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Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints

BACKGROUND: In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical diffic...

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Autores principales: Liu, Jie, Su, Minyi, Liu, Zhihai, Li, Jie, Li, Yan, Wang, Renxiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516336/
https://www.ncbi.nlm.nih.gov/pubmed/28720122
http://dx.doi.org/10.1186/s12859-017-1750-5
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author Liu, Jie
Su, Minyi
Liu, Zhihai
Li, Jie
Li, Yan
Wang, Renxiao
author_facet Liu, Jie
Su, Minyi
Liu, Zhihai
Li, Jie
Li, Yan
Wang, Renxiao
author_sort Liu, Jie
collection PubMed
description BACKGROUND: In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193–208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively. RESULTS: In this study, we have developed an upgraded version, i.e. KGS2, by employing 3D protein-ligand interaction fingerprints in reference selection. KGS2 was evaluated in combination with four scoring functions (X-Score, ChemPLP, ASP, and GoldScore) on five drug targets (HIV-1 protease, carbonic anhydrase 2, beta-secretase 1, beta-trypsin, and checkpoint kinase 1). In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases. CONCLUSIONS: KGS2 can be applied as a convenient “add-on” to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1750-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-55163362017-07-20 Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints Liu, Jie Su, Minyi Liu, Zhihai Li, Jie Li, Yan Wang, Renxiao BMC Bioinformatics Methodology Article BACKGROUND: In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193–208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively. RESULTS: In this study, we have developed an upgraded version, i.e. KGS2, by employing 3D protein-ligand interaction fingerprints in reference selection. KGS2 was evaluated in combination with four scoring functions (X-Score, ChemPLP, ASP, and GoldScore) on five drug targets (HIV-1 protease, carbonic anhydrase 2, beta-secretase 1, beta-trypsin, and checkpoint kinase 1). In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases. CONCLUSIONS: KGS2 can be applied as a convenient “add-on” to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1750-5) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-18 /pmc/articles/PMC5516336/ /pubmed/28720122 http://dx.doi.org/10.1186/s12859-017-1750-5 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Liu, Jie
Su, Minyi
Liu, Zhihai
Li, Jie
Li, Yan
Wang, Renxiao
Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints
title Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints
title_full Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints
title_fullStr Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints
title_full_unstemmed Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints
title_short Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints
title_sort enhance the performance of current scoring functions with the aid of 3d protein-ligand interaction fingerprints
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516336/
https://www.ncbi.nlm.nih.gov/pubmed/28720122
http://dx.doi.org/10.1186/s12859-017-1750-5
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