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March 7th, 2025
Version: 2
Peking University
bioinformatics
biorxiv

Protein stability prediction with dual-view ensemble learning from single sequence

Nie, Z.Open in Google Scholar•Ma, Y.Open in Google Scholar•Liu, Y.Open in Google Scholar•Huang, X.Open in Google Scholar•Liu, Z.Open in Google Scholar•Yang, P.Open in Google Scholar•Xu, F.Open in Google Scholar•Yin, F.Open in Google Scholar•Li, Z.Open in Google Scholar•Fu, J.Open in Google Scholaret al.

Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for protein stability prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability not only achieves comparable prediction performance to state-of-the-art methods, but also achieves superior performance in predicting rare beneficial mutations that are critical for directed evolution of proteins. More importantly, DVE-stability identifies important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for protein stability prediction in an interpretable ensemble learning manner.

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