Cargando…
Assessing multiple score functions in Rosetta for drug discovery
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...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7549810/ https://www.ncbi.nlm.nih.gov/pubmed/33044994 http://dx.doi.org/10.1371/journal.pone.0240450 |
_version_ | 1783592852490551296 |
---|---|
author | Smith, Shannon T. Meiler, Jens |
author_facet | Smith, Shannon T. Meiler, Jens |
author_sort | Smith, Shannon T. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7549810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-75498102020-10-20 Assessing multiple score functions in Rosetta for drug discovery Smith, Shannon T. Meiler, Jens PLoS One Research Article 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. Public Library of Science 2020-10-12 /pmc/articles/PMC7549810/ /pubmed/33044994 http://dx.doi.org/10.1371/journal.pone.0240450 Text en © 2020 Smith, Meiler http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Smith, Shannon T. Meiler, Jens Assessing multiple score functions in Rosetta for drug discovery |
title | Assessing multiple score functions in Rosetta for drug discovery |
title_full | Assessing multiple score functions in Rosetta for drug discovery |
title_fullStr | Assessing multiple score functions in Rosetta for drug discovery |
title_full_unstemmed | Assessing multiple score functions in Rosetta for drug discovery |
title_short | Assessing multiple score functions in Rosetta for drug discovery |
title_sort | assessing multiple score functions in rosetta for drug discovery |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7549810/ https://www.ncbi.nlm.nih.gov/pubmed/33044994 http://dx.doi.org/10.1371/journal.pone.0240450 |
work_keys_str_mv | AT smithshannont assessingmultiplescorefunctionsinrosettafordrugdiscovery AT meilerjens assessingmultiplescorefunctionsinrosettafordrugdiscovery |