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Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm

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Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold.

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Data Availability

All data needed to evaluate the conclusions are present in the paper and the Supplementary Materials. The online server at http://zhanglab-bioinf.com/MultiSFold.

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Acknowledgements

This work is supported by the National Key R&D Program of China (2022ZD0115103), the National Nature Science Foundation of China (No. 62173304), and the Key Project of Zhejiang Provincial Natural Science Foundation of China (No. LZ20F030002).

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Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Minghua Hou, Sirong Jin, Xinyue Cui, Chunxiang Peng, Kailong Zhao & Guijun Zhang

  2. BioMap & MBZUAI, Beijing, 100038, China

    Le Song

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Hou, M., Jin, S., Cui, X. et al. Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-023-00597-5

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