Optimal water networks in protein cavities with GAsol and 3D-RISM

Fusani, Lucia and Wall, Ian and Palmer, David and Cortes, Alvaro (2018) Optimal water networks in protein cavities with GAsol and 3D-RISM. Bioinformatics. ISSN 1367-4803 (https://doi.org/10.1093/bioinformatics/bty024)

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Abstract

Motivation: Water molecules in protein binding sites play essential roles in biological processes. The popular 3D-RISM prediction method can calculate the solvent density distribution within minutes, but is difficult to convert it into explicit water molecules. Results: We present GAsol, a tool that is capable of finding the network of water molecules that best fits a particular 3D-RISM density distribution in a fast and accurate manner and that outperforms other available tools by finding the globally optimal solution thanks to its genetic algorithm. Availability: https://github.com/accsc/GAsol. BSD 3-clauses license.

ORCID iDs

Fusani, Lucia, Wall, Ian, Palmer, David ORCID logoORCID: https://orcid.org/0000-0003-4356-9144 and Cortes, Alvaro;
  • Item type:Article
    ID code:62891
    Dates:
    Date
    Event
    15 January 2018
    Published
    15 January 2018
    Published Online
    8 January 2018
    Accepted
    Notes:This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Fusani, L., Wall, I., Palmer, D., & Cortes, A. (2018). Optimal water networks in protein cavities with GAsol and 3D-RISM. Bioinformatics. is available online at: https://doi.org/10.1093/bioinformatics/bty024
    Keywords:3DRISM, protein-water interactions, placevent, drug discovery, computational chemistry, molecular docking, molecular integral equation theory, software, Chemistry, Biochemistry, Computational Mathematics, Molecular Biology
    Subjects:Science > Chemistry
    Department:Faculty of Science > Pure and Applied Chemistry
    Depositing user: Pure Administrator
    Date deposited:16 Jan 2018 11:51
    Last modified:10 May 2022 00:22
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/62891
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