SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions.
BACKGROUND
Docking allows to predict ligand binding to proteins, since the 3D-structure for the target is available. Several docking studies have been carried out to identify potential ligands for drug targets. Many of these studies resulted in the leads that were later developed as drugs.


OBJECTIVE
Our goal here is to describe the development of an integrated computational tool to assess docking accuracy and build new scoring functions to predict ligandbinding affinity.


METHODS
We carried out docking simulations using MVD program for a data set available on CSAR 2014 database (coagulation factor Xa) for which ligand-binding information and structures are available. These docking results were analyzed using SAnDReS available at www.sandres.net. Machine learning methods were applied to build new scoring functions and our results were compared with previously published benchmarks.


RESULTS
Our integrated docking strategy generated poses with docking accuracy higher than previously published benchmarks. In addition, the new scoring function developed using SAnDReS shows better performance than well-established scoring functions such the ones available in Autodock, Autodock- Vina, Gold, Glide, and MVD.


CONCLUSIONS
The big data generated during docking lacked an integrated computational tool for statistical analysis of the influence of structural parameters on docking and scoring function performance. Here we describe methods to evaluate docking results using SAnDReS, a computational environment for statistical analysis of docking results and development of scoring functions. We believe that SAnDReS is a computational tool with potential to improve accuracy in docking projects.
Mariana Morrone
MM
Xavier
Gabriela Sehnem
GS
Heck
Mauricio Boff de
MB
Avila
Nayara Maria Bernhardt
NMB
Levin
Val Oliveira
VO
Pintro
Nathalia Lemes
NL
Carvalho
Faculty of Biosciences - Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre-RS 90619-900, Brazil.. Brazil.
Walter Filgueira de
WF
Azevedo
D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D000069550:Machine Learning; D062105:Molecular Docking Simulation
Dock; drug; machine learning.; protein; target