Combining fragment docking with graph theory to improve ligand docking for homology model structures.

ABSTRACT: Computational protein-ligand docking is well-known to be prone to inaccuracies in input receptor structures, and it is challenging to obtain good docking results with computationally predicted receptor structures (e.g. through homology modeling). Here we introduce a fragment-based docking method and test if it reduces requirements on the accuracy of an input receptor structures relative to non-fragment docking approaches. In this method, small rigid fragments are docked first using AutoDock Vina to generate a large number of favorably docked poses spanning the receptor binding pocket. Then a graph theory maximum clique algorithm is applied to find combined sets of docked poses of different fragment types onto which the complete ligand can be properly aligned. On the basis of these alignmen

SUBMITTER: Sarfaraz S 

PROVIDER: S-EPMC7544562 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature