Abstract
Rosetta is a computational software suite containing algorithms for a wide variety of macromolecular structure prediction and design tasks including small molecule protocols commonly used in drug discovery or enzyme design. Here, we benchmark RosettaLigand score functions and protocols in comparison to results of other software recently published in the Comparative Assessment of Score Functions (CASF-2016). The CASF-2016 benchmark covers a wide variety of tests including scoring and ranking multiple compounds against a target, ligand docking of a small molecule to a target, and virtual screening to extract binders from a compound library. Direct comparison to the score functions provided by CASF-2016 results shows that the original RosettaLigand score function ranks among the top software for scoring, ranking, docking and screening tests. Most notably, the RosettaLigand score function ranked 2/34 among other report score functions in CASF-2016. We additionally perform a ligand docking test with full sampling to mimic typical use cases. Despite improved performance of newer score functions in canonical protein structure prediction and design, we demonstrate here that more recent Rosetta score functions have reduced performance across all small molecule benchmarks. The tests described here have also been uploaded to the Rosetta scientific benchmarking server and will be run weekly to track performance as the code is continually being developed.
Highlights
Computational structure-based drug discovery is a powerful strategy used to identify and optimize ligands
We demonstrate that the newer score functions, which have been heavily optimized for protein structure prediction and design, perform worse in comparison to the original RosettaLigand score function for different small molecule tasks (Fig 3)
This paper investigates the use of multiple score functions within the Rosetta framework to determine the best available methods for small molecule protocols
Summary
Computational structure-based drug discovery is a powerful strategy used to identify and optimize ligands. Computational protocols that study protein-small molecule (aka ligand) interactions are used in tandem with experiments in structure-based computer aided drug discovery (SB-CADD) [1,2,3,4]. A central aspect of SB-CADD methods is the development of an accurate score function to quantify the physicochemical interactions of protein-ligand complexes [5,6]. Score functions have been continually developed since their inception; reliably calculating interactions between a protein target and a small molecule remains a formidable challenge [7,8]. In terms of ligand docking of one small molecule to a target, the score function.
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