IChemPIC: A Random Forest Classifier of Biological and Crystallographic Protein-Protein Interfaces

Journal of Chemical Information and Modeling
Franck Da SilvaDidier Rognan

Abstract

Protein-protein interactions are becoming a major focus of academic and pharmaceutical research to identify low molecular weight compounds able to modulate oligomeric signaling complexes. As the number of protein complexes of known three-dimensional structure is constantly increasing, there is a need to discard biologically irrelevant interfaces and prioritize those of high value for potential druggability assessment. A Random Forest model has been trained on a set of 300 protein-protein interfaces using 45 molecular interaction descriptors as input. It is able to predict the nature of external test interfaces (crystallographic vs biological) with accuracy at least equal to that of the best state-of-the-art methods. However, our method presents unique advantages in the early prioritization of potentially ligandable protein-protein interfaces: (i) it is equally robust in predicting either crystallographic or biological contacts and (ii) it can be applied to a wide array of oligomeric complexes ranging from small-sized biological interfaces to large crystallographic contacts.

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Citations

Mar 25, 2016·Journal of Chemical Information and Modeling·Durmus U KaratayDavid S Ginger
Oct 25, 2016·Journal of Molecular Biology·Christine YuehSandor Vajda
Mar 23, 2017·Journal of Medicinal Chemistry·Zheng ZhaoPhilip E Bourne
Jan 29, 2019·Chemical Biology & Drug Design·Yanmin ZhangHaichun Liu
Aug 9, 2019·Chemical Biology & Drug Design·Yanmin ZhangHaichun Liu
Aug 3, 2017·Journal of Computer-aided Molecular Design·Priscila da Silva Figueiredo Celestino GomesDidier Rognan
May 5, 2017·Nucleic Acids Research·Rainer FährrolfesMatthias Rarey
Oct 12, 2019·Journal of Medicinal Chemistry·Franck Da SilvaDidier Rognan

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