SeekRBP: Leveraging Sequence-Structure Integration with Reinforcement Learning for Receptor-Binding Protein Identification

TL;DR

SeekRBP introduces a novel sequence-structure and reinforcement learning-based framework for accurately identifying receptor-binding proteins, overcoming challenges of sequence divergence and class imbalance in viral host prediction.

Contribution

It is the first to model negative sampling as a sequential decision process using reinforcement learning in RBP identification, integrating sequence and structural data.

Findings

Outperforms static sampling methods in benchmarks

Effectively identifies RBPs in Vibrio phages

Enhances host prediction accuracy

Abstract

Motivation: Receptor-binding proteins (RBPs) initiate viral infection and determine host specificity, serving as key targets for phage engineering and therapy. However, the identification of RBPs is complicated by their extreme sequence divergence, which often renders traditional homology-based alignment methods ineffective. While machine learning offers a promising alternative, such approaches struggle with severe class imbalance and the difficulty of selecting informative negative samples from heterogeneous tail proteins. Existing methods often fail to balance learning from these ``hard negatives'' while maintaining generalization. Results: We present SeekRBP, a sequence--structure framework that models negative sampling as a sequential decision-making problem. By employing a multi-armed bandit strategy, SeekRBP dynamically prioritizes informative non-RBP sequences based on real-time training feedback, complemented by a multimodal fusion of protein language and structural embeddings. Benchmarking demonstrates that SeekRBP consistently outperforms static sampling strategies. Furthermore, a case study on Vibrio phages validates that SeekRBP effectively identifies RBPs to improve host prediction, highlighting its potential for large-scale annotation and synthetic biology applications.