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Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change 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 achieved state-of-the-art prediction performance on seven single-point mutation benchmark datasets, and comprehensively surpassed other methods on five of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.<\/jats:p>","DOI":"10.1093\/bib\/bbaf319","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T07:36:32Z","timestamp":1750145792000},"source":"Crossref","is-referenced-by-count":1,"title":["Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence"],"prefix":"10.1093","volume":"26","author":[{"given":"Zhiwei","family":"Nie","sequence":"first","affiliation":[{"name":"School of Electronic and Computer Engineering , Peking University, Shenzhen,","place":["China"]},{"name":"Pengcheng Laboratory , Shenzhen,","place":["China"]},{"name":"AI for Science (AI4S)-Preferred Program , Peking University Shenzhen Graduate School, Shenzhen,","place":["China"]}]},{"given":"Yiming","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering , Peking University, Shenzhen,","place":["China"]},{"name":"AI for Science (AI4S)-Preferred Program , Peking University Shenzhen Graduate School, Shenzhen,","place":["China"]}]},{"given":"Yutian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science , Peking University, Beijing,","place":["China"]}]},{"given":"Xiansong","family":"Huang","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory , Shenzhen,","place":["China"]}]},{"given":"Zhihong","family":"Liu","sequence":"additional","affiliation":[{"name":"Pingshan Translational Medicine Center , Shenzhen Bay Laboratory, Shenzhen,","place":["China"]}]},{"given":"Peng","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing National Laboratory for Molecular Sciences , Key Laboratory of Polymer Chemistry & Physics of Ministry of Education, Center for Soft Matter Science and Engineering, College of Chemistry and Molecular Engineering, Peking University, Beijing,","place":["China"]},{"name":"Key Laboratory of Polymer Chemistry & Physics of Ministry of Education , Center for Soft Matter Science and Engineering, College of Chemistry and Molecular Engineering, Peking University,","place":["China"]}]},{"given":"Fan","family":"Xu","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory , Shenzhen,","place":["China"]}]},{"given":"Feng","family":"Yin","sequence":"additional","affiliation":[{"name":"Pingshan Translational Medicine Center , Shenzhen Bay Laboratory, Shenzhen,","place":["China"]}]},{"given":"Zigang","family":"Li","sequence":"additional","affiliation":[{"name":"Pingshan Translational Medicine Center , Shenzhen Bay Laboratory, Shenzhen,","place":["China"]},{"name":"State Key Laboratory of Chemical Oncogenomics , School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, 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