Taba: A Tool to Analyze the Binding Affinity

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. © 2019 Wiley Periodicals, Inc. Taba is a program for the development of models to predict the affinity between ligands and proteins. The program uses information extracted from the 3D structures of protein‐ligand complexes. The basic idea behind the Taba is that the determinant structural features responsible for ligand‐binding affinity are already somehow imprinted in the structures of proteinligand complexes. Taba considers the protein‐ligand structure as a mass‐spring system and makes use of machine‐learning techniques to develop targeted scoring functions.