Abstract
MotivationThe use of experimental information has been demonstrated to increase the success rate of computational macromolecular docking. Many methods use information to post-filter the simulation output while others drive the simulation based on experimental restraints, which can become problematic for more complex scenarios such as multiple binding interfaces.ResultsWe present a novel method for including interface information into protein docking simulations within the LightDock framework. Prior to the simulation, irrelevant regions from the receptor are excluded for sampling (filter of initial swarms) and initial ligand poses are pre-oriented based on ligand input information. We demonstrate the applicability of this approach on the new 55 cases of the Protein–Protein Docking Benchmark 5, using different amounts of information. Even with incomplete or incorrect information, a significant improvement in performance is obtained compared to blind ab initio docking.Availability and implementationThe software is supported and freely available from https://github.com/brianjimenez/lightdock and analysis data from https://github.com/brianjimenez/lightdock_bm5.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Computational tools are essential to predict and describe threedimensional (3D) interactions between biomolecules
We describe and benchmark an updated implementation of LightDock that supports the use of information to drive or bias the docking simulation by filtering out swarms, pre-orienting ligand poses based on the available information and biasing the scoring energy upon satisfied residue contact restraints
The latest release of LightDock (0.7.0) (Jimenez et al, 2019), which supports the use of information to drive the docking in the format
Summary
Computational tools are essential to predict and describe threedimensional (3D) interactions between biomolecules. LightDock (Jimenez-Garcıa et al, 2018) is a multiscale flexible framework for the 3D determination of binary protein complexes based on the Glowworm Swarm Optimization (GSO) (Krishnanand and Ghose, 2009) algorithm that systematically optimizes the generated docking poses towards those energetically more favourable at every simulation step. Introducing restraints or biases in docking is a powerful mechanism to drive the simulation towards poses that satisfy those restraints (Dominguez et al, 2003). We describe and benchmark an updated implementation of LightDock that supports the use of information to drive or bias the docking simulation by filtering out swarms, pre-orienting ligand poses based on the available information and biasing the scoring energy upon satisfied residue contact restraints
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