Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy

TL;DR

This paper introduces FeatNN, an end-to-end model that uses a coevolutionary strategy to jointly represent protein structure and sequence information, significantly improving compound-protein binding affinity prediction accuracy and generalization.

Contribution

The paper presents a novel coevolutionary approach within an end-to-end architecture to effectively integrate multimodal protein data for enhanced CPA prediction.

Findings

FeatNN outperforms state-of-the-art methods in virtual drug screening.

The coevolutionary strategy improves the representation of protein multimodal information.

Utilizing both high- and low-quality databases enhances model accuracy and robustness.

Abstract

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug screening tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.