Cargando…

Extracting prime protein targets as possible drug candidates: machine learning evaluation

Extracting “high ranking” or “prime protein targets” (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD...

Descripción completa

Detalles Bibliográficos
Autores principales: Chattopadhyay, Subhagata, Do, Nhat Phuong, Flower, Darren R., Chattopadhyay, Amit K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582137/
https://www.ncbi.nlm.nih.gov/pubmed/37608081
http://dx.doi.org/10.1007/s11517-023-02893-0
_version_ 1785122263449731072
author Chattopadhyay, Subhagata
Do, Nhat Phuong
Flower, Darren R.
Chattopadhyay, Amit K.
author_facet Chattopadhyay, Subhagata
Do, Nhat Phuong
Flower, Darren R.
Chattopadhyay, Amit K.
author_sort Chattopadhyay, Subhagata
collection PubMed
description Extracting “high ranking” or “prime protein targets” (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations — ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository (http://DUD-E.decoys.org) are matched against 1040 ligands using “forward modeling” machine learning for initial “data mining and modeling” (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called “reverse modeling” (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data. GRAPHICAL ABSTRACT: [Image: see text]
format Online
Article
Text
id pubmed-10582137
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-105821372023-10-19 Extracting prime protein targets as possible drug candidates: machine learning evaluation Chattopadhyay, Subhagata Do, Nhat Phuong Flower, Darren R. Chattopadhyay, Amit K. Med Biol Eng Comput Original Article Extracting “high ranking” or “prime protein targets” (PPTs) as potent MRSA drug candidates from a given set of ligands is a key challenge in efficient molecular docking. This study combines protein-versus-ligand matching molecular docking (MD) data extracted from 10 independent molecular docking (MD) evaluations — ADFR, DOCK, Gemdock, Ledock, Plants, Psovina, Quickvina2, smina, vina, and vinaxb to identify top MRSA drug candidates. Twenty-nine active protein targets (APT) from the enhanced DUD-E repository (http://DUD-E.decoys.org) are matched against 1040 ligands using “forward modeling” machine learning for initial “data mining and modeling” (DDM) to extract PPTs and the corresponding high affinity ligands (HALs). K-means clustering (KMC) is then performed on 400 ligands matched against 29 PTs, with each cluster accommodating HALs, and the corresponding PPTs. Performance of KMC is then validated against randomly chosen head, tail, and middle active ligands (ALs). KMC outcomes have been validated against two other clustering methods, namely, Gaussian mixture model (GMM) and density based spatial clustering of applications with noise (DBSCAN). While GMM shows similar results as with KMC, DBSCAN has failed to yield more than one cluster and handle the noise (outliers), thus affirming the choice of KMC or GMM. Databases obtained from ADFR to mine PPTs are then ranked according to the number of the corresponding HAL-PPT combinations (HPC) inside the derived clusters, an approach called “reverse modeling” (RM). From the set of 29 PTs studied, RM predicts high fidelity of 5 PPTs (17%) that bind with 76 out of 400, i.e., 19% ligands leading to a prediction of next-generation MRSA drug candidates: PPT2 (average HPC is 41.1%) is the top choice, followed by PPT14 (average HPC 25.46%), and then PPT15 (average HPC 23.12%). This algorithm can be generically implemented irrespective of pathogenic forms and is particularly effective for sparse data. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2023-08-23 2023 /pmc/articles/PMC10582137/ /pubmed/37608081 http://dx.doi.org/10.1007/s11517-023-02893-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Chattopadhyay, Subhagata
Do, Nhat Phuong
Flower, Darren R.
Chattopadhyay, Amit K.
Extracting prime protein targets as possible drug candidates: machine learning evaluation
title Extracting prime protein targets as possible drug candidates: machine learning evaluation
title_full Extracting prime protein targets as possible drug candidates: machine learning evaluation
title_fullStr Extracting prime protein targets as possible drug candidates: machine learning evaluation
title_full_unstemmed Extracting prime protein targets as possible drug candidates: machine learning evaluation
title_short Extracting prime protein targets as possible drug candidates: machine learning evaluation
title_sort extracting prime protein targets as possible drug candidates: machine learning evaluation
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582137/
https://www.ncbi.nlm.nih.gov/pubmed/37608081
http://dx.doi.org/10.1007/s11517-023-02893-0
work_keys_str_mv AT chattopadhyaysubhagata extractingprimeproteintargetsaspossibledrugcandidatesmachinelearningevaluation
AT donhatphuong extractingprimeproteintargetsaspossibledrugcandidatesmachinelearningevaluation
AT flowerdarrenr extractingprimeproteintargetsaspossibledrugcandidatesmachinelearningevaluation
AT chattopadhyayamitk extractingprimeproteintargetsaspossibledrugcandidatesmachinelearningevaluation