Identification of Potential Inhibitors Targeting GTPase-Kirsten RAt Sarcoma Virus (K-Ras) Driven Cancers via E-Pharmacophore-Based Virtual Screening and Drug Repurposing Approach.

BACKGROUND: Mutations in the K-Ras gene are among the most frequent genetic alterations in various cancers, and inhibiting RAS signaling has shown promising results in treating solid tumors. However, finding effective drugs that can bind to the RAS protein remains challenging. This drove us to explore new compounds that could inhibit tumor growth, particularly in cancers that harbor K-Ras mutations. METHODS: Our study used bioinformatic techniques such as E-pharmacophore virtual screening, molecular simulation, principal component analysis (PCA), extra precision (XP) docking, and ADMET analyses to identify potential inhibitors for K-Ras mutants G12C and G12D. RESULTS: In our study, we discovered that inhibitors such as afatinib, osimertinib, and hydroxychloroquine strongly inhibit the G12C mutant. Similarly, hydroxyzine, zuclopenthixol, fluphenazine, and doxapram were potent inhibitors for the G12D mutant. Notably, all six of these molecules exhibit a high binding affinity for the H95 cryptic groove present in the mutant structure. These molecules exhibited a unique affinity mechanism at the molecular level, which was further enhanced by hydrophobic interactions. Molecular simulations and PCA revealed the formation of stable complexes within switch regions I and II. This was particularly evident in three complexes: G12C-osimertinib, G12D-fluphenazine, and G12D-zuclopenthixol. Despite the dynamic nature of switches I and II in K-Ras, the interaction of inhibitors remained stable. According to QikProp results, the properties and descriptors of the selected molecules fell within an acceptable range compared to sotorasib. CONCLUSIONS: We have successfully identified potential inhibitors of the K-Ras protein, laying the groundwork for the development of targeted therapies for cancers driven by K-Ras mutations.

Exome sequence analysis of rare frequency variants in Late-Onset Alzheimer Disease.

Alzheimer disease (AD) is a leading cause of dementia in elderly patients who continue to live between 3 and 11 years of diagnosis. A steep rise in AD incidents is observed in the elderly population in East-Asian countries. The disease progresses through several changes, including memory loss, behavioural issues, and cognitive impairment. The etiology of AD is hard to determine because of its complex nature. The whole exome sequences of late-onset AD (LOAD) patients of Korean origin are investigated to identify rare genetic variants that may influence the complex disorder. Computational annotation was performed to assess the function of candidate variants in LOAD. The in silico pathogenicity prediction tools such as SIFT, Polyphen-2, Mutation Taster, CADD, LRT, PROVEAN, DANN, VEST3, fathmm-MKL, GERP + + , SiPhy, phastCons, and phyloP identified around 17 genes harbouring deleterious variants. The variants in the ALDH3A2 and RAD54B genes were pathogenic, while in 15 other genes were predicted to be variants of unknown significance. These variants can be potential risk candidates contributing to AD. In silico computational techniques such as molecular docking, molecular dynamic simulation and steered molecular dynamics were carried out to understand the structural insights of RAD54B with ATP. The simulation of mutant (T459N) RAD54B with ATP revealed reduced binding strength of ATP at its binding site. In addition, lower binding free energy was observed when compared to the wild-type RAD54B. Our study shows that the identified uncommon variants are linked to AD and could be probable predisposing genetic factors of LOAD.

Molecular dynamics simulations corroborate recombinant expression studies carried out on three αIIb ß-propeller mutations reported in Indian Glanzmann thrombasthenia patients.

Mutations in the αIIb ß-propeller domain have long been known to disrupt heterodimerization and intracellular trafficking of αIIbß3 complexes leading to diminished surface expression and/or function, resulting in Glanzmann thrombasthenia. Our previous study on three ß-propeller mutations, namely G128S, S287L, and G357S, showed variable defects in protein transport correlated with the patient's clinical phenotypes. Pulse-chase experiments revealed differences in αIIbß3 complex maturation among the three mutations. Hence, the current study aims to correlate conformational changes caused by each one of them. Evolutionary conservation analysis, stability analysis, and molecular dynamics simulations of the three mutant structures were carried out. Stability analysis revealed that, while G128S and G357S mutations destabilized the ß-propeller structure, S287L retained the stability. Wild-type and mutant ß-propeller structures, when subjected to molecular dynamics simulations, confirmed that G128S and G357S were both destabilizing in nature when compared with the wild-type and S287L based on several parameters studied, like RMSD, RMSF, Rg, FEL, PCA, secondary structure, and hydrogen bonds. In our previous study, we demonstrated that mutant S287L αIIbß3 complexes were more stable than the wild-type αIIbß3 complexes, as evidenced in pulse-chase experiments. These findings corroborate variable intracellular fates of mutant αIIbß3 complexes as a result of these ß-propeller mutations.

The targeted next-generation sequence revealed SMAD4, AKT1, and TP53 mutations from circulating cell-free DNA of breast cancer and its effect on protein structure - A computational approach.

Breast cancer biomarkers that detect marginally advanced stages are still challenging. The detection of specific abnormalities, targeted therapy selection, prognosis, and monitoring of treatment effectiveness over time are all made possible by circulating free DNA (cfDNA) analysis. The proposed study will detect specific genetic abnormalities from the plasma cfDNA of a female breast cancer patient by sequencing a cancer-related gene panel (MGM455 - Oncotrack Ultima), including 56 theranostic genes (SNVs and small INDELs). Initially, we determined the pathogenicity of the observed mutations using PredictSNP, iStable, Align-GVGD, and ConSurf servers. As a next step, molecular dynamics (MD) was implemented to determine the functional significance of SMAD4 mutation (V465M). Lastly, the mutant gene relationships were examined using the Cytoscape plug-in GeneMANIA. Using ClueGO, we determined the gene's functional enrichment and integrative analysis. The structural characteristics of SMAD4 V465M protein by MD simulation analysis further demonstrated that the mutation was deleterious. The simulation showed that the native structure was more significantly altered by the SMAD4 (V465M) mutation. Our findings suggest that SMAD4 V465M mutation might be significantly associated with breast cancer, and other patient-found mutations (AKT1-E17K and TP53-R175H) are synergistically involved in the process of SMAD4 translocate to nuclease, which affects the target gene translation. Therefore, this combination of gene mutations could alter the TGF-ß signaling pathway in BC. We further proposed that the SMAD4 protein loss may contribute to an aggressive phenotype by inhibiting the TGF-ß signaling pathway. Thus, breast cancer's SMAD4 (V465M) mutation might increase their invasive and metastatic capabilities.Communicated by Ramaswamy H. Sarma.

Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma.

Introduction: Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease. Methods: In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks. Results: Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc. Conclusion: To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

Effects of the pathological E200K mutation on human prion protein: A computational screening and molecular dynamics approach.

The human prion protein gene (PRNP) is mapped to the short arm of chromosome 20 (20pter-12). Prion disease is associated with mutations in the prion protein-encoding gene sequence. Earlier studies found that the mutation G127V in the PRNP increases protein stability. In contrast, the mutation E200K, which has the highest mutation rate in the prion protein, causes Creutzfeldt-Jakob disease (CJD) in humans and induces protein aggregation. We aimed to identify the structural mechanisms of E200k and G127V mutations causing CJD. We used a variety of bioinformatic algorithms, including SIFT, PolyPhen, I-Mutant, PhD-SNP, and SNP& GO, to predict the association of the E200K mutation with prion disease. MD simulation is performed, and graphs for root mean square deviation, root mean square fluctuation, radius of gyration, DSSP, principal component analysis, porcupine, and free energy landscape are generated to confirm and prove the stability of the wild-type and mutant protein structures. The protein is analyzed for aggregation, and the results indicate more fluctuations in the protein structure during the simulation owing to the E200K mutation; however, the G127V mutation makes the protein structure stable against aggregation during the simulation.

An integrated investigation of structural and pathway alteration caused by PIK3CA and TP53 mutations identified in cfDNA of metastatic breast cancer.

In peripheral blood, cell-free DNA (cfDNA) contains circulating tumor DNA (ctDNA), which indicates molecular abnormalities in metastatic breast tumor tissue. The sequencing of cfDNA of Metastatic Breast Cancer (MBC) patients allows assessment of therapy response and noninvasive treatment. In the proposed study, clinically significant alterations in PIK3CA and TP53 genes associated with MBC resulting in a missense substitution of His1047Arg and Arg282Trp from an next-generation sequencing-based multi-gene panel were reported in a cfDNA of a patient with MBC. To investigate the impact of the reported mutation, we used molecular docking, molecular dynamics simulation, network analysis, and pathway analysis. Molecular Docking analysis determined the distinct binding pattern revealing H1047R-ATP complex has a higher number of Hydrogen bonds (H-bonds) and binding affinity with a slight difference compared to the PIK3CA-ATP complex. Following, molecular dynamics simulation for 200 ns, of which H1047R-ATP complex resulted in the instability of PIK3CA. Similarly, for TP53 mutant R282W, the zinc-free state (apo) and zinc-bounded (holo) complexes were investigated for conformational change between apo and holo complexes, of which the holo complex mutant R282W was unstable. To validate the conformational change of PIK3CA and TP53, 80% mutation of H1047R in the kinase domain of p110α expressed ubiquitously in PIK3CA protein that alters PI3K pathway, while R282W mutation in DNA binding helix (H2) region of P53 protein inhibits the transcription factor in P53 pathway causing MBC. According to our findings, the extrinsic (hypoxia, oxidative stress, and acidosis); intrinsic factors (MYC amplification) in PIK3CA and TP53 mutations will provide potential insights for developing novel therapeutic methods for MBC therapy.

Identification of potential inhibitors, conformational dynamics, and mechanistic insights into mutant Kirsten rat sarcoma virus (G13D) driven cancers.

The mutations at the hotspot region of K-Ras result in the progression of cancer types. Our study aimed to explore the small molecule inhibitors against the G13D mutant K-Ras model with anti-cancerous activity from food and drug administration (FDA)-approved drug compounds. We implemented several computational strategies such as pharmacophore-based virtual screening, molecular docking, absorption, distribution, metabolism and excretion features, and molecular simulation to ensure the identified hit compounds have potential efficacy against G13D K-Ras. We found that the FDA-approved compounds, namely, azelastine, dihydrocodeine, paroxetine, and tramadol, are potential candidates to inhibit the action of G13D mutant K-Ras. All four compounds exhibited similar binding patterns of sotorasib, and a structural binding mechanism with significant hydrophobic contacts. The descriptor features from the QikProp of all four compounds are within allowable limits compared to sotorasib drug. Consequently, a molecular simulation result emphasized that the dihydrocodeine and tramadol exhibited less fluctuation, minimal basin, significant h-bonds, and potent inhibition against G13D K-Ras. As a result, the current research identifies prospective K-Ras inhibitors that could be further improved with biochemical analysis for precision medicine against K-Ras-driven cancers.

Whole-exome sequencing analysis of NSCLC reveals the pathogenic missense variants from cancer-associated genes.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is the most common type of lung cancer. NSCLC accounts for 84% of all lung cancer cases. In recent years, advances in pathway understanding, methods for discovering novel genetic biomarkers, and new drugs designed to inhibit the signaling cascades have enabled clinicians to personalize therapy for NSCLC. OBJECTIVES: The primary aim of this study is to identify the genes associated with NSCLC that harbor pathogenic variants that could be causative for NSCLC. The second aim is to investigate their roles in different pathways that lead to NSCLC. METHODS: We examined exome-sequencing datasets from 54 NSCLC patients to characterize the variants associated with NSCLC. RESULTS: Our findings revealed that 17 variants in 14 genes were considered highly pathogenic, including CDKN2A, ERBB2, FOXP1, IDH1, JAK3, KMT2D, K-Ras, MSH3, MSH6, POLE, RNF43, TCF7L2, TP53, and TSC1. Gene set enrichment analysis revealed the involvement of transmembrane receptor protein tyrosine kinase activity, protein binding, ATP binding, phosphatidylinositol-4,5-bisphosphate 3-kinase, and Ras guanyl-nucleotide exchange factor activity. Pathway analysis of these genes yielded different cancer-related pathways, including colorectal, prostate, endometrial, pancreatic, PI3K-Akt signaling pathways, and signaling pathways regulating pluripotency of stem cells. Module 1 from protein-protein interactions (PPIs) identified genes that harbor pathogenic SNPs. Three of the most deleterious SNPs are ERBB2 (rs1196929947), K-Ras (rs121913529), and POLE (rs751425952). Interestingly, one patient has a pathogenic K-Ras variant (rs121913529) co-occurred with the missense variant (rs752054698) inTSC1 gene. CONCLUSION: This study maps highly pathogenic variants associated with NSCLC and investigates their contributions to the pathogenesis of NSCLC. This study sheds light on the potential applications of precision medicine in patients with NSCLC.

Computational investigation to identify potent inhibitors of the GTPase-Kirsten RAt sarcoma virus (K-Ras) mutants G12C and G12D.

K-Ras mutations are frequent in various cancer types, and according to recent research, K-Ras possesses four-drug targeting sites. This increased our interest in finding potential small molecule inhibitors with anticancer activity to treat K-Ras-driven cancers. We utilized integrated bioinformatic strategies, such as XP docking, MM-GBSA, cell-line cytotoxicity prediction, ADMET, and molecular simulation, to discover potential inhibitors of G12C and G12D mutants compared to sotorasib, which is a recent FDA-approved inhibitor of G12C. We identified compounds, such as flupentixol, amlodipine, and fluvoxamine, for the G12C mutant and paroxetine, flupentixol, and zuclopenthixol for the G12D mutant with significant inhibitory functions. All five compounds bound to the H95 cryptic groove of mutant K-Ras with high efficiency and, like sotorasib, retained a novel binding mechanism with additional hydrophobic interactions at the molecular level. Furthermore, the simulation studies suggested that the binding of flupentixol and amlodipine to G12C stabilizes switch I and switch II. In contrast, paroxetine and flupentixol to G12D showed a similar trend compared to sotorasib complexes. Thus, despite the very dynamic functionality of K-Ras switches I and II, the binding of shortlisted compounds is highly stable. Therefore, the reported study provides potential drug candidates for K-Ras inhibition that can be further developed with in vitro and in vivo evidence for targeted therapy.

Residue interaction networks of K-Ras protein with water molecules identifies the potential role of switch II and P-loop.

The mutant K-Ras with aberrant signaling is the primary cause of several cancers. The proposed study investigated the influence of water molecules in K-Ras crystal structure, where they have a significant function by understanding their residue interaction networks (RINs). We analyzed the RINs of K-Ras with and without water molecules and determined their interaction properties. RINs were developed with the help of StructureViz2 and RINspector; further, the changes in K-Ras backbone flexibility were predicted with the DynaMine. We found that the residues K42, I142, and L159 are the hotspots from water, including the K-Ras-GTP complex with the highest residue centrality analysis (RCA) Z-score. The DynaMine prediction calculated the NMR S2 value for the frequently mutated positions G12, G13, and Q61 showing a minor shift in flexibility, which make up the P-Loop and switch II of the K-Ras protein. This flexibility shift can account for changes in conformational activity and the protein's GTPase activity, making it difficult to recognize by the effectors and exchange factors. Taken together, our study helps in understanding the functional importance of the water molecules in K-Ras protein and the impact of mutation that modulate the conformational state of the protein.

Comparison of potential inhibitors and targeting fat mass and obesity-associated protein causing diabesity through docking and molecular dynamics strategies.

Genome-wide association studies (GWAS) have identified an association between polymorphisms in the FTO gene and obesity. The FTO: rs9939609, an intronic variant, is considered a risk allele for developing diabesity in homozygous and heterozygous forms. This study aimed to investigate the molecular structure of the available inhibitors specific to the FTO mutations along with the rs9939609 variant. We identified the best-suited inhibitor molecules for each mutant type containing the rs9939609 risk allele. Missense mutations unique to obesity and containing the risk allele of rs9939609 were retrieved from dbSNP for this study. Further stability testing for the mutations were carried out using DynaMut and iStable tools. Three mutations (G187A, M223V, and I492V) were highly destabilizing the FTO structure. These three mutants and native FTO were docked with each of the nine-inhibitor molecules collected from literature studies with the help of PyRx and AutoDock. Further structural behavior of the mutants and native FTO were identified with molecular dynamics simulations and MM-PBSA analyses, along with the 19complex inhibitor compound. We found the compound 19complex exhibited better binding interactions and is the top candidate inhibitor for the M223V and I492V mutants. This study provided insights into the structural changes caused due to mutations in FTO, and the binding mechanism of the inhibitor molecules. It could aid in developing antiobesity drugs for treating patients with mutations and risk alleles predisposing to obesity.

A review of novel coronavirus disease (COVID-19): based on genomic structure, phylogeny, current shreds of evidence, candidate vaccines, and drug repurposing.

Coronavirus disease (COVID-19) pandemic is instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of March 13, 2021, more than 118.9 million cases were infected with COVID-19 worldwide. SARS-CoV-2 is a positive-sense single-stranded RNA beta-CoV. Most COVID-19 infected individuals recover within 1-3 weeks. Nevertheless, approximately 5% of patients develop acute respiratory distress syndrome and other systemic complications, leading to death. Structural genetic analyses of SARS-CoV-2 have shown genomic resemblances but a low evolutionary correlation to SARS-CoV-1 responsible for the 2002-2004 outbreak. The S glycoprotein is critical for cell adhesion and the entrance of the virus into the host. The process of cell entry uses the cellular receptor named angiotensin-converting enzyme 2. Recent evidence proposed that the CD147 as a SARS-CoV-2's potential receptor. The viral genome is mainly held by two non-structural proteins (NSPs), ORF1a and ORF1ab, along with structural proteins. Although NSPs are conserved among the ßCoVs, mutations in NSP2 and NSP3 may play critical roles in transmitting the virus and cell tropism. To date, no specific/targeted anti-viral treatments exist. Notably, more than 50 COVID-19 candidate vaccines in clinical trials, and a few being administered. Preventive precautions are the primary strategy to limit the viral load transmission and spread, emphasizing the urgent need for developing significant drug targets and vaccines against COVID-19. This review provides a cumulative overview of the genomic structure, transmission, phylogeny of SARS-CoV-2 from Indian clusters, treatment options, updated discoveries, and future standpoints for COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-02749-0.

Structure-Based Virtual Screening to Identify Novel Potential Compound as an Alternative to Remdesivir to Overcome the RdRp Protein Mutations in SARS-CoV-2.

The number of confirmed COVID-19 cases is rapidly increasing with no direct treatment for the disease. Few repurposed drugs, such as Remdesivir, Chloroquine, Hydroxychloroquine, Lopinavir, and Ritonavir, are being tested against SARS-CoV-2. Remdesivir is the drug of choice for Ebola virus disease and has been authorized for emergency use. This drug acts against SARS-CoV-2 by inhibiting the RNA-dependent-RNA-polymerase (RdRp) of SARS-CoV-2. RdRp of viruses is prone to mutations that confer drug resistance. A recent study by Pachetti et al. in 2020 identified the P323L mutation in the RdRp protein of SARS-CoV-2. In this study, we aimed to determine the potency of lead compounds similar to Remdesivir, which can be used as an alternative when variants of SARS-CoV-2 develop resistance due to RdRp mutations. The initial screening yielded 704 compounds that were 90% similar to the control drug, Remdesivir. On further evaluation through drugability and antiviral inhibition percentage analyses, we shortlisted 32 and seven compounds, respectively. These seven compounds were further analyzed for their molecular interactions, which revealed that all seven compounds interacted with RdRp with higher affinity than Remdesivir under native conditions. However, three compounds failed to interact with the mutant protein with higher affinity than Remdesivir. Dynamic cross-correlation matrix (DCCM) and vector field collective motions analyses were performed to identify the precise movements of docked complexes' residues. Furthermore, the compound SCHEMBL20144212 showed a high affinity for native and mutant proteins and might provide an alternative against SARS-CoV-2 variants that might confer resistance to Remdesivir. Further validations by in vitro and in vivo studies are needed to confirm the efficacy of our lead compounds for their inhibition against SARS-CoV-2.

Molecular dynamics, residue network analysis, and cross-correlation matrix to characterize the deleterious missense mutations in GALE causing galactosemia III.

Epimerase-deficiency galactosemia (EDG) is caused by mutations in the UDP-galactose 4'-epimerase enzyme, encoded by gene GALE. Catalyzing the last reaction in the Leloir pathway, UDP-galactose-4-epimerase catalyzes the interconversion of UDP-galactose and UDP-glucose. This study aimed to use in-depth computational strategies to prioritize the pathogenic missense mutations in GALE protein and investigate the systemic behavior, conformational spaces, atomic motions, and cross-correlation matrix of the GALE protein. We searched four databases (dbSNP, ClinVar, UniProt, and HGMD) and major biological literature databases (PubMed, Science Direct, and Google Scholar), for missense mutations that are associated with EDG patients, our search yielded 190 missense mutations. We applied a systematic computational prediction pipeline, including pathogenicity, stability, biochemical, conservational, protein residue contacts, and structural analysis, to predict the pathogenicity of these mutations. We found three mutations (p.K161N, p.R239W, and p.G302D) with a severe phenotype in patients with EDG that correlated with our computational prediction analysis; thus, they were selected for further structural and simulation analyses to compute the flexibility and stability of the mutant GALE proteins. The three mutants were subjected to molecular dynamics simulation (MDS) with native protein for 200 ns using GROMACS. The MDS demonstrated that these mutations affected the beta-sheets and helical region that are responsible for the catalytic activity; subsequently, affects the stability and flexibility of the mutant proteins along with a decrease and more deviations in compactness when compared to that of a native. Also, three mutations created major variations in the combined atomic motions of the catalytic and C-terminal regions. The network analysis of the residues in the native and three mutant protein structures showed disturbed residue contacts occurred owing to the missense mutations. Our findings help to understand the structural behavior of a protein owing to mutation and are intended to serve as a platform for prioritizing mutations, which could be potential targets for drug discovery and development of targeted therapeutics.

Molecular dynamics simulations to decipher the structural and functional consequences of pathogenic missense mutations in the galactosylceramidase (GALC) protein causing Krabbe's disease.

Krabbe disease (KD), also known as globoid cell leukodystrophy disease, is an autosomal recessive lysosomal storage genetic disorder, which is caused by the deficiency of galactocerebrosidase (GALC) coding gene (GALC). This study aimed to use extensive computational pipelines in understanding the missense mutations in GALC. We retrieved 176 mutations from the public databases and subjected them to pathogenicity, stability, and conservation analyses. The PredictSNP, iStable, and ConSurf prediction tools predicted 45, 95, and 47 mutations to be deleterious, destabilizing, and highly conserved, respectively. The R396L and R396W were the most deleterious and destabilizing to GALC, and were therefore prioritized for further analysis. Systematic validation on the impact of the R396L and R396W mutations to the chaperone alpha lobeline was performed using the molecular docking approach. The docking analysis revealed that the mutant R396W interacted with minimal binding affinity compared with both the R396L mutant and native GALC. Furthermore, the repetitive molecular dynamics simulation analysis showed that the mutant R396W demonstrated less compactness and reduced number of intramolecular hydrogen bonds compared with the mutant R396L and the native GALC. Overall, we observed higher structural and functional modifications in R396W positioned in the substrate-binding site. This was highly supported by the MMPBSA and DSSP analysis of the GROMACS. DSSP showed the transformation of turns to bends, indicating a loss of stability due to the R396W mutation. This study is expected to serve as a platform for prioritizing mutant proteins that could be a platform for both drug and target therapeuticsCommunicated by Ramaswamy H. Sarma.

The Rise and Impact of COVID-19 in India.

The coronavirus disease (COVID-19) pandemic, which originated in the city of Wuhan, China, has quickly spread to various countries, with many cases having been reported worldwide. As of May 8th, 2020, in India, 56,342 positive cases have been reported. India, with a population of more than 1.34 billion-the second largest population in the world-will have difficulty in controlling the transmission of severe acute respiratory syndrome coronavirus 2 among its population. Multiple strategies would be highly necessary to handle the current outbreak; these include computational modeling, statistical tools, and quantitative analyses to control the spread as well as the rapid development of a new treatment. The Ministry of Health and Family Welfare of India has raised awareness about the recent outbreak and has taken necessary actions to control the spread of COVID-19. The central and state governments are taking several measures and formulating several wartime protocols to achieve this goal. Moreover, the Indian government implemented a 55-days lockdown throughout the country that started on March 25th, 2020, to reduce the transmission of the virus. This outbreak is inextricably linked to the economy of the nation, as it has dramatically impeded industrial sectors because people worldwide are currently cautious about engaging in business in the affected regions.

An extensive computational approach to analyze and characterize the functional mutations in the galactose-1-phosphate uridyl transferase (GALT) protein responsible for classical galactosemia.

Type I galactosemia is a very rare autosomal recessive genetic metabolic disorder that occurs because of the mutations present in the galactose-1-phosphate uridyl transferase (GALT) gene, resulting in a deficiency of the GALT enzyme. The action of the GALT enzyme is to convert galactose-1-phosphate and uridine diphosphate glucose into glucose-1-phosphate (G1P) and uridine diphosphate-galactose, a crucial second step of the Leloir pathway. A missense mutation in the GALT enzyme leads to variable galactosemia's clinical presentations, ranging from mild to severe. Our study aimed to employ a comprehensive computational pipeline to analyze the most prevalent missense mutations (p.S135L, p.K285 N, p.Q188R, and p.N314D) responsible for galactosemia; these genes could serve as potential targets for chaperone therapy. We analyzed the four mutations through different computational analyses, including amino acid conservation, in silico pathogenicity and stability predictions, and macromolecular simulations (MMS) at 50 ns The stability and pathogenicity predictors showed that the p.Q188R and p.S135L mutants are the most pathogenic and destabilizing. In agreement with these results, MMS analysis demonstrated that the p.Q188R and p.S135L mutants possess higher deviation patterns, reduced compactness, and intramolecular H-bonds of the protein. This could be due to the physicochemical modifications that occurred in the mutants p.S135L and p.Q188R compared to the native. Evolutionary conservation analysis revealed that the most prevalent mutations positions were conserved among different species except N314. The proposed research study is intended to provide a basis for the therapeutic development of drugs and future treatment of classical galactosemia and possibly other genetic diseases using chaperone therapy.

Integrative Bioinformatics Approaches to Map Potential Novel Genes and Pathways Involved in Ovarian Cancer.

Background and aims: Ovarian cancer (OC) is the seventh most commonly detected cancer among women. This study aimed to map the hub and core genes and potential pathways that might be involved in the molecular pathogenesis of OC. Methods: In the present work, we analyzed a microarray dataset (GSE126519) from the Gene Expression Omnibus (GEO) database and used the GEO2R tool to screen OC cells and ovarian SINE-resistant cancer cells for differentially expressed genes (DEGs). For the functional annotation of the DEGs, we conducted Gene Ontology (GO) and pathway enrichment analyses (KEGG) using the DAVID v6.8 online server and GenoGo Metacore™, Cortellis Solution software. Protein-protein interaction (PPI) networks were constructed using the STRING database, and Cytoscape software was used for visualization. The survival analysis was performed using the online platform GEPIA2 to determine the prognostic value of the expression of hub genes in cell lines from OC patients. Results: We identified a total of 809 upregulated and 700 downregulated DEGs. GO analysis revealed that the genes with statistically significant differences in expression were mainly associated with biological processes involved in the cell cycle, the mitotic cell cycle, mitotic nuclear division, organ morphogenesis, cell development, and cell morphogenesis. By using the Analyze Networks (AN) algorithm in GeneGo, we identified the most relevant biological networks involving DEGs that were mainly enriched in the cell cycle (in metaphase checkpoints) and revealed the role of APC in cell cycle regulation pathways. We found 10 hub genes and four core genes (FZD6, FZD8, CDK2, and RBBP8) that are strongly linked to OC. Conclusion: This study sheds light on the molecular pathogenesis of OC and is expected to provide potential molecular biomarkers that are beneficial for the treatment and clinical molecular diagnosis of OC.