Biology subjects

Abeer, A. N. M. N.

Publications and source records attributed to Abeer, A. N. M. N..

3 recordsLinked to original sources

bioRxiv2026

An Integrated Computational Antigen Discovery Pipeline with Hierarchical Filtering for Emerging Viral Variants

Emerging and evolving viral diseases, such as SARS-CoV-2, continue to pose significant global health challenges, underscoring the urgent need for rapid and scalable antigen discovery pipelines. This work presents a computational pipeline that integrates diverse computational tools and machine learning models to accelerate the identification and optimization of antigen candidates. The pipeline employs efficient filtering and consensus-based strategies to highlight epitopes with high therapeutic potential. We demonstrate its utility by significantly narrowing the antigen search space for Rift Valley fever virus (RVFV) and Mayaro virus (MAYV), and by effectively identifying conserved neutralizing epitopes in SARS-CoV-2. Our proposed computational antigen pipeline offers a powerful framework for expediting the development of future vaccines and therapeutics in response to emerging pathogens.

bioinformatics↗

bioRxiv2026

Structure-Based TCR-pMHC Binding Prediction and Generalization to Unseen Peptides

The interaction between T-cell receptors (TCRs) with the peptide-bound major histocompatibility complex (MHC) intricately impacts the functional specificity of T-cell-mediated adaptive immune response. Consequently, implication in immunotherapy has contributed to the ever-growing computational methods for TCR recognition, which have recently attracted structure-based approaches due to advancements in protein structure modeling. Despite access to structural information of the predicted binding interface, graph neural network (GNN)-based TCR-pMHC binding specificity classifiers tend to show poor accuracy for samples with unseen peptides. In this work, we comprehensively assess the potential factors that critically impact the generalization performance of classifiers trained with computationally predicted structures. Specifically, our experiments focus on analyzing the sensitivity of such predictors to the interaction features in the TCR-pMHC interface and the structural uncertainty. Building on the analysis, we demonstrate how the design of classifier architecture with auxiliary training objectives can improve the generalization performance to novel peptides not yet seen during model training. Overall, our work highlights the challenges of unseen peptide generalization from different perspectives of the GNN-based classifier paradigm, showcasing the strengths and weaknesses of the current state-of-the-art approaches in the generalization landscape.

bioinformatics↗

bioRxiv2025

Accelerating Antibody Development: Sequence and Structure-Based Models for Predicting Developability Properties through Size Exclusion Chromatography

Experimental screening for biopharmaceutical developability properties typically relies on resource-intensive, and time-consuming assays such as size exclusion chromatography (SEC). This study highlights the potential of in silico models to accelerate the screening process by exploring sequence and structure-based machine learning techniques. Specifically, we compared surrogate models based on pre-computed features extracted from sequence and predicted structure with sequence-based approaches using protein language models (PLMs) like ESM-2. In addition to different end-to-end fine-tuning strategies for PLM, we have also investigated the integration of the structural information of the antibodies into the prediction pipeline through graph neural networks (GNN). We applied these different methods for predicting protein aggregation propensity using a dataset of approximately 1200 Immunoglobulin G (IgG1) molecules. Through this empirical evaluation, our study identifies the most effective in silico approach for predicting developability properties for SEC assays, thereby adding insights to existing screening efforts for accelerating the antibody development process.

bioinformatics↗