Biology subjects

Hy, T.-S.

Publications and source records attributed to Hy, T.-S..

3 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

bioinformatics↗

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

bioinformatics↗

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.

bioinformatics↗