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State-specific protein–ligand complex structure prediction with a multiscale deep generative model

Nature Machine Intelligence volume 6, pages 195–208 (2024)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life. Despite recent advancements in protein structure prediction, existing algorithms are so far unable to systematically predict the binding ligand structures along with their regulatory effects on protein folding. To address this discrepancy, we present NeuralPLexer, a computational approach that can directly predict protein–ligand complex structures solely using protein sequence and ligand molecular graph inputs. NeuralPLexer adopts a deep generative model to sample the three-dimensional structures of the binding complex and their conformational changes at an atomistic resolution. The model is based on a diffusion process that incorporates essential biophysical constraints and a multiscale geometric deep learning system to iteratively sample residue-level contact maps and all heavy-atom coordinates in a hierarchical manner. NeuralPLexer achieves state-of-the-art performance compared with all existing methods on benchmarks for both protein–ligand blind docking and flexible binding-site structure recovery. Moreover, owing to its specificity in sampling both ligand-free-state and ligand-bound-state ensembles, NeuralPLexer consistently outperforms AlphaFold2 in terms of global protein structure accuracy on both representative structure pairs with large conformational changes and recently determined ligand-binding proteins. NeuralPLexer predictions align with structure determination experiments for important targets in enzyme engineering and drug discovery, suggesting its potential for accelerating the design of functional proteins and small molecules at the proteome scale.

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Fig. 1: NeuralPLexer enables accurate prediction of protein–ligand complex structure and conformational changes.
Fig. 2: Architecture details.
Fig. 3: Model performance on benchmarking problems.
Fig. 4: Model predictions for contrasting apo–holo pairs from the PocketMiner dataset.
Fig. 5: Model predictions for recently determined structures.

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Data availability

All datasets and predictions used to generate the reported results are available on Code Ocean86 and also on Zenodo at https://doi.org/10.5281/zenodo.10373581.

Code availability

The code, scripts and interactive data analysis notebooks are available on Code Ocean86 and also on GitHub at https://github.com/zrqiao/NeuralPLexer.

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Acknowledgements

Z.Q. acknowledges graduate research funding from Caltech and partial support from the Amazon-Caltech AI4Science fellowship. T.F.M. acknowledges partial support from the Caltech DeLogi fund, and A.A. acknowledges support from a Caltech Bren professorship. We thank M. Welborn, F. R. Manby, C. Zhang and V. Bhethanabotla for discussions on the work and for comments on the manuscript. We thank A. Meller and J. Borowsky for sharing the PocketMiner dataset.

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Authors and Affiliations

  1. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA

    Zhuoran Qiao

  2. Iambic Therapeutics, San Diego, CA, USA

    Zhuoran Qiao & Thomas F. Miller III

  3. Nvidia Corporation, Santa Clara, CA, USA

    Weili Nie, Arash Vahdat & Animashree Anandkumar

  4. Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

    Animashree Anandkumar

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Contributions

Z.Q., W.N., A.V., T.F.M. and A.A. conceived and designed the experiments. Z.Q. performed the experiments. Z.Q., W.N., A.V., T.F.M. and A.A. analysed the data. Z.Q. contributed analysis tools. Z.Q. and A.A. wrote the paper.

Corresponding authors

Correspondence to Zhuoran Qiao, Thomas F. Miller III or Animashree Anandkumar.

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Competing interests

Z.Q. and T.F.M. are currently employees of Iambic Therapeutics or its affiliates. A provisional patent application related to this work has been filed (US Patent App. provisional 63/496,899). The remaining authors declare no competing interests.

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Nature Machine Intelligence thanks Shigenori Tanaka, Anastassis Perrakis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Structure prediction accuracy on all targets.

Comparing AlphaFold2 (AF2), NeuralPLexer, and NeuralPLexer (no ligand) in terms of TM-score against all structure prediction targets described in this study, including PocketMiner and recent structures. All NeuralPLexer results shown in this figure are obtained using the LSA-SDE sampler and are based on the structure with the highest average protein pLDDT among the 8 generated structures for each prediction target.

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Supplementary results and discussions and Algorithms 1–12, Figs. 1–5 and Tables 1–6.

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Qiao, Z., Nie, W., Vahdat, A. et al. State-specific protein–ligand complex structure prediction with a multiscale deep generative model. Nat Mach Intell 6, 195–208 (2024). https://doi.org/10.1038/s42256-024-00792-z

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