about FATP1 is, thus, extremely important for understanding the mechanism of action of this protein and developing efficient treatments against its anomalous expression and dysregulation, which are often associated with pathological conditions such as breast cancer. As of now, there has been no prior prediction or evaluation of the 3D configuration of the human FATP1 protein, hindering a comprehensive understanding of the distinct functional roles of its individual domains. In our pursuit to unravel the structure of the most commonly expressed isoforms of FATP1, we employed the cutting-edge ALPHAFOLD 2 model for an initial prediction of the entire protein's structure. This prediction was complemented by molecular dynamics simulations, focusing on the most promising model. We predicted the structure of FATP1 in silico and thoroughly refined and validated it using coarse and molecular dynamics in the absence of the complete crystal structure. Their relative dynamics revealed the different properties of the characteristic FATP1.

Differential gene expression analysis combined with molecular dynamics simulation study to elucidate the novel potential biomarker involved in pulmonary TB

Tuberculosis (TB) is a lethal multisystem disease that attacks the lungs' first line of defense. A substantial threat to public health and a primary cause of death is pulmonary TB. This study aimed to identify and investigate the probable differentially expressed genes (DEGs) primarily involved in Pulmonary TB. Accordingly, three independent gene expression data sets, numbered GSE139825, GSE139871, and GSE54992, were utilized for this purpose. The identified DEGs were used for bioinformatics-based analysis, including physical gene interaction, Gene Ontology (GO), network analysis and pathway studies using the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG). The computational analysis predicted that TNFAIP6 is the significant DEG in the gene expression profiling of TB datasets. According to gene ontology analysis, TNFAIP6 is also essential in injury and inflammation. Further, TNFA1P6 is strongly linked to arsenic poisoning, evident from the results of NetworkAnalyst, a comprehensive and interactive platform for gene expression profiling via network visual analytics. As a result, the TNFAIP6 gene was ultimately chosen as a candidate DEG and subsequently employed for in silico structural characterization studies. The tertiary structure of TNFAIP6 was modelled using the ROBETTA server, followed by validation with SAVES and ProSA webserver. Additionally, structural dynamic studies, including molecular dynamics simulation (MDS) and essential dynamics analysis, including principal component (PC) based free energy landscape (FEL) analysis, was used for checking the stability of TNFAIP6 models. The dynamics result established the structural rigidity of modelled TNFAIP6 through RMSD, RMSF and RoG results. The FEL analysis revealed the restricted conformational flexibility of TNFAIP6 by displaying a single minimum energy basin in the contour plot. The comprehensive computational analysis established that TNFAIP6 could serve as a viable biomarker to assess the severity of pulmonary TB.

Translational protein RpsE as an alternative target for novel nucleoside analogues to treat MDR Enterobacter cloacae ATCC 13047: network analysis and molecular dynamics study

The pathogenic Enterobacter cloacae subsp. cloacae str. ATCC 13047 has contemporarily emerged as a multi-drug resistant strain. To formulate an effective treatment option, alternative therapeutic methods need to be explored. The present study focused on Gene Interaction Network study of 46 antimicrobial resistance genes to reveal the densely interconnecting and functional hub genes in E. cloacae ATCC 13047. The AMR genes were subjected to clustering, topological and functional enrichment analysis, revealing rpsE (RpsE), acrA (AcrA) and arnT (ArnT) as novel therapeutic drug targets for hindering drug resistance in the pathogenic strain. Network topology further indicated translational protein RpsE to be exploited as a promising drug-target candidate for which the structure was predicted, optimized and validated through molecular dynamics simulations (MDS). Absorption, distribution, metabolism and excretion screening recognized ZINC5441082 (N-Isopentyladenosine) (Lead_1) and ZINC1319816 (cyclopentyl-aminopurinyl-hydroxymethyl-oxolanediol) (Lead_2) as orally bioavailable compounds against RpsE. Molecular docking and MDS confirmed the binding efficacy and protein-ligand complex stability. Furthermore, binding free energy (G_bind) calculations, principal component and free energy landscape analyses affirmed the predicted nucleoside analogues against RpsE protein to be comprehensively examined as a potential treatment strategy against E. cloacae ATCC 13047.

Structural chemistry and molecular-level interactome reveals histidine kinase EvgS to subvert both antimicrobial resistance and virulence in Shigella flexneri 2a str. 301

Multi-drug resistant (MDR) Shigella flexneri 2a, one of the leading bacterial agents of diarrhoeal mortality, has posed challenges in treatment strategies. The present study was conducted to identify potential therapeutic biomarkers using gene interaction network (GIN) in order to understand the cellular and molecular level interactions of both antimicrobial resistance (AMR) and virulence genes through topological and clustering metrics. Statistically significant differential gene expression (DGE), structural chemistry and dynamics were incorporated to elucidate biomarker for sustainable therapeutic regimen against MDR S. flexneri. Functional enrichments and topological metrics revealed evgS, ybjZ, tolC, gyrA, parC and their direct interactors to be associated with diverse AMR mechanisms. Histidine kinase EvgS was considered as the hub protein due to its highest prevalence in the molecular interactome profiles of both the AMR (71.6%) and virulence (45.8%) clusters interconnecting several genes concerning two-component system (TCS). DGE profiles of ΔPhoPQ (deleted regulatory PhoP and sensor PhoQ) led to the upregulation of TCS comprising EvgSA thereby validating EvgS as a promising therapeutic biomarker. Druggability and structural stability of EvgS was assessed through thermal shifts, backbone stability and coarse dynamics refinement. Structure-function relationship was established revealing the C-terminal extracellular domain as the drug-binding site which was further validated through molecular dynamics simulation. Structure elucidation of identified biomarker followed by secondary and tertiary structural validation would prove pivotal for future therapeutic interventions against subverting both AMR and virulence posed by this strain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03325-w.

Datasets comprising the quality validations of simulated protein-ligand complexes and SYBYL docking scores of bioactive natural compounds as inhibitors of Mycobacterium tuberculosis protein-targets

Docking scores and simulation parameters to study the potency of natural compounds against protein targets in Mycobacterium tuberculosis (Mtb) were retrieved through molecular docking and in-silico structural investigation. The molecular docking datasets comprised 15 natural compounds, seven conventional anti-tuberculosis (anti-TB) drugs and their seven corresponding Mtb target proteins. Mtb protein targets were actively involved in translation mechanism, nucleic acid metabolism and membrane integrity. Standard structural screening and stereochemical optimizations were adopted to generate the 3D protein structures and their corresponding ligands prior to molecular docking. Force-field integration and energy minimization were further employed to obtain the proteins in their ideal geometry. Surflex-dock algorithm using Hammerhead scoring functions were used to finally produce the docking scores between each protein and the corresponding ligand(s). The best-docked complexes selected for simulation studies were subjected to topology adjustments, charge neutralizations, solvation and equilibrations (temperature, volume and pressure). The protein-ligand complexes and molecular dynamics parameter files have been provided. The trajectories of the simulated parameters such as density, pressure and temperature were generated with integrated tools of the simulation suite. The datasets can be useful to computational and molecular medicine researchers to find therapeutic leads relevant to the chemical behaviours of a specific class of compounds against biological systems. Structural parameters and energy functions provided a set of standard values that can be utilised to design simulation experiments regarding similar macromolecular interactions.

Designing Anti-Microbial Peptides Against Major β-Lactamase Enzymes in Clinically Important Gram-Negative Bacterial Pathogens: An In-Silico Study

Anti-microbial resistance (AMR) creating healthcare concerns worldwide requires ardent exploration of therapeutic alternatives. Although anti-microbial peptides (AMP) are popular for broad-spectrum activity, recent evidence of increasing resistance to membrane-acting AMPs by ESKAPE pathogens has compelled us to design novel AMPs as therapeutic candidates. A library of 60 AMPs comprising natural AMPs and their mutants was constructed through in-silico methods. After physico-chemical property evaluations, each peptide in the library was subjected to flexible molecular docking against four major β-lactamases in Gram-negative ESKAPE pathogens. Among the potent AMP mutants, a Lactoferricin B-Mutant (M4) possessed uniformly high affinity with SHV1, OXA48, NDM1, and AmpC having energies -842.0Kcal/mol, -774.8Kcal/mol, -1103.3Kcal/mol, and -858.8Kcal/mol respectively. Coarse-grained clustering and flexibility analysis further accounted for the residue-level stable configurations of the protein-peptide complexes with high affinity. Highest affinity of Lactoferricin B_M4 was found with NDM1 due to H-bonds, salt-bridges, and hydrophobic interactions with the metallo-β-lactamase domain including crucial active-site residue Asp124. Molecular dynamics simulation further confirmed the stability of Lactoferricin B_M4-NDM1 complex having low residue-level root-mean square deviations (RMSD), atomic-level fluctuations, and radius of gyration (Rg). The study encourages experimental validations and similar methods to identify potential AMPs against drug-resistant pathogens.

Network metrics, structural dynamics and density functional theory calculations identified a novel Ursodeoxycholic Acid derivative against therapeutic target Parkin for Parkinson's disease

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GIN analysis revealed PARK2, LRRK2, PARK7, PINK1 and SNCA as hub-genes.

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Topologically favoured Parkin was considered as a therapeutic target.

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ADMET screening identified a novel UDCA derivative as potential lead candidate.

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Chemical reactivity and ligand stability were analysed through DFT simulation.

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Docking and MDS established novel lead as potential Parkin inhibitor.

Parkinson's disease (PD) has been designated as one of the priority neurodegenerative disorders worldwide. Although diagnostic biomarkers have been identified, early onset detection and targeted therapy are still limited. An integrated systems and structural biology approach were adopted to identify therapeutic targets for PD. From a set of 49 PD associated genes, a densely connected interactome was constructed. Based on centrality indices, degree of interaction and functional enrichments, LRRK2, PARK2, PARK7, PINK1 and SNCA were identified as the hub-genes. PARK2 (Parkin) was finalized as a potent theranostic candidate marker due to its strong association (score > 0.99) with α-synuclein (SNCA), which directly regulates PD progression. Besides, modeling and validation of Parkin structure, an extensive virtual-screening revealed small (commercially available) inhibitors against Parkin. Molecule-258 (ZINC5022267) was selected as a potent candidate based on pharmacokinetic profiles, Density Functional Theory (DFT) energy calculations (ΔE = 6.93 eV) and high binding affinity (Binding energy = -6.57 ± 0.1 kcal/mol; Inhibition constant = 15.35 µM) against Parkin. Molecular dynamics simulation of protein-inhibitor complexes further strengthened the therapeutic propositions with stable trajectories (low structural fluctuations), hydrogen bonding patterns and interactive energies (>0kJ/mol). Our study encourages experimental validations of the novel drug candidate to prevent the auto-inhibition of Parkin mediated ubiquitination in PD.

In silico structure evaluation of BAG3 and elucidating its association with bacterial infections through protein-protein and host-pathogen interaction analysis

BAG3, a co-chaperone protein with a Bcl-2-associated athanogene (BAG) domain, has diverse functionalities in protein-folding, apoptosis, inflammation, and cell cycle regulatory cross-talks. It has been well characterised in cardiac diseases, cancers, and viral pathogenesis. The multiple roles of BAG3 are attributed to its functional regions like BAG, Tryptophan-rich (WW), isoleucine-proline-valine-rich (IPV), and proline-rich (PXXP) domains. However, to study its structural impact on various functions, the experimental 3D structure of BAG3 protein was not available. Hence, the structure was predicted through in silico modelling and validated through computational tools and molecular dynamics simulation studies. To the best of our knowledge, the role of BAG3 in bacterial infections is not explicitly reported. We attempted to study them through an in-silico protein-protein interaction network and host-pathogen interaction analysis. From structure-function relationships, it was identified that the WW and PXXP domains were associated with cellular cytoskeleton rearrangement and adhesion-mediated response, which might be involved in BAG3-related intracellular bacterial proliferation. From functional enrichment analysis, Gene Ontology terms and topological matrices, 18 host proteins and 29 pathogen proteins were identified in the BAG3 interactome pertaining to Legionellosis, Tuberculosis, Salmonellosis, Shigellosis, and Pertussis through differential phosphorylation events associated with serine metabolism. Furthermore, it was evident that direct (MAPK8, MAPK14) and associated (MAPK1, HSPD1, NFKBIA, TLR2, RHOA) interactors of BAG3 could be considered as therapeutic markers to curb down intracellular bacterial propagation in humans.

Novel cyclohexanone compound as a potential ligand against SARS-CoV-2 main-protease

No commercially available drug candidate has yet been devised which is unique to and not repurposed against SARS-CoV-2 and has high efficacy or safe toxicity profile or both. Taking curcumin as a reference compound, we identified a new commercially available cyclohexanone compound, ZINC07333416 with binding energy (-8.72 kcal/mol) better than that of popularly devised anti-Covid-19 drugs like viral protease inhibitor Lopinavir, nucleoside analogue Remdesivir and the repurposed drug hydroxychloroquine when targeted to the active-site of SARS-CoV-2 Main protease (Mpro) through docking studies. The ligand ZINC07333416 exhibits crucial interactions with major active site residues of SARS-CoV-2 Mpro viz. Cys145 and His41 involving in the protease activity; as well as GLU-166 and ASN-142 which plays the pivotal role in the protein-dimerization. The protein-ligand stable interaction was further confirmed with molecular dynamics simulation (MDS) studies. Based on virtual assessment, ZINC07333416 also have significant values in terms of medicinal chemistry, pharmacokinetics, synthetic accessibility and anti-viral activity that encourage its experimental applications against COVID-19.

Insight into the structural and functional analysis of the impact of missense mutation on cytochrome P450 oxidoreductase

Cytochrome P450 oxidoreductase (POR) is a steroidogenic and drug-metabolizing enzyme. It helps in the NADPH dependent transfer of electrons to cytochrome P450 (CYP) enzymes for their biological activity. In this study, we employed integrative computational approaches to decipher the impact of proline to leucine missense mutation at position 384 (P384L) in the connecting/hinge domain region which is essential for the catalytic activity of POR. Analysis of protein stability using DUET, MUpro, CUPSAT, I-Mutant2.0, iStable and SAAFEC servers predicted that mutation might alter the structural stability of POR. The significant conformational changes induced by the mutation to the POR structure were analyzed by long-range molecular dynamics simulation. The results revealed that missense mutation decreased the conformational stability of POR as compared to wild type (WT). The PCA based FEL analysis described the mutant-specific conformational alterations and dominant motions essential for the biological activity of POR. The LIGPLOT interaction analysis showed the different binding architecture of FMN, FAD, and NADPH as a result of mutation. The increased number of hydrogen bonds in the FEL conformation of WT proved the strong binding of cofactors in the binding pocket as compared to the mutant. The porcupine plot analysis associated with cross-correlation analysis depicted the high-intensity flexible motion exhibited by functionally important FAD and NADPH binding domain regions. The computational findings unravel the impact of mutation at the structural level, which could be helpful in understanding the molecular mechanism of drug metabolism.