PSSP-MFFNet: A Multifeature Fusion Network for Protein Secondary Structure Prediction.

ABSTRACT: Protein secondary structure prediction (PSSP) is a fundamental task in modern bioinformatics research and is particularly important for uncovering the functional mechanisms of proteins. To improve the accuracy of PSSP, various general and essential features generated from amino acid sequences are often used for predicting the secondary structure. In this paper, we propose PSSP-MFFNet, a deep learning-based multi-feature fusion network for PSSP, which incorporates a multi-view deep learning architecture with the multiple sequence alignment (MSA) Transformer to efficiently capture global and local features of protein sequences. In practice, PSSP-MFFNet adopts a multi-feature fusion strategy, integrating different features generated from protein sequences, including MSA, sequence information,

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10851352 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature