Biology subjects

Nguyen, V. T. D.

Publications and source records attributed to Nguyen, V. T. D..

7 recordsLinked to original sources

bioRxiv2026

Towards a Unified Framework for Protein Interaction Site Prediction Using Hierarchical Protein Representations

Proteins interact with a wide range of biomolecular partners, yet identifying the interaction sites that mediate these events remains a major challenge for understanding cellular function and guiding drug discovery. Existing computational methods are typically designed for specific interaction classes and often rely on task-specific features. Here, we introduce PRIME-ISP, a structure-based framework for predicting interaction sites across diverse biomolecular interaction types using a unified architecture. PRIME-ISP represents proteins at five organizational levels, including solvent-excluded surfaces, atoms, residues, secondary-structure elements, and whole proteins, and integrates information across these scales through coupled bottom-up and top-down propagation. This hierarchical representation jointly captures local geometry and global structural context, enabling a single architecture to address chemically and geometrically distinct binding problems without task-specific modifications. We evaluate PRIME-ISP on protein, peptide, DNA, RNA, small-molecule, metal, and carbohydrate interaction-site prediction benchmarks. Across these tasks, PRIME-ISP consistently outperforms a recent unified sequence-based predictor and achieves performance comparable to specialized methods while requiring no interaction-specific features. These results demonstrate that hierarchical structural representations provide a flexible foundation for interaction-site prediction across diverse biomolecular interactions. Source code and data are publicly available at https://github.com/HySonLab/PRIME-ISP

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bioRxiv2026

MutaStruct: Revisiting Benchmarks for Single-Site Mutation-Induced Structural Change Prediction

Predicting the structural consequences of single-site mutations is fundamental to understanding protein function, disease mechanisms, and protein engineering. However, existing benchmarks for mutation-induced structural change prediction are often affected by duplicate mutation events, highly similar structural pairs, and experimental-state confounders, which can inflate reported performance. We introduce MutaStruct, a curated benchmark of experimentally observed single-mutation protein structure pairs designed for rigorous evaluation of mutation-induced structural changes. MutaStruct combines residue-level sequence validation, coordinate-level quality control, context-aware filtering, event-level deduplication, and leakage-aware data splitting to improve dataset reliability and evaluation rigor. We benchmark sequence-only structure prediction, physics-based refinement, and learning-based refinement methods together with a Copy-WT diagnostic control. Copy-WT remains highly competitive on backbone-based metrics, while method rankings change substantially across local and mutation-site measures. These results show that overall structural fidelity does not necessarily imply accurate modeling of mutation-associated structural change, underscoring the need for further methodological development beyond current paradigms. The benchmark dataset is currently undergoing further curation and expansion and will be released in a future journal-version update. The codebase, trained models, and evaluation pipeline are publicly available at https://github.com/HySonLab/MutaStruct

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bioRxiv2025

EVOSYNTH: Enabling Multi-Target Drug Discovery through Latent Evolutionary Optimization and Synthesis-Aware Prioritization

Complex diseases, such as cancer and neurodegeneration, feature interconnected pathways, making single-target therapies ineffective due to pathway redundancy and compensatory mechanisms. Polypharmacy, which combines multiple drugs to target distinct proteins, addresses this but often leads to drug-drug interactions, cumulative toxicity, and complex pharmacokinetics. To overcome these challenges, we introduce EO_SCPLOWVOC_SCPLOWSO_SCPLOWYNTHC_SCPLOW, a modular framework for multi-target drug discovery that combines latent evolution and synthesis-aware prioritization to generate and prioritize candidates with high translational potential. Latent evolution navigates a chemically and functionally informed latent space to identify candidates with strong predicted affinity across multiple targets. Synthesis-aware prioritization evaluates both retrosynthetic feasibility and the trade-off between synthetic cost and therapeutic reward, enabling practical and efficient candidate selection. Applied to dual inhibition of JNK3 and GSK3{beta} in Alzheimers disease and PI3K and PARP1 in ovarian cancer, EO_SCPLOWVOC_SCPLOWSO_SCPLOWYNTHC_SCPLOW consistently outperforms baseline generative models, achieving higher predicted affinities, improved scaffold diversity, and lower synthesis costs. These findings highlight EO_SCPLOWVOC_SCPLOWSO_SCPLOWYNTHC_SCPLOWs ability to integrate target-driven generation with practical synthesizability, establishing a scalable framework for multi-target and polypharmacological drug discovery. Our source code and data to reproduce all experiments is publicly available on GitHub at: https://github.com/HySonLab/EvoSynth.

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bioRxiv2024

Generative AI-assisted Virtual Screening Pipeline for Generalizable and Efficient Drug Repurposing

Drug repurposing presents a valuable strategy to expedite drug discovery by identifying new therapeutic uses for existing compounds, especially for diseases with limited treatment options. We propose a Generative AI-assisted Virtual Screening Pipeline that combines generative modeling, binding pocket prediction, and similarity-based searches within drug databases to achieve a generalizable and efficient approach to drug repurposing. Our pipeline enables blind screening of any protein target without requiring prior structural or functional knowledge, allowing it to adapt to a wide range of diseases, including emerging health threats and novel targets where information is scarce. By rapidly generating potential ligands and efficiently identifying and ranking drug candidates, our approach accelerates the drug discovery process, broadening the scope and impact of repurposing efforts and offering new possibilities for therapeutic development. Detailed results and implementation can be accessed at https://github.com/HySonLab/DrugPipe.

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bioRxiv2024

Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models

Proteins, serving as the fundamental architects of biological processes, interact with ligands to perform a myriad of functions essential for life. Designing functional ligand-binding proteins is pivotal for advancing drug development and enhancing therapeutic efficacy. In this study, we introduce ProteinReDiff, an efficient computational framework targeting the redesign of ligand-binding proteins. Using equivariant diffusion-based generative models, ProteinReDiff enables the creation of high-affinity ligand-binding proteins without the need for detailed structural information, leveraging instead the potential of initial protein sequences and ligand SMILES strings. Our evaluations across sequence diversity, structural preservation, and ligand binding affinity underscore ProteinReDiffs potential to advance computational drug discovery and protein engineering. Our source code is publicly available at https://github.com/HySonLab/Protein_Redesign.

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bioRxiv2024

Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design

Directed evolution has been the most effective method for protein engineering that optimizes biological functionalities through a resource-intensive process of screening or selecting among a vast range of mutations. To mitigate this extensive procedure, recent advancements in machine learning-guided methodologies center around the establishment of a surrogate sequence-function model. In this paper, we propose Latent-based Directed Evolution (LDE), an evolutionary algorithm designed to prioritize the exploration of high-fitness mutants in the latent space. At its core, LDE is a regularized variational autoencoder (VAE), harnessing the capabilities of the state-of-the-art Protein Language Model (pLM), ESM-2, to construct a meaningful latent space of sequences. From this encoded representation, we present a novel approach for efficient traversal on the fitness landscape, employing a combination of gradient-based methods and directed evolution. Experimental evaluations conducted on eight protein sequence design tasks demonstrate the superior performance of our proposed LDE over previous baseline algorithms. Our implementation is publicly available at https://github.com/HySonLab/LatentDE.

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bioRxiv2023

Multimodal Pretraining for Unsupervised Protein Representation Learning

In this paper, we introduce a framework of symmetry-preserving multimodal pretraining to learn a unified representation of proteins in an unsupervised manner, encompassing both primary and tertiary structures. Our approach involves proposing specific pretraining methods for sequences, graphs, and 3D point clouds associated with each protein structure, leveraging the power of large language models and generative models. We present a novel way to combining representations from multiple sources of information into a single global representation for proteins. We carefully analyze the performance of our framework in the pretraining tasks. For the fine-tuning tasks, our experiments have shown that our new multimodal representation can achieve competitive results in protein-ligand binding affinity prediction, protein fold classification, enzyme identification and mutation stability prediction. We expect that this work will accelerate future research in proteins. Our source code in PyTorch deep learning framework is publicly available at https://github.com/HySonLab/Protein_Pretrain.

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