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J Comput Chem. 2019 Aug 13. doi: 10.1002/jcc.26048. [Epub ahead of print]

Taba: A Tool to Analyze the Binding Affinity.

Author information

1
Laboratory of Computational Systems Biology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Ipiranga Avenue, 6681 Partenon, 90619-900, Porto Alegre/RS, Brazil.
2
Specialization Program in Bioinformatics, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Ipiranga Avenue, 6681 Partenon, 90619-900, Porto Alegre/RS, Brazil.

Abstract

Evaluation of ligand-binding affinity using the atomic coordinates of a protein-ligand complex is a challenge from the computational point of view. The availability of crystallographic structures of complexes with binding affinity data opens the possibility to create machine-learning models targeted to a specific protein system. Here, we describe a new methodology that combines a mass-spring system approach with supervised machine-learning techniques to predict the binding affinity of protein-ligand complexes. The combination of these techniques allows exploring the scoring function space, generating a model targeted to a protein system of interest. The new model shows superior predictive performance when compared with classical scoring functions implemented in the programs Molegro Virtual Docker, AutoDock4, and AutoDock Vina. We implemented this methodology in a new program named Taba. Taba is implemented in Python and available to download under the GNU license at https://github.com/azevedolab/taba.

KEYWORDS:

binding affinity; drug design; machine learning; protein-ligand interactions; scoring function

PMID:
31410856
DOI:
10.1002/jcc.26048

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