Biology subjects

Mollah, M. N. H.

Publications and source records attributed to Mollah, M. N. H..

7 recordsLinked to original sources

bioRxiv2026

FlexAutoDock: A Flexible Platform for Automated Molecular Docking and Virtual Screening of Natural and Synthetic Compounds

Drug discovery (DD) is a complex, time-consuming, and resource-intensive process that involves the identification of therapeutic targets, selection of bioactive compounds, and extensive experimental validation. The discovery of promising therapeutic compounds from large libraries of phytochemicals and synthetic molecules remains a major challenge in modern drug development. Screening millions of compounds through conventional experimental approaches requires substantial time, cost, and computational resources. In recent years, in- silico molecular docking has emerged as an important computational approach for predicting interactions between small molecules and target proteins, thereby helping researchers prioritize promising compounds for further investigation. Several molecular docking webservers, including iScreen, SwissDock, CB-Dock2, DockThor, and MTiOpenScreen, have been developed to support virtual screening studies. However, many currently available platforms still face some important limitations. Most existing tools lack integrated repositories of medicinal plant-derived phytochemicals and organism-derived bioactive compounds, automated mapping between plants and their associated phytochemicals, and flexible ligand retrieval using chemical names, SMILES strings, PubChem CIDs, or drug names. In addition, many platforms require extensive manual protein and ligand preparation, provide limited support for AlphaFold-predicted protein structures, and lack efficient large-scale multi-target virtual screening. Most existing docking platforms offer limited support for interactive inspection of docked protein-ligand complexes, often requiring users to download the results and analyse them using external molecular visualization software. To address these limitations, we developed FlexAutoDock, an automated cloud-based molecular docking platform that provides a unified environment for protein-ligand docking and large-scale virtual screening. Unlike existing web servers, FlexAutoDock integrates curated repositories of medicinal plant- derived phytochemicals, organism-derived bioactive compounds, and synthetic compounds from the ZINC database while supporting flexible ligand acquisition through medicinal plant or organism selection, chemical names, SMILES strings, PubChem CIDs, and drug-name queries. The platform further streamlines the docking workflow through automated protein structure retrieval from the Protein Data Bank and AlphaFold databases, receptor and ligand preparation, chain-specific protein selection, blind and site-specific docking, interactive visualization of predicted protein-ligand complexes, and scalable multi-target virtual screening. The resulting platform enables rapid, flexible, and large-scale virtual screening while simplifying the molecular docking workflow, providing researchers with an accessible computational resource for accelerating early-stage drug discovery. FlexAutoDock offers a fast, reliable, and accessible computational platform for molecular docking and virtual screening, freely available to the scientific community at http://103.99.177.82:3000/.

bioinformatics↗

bioRxiv2026

In silico transcriptomic analysis reveals shared molecular signatures and immune-associated pathways between Hashimotos thyroiditis and type 2 diabetes with exploratory drug repurposing

The management of Hashimotos thyroiditis (HT), one of the most prevalent autoimmune disorders worldwide, becomes more complex when it coexists with type 2 diabetes (T2D) compared with the management of either disease alone. This complexity may arise from overlapping genetic, metabolic, and immune dysregulation, as well as potential therapeutic conflicts. Although HT and T2D are known to co-occur and share immune and metabolic features, the molecular characteristics underlying these overlaps have not been systematically explored. This study aimed to identify shared gene expression signatures and associated biological pathways between HT and T2D using an in silico, hypothesis-generating approach, and to explore candidate compounds that may be relevant to both conditions. Independent transcriptomic datasets (GSE138198 for control/HT and GSE29231 for control/T2D) were analyzed, leading to the identification of 59 genes that were differentially expressed in both HT and T2D compared with control samples. Protein-protein interaction (PPI) network analysis prioritized five shared key genes (sKGs): CDC42, CD74, FOS, RAC2, and YWHAB. Functional enrichment analysis of these sKGs revealed overlapping biological processes, molecular functions, cellular components, and immune-related signaling pathways, as well as shared regulatory networks involving transcription factors (FOXC1 and HNF4A) and microRNAs (hsa-miR-221-3p and hsa-miR-29a-3p). Immune infiltration analysis demonstrated broadly similar patterns of immune dysregulation in both diseases, providing additional biological context for the observed shared molecular signatures. Finally, an exploratory in silico drug repurposing pipeline incorporating molecular docking, ADMET profiling, drug-likeness assessment, and molecular dynamics simulations prioritized three candidate compounds: gliquidone, oleanolic acid, and glipizide for further investigation. Overall, this study provides a hypothesis-generating framework highlighting shared molecular features between HT and T2D, which may inform future experimental validation and clinical research. Author SummaryIn this work, we wanted to better understand why Hashimotos thyroiditis, an autoimmune condition that affects the thyroid gland, is often seen in people who also have type 2 diabetes. Treating patients who live with both conditions can be difficult, and we were interested in finding out whether they share common biological causes. To do this, we examined genetic data from individuals with each disease and looked for patterns that appeared in both groups. We discovered several genes that seem to act in similar ways in the two conditions, particularly genes linked to immune system activity and associated pathways. This finding suggests that shared molecular signatures and immune-associated pathways may play a role in the development of both diseases. We also explored how these shared genetic features influence larger biological processes and immune responses. The similarities we found support the idea that the two diseases may be connected through related biological pathways. In addition, we used computer-based screening methods to identify existing drugs that might influence these shared pathways. While these results need further testing, we hope our findings help open new directions for research and eventually contribute to better care for patients affected by both conditions.

bioinformatics↗

bioRxiv2024

Detection of Potential Phytochemicals against ctxAB Toxin to Combat Cholera

Vibrio cholerae is a gram-negative curved rod-shaped bacterium responsible for cholera, an intestinal infection characterized by severe acute watery diarrhea that can be fatal if untreated. The major virulence factor for Vibrio cholerae is cholera toxin (ctx), a potent toxin which has two subunits, A and B (ctxAB), that are crucial for the progression of the deadly disease. The B subunit is a pentavalent protein that binds to the intestinal mucosa to allow internalization of the A subunit to initiate rice-watery diarrhea. This study explored potential phytochemicals to inhibit the B subunit from binding to the host mucosa by examining their interaction with ctxB. Proteins encoded by the three genotypes of the ctxB gene (ctxB1, ctxB3, and ctxB7) present in the El Tor biotype strains of V. cholerae O1 responsible for the currently ongoing seventh cholera pandemic were targeted. Analysis of 52 phytochemicals obtained from PubChem identified a group of phytochemicals based on their binding affinity scores with the proteins of ctxB genotypes. Assessment of drug-likeliness and toxicity risk highlighted two phytochemicals, Limonin and Emodin, as the best potential candidates for inhibiting the mode of action of cholera toxin. Molecular dynamic (MD) simulation-based MM-PBSA analysis suggested these compounds stably interact with the receptors. Molecular docking and molecular dynamic simulations revealed that these phytochemicals might have potential to inhibit the cholera toxin function and reduce the severity of cholera. Author SummaryCholera toxin (ctx) subunits A and B (ctxAB) play a significant role in the disease, with the B subunit facilitating the binding of the toxin to the intestinal cells, and the A subunit entering the cells, resulting in the cellular changes that lead to watery diarrhea. To inhibit the B subunit of cholera toxin from binding host mucosa, we explored candidate phytochemicals instead of synthetic molecules as drug agents by examining their binding behavior with ctxB using a molecular docking approach. The molecular docking, molecular dynamic simulations, and toxicity analyses identified two phytochemical compounds that have the potential to inhibit the mechanism of cholera toxin.

bioinformatics↗

bioRxiv2023

Genome-wide identification and characterization of major RNAi genes highlighting their regulatory factors in Cowpea (Vigna unguiculata (L.) Walp.)

In different regions of the world, cowpea (Vigna unguiculata (L.) Walp.) is an important vegetable and an excellent source of protein. It lessens the malnutrition of the underprivileged in developing nations and has some positive effects on health, such as a reduction in the prevalence of cancer and cardiovascular disease. However, occasionally, certain biotic and abiotic stresses caused a sharp fall in cowpea yield. Currently, short RNAs (sRNAs) are thought to play a crucial role in controlling how stress-related genes are expressed in plants as they go through various developmental phases. Major RNA interference (RNAi) genes like Dicer-like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) are essential for sRNA synthesis processes and shield plants from biotic and abiotic stresses. In this study, applying BLASTP search and phylogenetic tree analysis with reference to the Arabidopsis RNAi (AtRNAi) genes, we discovered 28 VuRNAi genes, including 7 VuDCL, 14 VuAGO, and 7 VuRDR genes in cowpea. We looked at the domains, motifs, gene structures, chromosomal locations, sub-cellular locations, gene ontology (GO) terms and regulatory factors (transcription factors, micro-RNAs, and cis-acting factors) to characterize the VuRNAi genes and proteins in cowpea. Predicted VuDCL1, VuDCL2(a, b), VuAGO7, VuAGO10 and VuRDR6 genes might have an impact on cowpea growth, development of the vegetative and flowering stages and antiviral defense. The VuRNAi gene regulatory features miR395 and miR396 might contribute to grain quality improvement, immunity boosting, and pathogen infection resistance under salinity and drought conditions. Predicted cis-acting elements (CAEs) from the VuRNAi gene associated with light responsiveness (LR), stress responsiveness (SR) and hormone responsiveness (HR) might play a role in plant growth and development, improving grain quality and production and protecting plants from biotic and abiotic stresses. Therefore, our study provides crucial information about the functional roles of VuRNAi genes and their regulatory components, which would aid in the development of future cowpeas that are more resilient to biotic and abiotic stress.

bioinformatics↗

bioRxiv2020

Bioinformatic analysis based genome-wide identification, characterization, diversification and regulatory transcription components of RNA silencing machinery genes in wheat (Triticum aestivum L.)

Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) gene families are known as RNA silencing machinery genes or RNAi genes. They have important activities at post-transcriptional and chromatin modification levels. They regulate gene expression relating to different stresses, growth, and development in eukaryotes. A complete cycle of gene silencing is occurred by the collaboration of these three families. However, these gene families are not yet rigorously studied in the economically important wheat genome. Our bioinformatic analysis based genome-wide identification, characterization, diversification and regulatory components of these gene families identified 7 TaDCL, 39 TaAGO and 16 TaRDR genes from wheat genome against RNAi genes of Arabidopsis thaliana. Phylogenetic analysis of wheat genome with Arabidopsis and rice RNAi genes showed that TaDCL, TaAGO and TaRDR proteins are clustered into four, eight and four subgroups respectively. Domain, motif and exon-intron structure analyses showed that the TaDCL, TaAGO and TaRDR proteins conserve identical characteristics within groups while retain diverse differences between groups. GO annotations implied that a number of biological and molecular pathways are linked to RNAi mechanism in wheat. Gene networking between transcription factors and RNAi proteins indicates that ERF is the leading family linked to maximum RNAi genes followed by MIKC-MADS, C2H2, BBR-BPC, MYB, and Dof. Cis-regulatory elements associated to RNAi genes are predicted to act as regulatory components against various environmental conditions. Expressed sequence tag analysis showed that larger numbers of RNAi genes are expressed in different tissues and organs predicted to play roles for healthy plants and grains. Expression analysis of 7 TaDCL genes using qRT-PCR showed that only TaDCL3a and TaDCL3b had root specific significant expression (p-value<0.05) with no expression in leaf validated EST results. Besides, TaDCL3b and TaDCL4 significantly prompted in drought condition indicating their potential role in drought stress tolerance. Overall results would however help researchers for in-depth biological investigation of these RNAi genes in wheat crop improvement.

genomics↗

bioRxiv2020

Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

Toxicogenomics combines high throughput molecular technologies with statistical and machine learning approaches to discover a similar group of doses of chemical compounds (DCCs) and genes to explore toxicogenomic biomarkers and their regulatory DCCs. This is also very important in the toxicity study of environmental stressors, synthetic chemicals and drug discovery and development process. Different clustering algorithms are concerned with the discovering of interesting clusters/groups of row or column entities of a dataset. Among those hierarchical clustering (HC) and logistic probabilistic hidden variable model (LPHVM) can identify toxicogenomic biomarkers and their regulatory DCCs forming co-cluster. However, the HC method is very sensitive to outlying observations. On the other hand, though LPHVM is a robust approach, it consumes more time for calculation since it is Expectation-Maximization (EM) based iterative approach. Additionally, the LPHVM creates artificiality problem taking absolute value of the data matrix. Therefore, to overcome these problems in this paper, we proposed a robust hierarchical co-clustering (RHCOC) algorithm to co-cluster genes and DCCs simultaneously with a view to explore toxicogenomic biomarkers and their regulatory DCCs. The performance of the proposed RHCOC algorithm over the conventional HC for clustering genes and DCCs of toxicogenomic data has been investigated based on the simulation study. The results of the simulation study have shown that the RHCOC approaches produce far lower clustering error rate (ER) than the conventional HC approaches in presence of outlying observations in the dataset. Otherwise they perform equally in absence of outlier in the dataset. To explore biomarker co-clusters consisting of toxicogenomic biomarker genes and their regulatory DCCs we used control chart for individual measurement (CCIM). We have also investigated the performance of the proposed approach in the case of the pathway level real life fold change gene expression (FCGE) toxicogenomic data analysis. The biomarker co-clusters consisting of toxicogenomic biomarker genes and their regulatory DCCs and biomarker genes explored by the proposed approaches have been validated by the literature and functional annotation. Our method is implemented in R package "rhcoclust" available on github (https://github.com/mdbahadur/rhcoclust).

bioinformatics↗

bioRxiv2020

In Silico Identification, Characterization and Diversity Analysis of RNAi Genes and their Associated Regulatory Elements in Sweet Orange (Citrus sinensis L)

RNA interference (RNAi) plays key roles in post-transcriptional and chromatin modification levels as well as regulates various eukaryotic gene expressions which involved in stress responses, development and maintenance of genome integrity during developmental stages. The whole mechanism of RNAi pathway is directly involved with the gene-silencing process by the interaction of Dicer-Like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) gene families. However, the genes of these three RNAi families are largely unknown yet in sweet orange (Citrus sinensis), though it is an economically important fruit plant all over the world. Therefore, a comprehensive investigation for genome-wide identification, characterization and diversity analysis of RNA silencing genes in C. sinensis was conducted and identified 4 CsDCL, 8 CsAGO and 4 CsRDR as RNAi genes. To characterize and validate the predicted genes of RNAi families, various bioinformatics analysis was conducted. Phylogenetic analysis clustered the predicted CsDCLs, CsAGOs and CsRDRs genes into four, six and four subgroups with the relevant genes of Arabidopsis respectively. The domain and motif composition analysis, the gene structure for all three-gene families exhibited almost homogeneity within the same group members while showed significant differences in between groups. The GO enrichment analysis results clearly indicated that the predicted genes have direct involvement into the RNAi process as expected in C. sinensis. Moreover, Cis-regulatory elements and regulatory transcription factor analysis of the reported RNAi genes demonstrated the diverse connection to the huge biological functions and regulatory pathways. The expressed sequence tag (EST) analysis showed that these genes are highly expressed in fruit and leaves which indicate that these reported genes have great involvement in C. sinensis food, flowering and fruit production. The expression analysis of the reported RNAi genes might be more useful to explore the most effective RNAi genes in C. sinensis for further biotechnological application.

genomics↗