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...

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Bibliographic Details
Published in:Medical & biological engineering & computing Vol. 61; no. 11; pp. 3035 - 3048
Main Authors: Chattopadhyay, Subhagata, Do, Nhat Phuong, Flower, Darren R., Chattopadhyay, Amit K.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023
Springer Nature B.V
Subjects:
Algorithms
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Cluster analysis
Clustering
Computer Applications
Data mining
Decoys
Drug Design
Drug development
Drug resistance
drugs
head
Human Physiology
Imaging
Learning algorithms
Ligands
Machine Learning
methicillin-resistant Staphylococcus aureus
Modelling
Molecular docking
Molecular Docking Simulation
Original
Original Article
Outliers (statistics)
prediction
Probabilistic models
Proteins
Radiology
tail
Vector quantization
Forward modeling
Reverse modeling
Machine learning (ML)
DBSCAN
Data mining
Molecular docking
K-means clustering
Gaussian mixture model
Protein–ligand interaction
DUD-E repository
Ligands
Drug design
Protein targets
ISSN:0140-0118, 1741-0444, 1741-0444
Online Access:Get full text
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Summary: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
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ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-023-02893-0