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Mapping Protein Conformational Landscapes from Crystallographic Drug Fragment Screens

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Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Inf. Model. 2024, 64, 23, 8937-8951
    Computational Biochemistry

    Mapping Protein Conformational Landscapes from Crystallographic Drug Fragment Screens
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    • Ammaar A. Saeed
      Ammaar A. Saeed
      Department of Molecular & Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, United States
      More by Ammaar A. Saeed
    • Margaret A. Klureza
      Margaret A. Klureza
      Department of Chemistry & Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
      More by Margaret A. Klureza
    • Doeke R. Hekstra*
      Doeke R. Hekstra
      Department of Molecular & Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, United States
      School of Engineering & Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
      *Email: [email protected]
      More by Doeke R. Hekstra
      Orcidhttps://orcid.org/0000-0003-2332-9223
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2024, 64, 23, 8937–8951
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    https://doi.org/10.1021/acs.jcim.4c01380
    Published November 12, 2024
    Copyright © 2024 The Authors. Published by American Chemical Society
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    Abstract

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    Proteins are dynamic macromolecules. Knowledge of a protein’s thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein’s structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COnformational LAndscape Visualization (COLAV), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens enable detailed mapping of proteins’ conformational landscapes.

    Copyright © 2024 The Authors. Published by American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.4c01380.

    • Additional figures validating the COLAV method and an additional table describing COLAV functions (PDF)

    • Table file describing MPro structure files used in this analysis, with the following columns: Name specifying the name of the corresponding PDB file, space group specifying the space group of the structure (if deposited), resolution specifying the resolution of the structure (if deposited), PDB ID specifying the PDB code for the structure (if applicable), SMILES specifying the SMILES code of the primary ligand, binding specifying the binding mode of the primary ligand (active site or allosteric) or apo if no primary ligand, Ligand specifying the three-letter ID for each ligand, and fragment specifying whether the structure originated from a fragment screen (Yes) or not (No) (PTP1B) (CSV)

    • Table file describing MPro structure files used in this analysis, with the following columns: Name specifying the name of the corresponding PDB file, space group specifying the space group of the structure (if deposited), resolution specifying the resolution of the structure (if deposited), PDB ID specifying the PDB code for the structure (if applicable), SMILES specifying the SMILES code of the primary ligand, binding specifying the binding mode of the primary ligand (active site or allosteric) or apo if no primary ligand, ligand specifying the three-letter ID for each ligand, and fragment specifying whether the structure originated from a fragment screen (Yes) or not (No) (MPRO) (CSV)

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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2024, 64, 23, 8937–8951
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jcim.4c01380
    Published November 12, 2024
    Copyright © 2024 The Authors. Published by American Chemical Society
    Request reuse permissions

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