Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand

By Samuel L. DeLuca, Karen R. Khar, Jens Meiler. 2015

136 citations recorded by the source corpus.

RosettaLigand has been successfully used to predict binding poses in protein-small molecule complexes. However, the RosettaLigand docking protocol is comparatively slow in identifying an initial starting pose for the small molecule (ligand) making it unfeasible for use in virtual High Throughput Screening (vHTS). To overcome this limitation, we developed a new sampling approach for placing the ligand in the protein binding site during the initial ‘low-resolution’ docking step. It combines the translational and rotational adjustments to the ligand pose in a single transformation step. The new algorithm is both more accurate and more time-efficient. The docking success rate is improved by 10–15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150. The average time to generate a model is reduced from 50 seconds to 10 seconds. As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries. We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the ‘high-resolution’ full atom refinement step.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Materials Chemistry

In Molecular Biology

Citations in the corpus

RosettaLigandEnsemble: A Small-Molecule Ensemble-Driven Docking Approach (2018, unknown): “Initial parameters for RLE are derived from the latest features of the RosettaLigand algorithm 36,37 and optimized for sampling e ﬃ ciency.”
Web‐accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE) (2018, unknown): “53,54 p K a .”
Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery (2022, background): “In SB molecular docking, small molecule ﬂ exibility is often represented through the inclusion of multiple discrete pre-generated conformers (Brylinski and Skolnick, 2008; Morris et al., 2009; Lemmon and Meiler, 2012; Combs et al., 2013; DeLuca et al., 2015).”
Software for molecular docking: a review (2017, methodology): “Furthermore, ‘RosettaLigand’ uses a low-resolution docking in the initial step combined with translational and rotational adjustments (DeLuca et al. 2015).”