An end-to-end framework for the prediction of protein structure and fitness from single sequence

Yinghui Chen, Yunxin Xu, Di Liu, Yaoguang Xing and Haipeng Gong ()
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Yinghui Chen: Tsinghua University
Yunxin Xu: Tsinghua University
Di Liu: Tsinghua University
Yaoguang Xing: Tsinghua University
Haipeng Gong: Tsinghua University

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: Abstract Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction accuracy, showing promise for many downstream prediction tasks. Here, we propose SPIRED, a single-sequence-based structure prediction model that exhibits comparable performance to the state-of-the-art methods but with approximately 5-fold acceleration in inference and at least one order of magnitude reduction in training consumption. By integrating SPIRED with downstream neural networks, we compose an end-to-end framework named SPIRED-Fitness for the rapid prediction of both protein structure and fitness from single sequence with satisfactory accuracy. Moreover, SPIRED-Stab, the derivative of SPIRED-Fitness, achieves state-of-the-art performance in predicting the mutational effects on protein stability.

Date: 2024
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DOI: 10.1038/s41467-024-51776-x

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