Biology subjects

Alqarghuli, S.

Publications and source records attributed to Alqarghuli, S..

3 recordsLinked to original sources

bioRxiv2025

Zero-Shot Protein-Ligand Binding Site Prediction from Protein Sequence and SMILES

Accurate identification of protein-ligand binding sites is critical for mechanistic biology and drug discovery, yet performance varies widely across ligand families and data regimes. We present a systematic prediction and evaluation framework that stratifies ligands into three settings, overrepresented (many examples), under-represented (tens of examples; few-shot), and zero-shot (unseen at training). We developed a novel three-stage, sequence-based modeling suite that progressively adds ligand conditioning and zero-shot capability, and used an evaluation frame-work to assess the suite. Stage 1 trains per-ligand predictors using a pretrained protein language model (PLM). Stage 2 introduces ligand-aware conditioning via an embedding table, enabling a single multi-ligand model. Stage 3 replaces the table with a pretrained chemical language model (CLM) operating on SMILES, enabling zero-shot generalization. We show Stage 2 improves Macro F1 on the overrepresented test set from 0.4769 (Stage 1) to 0.5832 and outperforms sequence- and structure-based baselines. Stage 3 attains zero-shot performance (F1 = 0.3109) on 5 612 previously unseen ligands while remaining competitive on represented ligands. Ablations across five PLM scales and multiple CLMs reveal larger PLM backbones consistently increase Macro F1 across all regimes, whereas scaling the CLM yields modest or inconsistent gains, which need further investigation. Our results demonstrate that zero-shot residue-level prediction from sequence and SMILES is feasible and identifies the PLM scale as the dominant lever for further advances. The code is fully open source at GitHub.

bioinformatics↗

bioRxiv2025

Using Autoregressive-Transformer Model for Protein-Ligand Binding Site Prediction

AO_SCPLOWBSTRACTC_SCPLOWAccurate prediction of protein-ligand binding sites is critical for understanding molecular interactions and advancing drug discovery. Existing computational approaches often suffer from limited generality, restricting their applicability to a small subset of ligands, while data scarcity further impairs performance, particularly for underrepresented ligand types. To address these challenges, we introduce a unified model that integrates a protein language model with an autoregressive transformer for protein-ligand binding site prediction. By framing the task as a language modeling problem and incorporating task-specific tokens, our method achieves broad ligand coverage while relying solely on protein sequence input. We systematically analyze ligand-specific task token embeddings, demonstrating that they capture meaningful biochemical properties through clustering and correlation analyses. Furthermore, our multi-task learning strategy enables effective knowledge transfer across ligands, significantly improving predictions for those with limited training data. Experimental evaluations on 41 ligands highlight the models superior generalization and applicability compared to existing methods. This work establishes a scalable generative AI framework for binding site prediction, laying the foundation for future extensions incorporating structural information and richer ligand representations. The code, model, and datasets are available at this link.

bioinformatics↗

bioRxiv2025

Extending Prot2Token: Aligning Protein Language Models for Unified and Diverse Protein Prediction Tasks

AO_SCPLOWBSTRACTC_SCPLOWComprehensive protein function and property prediction remains a major challenge due to the vast diversity of sequences, structural variations, and limited labeled data. Existing models are often specialized to be task-specific, requiring independent training, which limits scalability. To address this, we extend Prot2Token, a unified autoregressive framework that focuses on the post-training alignment of pre-trained protein language models (PLMs), to new applications. Our approach enables next-token prediction across new applications of proteinprediction tasks, including protein-protein structure similarity, 3D structure prediction, mutation stability, post-translational modifications (PTMs), substratekinase phosphorylation sites, protein-protein affinity, and protein-ion binding sites. We introduce a self-supervised pre-training stage for the decoder, enhancing model initialization and improving downstream predictions. By integrating a causal autoregressive transformer with a pre-trained ESM-2 encoder, our model effectively aligns diverse protein tasks within a single framework. Additionally, we discuss the opportunities and limitations of this approach, providing insights for future research in optimizing PLMs as a general tool for broader biological applications. Code is available on GitHub Repository.

bioinformatics↗