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3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases

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Abstract

The high similarity between certain sub-pockets of serine proteases may lead to low selectivity of protease inhibitors. Therefore the application of proteochemometrics (PCM), which quantifies the relationship between protein/ligand descriptors and affinity for multiple ligands and targets simultaneously, is useful to understand and improve the selectivity profiles of potential inhibitors. In this study, protein field-based PCM that uses knowledge-based and WaterMap derived fields to describe proteins in combination with 2D (RDKit and MOE fingerprints) and 3D (4 point pharmacophoric fingerprints and GRIND) ligand descriptors was used to model the bioactivities of 24 homologous serine proteases and 5863 inhibitors in an integrated fashion. Of the multiple field-based PCM models generated based on different ligand descriptors, RDKit fingerprints showed the best performance in terms of external prediction with Rtest2 of 0.72 and RMSEP of 0.81. Further, visual interpretation of the models highlights sub-pocket specific regions that influence affinity and selectivity of serine protease inhibitors.

Graphical abstract: 3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases

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Publication details

The article was received on 16 Dec 2016, accepted on 14 Mar 2017 and first published on 15 Mar 2017


Article type: Research Article
DOI: 10.1039/C6MD00701E
Citation: Med. Chem. Commun., 2017, Advance Article
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    3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases

    V. Subramanian, Q. U. Ain, H. Henno, L. Pietilä, J. E. Fuchs, P. Prusis, A. Bender and G. Wohlfahrt, Med. Chem. Commun., 2017, Advance Article , DOI: 10.1039/C6MD00701E

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