A1 Refereed original research article in a scientific journal
Building shape-focused pharmacophore models for effective docking screening
Authors: Moyano-Gómez, Paola; Lehtonen, Jukka V.; Pentikäinen, Olli T.; Postila, Pekka A.
Publisher: BioMed Central
Publication year: 2024
Journal: Journal of cheminformatics
Journal name in source: Journal of cheminformatics
Journal acronym: J Cheminform
Article number: 97
Volume: 16
Issue: 1
eISSN: 1758-2946
DOI: https://doi.org/10.1186/s13321-024-00857-6
Web address : https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00857-6
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/457542365
The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.
Downloadable publication This is an electronic reprint of the original article. |  |
Funding information in the publication:
This research was funded by Novo Nordisk Foundation (O.T.P., Pioneer Innovator (0068926) and Distinguished Innovator (0075825) Grants). This research was also supported by the Research Council of Finland’s Flagship InFLAMES (P.A.P). The funding decision numbers are 337530 and 357910.
