Biology subjects

Wahid, A.

Publications and source records attributed to Wahid, A..

5 recordsLinked to original sources

bioRxiv2026

Immunoinformatics-Guided Design and In Silico Evaluation of a Multi-Epitope Vaccine Against Influenza A H10N5 and H3N2 Strains Based on Hemagglutinin and Neuraminidase Proteins

Influenza A viruses H3N2 and H10N5 represent, respectively, a persistently dominant seasonal pathogen and a newly documented zoonotic threat with the latter strain variants responsible for the first confirmed human fatality in January 2024, yet no vaccine platform currently addresses co-protection against both subtypes within a unified immunogen. We report here the immunoinformatics based vaccine design and multi-layered computational validation of a 419-amino-acid multi-epitope subunit vaccine construct targeting conserved hemagglutinin (HA) and neuraminidase (NA) antigens identified through multiple sequence alignment of the avian H10N5 (A/swine/Hubei/10/2008) and H3N2 human reference strain sequences to identify viral agents undergoing mammalian adaptations. Linear B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were predicted using ABCpred, BCEpred, BepiPred 2.0, NetMHCpan 2.1, and NetMHCpan 4.0, then filtered through VaxiJen 3.0, AllerTOP v2.1, and ToxinPred to retain only antigenic, non-allergenic, non-toxic candidates. The final construct, incorporating an avian {beta}-defensin N-terminal adjuvant with GPGPG, AAY, and EAAAK linkers, exhibited a molecular weight of 43.9 kDa, instability index of 31.15, and SOLPro solubility probability of 0.763. Tertiary structure modeling via I-TASSER and GalaxyRefine achieved 84.4% Ramachandran-favored residues. Molecular docking against TLR3 and TLR7 yielded binding free energies of -16.1 and -16.8 kcal/mol with picomolar dissociation constants. Molecular dynamics simulations confirmed complex stability over extended trajectories. Furthermore, codon optimization produced a Codon Adaptation Index of 1.0 for E. coli K12 expression. In silico immune simulation demonstrated robust activation of humoral and cellular immunity including elevated IgG1, IgM, IFN-{gamma}, IL-2, rapid NK cell expansion, and broad B-cell clonal diversity. These findings establish a computationally validated candidate capable of providing protection against influenza in multiple host organisms, warranting experimental advancement.

bioinformatics↗

bioRxiv2026

MetaResNet: Enhancing Microbiome-Based Disease Classification through Colormap Optimization and Imbalance Handling

Image-based representations of metagenomic data enable convolutional neural network (CNN) applications for microbiome disease classification. However, the impact of colormap selection on model performance remains unexplored. Current approaches arbitrarily select visualization parameters despite evidence that colormap choices can suppress minority-class features in imbalanced microbiome datasets. This study systematically evaluates colormap effects on metagenomic disease classification to establish evidence-based visualization guidelines. We developed MetaResNet, a custom CNN architecture incorporating residual blocks and attention mechanisms, to assess five colormap schemes (Jet, YlGnBu, Reds, Paired and nipy spectral) across four benchmark datasets: inflammatory bowel disease (n=110), colon cancer (n=121), women type 2 diabetes (n=96), and obesity (n=253). Class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE) versus class weighting strategies. Custom data augmentation preserved taxonomic abundance relationships while enhancing generalization. Performance evaluation employed F1-score, Receiver Operating Characteristic and Area Under the Curve (AUC-ROC), Matthews correlation coefficient (MCC), precision, and recall to address accuracy limitations in imbalanced scenarios. Results identified the Jet colormap coupled with SMOTE as the optimal global configuration, maximizing signal retention and achieving peak performance (AUC 1.00 in Colon). SMOTE significantly improved minority-class recall over class weighting (0.81 {+/-} 0.09 vs. 0.69 {+/-} 0.11, p = 0.003). MetaResNet achieved performance comparable to current state-of-the-art frameworks, while statistically outperforming established deep learning baselines (e.g., DeepMicro, PopPhy-CNN; p = 0.025) in discriminatory power (AUC), with peak values exceeding 0.96. These findings demonstrate that visualization efficacy is strategy-dependent, establishing MetaResNet as a robust framework for microbiome-based diagnostics that supports evidence-based visualization strategies for precision medicine.

bioinformatics↗

bioRxiv2026

DynaBiomeX: An Interpretable Dual-Strategy Deep Learning Framework for Architectural Noise Filtration in Sparse Longitudinal Microbiome Data

ObjectiveLongitudinal microbiome datasets present unique challenges due to extreme sparsity, zero-inflation, and non-stationary behavior. Conventional Recurrent Neural Networks (RNNs) struggle to distinguish structural from sampling zeros in these contexts, limiting their utility for Clinical Decision Support (CDS). MethodsWe introduce DynaBiomeX, an interpretable framework specifically developed for sparse biomedical time-series. It integrates Stacking Ensembles (Bi-LSTM, GRU) with an adapted Temporal Fusion Transformer (TFT) in a unified Screener-Sentinel workflow. The Ensembles optimize collective decision boundaries to maximize sensitivity and minimize missed cases. Concurrently, the TFT functions as a Physiological Gatekeeper, utilizing Gated Residual Networks (GRN) to actively filter stochastic noise from real biological signals. We validated this approach on a multi-modal dataset of 1,871 hematopoietic cell transplantation (HCT) patients to detect gut dysbiosis. ResultsStacking ensembles maximized discriminative performance (ROC-AUC = 0.912), effectively serving as high-sensitivity screeners. In contrast, the Adapted TFT functioned as a precision Sentinel, achieving zero false positives (Precision = 1.0) and high stability (MCC = 0.646). Crucially, the TFT demonstrated superior probabilistic reliability with a low Expected Calibration Error (ECE = 0.0085), addressing the "black-box" overconfidence typical of deep learning models. Ablation studies confirmed predictive robustness even without clinical covariates (ROC-AUC > 0.81). ConclusionDynaBiomeX couples sensitive screening with precise, calibrated validation to robustly analyze sparse longitudinal data. Validated on microbiome dysbiosis, this framework offers a scalable template for zero-inflated domains like single-cell sequencing and EHR monitoring. HighlightsO_LIDynaBiomeX integrates Stacking Ensembles and Temporal Fusion Transformers for microbiome risk stratification. C_LIO_LIGated Residual Networks distinguish stochastic sampling zeros from structural zeros in sparse ASV data. C_LIO_LIA novel Screener-Sentinel workflow resolves the trade-off between surveillance sensitivity and diagnostic precision. C_LIO_LIClinical metadata functions as a physiological gatekeeper, filtering noise to achieve zero false positives (MCC 0.646). C_LIO_LIThe framework minimizes alarm fatigue in Auto-FMT decision support by validating latent dysbiosis signals. C_LI

bioinformatics↗

bioRxiv2019

A switch in cilia-mediated Hedgehog signaling controls muscle stem cell quiescence and cell cycle progression

Tissue homeostasis requires a tight control of stem cells to maintain quiescence in normal conditions, and ensure a balance between progenitor cell production and the need to preserve a stem cell pool in repair conditions. Using ex-vivo and in-vivo genetic approaches, we provide evidence that primary cilium-mediated repressive Hedgehog (Hh) signalling is required to maintain skeletal muscle stem cells (MuSCs) in a quiescent state. De-repression and further activation of Hh signalling initiates MuSC entry and progression through the cell cycle, and controls self-renewal to ensure efficient repair of injured muscles. We propose a model whereby disassembly of primary cilia upon MuSC activation induces a switch in Hh signalling from a repressive to active state that controls exit from quiescence. Positive Hh response in bi-potential muscle progenitor cells regulates also cell cycle progression and drives MuSC self-renewal. These findings identify Hh signalling as a major regulator of MuSC activity. HighlightsO_LICilia-containing quiescent MuSCs are Hh signalling suppressed C_LIO_LIMuSC activation coincides with a switch to active Hh signalling C_LIO_LISmo mutation delays cell cycle entry and progression, and causes impaired self-renewal C_LIO_LIPtch1 mutation promotes exit from quiescence, rapid cell cycle and increased self-renewal C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=147 HEIGHT=200 SRC="FIGDIR/small/884601v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1cbaa81org.highwire.dtl.DTLVardef@273898org.highwire.dtl.DTLVardef@f4b26corg.highwire.dtl.DTLVardef@edda2c_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2018

Utilisation of staphylococcal immune evasion protein Sbi as a novel vaccine adjuvant

Co-ligation of the B cell antigen receptor with complement receptor 2 on B-cells via a C3d-opsonised antigen complex significantly lowers the threshold required for B cell activation. Consequently, fusions of antigens with C3d polymers have shown great potential in vaccine design. However, these linear arrays of C3d multimers do not mimic the natural opsonisation of antigens with C3d. Here we investigate the potential of using the unique complement activating characteristics of Staphylococcal immune-evasion protein Sbi to develop a pro-vaccine approach that spontaneously coats antigens with C3 degradation products in a natural way. We show that Sbi rapidly triggers the alternative complement pathway through recruitment of complement regulators, forming a tripartite complex that acts as a competitive antagonist of factor H, resulting in enhanced complement consumption. These functional results are corroborated by the structure of this complement activating Sbi-III-IV:C3d:FHR-1 complex. Finally, we demonstrate that Sbi, fused with Mycobacterium tuberculosis antigen Ag85b, causes efficient opsonisation with C3 fragments, thereby enhancing the immune response significantly beyond that of Ag85b alone, providing proof of concept for our pro-vaccine approach.

biochemistry↗