Cargando…

Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS

A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound–Protein interactions (CPI)—one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via...

Descripción completa

Detalles Bibliográficos
Autores principales: Kanai, Chisato, Kawasaki, Enzo, Murakami, Ryuta, Morita, Yusuke, Yoshimori, Atsushi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434178/
https://www.ncbi.nlm.nih.gov/pubmed/34500569
http://dx.doi.org/10.3390/molecules26175131
_version_ 1783751536765042688
author Kanai, Chisato
Kawasaki, Enzo
Murakami, Ryuta
Morita, Yusuke
Yoshimori, Atsushi
author_facet Kanai, Chisato
Kawasaki, Enzo
Murakami, Ryuta
Morita, Yusuke
Yoshimori, Atsushi
author_sort Kanai, Chisato
collection PubMed
description A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound–Protein interactions (CPI)—one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via pairwise kernel-based support vector machine (SVM) is the main feature of CGBVS which gives high prediction accuracy, with simple implementation and easy handling. We studied whether the CGBVS technique can identify ligands for targets without ligand information (orphan targets) using data from G protein-coupled receptor (GPCR) families. As the validation method, we tested whether the ligand prediction was correct for a virtual orphan GPCR in which all ligand information for one selected target was omitted from the training data. We have specifically expressed the results of this study as applicability index and developed a method to determine whether CGBVS can be used to predict GPCR ligands. Validation results showed that the prediction accuracy of each GPCR differed greatly, but models using Multiple Sequence Alignment (MSA) as the protein descriptor performed well in terms of overall prediction accuracy. We also discovered that the effect of the type compound descriptors on the prediction accuracy was less significant than that of the type of protein descriptors used. Furthermore, we found that the accuracy of the ligand prediction depends on the amount of ligand information with regard to GPCRs related to the target. Additionally, the prediction accuracy tends to be high if a large amount of ligand information for related proteins is used in the training.
format Online
Article
Text
id pubmed-8434178
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-84341782021-09-12 Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS Kanai, Chisato Kawasaki, Enzo Murakami, Ryuta Morita, Yusuke Yoshimori, Atsushi Molecules Article A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound–Protein interactions (CPI)—one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via pairwise kernel-based support vector machine (SVM) is the main feature of CGBVS which gives high prediction accuracy, with simple implementation and easy handling. We studied whether the CGBVS technique can identify ligands for targets without ligand information (orphan targets) using data from G protein-coupled receptor (GPCR) families. As the validation method, we tested whether the ligand prediction was correct for a virtual orphan GPCR in which all ligand information for one selected target was omitted from the training data. We have specifically expressed the results of this study as applicability index and developed a method to determine whether CGBVS can be used to predict GPCR ligands. Validation results showed that the prediction accuracy of each GPCR differed greatly, but models using Multiple Sequence Alignment (MSA) as the protein descriptor performed well in terms of overall prediction accuracy. We also discovered that the effect of the type compound descriptors on the prediction accuracy was less significant than that of the type of protein descriptors used. Furthermore, we found that the accuracy of the ligand prediction depends on the amount of ligand information with regard to GPCRs related to the target. Additionally, the prediction accuracy tends to be high if a large amount of ligand information for related proteins is used in the training. MDPI 2021-08-24 /pmc/articles/PMC8434178/ /pubmed/34500569 http://dx.doi.org/10.3390/molecules26175131 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kanai, Chisato
Kawasaki, Enzo
Murakami, Ryuta
Morita, Yusuke
Yoshimori, Atsushi
Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS
title Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS
title_full Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS
title_fullStr Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS
title_full_unstemmed Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS
title_short Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS
title_sort computational prediction of compound–protein interactions for orphan targets using cgbvs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434178/
https://www.ncbi.nlm.nih.gov/pubmed/34500569
http://dx.doi.org/10.3390/molecules26175131
work_keys_str_mv AT kanaichisato computationalpredictionofcompoundproteininteractionsfororphantargetsusingcgbvs
AT kawasakienzo computationalpredictionofcompoundproteininteractionsfororphantargetsusingcgbvs
AT murakamiryuta computationalpredictionofcompoundproteininteractionsfororphantargetsusingcgbvs
AT moritayusuke computationalpredictionofcompoundproteininteractionsfororphantargetsusingcgbvs
AT yoshimoriatsushi computationalpredictionofcompoundproteininteractionsfororphantargetsusingcgbvs