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Evaluation of the Wilma-SIE Virtual Screening Method in Community Structure–Activity Resource 2013 and 2014 Blind Challenges
Author(s) -
Hervé Hogues,
Traian Sulea,
Enrico O. Purisima
Publication year - 2015
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.5b00278
Subject(s) - virtual screening , docking (animal) , computer science , computational biology , artificial intelligence , chemistry , machine learning , data mining , biology , stereochemistry , pharmacophore , medicine , nursing
Prospective assessments of the Wilma-SIE (solvated interaction energy) platform for ligand docking and ranking were performed during the 2013 and 2014 editions of the Community Structure-Activity Resource (CSAR) blind challenge. Diverse targets like a steroid-binding protein, a serine protease (factor Xa), a tyrosine kinase (Syk), and a nucleotide methyltransferase (TrmD) were included. Pose selection was achieved with high precision; in all 24 tests Wilma-SIE top-ranked the native pose among carefully generated sets of decoy conformations. Good separation for the native pose was also observed indicating robustness in pose scoring. Cross-docking was also accomplished with high accuracy for the various systems, with ligand median-RMSD values around 1 Å from the crystal structures. Larger deviations were occasionally obtained due to the rigid-target approach even if multiple target structures were used. Affinity ranking of congeneric ligands after cross-docking was reasonable for three of the four systems, with Spearman ranking coefficients around 0.6. Poor affinity ranking for FXa is possibly due to missing structural domains, which are present during measurements. Assignment of protonation states is critical for affinity scoring with the SIE function, as shown here for the Syk system. Including the FiSH model improved cross-docking but worsened affinity predictions, pointing to the need for further fine-tuning of this newer solvation model. The consistently strong performance of the Wilma-SIE platform in recent CSAR and SAMPL blind challenges validates its applicability for virtual screening on a broad range of molecular targets.

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