Biology subjects

Sakib, M. N.

Publications and source records attributed to Sakib, M. N..

9 recordsLinked to original sources

bioRxiv2026

Structure-Guided Design and Dynamic Evaluation of VP4-Targeting siRNAs Against Rotavirus A

Rotavirus is a major cause of severe diarrheal disease in children under the age of five, with reduced vaccine effectiveness in low-resource settings causing substantial morbidity and mortality. In the absence of approved antiviral therapeutics, treatment is largely supportive, urging the need for targeted and precision-based interventions. VP4 protein plays an essential role in viral attachment, entry, and infectivity, making it a suitable target for targeted therapy. In this context, RNA interference is a specific method for inhibiting viral gene expression with its efficacy depending on sequence conservation, target accessibility, and compatibility with the RISC-loading machinery. In the present study, an integrative in silico approach was employed to design and evaluate siRNAs targeting conserved regions of the VP4 gene across six geographically diverse countries. Candidate siRNAs were screened using established design rules and regression-based scoring with off-target filtering. Three optimized siRNAs were further assessed through structural modeling, molecular docking, and molecular dynamics simulations to examine interactions with human Dicer, TRBP, and Argonaute-2. Comparative dynamic analyses identified one siRNA with enhanced structural compatibility, reduced conformational fluctuations, and stable interactions with RISC-loading proteins. These findings provide a rational computational basis for VP4-targeted siRNA development, facilitating experimental validation.

bioinformatics↗

bioRxiv2025

Linking Genomic Landscape to Disease Mechanism: Core Genetic Factors Underlying Pathogenesis and Antimicrobial Resistance in Diarrheal Pathogens

BackgroundDiarrheal diseases remain a major global health burden, as they severely affect children, particularly in Bangladesh. After decades of research, the molecular mechanisms of diarrheal pathogens for disease pathogenesis and antibiotic resistance are still unknown, notably in Gram-negative bacteria. This pilot study fills the gap by employing whole genome sequencing and pan-genome analysis to analyze Bangladeshi diarrheal pathogens to identify genetic variables that cause disease pathogenesis and antibiotic resistance. ResultsHence, we investigated the genetic diversity of bacterial isolates from 31 clinical stool samples by a combination of whole-genome sequencing (WGS) and pan-genomic analysis. A core group of 50 genes, conserved across a significant number of strains, was identified via pan-genomic analysis, with considerable variation in accessory genes. This signifies a significant degree of genetic flexibility. Gene ontology analysis yielded substantial insights into prospective therapeutic targets by emphasizing the critical function of these core genes in bacterial survival and pathogenicity. Furthermore, the findings of the antimicrobial susceptibility test (AST) revealed concerning resistance trends, particularly to fluoroquinolones and beta-lactams, underscoring the necessity for enhanced surveillance and alternative therapeutic approaches. ConclusionThis study provides a comprehensive genetic framework to improve understanding of the complexity of diarrheal infections and the mechanisms underlying their resistance, fostering opportunities for potential therapeutic advancements.

microbiology↗

bioRxiv2025

APDeeM: A machine Learning strategy towards Effective Peptide Vaccine Candidates Identification against Different Types of Viruses

Viral infections pose significant global health challenges, underscoring the urgent need for improved medications. Nevertheless, traditional medicinal approaches depend significantly on labor-intensive laboratory tests, which impede efficient identification and prolong vaccine development, particularly when screening a huge number of samples. To address these obstacles, we present a comprehensive Antiviral Peptide (AVP) Detection Dataset, comprising 14 unique features to improve the characterization of antiviral and non-antiviral peptides. Subsequently, we introduce the Antiviral Peptide detection enhanced by Ensemble Machine Learning (APDeeM) system. This advanced computational framework considerably reduces the time required for AVP detection by utilizing ensemble learning methodologies. The APDeeM system incorporates Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), and AdaBoost algorithms to facilitate the swift selection of AVP candidates without requiring urgent laboratory testing. Our proposed ensemble methodology showed superior performance, with an accuracy of 85.99%, F1 score of 87.60%, recall of 88.91%, and precision of 86.32%, exceeding the efficacy of all tested antiviral peptide prediction models in this research. The APDeeM approach signifies a substantial improvement over conventional detection techniques, expediting the identification of prospective vaccine candidates and facilitating the advancement of more effective antiviral peptides. The most promising AVP candidates may urge laboratory validation, optimize resources, and accelerate vaccine development.

bioinformatics↗

bioRxiv2024

Dissecting Breast Cancer Heterogeneity Through Transcriptomics Insights of Diverse Etiological Factors for Common Biomarker Discovery

Breast cancer has many different causes, and the key to finding effective treatments is understanding the diseases heterogeneity. The present study used three gene expression datasets from 110 female samples related to stress, drug and hormonal imbalance, diet and nutrition, and physical activity and light exposure at night to predict differential gene expression. Interestingly, all gene expression datasets shared 22 upregulated and 4 downregulated genes, regardless of etiology. This suggests these genes share the core molecular mechanism and the biological pathway that causes breast cancer. Notably, these genes were significantly enriched in some important pathways, including cycle regulation, endoplasmic reticulum stress, and transcriptional regulation, demonstrating their potential as therapeutic targets. Further, we found UBE2J2 from upregulated genes and ZCCHC7 from downregulated genes as the top hub and bottleneck genes, which may help network connectivity and functional gene interactions. Computational study further asserted the strong binding affinity of drug-target complexes. Later, molecular dynamics simulations confirmed the predicted drug-target complexes stability and dynamic behavior, demonstrating these two genes as potential therapeutic targets. The findings from this analysis provide the molecular basis into the complex interplay between diverse etiologic factors and breast cancer pathogenesis, paving the way for innovative biomarker-targeted therapies.

bioinformatics↗

bioRxiv2024

Unveiling Novel Hyperglycosylated Analog of Human Erythropoietin: A Comprehensive Computational Exploration

IntroductionThe capability of human erythropoietin (hEPO) to treat anemia by stimulating erythropoiesis, has made it become one of the worlds leading biopharmaceuticals. An effective way to improve the efficiency of recombinant hEPO drugs is increasing glycosylation which can increase its serum half-life and thereby its in vivo biological activity. In this context, the objective of the present study was to explore potential glycosylation sites in hEPO to develop new hyperglycosylated analogous drug. Materials and MethodsA rational computational strategy was executed to select compatible region of hEPO for inserting N-linked glycosylation consensus motif (N-X-S/T), thus designing a number of analogs. The 3D models of the analogs were constructed by homology modeling and validated by various in silico tools. The probability of glycosylation was checked and hyperglycosylated models of the selected analogs were developed. Molecular docking and molecular dynamics simulation were performed to select the best analog. ResultsAmong 40 analogs designed in this study, Analog-71.1 showed most promising results. Four possible sites of this analog showed good probability for glycosylation. According to the molecular docking study, the analog obtained high docking score (2313.711) and formed highest number of (25) hydrogen bonds with erythropoietin receptors, which indicated strong and stable interaction with the receptors. Moreover, the steady trajectory of RMSD, Rgyr, RMSF and other findings obtained from MD simulation confirmed the structural stability of the analog. ConclusionsOur overall study has selected Analog-71.1 as a potential candidate to develop new analogous drug with enhanced efficiency, which needs further experimental analyses.

bioinformatics↗

bioRxiv2024

Mapping the PTEN Mutation Landscape: Structural and Functional Drivers of Lung Cancer

Lung cancer is the predominant form of cancer globally, arising from the dysfunction of genetic mutations. Although PTEN mutation is crucial in the aetiology of lung cancer, the mapping of these major drivers has to be determined. We leverage computational algorithms on 43,855 SNPs of PTEN to discover the mutational impact contributing to lung cancer. Fifteen variations were identified as detrimental, and no pertinent studies have previously addressed their structural and functional aspects. Notably, seven variations were identified as the most significant contributors to lethal effects in functional aberration, as demonstrated by the computational assessment. Subsequently, molecular simulation elucidated the structural instability associated with these alterations. Furthermore, drug binding experiments at the mutational site corroborated the destabilization experiments by demonstrating the conformational alteration of the structure, resulting in varied amino acid interactions. In summary, the present study elucidates the influence of mutations in PTEN structure on its functional architecture.

cancer biology↗

bioRxiv2023

Repurposing Remdesivir for COVID-19: Computational Drug Design Targeting SARS-CoV-2 RNA Polymerase and Main Protease using Molecular Dynamics Approach

The coronavirus disease of 2019 (COVID-19) is a highly contagious respiratory illness that has become a global health crisis with new variants, an unprecedented number of infections, and deaths and demands urgent manufacturing of potent therapeutics. Despite the success of vaccination campaigns around the globe, there is no particular therapeutics approved to date for efficiently treating infected individuals. Repositioning or repurposing previously effective antivirals against RNA viruses to treat COVID-19 patients is a feasible option. Remdesivir is a broad-spectrum antiviral drug that the Food and Drug Administration (FDA) licenses for treating COVID-19 patients who are critically ill patients. Remdesivirs low efficacy, which has been shown in some clinical trials, possible adverse effects, and dose-related toxicities are issues with its use in clinical use. Our study aimed to design potent derivatives of remdesivir through the functional group modification of the parent drug targeting RNA-dependent RNA polymerase (RdRp) and main protease (MPro) of SARS-CoV-2. The efficacy and stability of the proposed derivatives were assessed by molecular docking and extended molecular dynamics simulation analyses. Furthermore, the pharmacokinetic activity was measured to ensure the safety and drug potential of the designed derivatives. The derivatives were non-carcinogenic, chemically reactive, highly interactive, and stable with the target proteins. D-CF3 is one of the designed derivatives that finally showed stronger interaction than the parent drug, according to the docking and dynamics simulation analyses, with both target proteins. However, in vitro and in vivo investigations are guaranteed to validate the findings in the future.

bioinformatics↗

bioRxiv2023

Accelerating Cancer Vaccine Development for Human T-Lymphotropic Virus (HTLV) Using a High-Throughput Molecular Dynamics Approach

Human T-lymphotropic virus (HTLV), a retrovirus belonging to the oncovirus family, has long been linked to be associated with various inflammatory and immunosuppressive disorders. To combat the devastating impact of this virus, our study employed a reverse vaccinology approach to design a multi-epitope-based vaccine targeting the highly virulent subtypes of HTLV. We conducted a comprehensive analysis of the molecular interactions between the vaccine and Toll-like receptors (TLRs), providing valuable insights for future research on preventing and managing HTLV-related diseases and any possible outbreaks. The vaccine was designed by focusing on the envelope glycoprotein gp62, a crucial protein involved in the infectious process and immune mechanisms of HTLV inside the human body. Epitope mapping identified T cell and B cell epitopes with low binding energies, ensuring their immunogenicity and safety. Linkers and adjuvants were incorporated to enhance the vaccines stability, antigenicity, and immunogenicity. Two vaccine constructs were developed, both exhibiting high antigenicity and conferring safety. Vaccine construct 2 demonstrated expected solubility and structural stability after disulfide engineering. Molecular docking analyses revealed strong binding affinity between the vaccine construct 2 and both TLR2 and TLR4. Molecular dynamics simulations indicated that the TLR2-vaccine complex displayed enhanced stability, compactness, and consistent hydrogen bond formation, suggesting a favorable affinity. Contact analysis, Gibbs free energy landscapes, and DCC analysis further supported the stability of the TLR2-vaccine complex, while DSSP analysis confirmed stable secondary structures. MM-PBSA analysis revealed a more favorable binding affinity of the TLR4-vaccine complex, primarily due to lower electrostatic energy. In conclusion, our study successfully designed a multi-epitope-based vaccine targeting HTLV subtypes and provided valuable insights into the molecular interactions between the vaccine and TLRs. These findings should contribute to the development of effective preventive and treatment approaches against HTLV-related diseases.

bioinformatics↗

bioRxiv2020

Effects of prefrontal theta burst stimulation on neuronal activity and subsequent eating behavior: an interleaved rTMS and fNIRS study

The dorsolateral prefrontal cortex (dlPFC) and dorsomedial prefrontal cortex (dmPFC) are important nodes for self-control and decision-making, but through separable processes (cognitive control versus evaluative processing). This study aimed to examine the effects of excitatory brain stimulation (intermittent theta-burst stimulation; iTBS) targeting the dlPFC and dmPFC on food choice. iTBS was hypothesized to decrease consumption of appetitive snack foods, via enhanced interference control for dlPFC stimulation and reduced delay discounting for dmPFC stimulation. Using a single-blinded, between-subjects design, participants (N = 43) were randomly assigned to of the three conditions: 1) iTBS targeting the left dlPFC, 2) iTBS targeting bilateral dmPFC, or 3) sham. Participants then completed two cognitive tasks (delay discounting (DD) and Flanker), followed by a taste test. fNIRS imaging revealed increases in medial PFC activity were evident in the dmPFC stimulation group during the DD task; likewise, a neural efficiency effect was observed in the dlPFC stimulation group during the Flanker. Gender significantly moderated consumption during the taste test, with females in the dmPFC showing paradoxical increases in food consumption compared to sham. Findings are consistent with possible amplification of positive evaluative processing in the presence of dietary restraint, vis-a-vis excitation of the mPFC.

neuroscience↗