PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.

Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, and small-molecule secondary metabolites. Dysregulation of methyltransferases is implicated in various human cancers. However, in light of the well-recognized significance of PMTs, reliable and efficient identification methods are essential. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated, and prediction models were trained using various machine-learning algorithms using a tenfold cross-validation technique. After evaluating each model on the dataset, the SVM-based CKSAAP model achieved the highest prediction accuracy with balanced sensitivity and specificity. Also, this SVM model outperformed deep-learning algorithms for the prediction of PMTs. In addition, cross-database validation was performed to ensure the robustness of the model. Feature importance was assessed using shapley additive explanations (SHAP) values, providing insights into the contributions of different features to the model's predictions. Finally, the SVM-based CKSAAP model was implemented in a standalone tool, PMTPred, due to its consistent performance during independent testing and cross-database evaluation. We believe that PMTPred will be a useful and efficient tool for the identification of PMTs. The PMTPred is freely available for download at https://github.com/ArvindYadav7/PMTPred and http://www.bioinfoindia.org/PMTPred/home.html for research and academic use.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase.

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma.

Computational approach for assessing the involvement of SMYD2 protein in human cancers using TCGA data.

BACKGROUND: SMYD2 is a protein of the SET and MYND domain-containing family SMYD. It can methylate the lysine residue of various histone and nonhistone cancer-related proteins and plays a critical role in tumorigenesis. Although emerging evidence supports the association of SMYD2 in the progression of cancers, but its definitive effect is not yet clear. Therefore, further study of the gene in relation with cancer progression needs to be conducted. In the current study, investigators used TCGA data to determine the potential carcinogenic effect of SMYD2 in 11 cancer types. The transcriptional expression, survival rate, mutations, enriched pathways, and Gene Ontology of the SMYD2 were explored using different bioinformatics tools and servers. In addition, we also examined the correlation between SMYD2 gene expression and immunocyte infiltration in multiple cancer types. RESULTS: Findings revealed that higher expression of SMYD2 was significantly correlated with cancer incidents. In CESC and KIRC, the mRNA expression of SMYD2 was significantly correlated with overall survival (OS). In BRCA, KIRC, COAD, and HNSC, the mRNA expression of SMYD2 was significantly correlated with disease-free survival (DFS). We detected 15 missense, 4 truncating, 4 fusions, and 1 splice type of mutation. The expression of SMYD2 was significantly correlated with tumor purity and immunocyte infiltration in six cancer types. The gene GNPAT was highly associated with SMYD2. Significant pathways and Gene Ontology (GO) terms for co-expressed genes were associated to various processes linked with cancer formation. CONCLUSION: Collectively, our data-driven results may provide reasonably comprehensive insights for understanding the carcinogenic effect of SMYD2. It suggests that SMYD2 might be used as a significant target for identifying new biomarkers for various human tumors.

Computational screening of pathogenic missense nsSNPs in heme oxygenase 1 (HMOX1) gene and their structural and functional consequences.

Heme Oxygenase 1 (HMOX1) is a cytoprotective enzyme, exhibiting the highest activity in the spleen, catalyzing the heme ring breakdown into products of biological significance- biliverdin, CO, and Fe2+. In vascular cells, HMOX1 possesses strong anti-apoptotic, antioxidant, anti-proliferative, anti-inflammatory, and immunomodulatory actions. The majority of these activities are crucial for the prevention of atherogenesis. Single amino acid substitutions in proteins generated by missense non-synonymous single nucleotide polymorphism (nsSNPs) in the protein-encoding regions of genes are potent enough to cause significant medical challenges due to the alteration of protein structure and function. The current study aimed at characterizing and analyzing high-risk nsSNPs associated with the human HMOX1 gene. Preliminary screening of the total available 288 missense SNPs was performed through the lens of deleteriousness and stability prediction tools. Finally, a total of seven nsSNPs (Y58D, A131T, Y134H, F166S, F167S, R183S and M186V) were found to be most deleterious by all tools that are present at highly conserved positions. Molecular dynamics simulations (MDS) analysis explained the mutational effects on the dynamic action of the wild-type and mutant proteins. In a nutshell, R183S (rs749644285) was identified as a highly detrimental mutation that could significantly render the enzymatic activity of HMOX1. The finding of this computational analysis might help subject the experimental confirmatory analysis to characterize the role of nsSNPs in HMOX1.Communicated by Ramaswamy H. Sarma.

Comprehensive bioinformatics analysis of structural and functional consequences of deleterious missense mutations in the human QDPR gene.

Quinonoid dihydropteridine reductase (QDPR) is an enzyme that regulates tetrahydrobiopterin (BH4), a cofactor for enzymes involved in neurotransmitter synthesis and blood pressure regulation. Reduced QDPR activity can cause dihydrobiopterin (BH2) accumulation and BH4 depletion, leading to impaired neurotransmitter synthesis, oxidative stress, and increased risk of Parkinson's disease. A total of 10,236 SNPs were identified in the QDPR gene, with 217 being missense SNPs. Over 18 different sequence-based and structure-based tools were employed to assess the protein's biological activity, with several computational tools identifying deleterious SNPs. Additionally, the article provides detailed information about the QDPR gene and protein structure and conservation analysis. The results showed that 10 mutations were harmful and linked to brain and central nervous system disorders, and were predicted to be oncogenic by Dr. Cancer and CScape. Following conservation analysis, the HOPE server was used to analyse the effect of six selected mutations (L14P, V15G, G23S, V54G, M107K, G151S) on the protein structure. Overall, the study provides insights into the biological and functional impact of nsSNPs on QDPR activity and the potential induced pathogenicity and oncogenicity. In the future, research can be conducted to systematically evaluate QDPR gene variation through clinical studies, investigate mutation prevalence across different geographical regions, and validate computational results with conclusive experiments.Communicated by Ramaswamy H. Sarma.

Repurposing of drugs against methyltransferase as potential Zika virus therapies.

In recent years, the outbreak of infectious disease caused by Zika Virus (ZIKV) has posed a major threat to global public health, calling for the development of therapeutics to treat ZIKV disease. Several possible druggable targets involved in virus replication have been identified. In search of additional potential inhibitors, we screened 2895 FDA-approved compounds using Non-Structural Protein 5 (NS5) as a target utilizing virtual screening of in-silco methods. The top 28 compounds with the threshold of binding energy -7.2 kcal/mol value were selected and were cross-docked on the three-dimensional structure of NS5 using AutoDock Tools. Of the 2895 compounds screened, five compounds (Ceforanide, Squanavir, Amcinonide, Cefpiramide, and Olmesartan_Medoxomil) ranked highest based on filtering of having the least negative interactions with the NS5 and were selected for Molecular Dynamic Simulations (MDS) studies. Various parameters such as RMSD, RMSF, Rg, SASA, PCA and binding free energy were calculated to validate the binding of compounds to the target, ZIKV-NS5. The binding free energy was found to be -114.53, -182.01, -168.19, -91.16, -122.56, and -150.65 kJ mol-1 for NS5-SFG, NS5-Ceforanide, NS5-Squanavir, NS5-Amcinonide, NS5-Cefpiramide, and NS5-Ol_Me complexes respectively. The binding energy calculations suggested Cefpiramide and Olmesartan_Medoxomil (Ol_Me) as the most stable compounds for binding to NS5, indicating a strong rationale for their use as lead compounds for development of ZIKV inhibitors. As these drugs have been evaluated on pharmacokinetics and pharmacodynamics parameters only, in vitro and in vivo testing and their impact on Zika viral cell culture may suggest their clinical trials on ZIKV patients.

Comprehensive analysis of non-synonymous missense SNPs of human galactose mutarotase (GALM) gene: an integrated computational approach.

Missense Non-synonymous single nucleotide polymorphisms (nsSNPs) of Galactose Mutarotase (GALM) are associated with the Novel type of Galactosemia (Galactosemia type 4) together with symptoms such as high blood galactose levels and eye cataracts. The objective of the present study was to identify deleterious nsSNPs of GALM recorded on the dbSNP database through comprehensive insilico analysis. Among the 319 missense nsSNPs reported, various insilco tools predicted R78S, R82G, A163E, P210S, Y281C, E307G and F339C as the most deleterious mutations. Structural analysis, PTM analysis and molecular dynamics simulations (MDS) were carried out to understand the effect of these mutations on the structural and physicochemical properties of the GALM protein. The residues R82G and E307G were found to be part of the binding site that resulted in decreased surface accessibility. Replacing the charged wild-type residue with a neutral mutant type affected its substrate binding. All 7 mutations were found to increase the rigidity of the protein structure, which is unfavorable during ligand binding. The mutation F339E made the protein structure more rigid than all the other mutations. Y281 is a phosphorylated site, and therefore, less significant structural changes were observed when compared to other mutations; however, it may have significant differences in the usual functioning of the protein. In summary, the structural and functional analysis of missense SNPs of GALM is important to reduce the number of potential mutations to be evaluated in vitro to understand the association with some genetic diseases.Communicated by Ramaswamy H. Sarma.

Curative anti-typhoid effect of Detarium microcarpum Guill. & Perr. (Leguminosae) hydroethanolic extract root bark based-on in vivo and molecular docking analyses.

ETHNOPHARMACOLOGICAL RELEVANCE: Detarium microcarpum is used to treat typhoid fever, a major public health problem, by indigenous population in Africa. Though its preventive activities have been documented, the curative effect is still to be confirmed. AIM OF THE STUDY: This study aimed at evaluating the curative effects of the hydroethanolic extract of Detarium microcarpum root bark on Salmonella typhimurium-induced typhoid in rat and exploring the in-silico inhibition of some bacterial key enzymes. STUDY DESIGN: In vitro antioxydant, in vivo antisalmonella of the extract and in silico molecular docking assay on the isolated compounds were carried out to explore the anti-salmonella effects of Detarium microcarpum. MATERIAL AND METHODS: The in vitro antioxidant properties of the extract were evaluated using DPPH, ABTS and FRAP tests. The anti-salmonella activity of the extract was assessed through feacal sample from Salmonella typhimurium-infected rat cultured in Salmonella-Shigella agar (SS agar) medium. The affinity of isolated compounds (Rhinocerotinoic acid and Microcarposide) from the extract were performed on four key enzymes (Adenylosuccinate lyase, Acetyl coenzyme A synthetase, Thymidine phosphorylase and LuxS-Quorum sensor) using molecular docking simulation to elucidate the molecular level inhibition mechanism. RESULTS: Crude extract of D. microcarpum root bark showed variable activities on DPPH (RSa50: 6.09 ± 1.04 µg/mL), ABTS (RSa50: 24.46 ± 0.27), and FRAP (RSa50: 23.30 ± 0.23). The extract at all the doses exhibited significant healing effect of infected rats, with the complete clearance. The extract restored hematological, biochemical and histological parameters closed to the normal control. The molecular docking results indicates that rhinocerotinoic acid and microcarposide present more affinity to the LuxS-Quorum sensor and Acetyl coenzyme A synthetase protein as compared to the others. CONCLUSION: These results demonstrate potent anti-typhoid activities of the hydroethanolic of Detarium microcarpum root bark extract through antioxidant properties and high inhibitory affinity of its compounds on some bacterial key enzymes that justify its use as traditional medicine to typhoid fever.

A bioinformatics approach to the identification of novel deleterious mutations of human TPMT through validated screening and molecular dynamics.

Polymorphisms of Thiopurine S-methyltransferase (TPMT) are known to be associated with leukemia, inflammatory bowel diseases, and more. The objective of the present study was to identify novel deleterious missense SNPs of TPMT through a comprehensive in silico protocol. The initial SNP screening protocol used to identify deleterious SNPs from the pool of all TPMT SNPs in the dbSNP database yielded an accuracy of 83.33% in identifying extremely dangerous variants. Five novel deleterious missense SNPs (W33G, W78R, V89E, W150G, and L182P) of TPMT were identified through the aforementioned screening protocol. These 5 SNPs were then subjected to conservation analysis, interaction analysis, oncogenic and phenotypic analysis, structural analysis, PTM analysis, and molecular dynamics simulations (MDS) analysis to further assess and analyze their deleterious nature. Oncogenic analysis revealed that all five SNPs are oncogenic. MDS analysis revealed that all SNPs are deleterious due to the alterations they cause in the binding energy of the wild-type protein. Plasticity-induced instability caused by most of the mutations as indicated by the MDS results has been hypothesized to be the reason for this alteration. While in vivo or in vitro protocols are more conclusive, they are often more challenging and expensive. Hence, future research endeavors targeted at TPMT polymorphisms and/or their consequences in relevant disease progressions or treatments, through in vitro or in vivo means can give a higher priority to these SNPs rather than considering the massive pool of all SNPs of TPMT.

Physicochemical characterization of paclitaxel prodrugs with cytochrome 3A4 to correlate solubility and bioavailability implementing molecular docking and simulation studies.

Prodrugs are biologically inactive drug molecules that may be developed through rational drug design with an objective to improve a drug's pharmaceutical and pharmacokinetic properties. Paclitaxel, a highly potent anticancer drug, is directed against many cancers like breast cancer, ovarian cancer, lung cancer, head and neck tumors, non-small cell lung cancer, and Kaposi's sarcoma, etc. Along with its excellent antitumor activity the drug had a major limitation of low water solubility. To overcome this limitation of this nanomolar active drug many prodrugs were formed in the past. Though increase in the solubility of the drug was obtained but that may or may not account for its increase in bioavailability. CYP3A4 liver enzymes are responsible for the metabolism of fifty percent of the drugs and are major metabolizing enzyme for paclitaxel. Phosphate prodrugs are well known to account the insolubility of many drugs and thus increasing their bioavailability also. In this study, we calculated the ADMET properties of a dataset of twenty phosphate prodrugs of paclitaxel. On the basis of reflection of three favourable properties, ten prodrugs were chosen for further docking studies against CYP3A4. Finally, three prodrugs showing unfavourable binding affinities were selected for Molecular Dynamics Simulations and from this in-silico study we identified that all the three selected prodrugs were unstable as compared to the paclitaxel. The instability of these prodrugs showed their lesser interaction with the CYP3A4 and hence contributing more towards its bioavailability. Thus the three suggested prodrugs those were studied in-silico for oral bioavailability can be further validated for gastrointestinal cancer.Communicated by Ramaswamy H. Sarma.

Identification of small molecules against cyclin dependent kinase-5 using chemoinformatics approach for Alzheimer's disease and other tauopathies.

Alzheimer's disease (AD) is a multifactorial complex and wide spreading global disease. It is a chronic neurodegenerative disorder characterized by amyloid beta (Aß) and neurofibrillary tangles (NFTs). Several enzymes are involved in which CDK5 is a major tau phosphorylation enzyme. We have screened (n = 5,36,801) compounds against CDK5 and 392 compounds were selected for pharmacokinetics analysis. In the pharmacokinetics analysis, various descriptors were used for filtering the compounds. After that 16 compounds with the control compound Z3R were employed for the redocking using Autodock Vina and Autodock. Lastly, four compounds were selected and employed for 100 ns MDS studies. On the basis of various MD analysis like RMSD, RMSF, Rg, SASA, Number of hydrogen bonds, Principal component analysis and binding free energy (CDK5-ZINC6261568: -129.50 kJ.mol-1 and CDK5-ZINC14168360: -191.16 kJ.mol-1), we have found that ZINC6261568 and ZINC14168360 can act as a lead compound against the CDK5.Communicated by Ramaswamy H. Sarma.

Disruption of DYRK1A-induced hyperphosphorylation of amyloid-beta and tau protein in Alzheimer's disease: An integrative molecular modeling approach.

Alzheimer's disease (AD) is a neurological disorder caused by the abnormal accumulation of hyperphosphorylated proteins. Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a dual phosphorylation enzyme which phosphorylates the amyloid-ß (Aß) and neurofibrillary tangles (NFTs). A high throughput virtual screening approach was applied to screen a library of 98,071 compounds against DYRK1A using different programs including AutoDock Vina, Smina, and idock. Based on the binding affinities, we selected 330 compounds for absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. Various pharmacokinetics parameters were predicted using the admetSAR server, and based on the pharmacokinetics results, 14 compounds were selected for cross-docking analysis using AutoDock. Cross-docking analysis revealed four compounds, namely, ZINC3843365 (-11.07 kcal/mol-1), ZINC2123081 (-10.93 kcal/mol-1), ZINC5220992 (-10.63 kcal/mol-1), and ZINC68569602 (-10.35 kcal/mol-1), which had the highest negative affinity scores compared to the 10 other molecules analyzed. Density functional theory (DFT) analysis was conducted for all the four top-ranked compounds. The molecular interaction stability of these four compounds with DYRK1A has been evaluated using molecular dynamics (MD) simulations on 100 nanoseconds followed by principal component analysis (PCA) and binding free energy calculations. The Gibbs free energy landscape analysis suggested the metastable state and folding pattern of selected docking complexes. Based on the present study outcome, we propose four antagonists, viz., ZINC3843365, ZINC2123081, ZINC5220992, and ZINC68569602 as potential inhibitors against DYRK1A and to reduce the amyloid-ß and neurofibrillary tangle burden. These screened molecules can be further investigated using a number of in vitro and in vivo experiments.

Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study.

Disease-associated single nucleotide polymorphisms (SNPs) alter the natural functioning and the structure of proteins. Glutamic-oxaloacetic transaminase 1 (GOT1) is a gene associated with multiple cancers and neurodegenerative diseases which codes for aspartate aminotransferase. The present study involved a comprehensive in-silico analysis of the disease-associated SNPs of human GOT1. Four highly deleterious nsSNPs (L36R, Y159C, W162C and L345P) were identified through SNP screening using several sequence-based and structure-based tools. Conservation analysis and oncogenic analysis showed that most of the nsSNPs are at highly conserved residues, oncogenic in nature and cancer drivers. Molecular dynamics simulations (MDS) analysis was performed to understand the dynamic behaviour of native and mutant proteins. PTM analysis revealed that the nsSNP Y159C is at a PTM site and will mostly affect phosphorylation at that site. Based on the overall analyses carried out in this study, L36R is the most deleterious mutation amongst the aforementioned deleterious mutations of GOT1.

High-throughput screening of natural compounds and inhibition of a major therapeutic target HsGSK-3ß for Alzheimer's disease using computational approaches.

BACKGROUND: Alzheimer's disease is a leading neurodegenerative disease worldwide and is the 6th leading cause of death in the USA. AD is a very complex disease and the drugs available in the market cannot fully cure it. The glycogen synthase kinase 3 beta plays a major role in the hyperphosphorylation of tau protein which forms the neurofibrillary tangles which is a major hallmark of AD. In this study, we have used a series of computational approaches to find novel inhibitors against GSK-3ß to reduce the TAU hyperphosphorylation. RESULTS: We have retrieved a set of compounds (n=167,741) and screened against GSK-3ß in four sequential steps. The resulting analysis of virtual screening suggested that 404 compounds show good binding affinity and can be employed for pharmacokinetic analysis. From here, we have selected 20 compounds those were good in terms of pharmacokinetic parameters. All these compounds were re-docked by using Autodock Vina followed by Autodock. Four best compounds were employed for MDS and here predicted RMSD, RMSF, Rg, hydrogen bonds, SASA, PCA, and binding-free energy. From all these analyses, we have concluded that out of 167,741 compounds, the ZINC15968620, ZINC15968622, and ZINC70707119 can act as lead compounds against HsGSK-3ß to reduce the hyperphosphorylation. CONCLUSION: The study suggested three compounds (ZINC15968620, ZINC15968622, and ZINC70707119) have great potential to be a drug candidate and can be tested using in vitro and in vivo experiments for further characterization and applications.

Virtual screening, pharmacokinetics, molecular dynamics and binding free energy analysis for small natural molecules against cyclin-dependent kinase 5 for Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and characterized by brain cell death, memory loss and is the most common form of dementia. Although AD has devastating effects, however, drugs which can treat the AD remain limited. The cyclin-dependent kinase 5 (CDK5) has been recognized as being involved in the pathological hyperphosphorylation of tau protein, which leads to the formation of neurofibrillary tangles (NFTs). We utilized the structure-based virtual screening (SBVS) approach to find the potential inhibitors against HsCDK5. The natural compound subset from the ZINC database (n = 167,741) was retrieved and screened by using SBVS method. From here, we have predicted 297 potent inhibitors. These 297 compounds were evaluated through their pharmacokinetic properties by ADMET (absorption, distribution, metabolism, elimination/excretion and toxicity) descriptors. Finally, 17 compounds were selected and used for re-docking. After the refinement by molecular docking and by using drug-likeness analysis, we have identified four potential inhibitors (ZINC85877721, ZINC96114862, ZINC96115616 and ZINC96116231). All these four ligands were employed for 100 ns MDS study. From the root mean square deviation (RMSD), root mean square fluctuation (RMSF), Rg, number of hydrogen bonds, solvent accessible surface area (SASA), principal component analysis (PCA) and binding free energy analysis we have found that out of four inhibitors ZINC85877721 and ZINC96116231 showed good binding free energy of -198.84 and -159.32 kJ.mol-1, respectively, and also good in other structural analyses. Both compounds displayed excellent pharmacological and structural properties to be the drug candidates. Collectively, these findings recommend that two compounds have great potential to be a promising agent against AD to reduce the CDK5 induced hyperphosphorylation and could be considered as therapeutic agents for the AD.Communicated by Ramaswamy H. Sarma.

ABCD: Alzheimer's disease Biomarkers Comprehensive Database.

Alzheimer's disease (AD) is an age-related, non-reversible, and progressive brain disorder. Memory loss, confusion, and personality changes are major symptoms noticed. AD ultimately leads to a severe loss of mental function. Due to lack of effective biomarkers, no effective medication was available for the complete treatment of AD. There is a need to provide all AD-related essential information to the scientific community. Our resource Alzheimer's disease Biomarkers Comprehensive Database (ABCD) is being planned to accomplish this objective. ABCD is a huge collection of AD-related data of molecular markers. The web interface contains information concerning the proteins, genes, transcription factors, SNPs, miRNAs, mitochondrial genes, and expressed genes implicated in AD pathogenesis. In addition to the molecular-level data, the database has information for animal models, medicinal candidates and pathways involved in the AD and some image data for AD patients. ABCD is coupled with some major external resources where the user can retrieve additional general information about the disease. The database was designed in such a manner that user can extract meaningful information about gene, protein, pathway, and regulatory elements based search options. This database is unique in the sense that it is completely dedicated to specific neurological disorder i.e. AD. Further advance options like AD-affected brain image data of patients and structural compound level information add values to our database. Features of this database enable users to extract, analyze and display information related to a disease in many different ways. The database is available for academic purpose and accessible at http://www.bioinfoindia.org/abcd.

Identification of novel small molecules against GSK3ß for Alzheimer's disease using chemoinformatics approach.

Alzheimer's disease is a rapidly increasing neurodegenerative disease. It is a multifactorial disease and also a global threat. Several enzymes are implicated in the disease in which Glycogen Synthase Kinase 3 beta is a key enzyme to increase the disease progression by the hyperphosphorylation of the tau protein. We have used an integrative chemoinformatics and pharmacokinetics approach for the identification of novel small molecules. We have retrieved a subset from the ZINC database (n = 5,36,709) and screened against GSK3ß in four steps. From here top 298 potent compounds were selected and employed for their pharmacokinetics analysis. We had seen that 29 compounds showed the key characteristics to be a novel drug candidate therefore, all these compounds were employed for redocking studies using Autodock Vina and Autodock. This analysis revealed that four compounds were showing good binding affinity. All these four compounds were employed for MDS analysis of 100 ns From here using a bunch of MD analyses we have found that out of four compounds GSK3ß-ZINC21011059 and GSK3ß-ZINC21011066 act as a stable protein-ligand complex. Therefore we proposed ZINC21011059 and ZINC21011066 can serve as a novel compounds against GSK3ß and predicted scaffold can be used for further optimization towards the improvement of isoform selectivity, and warranting further investigations towards their in vitro and in vivo validation of the bioactivity.

Gene Expression Studies to Identify Significant Genes in AR, MTOR, MAPK Pathways and their Overlapping Regulatory Role in Prostate Cancer.

Gene expression studies revealed a large degree of variability in gene expression patterns particularly in tissues even in genetically identical individuals. It helps to reveal the components majorly fluctuating during the disease condition. With the advent of gene expression studies many microarray studies have been conducted in prostate cancer, but the results have varied across different studies. To better understand the genetic and biological regulatory mechanisms of prostate cancer, we conducted a meta-analysis of three major pathways i.e. androgen receptor (AR), mechanistic target of rapamycin (mTOR) and Mitogen-Activated Protein Kinase (MAPK) on prostate cancer. Meta-analysis has been performed for the gene expression data for the human species that are exposed to prostate cancer. Twelve datasets comprising AR, mTOR, and MAPK pathways were taken for analysis, out of which thirteen potential biomarkers were identified through meta-analysis. These findings were compiled based upon the quantitative data analysis by using different tools. Also, various interconnections were found amongst the pathways in study. Our study suggests that the microarray analysis of the gene expression data and their pathway level connections allows detection of the potential predictors that can prove to be putative therapeutic targets with biological and functional significance in progression of prostate cancer.

Computational and In-Vitro Validation of Natural Molecules as Potential Acetylcholinesterase Inhibitors and Neuroprotective Agents.

BACKGROUND: Cholinesterase inhibitors are the first line of therapy for the management of Alzheimer's disease (AD), however, it is now established that they provide only temporary and symptomatic relief, besides, having several inherited side-effects. Therefore, an alternative drug discovery method is used to identify new and safer 'disease-modifying drugs'. METHODS: Herein, we screened 646 small molecules of natural origin having reported pharmacological and functional values through in-silico docking studies to predict safer neuromodulatory molecules with potential to modulate acetylcholine metabolism. Further, the potential of the predicted molecules to inhibit acetylcholinesterase (AChE) activity and their ability to protect neurons from degeneration was determined through in-vitro assays. RESULTS: Based on in-silico AChE interaction studies, we predicted quercetin, caffeine, ascorbic acid and gallic acid to be potential AChE inhibitors. We confirmed the AChE inhibitory potential of these molecules through in-vitro AChE inhibition assay and compared results with donepezil and begacestat. Herbal molecules significantly inhibited enzyme activity and inhibition for quercetin and caffeine did not show any significant difference from donepezil. Further, the tested molecules did not show any neurotoxicity against primary (E18) hippocampal neurons. We observed that quercetin and caffeine significantly improved neuronal survival and efficiently protected hippocampal neurons from HgCl2 induced neurodegeneration, which other molecules, including donepezil and begacestat, failed to do. CONCLUSION: Quercetin and caffeine have the potential as "disease-modifying drugs" and may find application in the management of neurological disorders such as AD.

An integrative approach to develop computational pipeline for drug-target interaction network analysis.

Understanding the general principles governing the functioning of biological networks is a major challenge of the current era. Functionality of biological networks can be observed from drug and target interaction perspective. All possible modes of operations of biological networks are confined by the interaction analysis. Several of the existing approaches in this direction, however, are data-driven and thus lack potential to be generalized and extrapolated to different species. In this paper, we demonstrate a systems pharmacology pipeline and discuss how the network theory, along with gene ontology (GO) analysis, co-expression analysis, module re-construction, pathway mapping and structure level analysis can be used to decipher important properties of biological networks with the aim to propose lead molecule for the therapeutic interventions of various diseases.