Bioinformatic pipeline for Protein-Carbohydrate complexes structure prediction

MARTI, M; MODENUTTI, C; DI PAOLA, MATIAS; BLANCO, J

Capital Federal

Congreso; Reunión Conjunta de Sociedades de Biociencias; 2017

Proteins that bind carbohydrates are responsible for numerous important biological functions, such as signal transduction, cell adhesion, among many others. Despite the number of reported structures of protein-carbohydrates complexes (PCCs) is constantly increasing, they depends mainly on wet experiments, which are expensive and can be very difficult to perform successfully. Achieving accurate predictions of the sugar binding mode by computational methods remains one of the biggest challenges in computational Glycobiology. This is due mainly because the residues that form the Carbohydrate Binding Site (CBS) can differ from its ideal conformation in protein Homology Models HM, which can thereafter significantly affect Docking algorithms performance. In addition, while generally available docking programs work reasonably well for most drug-like compounds, carbohydrates and carbohydrate-like molecules are often problematic, because Force-Fields (FF) and Scoring Functions are typically designed to reproduce structures of protein-drug complexes.In this work, we present an integrated approach that combines conformational-space sampling of receptor structures builded from a wide range sequence identity templates with MODELLER software, boosted by Molecular Dynamics simulations with AMBER16 package and scored with a biased docking method, the Water-Site Biased Docking Method (WSBDM), an Autodock4 docking protocol with a key FF modification development in our lab. In order to obtain the most plausible PCCs conformations, clustering over final complexes structures predicted by docking and Energetic/Probabilistic analysis was applied. With WSBDM, we was able to reproduce a 93% of PCCs training set builded from Protein Data Bank against a 67% of the conventional docking method. The results show that this emerges as a promising tool to build reliable 3D-models, which can then be used for rational design or optimization of glycomimetic drugs.