Work

SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction

Year: 2025

Type: article

Abstract: Proteins are the basic building blocks of life and perform fundamental functions in biology. Predicting protein properties based on amino acid sequences and 3D structures has become a key approach to accelerating drug development. In this study, we propose a novel sequence- and structure-based framework, SST-ResNet, which consists of the multimodal language model ProSST and a multi-scale information integration module. This framework is designed to deeply explore the latent relationships between protein sequences and structures, thereby achieving superior synergistic prediction performance. Our method outperforms previous joint prediction models on Enzyme Commission (EC) numbers and Gene Ontology (GO) tasks. Furthermore, we demonstrate the necessity of multi-scale information integration for these two types of data and illustrate its exceptional performance on key tasks. We anticipate that this framework can be extended to a broader range of protein property prediction problems, ultimately facilitating drug development.

Source: International Journal of Molecular Sciences

DOI: 10.3390/ijms26062783

Authors: Guowei Zhou, Yanpeng Zhao, Song He, Xiaochen Bo

Institutions: Tianjin University, Academy of Military Medical Sciences, Shanghai University

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,499

Funder: National Key Research and Development Program of China