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Computational drug development for membrane protein targets

Nature Biotechnology volume 42, pages 229–242 (2024)Cite this article

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Abstract

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

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Fig. 1: Workflow of virtual ligand screening for membrane proteins with a GPCR as a prototypical example.
Fig. 2: An integrated structural biology approach.

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Acknowledgements

Funding from the following sources is acknowledged: the National Natural Science Foundation of China and Swiss National Science Foundation (NSFC-SNF 32161133022 to H.V. and H.S.); the Shenzhen Key Laboratory of Computer-Aided Drug Discovery, Advanced Technology, Chinese Academy of Sciences, Shenzhen (funding no. ZDSYS20201230165400001 to S.Y. and H.V.); the Chinese Academy of Science President’s International Fellowship Initiative (PIFI) (no. 2020FSB0003 to H.V.); Guangdong Retired Expert to H.V. (granted by Guangdong Province); Shenzhen Pengcheng Scientist to H.V; the AlphaMol and SIAT Joint Laboratory to S.Y. and H.V; and Shenzhen Government Top-Talent Working Funding and Guangdong Province Academician Work Funding to H.V.

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  1. These authors contributed equally: Haijian Li, Xiaolin Sun, Wenqiang Cui, Marc Xu, Junlin Dong, Babatunde Edukpe Ekundayo, Dongchun Ni, Zhili Rao, Liwei Guo.

Authors and Affiliations

  1. Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China

    Haijian Li, Xiaolin Sun, Wenqiang Cui, Marc Xu, Junlin Dong, Zhili Rao, Liwei Guo, Shuguang Yuan & Horst Vogel

  2. University of Chinese Academy of Sciences, Beijing, China

    Wenqiang Cui, Marc Xu & Junlin Dong

  3. Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland

    Babatunde Edukpe Ekundayo, Dongchun Ni & Henning Stahlberg

  4. Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Horst Vogel

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Li, H., Sun, X., Cui, W. et al. Computational drug development for membrane protein targets. Nat Biotechnol 42, 229–242 (2024). https://doi.org/10.1038/s41587-023-01987-2

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