A Dessen

AA Freitas

Ana T Winck

AR Leach

AT Winck

B Chandrika

CF Wong

CH da Silveira

CT Caskey

DA Case

Duncan DA Ruiz

E Althus

EK Schroeder

F Jiang

F Wang

F Österberg

G Jones

GA Jeffrey

GM Morris

GM Verkhivker

H Alonso

HM Berman

ID Kuntz

IH Witten

J Apostolakis

J Han

J-H Lin

JJ Irwin

JR Quinlan

JS Oliveira

Karina S Machado

KS Machado

M Totrov

ML Teodoro

MR Kuo

NA Kruh

Osmar Norberto de Souza

P Cozzini

P-N Tan

PD Lyne

RE Amaro

RMA Knegtel

S Huang

TP Lybrand

World Health Organization

Y Wang

Y-P Pang

BMC Genomics

English

PubMed

Springer - Publisher Connector

Mining flexible-receptor docking experiments to select promising protein receptor snapshots

Abstract
          
            Background
            Molecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands. Typically, molecular docking algorithms consider receptors as rigid bodies. Receptors are, however, intrinsically flexible in the cellular environment. The use of a time series of receptor conformations is an approach to explore its flexibility in molecular docking computer simulations, but it is extensively time-consuming. Hence, selection of the most promising conformations can accelerate docking experiments and, consequently, the RDD efforts.
          
          
            Results
            We previously docked four ligands (NADH, TCL, PIF and ETH) to 3,100 conformations of the InhA receptor from M. tuberculosis. Based on the receptor residues-ligand distances we preprocessed all docking results to generate appropriate input to mine data. Data preprocessing was done by calculating the shortest interatomic distances between the ligand and the receptor’s residues for each docking result. They were the predictive attributes. The target attribute was the estimated free-energy of binding (FEB) value calculated by the AutodDock3.0.5 software. The mining inputs were submitted to the M5P model tree algorithm. It resulted in short and understandable trees. On the basis of the correlation values, for NADH, TCL and PIF we obtained more than 95% correlation while for ETH, only about 60%. Post processing the generated model trees for each of its linear models (LMs), we calculated the average FEB for their associated instances. From these values we considered a LM as representative if its average FEB was smaller than or equal the average FEB of the test set. The instances in the selected LMs were considered the most promising snapshots. It totalized 1,521, 1,780, 2,085 and 902 snapshots, for NADH, TCL, PIF and ETH respectively.
          
          
            Conclusions
            By post processing the generated model trees we were able to propose a criterion of selection of linear models which, in turn, is capable of selecting a set of promising receptor conformations. As future work we intend to go further and use these results to elaborate a strategy to preprocess the receptors 3-D spatial conformation in order to predict FEB values. Besides, we intend to select other compounds, among the million catalogued, that may be promising as new drug candidates for our particular protein receptor target.
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https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/1471-2164-11-S5-S6?site=bmcgenomics.biomedcentral.com

Mining flexible-receptor docking experiments to select promising protein receptor snapshots

Abstract

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