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Integrating diverse experimental information to assist protein complex structure prediction by GRASP

Nature Methods volume 22, pages 2362–2374 (2025)Cite this article

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Abstract

Protein complex structure prediction is crucial for understanding of biological activities and advancing drug development. While various experimental methods can provide structural insights into protein complexes, the knowledge obtained is often sparse or approximate. A general tool is needed to integrate limited experimental information for high-throughput and accurate prediction. Here we introduce GRASP to efficiently and flexibly incorporate diverse forms of experimental information. GRASP outperforms existing tools in handling both simulated and real-world experimental restraints including those from crosslinking, covalent labeling, chemical shift perturbation and deep mutational scanning. For example, GRASP excels at predicting antigen–antibody complex structures, even surpassing AlphaFold3 when using experimental deep mutational scanning or covalent-labeling restraints. Beyond its accuracy and flexibility in restrained structure prediction, GRASP’s ability to integrate multiple forms of restraints enables integrative modeling. We also showcase its potential in modeling protein structural interactome under near-cellular conditions using previously reported large-scale in situ crosslinking data for mitochondria.

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Fig. 1: Scheme of GRASP to integrate generalized restraints.
Fig. 2: Performance of different methods on benchmark dataset with two types of basic restraint.
Fig. 3: The performance of GRASP and other methods using single-type experimental restraint data.
Fig. 4: GRASP improves antigen–antibody structure prediction accuracy.
Fig. 5: Applying GRASP in integrative modeling results in reasonable structures.
Fig. 6: Dimeric structure models are predicted by GRASP for PPIs captured by targets XL-MS in mitochondria.

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Data availability

All relevant evaluation datasets and results are available via OSF at https://doi.org/10.17605/OSF.IO/6KJUQ (ref. 70). Further training data for MEGAFold-Multimer and GRASP are released at http://ftp.cbi.pku.edu.cn/pub/psp/.

Code availability

The GRASP code is available via GitHub at https://github.com/xiergo/GRASP-JAX and via Zenodo at https://doi.org/10.5281/zenodo.15347070 (ref. 71) under the Apache v.2.0 license. Model weights have been deposited on OSF at https://doi.org/10.17605/OSF.IO/6KJUQ (ref. 70). In addition, a Colab notebook is available via GitHub at https://colab.research.google.com/github/xiergo/GRASP-JAX/blob/main/GRASP-JAX.ipynb for ease of use.

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Acknowledgements

This work was supported by the National Science and Technology Major Project (grant no. 2022ZD0115001 to Y.Q.G. and S. Liu), the National Natural Science Foundation of China (grant nos. 92353304 and T2495221 to Y.Q.G.) and New Cornerstone Science Foundation (grant no. NCI202305 to Y.Q.G.). We thank K. Stahl from Technische Universität Berlin for recommending AlphaLink configuration and providing simulated CASP15 restraints for debugging. We thank F. N. Hitawala from Johns Hopkins University for sharing the PDB IDs and sequence data of their curated benchmarking dataset. We thank Y. Kaiguang from Dalian Institute of Chemical Physics to provide guidance for usage of the targeted XL-MS in the mitochondria. We also thank Z. Zhu for helpful discussions on binding energy calculation.

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  1. These authors contributed equally: Yuhao Xie, Chengwei Zhang.

Authors and Affiliations

  1. Changping Laboratory, Beijing, China

    Yuhao Xie, Sirui Liu & Yi Qin Gao

  2. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Yuhao Xie

  3. Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China

    Chengwei Zhang, Yanjiao Lu & Yi Qin Gao

  4. School of Life Sciences, Peking University, Beijing, China

    Chengwei Zhang & Yanjiao Lu

  5. New Cornerstone Science Laboratory, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, China

    Shimian Li, Xinyu Du, Zhenyu Chen & Yi Qin Gao

  6. Huawei Technologies Co. Ltd, Hangzhou, China

    Min Wang & Yingtong Hu

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Contributions

Y.Q.G. and S. Liu designed and developed overall concepts in the paper and supervised the project. Y.X., C.Z. and X.D. developed the GRASP and Combfit methods. M.W. and Y.H. trained and evaluated MEGAFold-Multimer. Y.X., C.Z., S. Li, X.D., Y.L. and Z.C. performed data collection, evaluation and analysis. X.Y., C.Z., S. Li and X.D. wrote the initial draft of the paper. All authors contributed ideas to the work and assisted in editing and revision.

Corresponding authors

Correspondence to Sirui Liu or Yi Qin Gao.

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Competing interests

Changping Laboratory and Huawei Technologies Co., Ltd are in the process of applying for a patent covering the GRASP method, which lists authors including S. Liu, Y.X., C.Z. and Y.Q.G. as inventors. The other authors declare no competing interests.

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Supplementary information

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Supplementary Figs 1–24, Tables 1–7, Methods 1–7 and References.

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Detailed evaluation results and analytical data for each case across the six datasets.

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Xie, Y., Zhang, C., Li, S. et al. Integrating diverse experimental information to assist protein complex structure prediction by GRASP. Nat Methods 22, 2362–2374 (2025). https://doi.org/10.1038/s41592-025-02820-1

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