Biology subjects

Esmaili, F.

Publications and source records attributed to Esmaili, F..

5 recordsLinked to original sources

bioRxiv2025

Predicting Kinase-Substrate Phosphorylation Site Using Autoregressive Transformer

Accurately predicting kinase-specific phosphorylation sites remains difficult due to the diversity of kinases and the context-dependent nature of substrate recognition. Importantly, aberrant kinase overactivation is a hallmark of many cancers including colorectal, gastric, liver, and breast tumors where dysregulated kinase signaling promotes malignant transformation, tumor progression, and therapy resistance. This underscores the clinical importance of understanding kinase-substrate relationships and precisely mapping phosphorylation events. In this paper, we introduce two complementary sequence-based architectures that operate directly on full-length substrate and kinase sequences. Stage 1 extends a task-agnostic prediction method, named Prot2Token, to jointly support three tasks: kinase-group classification from substrate sequences alone, kinase-substrate interaction prediction, and kinase-specific phosphorylation-site prediction while incorporating a self-supervised decoder pretraining task that predicts amino-acid positions from encoder embeddings. This pretraining substantially strengthens site prediction. Stage 2 specializes the architecture for phosphorylation-site prediction by replacing causal decoding of Prot2Token with a bidirectional one, yielding further gains. On standard benchmarks, the specialized model consistently outperforms widely used baselines. Beyond in-distribution evaluation, across both in-distribution and zero-shot settings of understudied dark kinases, we show the sign of zero-shot kinase-specific phosphorylation-site prediction capability. Together, these results indicate that jointly modeling substrate and kinase sequences provides a straight-forward, scalable approach to state-of-the-art, zero-shot-capable phosphorylationsite prediction.

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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.

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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.

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bioRxiv2024

Comprehensive Evaluation of AlphaFold-Multimer, AlphaFold3 and ColabFold, and Scoring Functions in Predicting Protein-Peptide Complex Structures

Determining the three-dimensional structures of protein-peptide complexes is crucial for elucidating biological processes and designing peptide-based drugs. Protein-peptide docking has become essential for predicting complex structures. AlphaFold-Multimer, ColabFold and AlphaFold3 provided groundbreaking tools to enhance the protein-peptide docking accuracy. This study evaluates these three tools for predicting protein-peptide complex structures using Template-Based (TB) and Template-Free (TF) methods. AlphaFold-Multimer excels in TB predictions and performs moderately in TF scenarios in the prediction pool, but TF outperforms TB in the first-ranked models. ColabFold demonstrates versatility in both TB and TF settings. AlphaFold3 generates high-quality structures for more proteins, but the medium accuracy is not as good as AlphaFold-Multimer using a large model pool. We also assessed the performance of various scoring functions in ranking predicted protein-peptide complex structures. While the scoring function built in AlphaFold demonstrates the best performance, some other scoring functions, e.g., FoldX-Stability and HADDOCK-mdscore, provide complementary values. The findings suggest the potential for enhancing scoring functions targeting AlphaFold-based predictions by combining multiple scoring functions or using a consensus approach from many prediction models.

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bioRxiv2024

Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling

This paper proposes a versatile tokenization method and introduces Prot2Token, a model that combines autoregressive language modeling with protein language models (PLMs) to tackle various protein prediction tasks using protein sequences. Leveraging our tokenization method, Prot2Token adapts existing PLMs for multiple tasks such as protein-level prediction, residue-level prediction, and protein-protein interaction prediction through next-token prediction of tokenized target label sequences. By incorporating prompt tokens into the decoder, Prot2Token enables multi-task training in a single end-to-end session. Our results demonstrate that Prot2Token not only matches the performance of specialized models across various tasks but also paves the way for integrating protein tasks with large language models (LLMs), representing an important step towards creating general-purpose PLMs for advanced protein language processing (PLP). Additionally, we use Prot2Token to develop S-ESM, a structure-aware version of the ESM model, which achieves competitive performance with state-of-the-art methods in 3D structure-related tasks using only protein sequences. Code is available at: https://github.com/mahdip72/prot2token.

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