DeepMetal: A Hierarchical Coarse-to-Fine Framework for Metal-Binding Site Prediction via Protein Language Models and SE(3)-Equivariant Graph Neural Networks

Symphony-Bind: Prediction of Protein Binding Sites for 11 Representative Small Molecules and Ions via Fine-Tuning Protein Language Models and Grouped Multi-Task Learning

Accurately identifying protein binding sites for small molecules and ions is crucial for understanding biological processes and advancing drug discovery. Pretrained protein language models (pLMs) have emerged as powerful tools for this purpose, but existing prediction models often face a trade-off when using pLMs: freezing pLMs limits their adaptability, while fully fine-tuning them requires high computational costs. To address this trade-off, we attempted to introduce Parameter-Efficient Fine-Tuning (PEFT) as a promising solution to balance efficiency and performance. Specifically, we applied five PEFT strategies (i.e., LoRA, QLoRA, DoRA, AdaLoRA, and IA3) to fine-tune four pLMs (i.e., ProtT5, ProtBERT, ESM2–150M, and ESM2–650M) and assessed their predictive performance across protein binding site tasks for 11 representative small molecules and ions. The results clearly indicate that the LoRA-enhanced ESM2–650M consistently outperforms all other combinations. Despite this robust baseline, training independent models for specific small molecules remains challenging due to the scarcity of high-quality binding data. To bridge this gap, we implemented a Grouped Multi-Task Learning (GMTL) strategy, allowing the model to capture shared binding patterns among ligands with similar biological significance. Experimental results demonstrate that this strategy significantly enhances predictive performance. Building upon these insights, we present Symphony-Bind. It is a GMTL framework that leverages LoRA-enhanced ESM2–650M to extract embeddings, which are subsequently refined by a shared ConvBERT module and then processed by ligand-specific MLPs for precise binding site prediction. Performance evaluation on 11 representative ligand tasks shows that Symphony-Bind achieves average MCC values of 0.561, 0.629, and 0.324 for the nucleotide, cofactor, and inorganic ion groups, surpassing evaluated sequence-based state-of-the-art methods while remaining competitive with structure-based models.

Unveiling Large-Scale Kinase-Centric Protein-Protein Interactions through a Knowledge-Informed Workflow.

A pipeline reformulating kinase-substrate modeling as a Bayesian inference problem is presented and it is revealed that the interaction types and distances to the catalytic pocket significantly influence pathogenicity scores.

Multivalent ion binding site identification with structure-based deep learning

BiteNetI is a structure-based deep learning model that uses 3D convolutional neural networks to simultaneously localize ion-binding centers and predict binding residues for 14 biologically relevant ions, supporting comprehensive and large-scale annotation of protein-ion interactions.

MBPBERT: A Large Language Model for Metal-Binding Peptide Discovery.

M2-PRNet: multi-scale and multi-modal learning for protein–RNA binding affinity prediction

The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible complex structures are available.

Mavchen-1: A Conformational Ensemble Platform for Protein–Ligand Pose Prediction That Substantially Outperforms Static Structure Prediction in a Category-Stratified Benchmark

A category-stratified, statistically powered benchmark comparing pose prediction from receptor conformational ensembles against AlphaFold2, used as a matched static-structure baseline, across 29 protein–ligand systems spanning cryptic-pocket, induced-fit, water-mediated, and autoimmune-indication target classes is presented.