Author(s)
Lili X Peng
Published 1 Project
Bioinformatics Fragfeature Virtual Screening Allosteric Kinase Inhibitors Machine
Morgan Lawrenz
Published 1 Project
Bioinformatics Fragfeature Virtual Screening Allosteric Kinase Inhibitors Machine
Diwakar Shukla
Published 1 Project
Bioinformatics Fragfeature Virtual Screening Allosteric Kinase Inhibitors Machine
Grace W Tang
Published 1 Project
Bioinformatics Fragfeature Virtual Screening Allosteric Kinase Inhibitors Machine
Vijay S. Pande
Published 1 Project
Bioinformatics Fragfeature Virtual Screening Allosteric Kinase Inhibitors Machine
Russ B. Altman
Published 8 Projects
genomics Bioinformatics Pathology Drug Side Effect Prediction Drug Response
Content
Video Abstract (AI generated) (01:53) Paper PreprintRecent molecular dynamics (MD) simulations of the catalytic domain of the c-Src kinase revealed intermediate conformations with a potentially druggable allosteric pocket adjacent to the C-helix, bound by 8-anilino-1-naphthalene sulfonate. Towards confirming the existence of this pocket, we have developed a novel lead enrichment protocol using new target and lead enrichment software to identify sixteen allosteric lead ligands of the c-Src kinase. First, Markov State Models analysis was used to identify the most statistically significant c-Src target conformations from all MD-simulated conformations. The most statistically relevant candidate MSM targets were then prioritized by assessing how well each reproduced binding poses of ligands specific to the ATP-competitive and allosteric pockets. The top-performing MSM targets, identified by receiver-operating curve analysis, were then used to screen the ZINC library of 13 million ″clean, drug-like ligands″, all of which prioritized based on their empirical scoring function, binding pose consistency across MSM targets, and strong hydrogen bonding and hydrophobic interactions with Src residues. The FragFEATURE knowledgebase of fragment-protein pocket interactions was then used to identify fragments specific to the ATP-competitive and allosteric pockets. This information was used to identify seven Type II and nine Type III lead ligands with binding poses supported by fragment predictions. Of these, Type II lead ligands, ZINC13037947 and ZINC09672647, and Type III lead ligands, ZINC12530852 and ZINC30012975, exhibited the most favorable fragment profiles and are recommended for further experimental testing for the existence of the allosteric pocket in Src.
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Russ Altman. (2021, Oct 26).Application of new informatics tools for identifying allosteric lead ligands of the c-Src kinase[Video]. Scitok. https://scitok.com/project/p/8134f162
X Peng Lili. "Application of new informatics tools for identifying allosteric lead ligands of the c-Src kinase" Scitok, uploaded by Altman Russ, 26 Oct, 2021, https://scitok.com/project/p8134f162
Russ Altman. "Application of new informatics tools for identifying allosteric lead ligands of the c-Src kinase" Scitok. (Oct 26, 2021). https://scitok.com/project/p/8134f162
Russ Altman (Oct 26, 2021). Application of new informatics tools for identifying allosteric lead ligands of the c-Src kinase Scitok. https://scitok.com/project/p/8134f162
Russ Altman. Application of new informatics tools for identifying allosteric lead ligands of the c-Src kinase[video]. 2021 Oct 26. https://scitok.com/project/p/8134f162
@online{al2006link, title={ Application of new informatics tools for identifying allosteric lead ligands of the c-Src kinase }, author={ Altman, Russ }, organization={Scitok}, month={ Oct }, day={ 26 }, year={ 2021 }, url = {https://scitok.com/project/p/8134f162}, }