Elucidating the functional impact of G137V and G144R variants in Maroteaux Lamy's Syndrome by Molecular Dynamics Simulation.

Mucopolysaccharidoses VI (Maroteaux Lamy syndrome) is a metabolic disorder due to the loss of enzyme activity of N-acetyl galactosamine-4-sulphatase arising from mutations in the ARSB gene. The mutated ARSB is the origin for the accumulation of GAGs within the lysosome leading to severe growth deformities, causing lysosomal storage disease. The main focus of this study is to identify the deleterious variants by applying bioinformatics tools to predict the conservation, pathogenicity, stability, and effect of the ARSB variants. We examined 170 missense variants, of which G137V and G144R were the resultant variants predicted detrimental to the progression of the disease. The native along with G137V and G144R structures were fixed as the receptors and subjected to Molecular docking with the small molecule Odiparcil to analyze the binding efficiency and the varied interactions of the receptors towards the drug. The interaction resulted in similar docking scores of - 7.3 kcal/mol indicating effective binding and consistent interactions of the drug with residues CYS117, GLN118, THR182, and GLN517 for native, along with G137V and G144R structures. Molecular Dynamics were conducted to validate the stability and flexibility of the native and variant structures on ligand binding. The overall study indicates that the drug has similar therapeutic towards the native and variant based on the higher binding affinity and also the complexes show stability with an average of 0.2 nm RMS value. This can aid in the future development therapeutics for the Maroteaux Lamy syndrome.

A computational method to characterize the missense mutations in the catalytic domain of GAA protein causing Pompe disease.

Pompe disease is an autosomal recessive lysosomal storage disease caused by acid α-glucosidase (GAA) deficiency, resulting in intralysosomal accumulation of glycogen, including cardiac, skeletal, and smooth muscle cells. The GAA gene is located on chromosome 17 (17q25.3), the GAA protein consists of 952 amino acids; of which 378 amino acids (347-726) falls within the catalytic domain of the protein and comprises of active sites (518 and 521) and binding sites (404, 600, 616, and 674). In this study, we used several computational tools to classify the missense mutations in the catalytic domain of GAA for their pathogenicity and stability. Eight missense mutations (R437C, G478R, N573H, Y575S, G605D, V642D, L705P, and L712P) were predicted to be pathogenic and destabilizing to the protein structure. These mutations were further subjected to phenotyping analysis using SNPeffect 4.0 to predict the chaperone binding sites and structural stability of the protein. The mutations R437C and G478R were found to compromise the chaperone-binding activity with GAA. Molecular docking analysis revealed that the G478R mutation to be more significant and hinders binding to the DNJ (Miglustat) compared with the R437C. Further molecular dynamic analysis for the two mutations demonstrated that the G478R mutation was acquired higher deviation, fluctuation, and lower compactness with decreased intramolecular hydrogen bonds compared to the mutant R437C. These data are expected to serve as a platform for drug design against Pompe disease and will serve as an ultimate tool for variant classification and interpretations.

Computational approach to unravel the impact of missense mutations of proteins (D2HGDH and IDH2) causing D-2-hydroxyglutaric aciduria 2.

The 2-hydroxyglutaric aciduria (2-HGA) is a rare neurometabolic disorder that leads to the development of brain damage. It is classified into three categories: D-2-HGA, L-2-HGA, and combined D,L-2-HGA. The D-2-HGA includes two subtypes: type I and type II caused by the mutations in D2HGDH and IDH2 proteins, respectively. In this study, we studied six mutations, four in the D2HGDH (I147S, D375Y, N439D, and V444A) and two in the IDH2 proteins (R140G, R140Q). We performed in silico analysis to investigate the pathogenicity and stability changes of the mutant proteins using pathogenicity (PANTHER, PhD-SNP, SIFT, SNAP, and META-SNP) and stability (i-Mutant, MUpro, and iStable) predictors. All the mutations of both D2HGDH and IDH2 proteins were predicted as disease causing except V444A, which was predicted as neutral by SIFT. All the mutants were also predicted to be destabilizing the protein except the mutants D375Y and N439D. DSSP plugin of the PyMOL and Molecular Dynamics Simulations (MDS) were used to study the structural changes in the mutant proteins. In the case of D2HGDH protein, the mutations I147S and V444A that are positioned in the beta sheet region exhibited higher Root Mean Square Deviation (RMSD), decrease in compactness and number of intramolecular hydrogen bonds compared to the mutations N439D and D375Y that are positioned in the turn and loop region, respectively. While the mutants R140Q and R140QG that are positioned in the alpha helix region of the protein. MDS results revealed the mutation R140Q to be more destabilizing (higher RMSD values, decrease in compactness and number of intramolecular hydrogen bonds) compared to the mutation R140G of the IDH2 protein. This study is expected to serve as a platform for drug development against 2-HGA and pave the way for more accurate variant assessment and classification for patients with genetic diseases.

Computational modelling approaches as a potential platform to understand the molecular genetics association between Parkinson's and Gaucher diseases.

Gaucher's disease (GD) is a genetic disorder in which glucocerebroside accumulates in cells and specific organs. It is broadly classified into type I, type II and type III. Patients with GD are at high risk of Parkinson's disease (PD), and the clinical and pathological presentation of GD patients with PD is almost identical to idiopathic PD. Several experimental models like cell culture, animal models, and transgenic mice models were used to understand the molecular mechanism behind GD and PD association; however, such mechanism remains unclear. In this context, based on literature reports, we identified the most common mutations K198T, E326K, T369M, N370S, V394L, D409H, L444P, and R496H, in the Glucosylceramidase (GBA) protein that are known to cause GD1, and represent a risk of developing PD. However, to date, no computational analyses have designed to elucidate the potential functional role of GD mutations with increased risk of PD. The present computational pipeline allows us to understand the structural and functional significance of these GBA mutations with PD. Based on the published data, the most common and severe mutations were E326K, N370S, and L444P, which further selected for our computational analysis. PredictSNP and iStable servers predicted L444P mutant to be the most deleterious and responsible for the protein destabilization, followed by the N370S mutation. Further, we used the structural analysis and molecular dynamics approach to compare the most frequent deleterious mutations (N370S and L444P) with the mild mutation E326K. The structural analysis demonstrated that the location of E326K and N370S in the alpha helix region of the protein whereas the mutant L444P was in the starting region of the beta sheet, which might explain the predicted pathogenicity level and destabilization effect of the L444P mutant. Finally, Molecular Dynamics (MD) at 50 ns showed the highest deviation and fluctuation pattern in the L444P mutant compared to the two mutants E326K and N370S and the native protein. This was consistent with more loss of intramolecular hydrogen bonds and less compaction of the radius of gyration in the L444P mutant. The proposed study is anticipated to serve as a potential platform to understand the mechanism of the association between GD and PD, and might facilitate the process of drug discovery against both GD and PD.

Impact of missense mutations in survival motor neuron protein (SMN1) leading to Spinal Muscular Atrophy (SMA): A computational approach.

Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by the mutations in survival motor neuron 1 gene (SMN1). The molecular pathology of missense mutations in SMN1 is not thoroughly investigated so far. Therefore, we collected all missense mutations in the SMN1 protein, using all possible search terms, from three databases (PubMed, PMC and Google Scholar). All missense mutations were subjected to in silico pathogenicity, conservation, and stability analysis tools. We used statistical analysis as a QC measure for validating the specificity and accuracy of these tools. PolyPhen-2 demonstrated the highest specificity and accuracy. While PolyPhen-1 showed the highest sensitivity; overall, PolyPhen2 showed better measures in comparison to other in silico tools. Three mutations (D44V, Y272C, and Y277C) were identified as the most pathogenic and destabilizing. Further, we compared the physiochemical properties of the native and the mutant amino acids and observed loss of H-bonds and aromatic stacking upon the cysteine to tyrosine substitution, which led to the loss of aromatic rings and may reduce protein stability. The three mutations were further subjected to Molecular Dynamics Simulation (MDS) analysis using GROMACS to understand the structural changes. The Y272C and Y277C mutants exhibited maximum deviation pattern from the native protein as compared to D44V mutant. Further MDS analysis predicted changes in the stability that may have been contributed due to the loss of hydrogen bonds as observed in intramolecular hydrogen bond analysis and physiochemical analysis. A loss of function/structural impact was found to be severe in the case of Y272C and Y277C mutants in comparison to D44V mutation. Correlating the results from in silico predictions, physiochemical analysis, and MDS, we were able to observe a loss of stability in all the three mutants. This combinatorial approach could serve as a platform for variant interpretation and drug design for spinal muscular dystrophy resulting from missense mutations.

A Molecular Docking and Dynamics Approach to Screen Potent Inhibitors Against Fosfomycin Resistant Enzyme in Clinical Klebsiella pneumoniae.

Klebsiella pneumoniae, BA6753 was cultured from a patient in the Clinical Microbiology Laboratory of Christian Medical College. K. pneumoniae, BA6753 has a multidrug resistance plasmid encoding novel FosA variant-7, fosfomycin resistance enzyme. Minimal side effects and a wide range of bactericidal activity of fosfomycin have resulted in its expanded clinical use that prompts the rise of fosfomycin-resistant strains. At present, there are no effective inhibitors available to conflict the FosA-medicated fosfomycin resistance. To develop effective FosA inhibitors, it is crucial to understand the structural and dynamic properties of resistance enzymes. Hence, the present study focuses on the identification of potent inhibitors that can effectively bind to the fosfomycin resistance enzyme, thus predispose the target to inactivate by the second antibiotic. Initially, a series of active compounds were screened against the resistant enzyme, and the binding affinities were confirmed using docking simulation analysis. For efficient activity, the binding affinity of the resistance enzyme ought to be high with the inhibitor than the fosfomycin drug. Consequently, the enzyme-ligand complex which showed higher binding affinity than the fosfomycin was employed for subsequent analysis. The stability of the top scoring enzyme-ligand complex was further validated using molecular dynamics simulation studies. On the whole, we presume that the compound 19583672 demonstrates a higher binding affinity for the resistance enzyme comparing to other compounds and fosfomycin. We believe that further enhancement of the lead compound can serve as a potential inhibitor against resistance enzyme in drug discovery process. J. Cell. Biochem. 118: 4088-4094, 2017. © 2017 Wiley Periodicals, Inc.

Computational screening and structural analysis of Gly201Arg and Gly201Asp missense mutations in human cyclin-dependent kinase 4 protein.

The regulatory proteins, cyclins, and cyclin-dependent kinases (CDKs) control the cell cycle progression. CDK4 gene mutations are associated with certain cancers such as melanoma, breast cancer, and rhabdomyosarcoma. Therefore, understanding the mechanisms of cell cycle control and cell proliferation is essential in developing cancer treatment regimens. In this study, we obtained cancer-causing CDK4 mutations from the COSMIC database and subjected them to a series of in silico analyses to identify the most significant mutations. An overall of 238 mutations (119 missense mutations) retrieved from the COSMIC database were investigated for the pathogenic and destabilizing properties using the PredictSNP and iStable algorithms. Further, the amino acid position of the most pathogenic and destabilizing mutations were analyzed to understand the nature of amino acid conservation across the species during the evolution. We observed that the missense mutations G201R and G201D were more significant and the Glycine at position 201 was found to highly conserved. These significant mutations were subjected to molecular dynamics simulation analysis to understand the protein's structural changes. The results from molecular dynamics simulations revealed that both G201R and G201D of CDK4 are capable of altering the protein's native form. On comparison among the most significant mutations, G201R disrupted the protein structure higher than the protein with G201D.

Identification of dysregulated canonical pathways associated with pathogenesis and progression of Amyotrophic Lateral Sclerosis-An integrated bioinformatics approach.

The mechanisms responsible for the pathogenesis and progression of Amyotrophic Lateral Sclerosis (ALS) remain poorly understood, making the diagnosis of ALS challenging. We aimed to find the novel gene biomarkers via computationally analyzing microarray expression studies, in three different cell lineages, namely myotube cells, astrocyte cells and oligodendrocyte cells. Microarray gene expression profiles were obtained and analyzed for three cell types: myotube cell lineage (GSE122261), astrocyte, and oligodendrocyte cell lineage (GSE87385). A comprehensive computational pipeline, tailored explicitly for microarray gene expression profiling studies, was devised to analyze the sample groups, wherein the myotube sample group comprised of six control (GSM3462697, GSM3462698, GSM3462699, GSM3462700, GSM3462701, GSM3462702) & six diseased (GSM3462691, GSM3462692, GSM3462693, GSM3462694, GSM3462695, GSM3462696) samples were considered. Similarly, for the astrocyte sample group two samples each for the control (GSM2330040, GSM2330042) and the diseased (GSM2330039, GSM2330041), and for the oligodendrocyte sample group, 2 control (GSM2330043, GSM2330045) samples and two diseased (GSM2330044, GSM2330046) samples were considered for the current study. The in-depth interaction of these DEGs was studied using MCODE and subjected to preliminary functional analysis using ClueGO/CluePedia plug-in. Qiagen's IPA software was employed for enrichment analysis, which generated the key canonical pathways and a list of potential biomarker molecules specific to each sample group. The preliminary analysis yielded 512 DEGs across all 3-sample groups, wherein 139 DEGs belonged to the myotube sample group, 216 DEGs for the astrocyte sample group, and 157 DEGs for the oligodendrocytes sample group. The data suggests growth hormone signaling and its activity, ErbB signaling pathway, and JAK/STAT signaling pathway are some of the pathways that are significantly dysregulated and play a crucial role in the development and progression of ALS. KISS1R and CSHL1 are potential genes that could act as diagnostic biomarkers in myotube cell types. Also, KRAS, TGFB2, JUN, and SMAD6 genes may be used as prognostic biomarkers to differentiate between early and late-stage ALS-diseased patients.

Elucidating the mechanism of antimicrobial resistance in Mycobacterium tuberculosis using gene interaction networks.

Antimicrobial resistance (AMR) in microorganisms is an urgent global health threat. AMR of Mycobacterium tuberculosis is associated with significant morbidity and mortality. It is of great importance to underpin the resistance pathways involved in the mechanisms of AMR and identify the genes that are directly involved in AMR. The focus of the current study was the bacteria M. tuberculosis, which carries AMR genes that give resistance that lead to multidrug resistance. We, therefore, built a network of 43 genes and examined for potential gene-gene interactions. Then we performed a clustering analysis and identified three closely related clusters that could be involved in multidrug resistance mechanisms. Through the bioinformatics pipeline, we consistently identified six-hub genes (dnaN, polA, ftsZ, alr, ftsQ, and murC) that demonstrated the highest number of interactions within the clustering analysis. This study sheds light on the multidrug resistance of MTB and provides a protocol for discovering genes that might be involved in multidrug resistance, which will improve the treatment of resistant strains of TB.

Molecular characterization of circadian gene expression and its correlation with survival percentage in colorectal cancer patients.

Colorectal cancer (CRC) is a form of cancer characterized by many symptoms and readily metastasizes to different organs in the body. Circadian rhythm is one of the many processes that is observed to be dysregulated in CRC-affected patients. In this study, we aim to identify the dysregulated physiological processes in CRC-affected patients and correlate the expression profiles of the circadian clock genes with CRC-patients' survival rates. We performed an extensive microarray gene expression pipeline, whereby 471 differentially expressed genes (DEGs) were identified, following which, we streamlined our search to 43 circadian clock affecting DEGs. The Circadian Gene Database was accessed to retrieve the circadian rhythm-specific genes. The DEGs were then subjected to multi-level functional annotation, i.e., preliminary analysis using ClueGO/CluePedia and pathway enrichment using DAVID. The findings of our study were interesting, wherein we observed that the survival percentage of CRC-affected patients dropped significantly around the 100th-month mark. Furthermore, we identified hormonal activity, xenobiotic metabolism, and PI3K-Akt signaling pathway to be frequently dysregulated cellular functions. Additionally, we detected that the ZFYVE family of genes and the two genes, namely MYC and CDK4 were the significant DEGs that are linked to the pathogenesis and progression of CRC. This study sheds light on the importance of bioinformatics to simplify our understanding of the interactions of different genes that control different phenotypes.

Identification of potential circadian genes and associated pathways in colorectal cancer progression and prognosis using microarray gene expression analysis.

Colorectal cancer (CRC) is third cancer causing death in the world. CRC is associated with disrupting the circadian rhythm (CR), closely associating the CRC progression and the dysregulation of genes involved in the biological clock. In this study, we aimed to understand the circadian rhythm changes in patients diagnosed with CRC. We used the GEO database with the ID GSE46549 for our analysis, which consists of 32 patients with CRC and one as normal control. Our study has identified five essential genes involved in CRC, HAPLN1, CDH12, IGFBP5, DCHS2, and DOK5, and had different enriched pathways, such as the Wnt-signaling pathway, at different time points of study. As a part of our study, we also identified various related circadian genes, such as CXCL12, C1QTNF2, MRC2, and GLUL, from the Circadian Gene Expression database, that played a role in circadian rhythm and CRC development. As circadian timing can influence the host tissue's ability to tolerate anticancer medications, the genes reported can serve as a potential drug target for treating CRC and become beneficial to translational settings.

Controlling cell proliferation by targeting cyclin-dependent kinase 6 using drug repurposing approach.

Cyclin-dependent kinase 6 (CDK6) is an essential kinase in cell cycle progression, which is a viable target for inhibitors in various malignancies, including breast cancer. This study aimed to virtually screen efficient compounds as new leads in treating breast cancer using a drug repurposing approach. Apoptosis regulatory compounds were taken from the seleckchem database. Molecular docking experiments were carried out in the presence of abemaciclib, a routinely used FDA drug. Compared to conventional drugs, the two compounds demonstrated a higher binding affinity for CDK6. Compounds (N-benzyl-6-[(4-hydroxyphenyl)methyl]-8-(naphthalen-1-ylmethyl)-4,7-dioxo-3,6,9,9a-tetrahydro-2H-pyrazino[1,2-a]pyrimidine-1-carboxamide) and (1'-[4-[1-(4-fluorophenyl)indol-3-yl]butyl]spiro[1H-2-benzofuran-3,4'-piperidine]) were discovered to have an inhibitory effect against CDK6 at -8.49 and -6.78kcal/mol, respectively, compared to -8.09kcal/mol of the control molecule, the interacting residues of these two new compounds were found to fall within the binding site of the CDK6 molecule. Both compounds exhibited equal ADME features compared with abemaciclib and would be well distributed and metabolized by the body with an appropriate druglikeness range. Lastly, molecular dynamics was initiated for 200ns for the selected potent inhibitors and abemaciclib as complexed with CDK6. The RMSD, RMSF, Rg, H-Bond interactions, SASA, PCA, FEL, and MM/PBSA analysis were performed for the complexes to assess the stability, fluctuations, radius of gyration, hydrogen bond interaction, solvent accessibility, essential dynamics, free energy landscape, and MM/PBSA. The selected two compounds are small molecules in the appropriate druglikeness range. The results observed in molecular docking and molecular dynamics simulations were most promising for two compounds, suggesting their potent inhibitory effect against CDK6. We propose that these candidate compounds can undergo in vitro validation and in vivo testing for their further use against cancer.

Computational and structural investigation of Palmitoyl-Protein Thioesterase 1 (PPT1) protein causing Neuronal Ceroid Lipofuscinoses (NCL).

The Neuronal Ceroid Lipofuscinoses (NCL) are a group of progressive neurodegenerative disorders, associated with 14 Ceroid Lipofuscinosis Neuronal genes (CLN1-14). The mutations in the Palmitoyl-Protein Thioesterase 1 (PPT1) protein serve as one of the major reasons for the causative of NCL. The PPT1 involves degrading and modifying cysteine residues in proteins or peptides by removing thioester-linked fatty acyl groups like palmitate prefers acyl chains of 14-18 carbons in length. In this study, we have analyzed the impact of PPT1 mutations on the deleteriousness, stability, conservative nature of amino acid, and impact of mutations on the protein structure. We have also used molecular dynamics simulations using GROMACS to perceive the alteration in the dynamic behavior of the PPT1 at the residual level. In this study, we have retrieved 23 PPT1 mutations from the UniProt database, and these were subjected to a series of analyses using varied computer algorithms. From these analyses, out of 23 mutations, 16 mutations were identified as deleterious. Among 16, eight mutations were identified to destabilize the protein structure, and finally, two mutations (W38C and L222P) were found to be positioned in the highly conserved region. The structural impact study observed that the mutant proline could disrupt the alpha helix formed by the leucine at position 222. Finally, from the molecular dynamics simulations, we observed that due to the mutations (W38C and L222P), the protein had experienced higher deviation, fluctuation, and lower compactness. These structural changes elucidate that these mutations can impact the structure and function of the PPT1 protein.

Deciphering the effect of mutations in MMAA protein causing methylmalonic acidemia-A computational approach.

Methylmalonic acidemia (MMA) is a rare genetic disorder affecting multiple body systems. We aimed to investigate the pathogenic mutations in MMAA that are associated with isolated methylmalonic acidemia to identify the structural behavior of MMAA upon mutation. The algorithms such as PredictSNP, iStable, ConSurf, and Align GVGD were employed to analyze the consequence of the mutations. Molecular docking was carried out for the native MMAA, L89P, G274D, and R359G to interpret its interactions with the GDP substrate. The docked complexes were simulated for 200ns aiding GROMACS in apprehending the behavior of MMAA upon mutation and GDP binding. After simulation, cα disruptions were observed using the RMSF plot, which indicated that several regions of mutant MMAAs have highly fluctuated. The gyration and H-bond plots were used to understand the compactness and intermolecular interaction with the GDP molecule. The MDS analysis showed that the mutations L89P, G274D, and R359G are highly unstable even after GDP binding, with the least compactness, fewer H-bonds, and larger conformational cα motions. Our study provided structural and dynamic insights into MMAA protein, which further helps to characterize these mutants and provide potential treatment strategies for MMA patients.

Computational structural assessment of BReast CAncer type 1 susceptibility protein (BRCA1) and BRCA1-Associated Ring Domain protein 1 (BARD1) mutations on the protein-protein interface.

Breast cancer type 1 susceptibility protein (BRCA1) is closely related to the BRCA2 (breast cancer type 2 susceptibility protein) and BARD1 (BRCA1-associated RING domain-1) proteins. The homodimers were formed through their RING fingers; however they form more compact heterodimers preferentially, influencing BRCA1 residues 1-109 and BARD1 residues 26-119. We implemented an integrative computational pipeline to screen all the mutations in BRCA1 and identify the most significant mutations influencing the Protein-Protein Interactions (PPI) in the BRCA1-BARD1 protein complex. The amino acids involved in the PPI regions were identified from the PDBsum database with the PDB ID: 1JM7. We screened 2118 missense mutations in BRCA1 and none in BARD1 for pathogenicity and stability and analyzed the amino acid sequences for conserved residues. We identified the most significant mutations from these screenings as V11G, M18K, L22S, and T97R positioned in the PPI regions of the BRCA1-BARD1 protein complex. We further performed protein-protein docking using the ZDOCK server. The native protein-protein complex showed the highest binding score of 2118.613, and the V11G mutant protein complex showed the least binding score of 1992.949. The other three mutation protein complexes had binding scores between the native and V11G protein complexes. Finally, a molecular dynamics simulation study using GROMACS was performed to comprehend changes in the BRCA1-BARD1 complex's binding pattern due to the mutation. From the analysis, we observed the highest deviation with lowest compactness and a decrease in the intramolecular h-bonds in the BRCA1-BARD1 protein complex with the V11G mutation compared to the native complex or the complexes with other mutations.

Understanding the activating mechanism of the immune system against COVID-19 by Traditional Indian Medicine: Network pharmacology approach.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmissions are occurring rapidly; it is raising the alarm around the globe. Though vaccines are currently available, the evolution and mutations in the SARS-CoV-2 threaten available vaccines' significance. The drugs are still undergoing clinical trials, and certain medications are approved for "emergency use" or as an "off-label" drug during the pandemic. These drugs have been effective yet accommodating side effects, which also can be lethal. Complementary and alternative medicine is highly demanded since it embraces a holistic approach. Since ancient times, natural products have been used as drugs to treat various diseases in the medical field and are still widely practiced. Medicinal plants contain many active compounds that serve as the key to an effective drug design. The Kabasura kudineer and Nilavembu kudineer are the two most widely approved formulations to treat COVID-19. However, the mechanism of these formulations is not well known. The proposed study used a network pharmacology approach to understand the immune-boosting mechanism by the Kabasura kudineer, Nilavembu kudineer, and JACOM in treating COVID-19. The plants and phytochemical chemical compounds in the Kabasura kudineer, Nilavembu kudineer, and JACOM were obtained from the literature. The Swiss target prediction algorithm was used to predict the targets for these phytochemical compounds. The common genes for the COVID-19 infection and the drug targets were identified. The gene-gene interaction network was constructed to understand the interactions between these common genes and enrichment analyses to determine the biological process, molecular functions, cellular functions, pathways involved, etc. Finally, virtual screening and molecular docking studies were performed to identify the most potential targets and significant phytochemical compounds to treat the COVID-19. The present study identified potential targets as ACE, Cathepsin L, Cathepsin B, Cathepsin K, DPP4, EGFR, HDAC2, IL6, RIPK1, and VEGFA. Similarly, betulinic acid, 5″-(2⁗-Hydroxybenzyl) uvarinol, antofine, (S)-1'-methyloctyl caffeate, (Z)-3-phenyl-2-propenal, 7-oxo-10α-cucurbitadienol, and PLX-4720 collectively to be potential treatment agents for COVID-19.

Investigating mutations at the hotspot position of the ERBB2 and screening for the novel lead compound to treat breast cancer - a computational approach.

Membrane proteins are the most common types of cancer that are active in the prognosis. Membrane proteins are a distinguishing characteristic of a cancer cell. In tumor cell therapy, the overexpressed membrane proteins are becoming ever more relevant. The 3-kinase (PI3K)/AKT phosphatidylinositol pathway is downstream triggered by different extracellular signals, and this signaling pathway activation impacts a variety of proliferation of the cellular processes like cell growth and surviving. Frequent PI3K/AKT dysregulation in human cancer has rendered proteins of this pathway desirable for diagnostic markers. Members of the ERBB family-like ERBB2 and ERBB3 activate intracellular signaling pathways such as PI3K/AKT. The mutations in these proteins dysfunctions the proteins in the downstream. Considering this importance, we have developed a computational pipeline to identify the mutation position with a highest number of mutations and to screen them for pathogenicity, stability, conservation, and structural changes using PredictSNP, iStable, ConSurf, and GROMACS simulation software respectively. Further, a virtual screening approach was initiated to find the most similar non-toxic lead compound, which could be an alternative to the currently used lapatinib. To conclude, protein-ligand dynamics were undertaken to study the actions of native and mutants with the lapatinib and the lead compound. From the overall analysis, we identified position 755 with leucine in the native condition is prone to frequent mutations. The leucine at 755th position is more prone to mutate as serine and tryptophan. Further from the computational analysis, we identified that the mutation L755S is more significant than the L755W mutation. We have witnessed CID140590176 be a potential lead compound with no toxicity. The behavior of the lead compound has shown more compactness with an increased number of intermolecular hydrogen bonds in the ERBB2 with L755S. This lead compound can be further taken for experimental validations, and we believe that this lead compound could be a potent ERBB2 inhibitor.

Identification of potential inhibitors against pathogenic missense mutations of PMM2 using a structure-based virtual screening approach.

The autosomal recessive phosphomannomutase 2-congenital disorder of glycosylation (PMM2-CDG) is characterized by defective functioning of the PMM2 enzyme, which is necessary for the conversion of mannose-6-phosphate into mannose-1-phosphate. Here, a computational pipeline was drawn to identify the most significant mutations, and further, we used a virtual screening approach to identify a new lead compound to treat the identified significant mutations. We searched for missense mutation data related to PMM2-CDG in HGMD®, UniProt, and ClinVar. Our search yielded a total of 103 mutations, of which 91 are missense mutations. The D65Y, I132N, I132T, and F183S mutations were classified as deleterious, destabilizing, and altering the biophysical properties using the PredictSNP, iStable, and Align GVGD in silico prediction tools. Additionally, we applied a multistep protocol to screen for an alternative lead compound to the existing CID2876053 (1-(3-chlorophenyl)-3,3-bis(pyridine-2-yl)urea) with affinity to these identified significant mutants. Two compounds, CHEMBL1491007 (6-chloro-4-phenyl-3-(4-pyridin-2-ylpiperazin-1-yl)-1H-quinolin-2-one) and CHEMBL3653029 (5-chloro-4-[6-[(3-fluorophenyl)methylamino]pyridin-2-yl]-N-(piperidin-4-ylmethyl)pyridin-2-amine), exhibited the highest binding affinity with the selected mutants and were chosen for further analysis. Through molecular docking, molecular dynamics simulation, and MMPBSA analysis, we found that the known compound, i.e. CID2876053, has stronger interaction with the D65Y mutant. The newly identified lead compound CHEMBL1491007 showed stronger interaction with the I132N and I132T mutants, whereas the most deleterious mutant, F183S, showed stronger interaction with CHEMBL3653029. This study is expected to aid in the field of precision medicine, and further to in vivo and in vitro analysis of these lead compounds might shed light on the treatment of PMM2-CDG. Communicated by Ramaswamy H. Sarma.

A systemic approach to explore the mechanisms of drug resistance and altered signaling cascades in extensively drug-resistant tuberculosis.

BACKGROUND AND AIM: The persistence of extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis (MTB) continue to pose a significant challenge to the treatment and control of tuberculosis infections worldwide. XDR-MTB strains exhibit resistance against first-line anti-TB drugs, fluoroquinolones, and second-line injectable drugs. The mechanisms of drug resistance of MTB remains poorly understood. Our study aims at identifying the differentially expressed genes (DEGs), associated gene networks, and signaling cascades involved in rendering this pathogen resistant to multiple drugs, namely, isoniazid, rifampicin, and capreomycin. METHODS: We used the microarray dataset GSE53843. The GEO2R tool was used to prioritize the most significant DEGs (top 250) of each drug exposure sample between XDR strains and non-resistant strains. The validation of the 250 DEGs was performed using volcano plots. Protein-protein interaction networks of the DEGs were created using STRING and Cytoscape tools, which helped decipher the relationship between these genes. The significant DEGs were functionally annotated using DAVID and ClueGO. The concomitant biological processes (BP) and molecular functions (MF) were represented as dot plots. RESULTS AND CONCLUSION: We identified relevant molecular pathways and biological processes, such as cell wall biogenesis, lipid metabolic process, ion transport, phosphopantetheine binding, and triglyceride lipase activity. These processes indicated the involvement of multiple interconnected mechanisms in drug resistance. Our study highlighted the impact of cell wall permeability, with the dysregulation of the mur family of proteins, as essential factors in the inference of resistance. Additionally, upregulation of genes responsible for ion transport such as ctpF, arsC, and nark3, emphasizes the importance of transport channels and efflux pumps in potentially driving out stress-inducing compounds. This study investigated the upregulation of the Lip family of proteins, which play a crucial role in triglyceride lipase activity. Thereby illuminating the potential role of drug-induced dormancy and subsequent resistance in the mycobacterial strains. Multiple mechanisms such as carboxylic acid metabolic process, NAD biosynthetic process, triglyceride lipase activity, phosphopantetheine binding, organic acid biosynthetic process, and growth of symbiont in host cell were observed to partake in resistance of XDR-MTB. This study ultimately provides a platform for important mapping targets for potential therapeutics against XDR-MTB.

Network analysis of transcriptomics data for the prediction and prioritization of membrane-associated biomarkers for idiopathic pulmonary fibrosis (IPF) by bioinformatics approach.

Idiopathic pulmonary fibrosis (IPF) is a rare yet crucial persistent lung disorder that actuates scarring of lung tissues, which makes breathing difficult. Smoking, environmental pollution, and certain viral infections could initiate lung scarring. However, the molecular mechanism involved in IPF remains elusive. To develop an efficient therapeutic arsenal against IPF, it is vital to understand the pathology and deviations in biochemical pathways that lead to disorder. In this study, we availed network analysis and other computational pipelines to delineate the prominent membrane proteins as diagnostic biomarkers and therapeutic targets for IPF. This study yielded a significant role of glycosaminoglycan binding, endothelin, and GABA-B receptor signaling pathway in IPF pathogenesis. Furthermore, ADCY8, CRH, FGB, GPR17, MCHR1, NMUR1, and SAA1 genes were found to be immensely involved with IPF, and the enrichment pathway analysis suggests that most of the pathways were corresponding to membrane transport and signal transduction functionalities. This analysis could help in better understanding the molecular mechanism behind IPF to develop an efficient therapeutic target or biomarkers for IPF.