M.sup.propred: A machine learning antagonists

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the o...

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Bibliographic Details
Published in:PloS one 2023-06, Vol.18 (6), p.e0287179
Main Authors: Ferdous, Nadim, Reza, Mahjerin Nasrin, Hossain, Mohammad Uzzal, Mahmud, Shahin, Napis, Suhami, Chowdhury, Kamal, Mohiuddin, A. K. M
Format: Article
Language:English
Subjects:
Analysis
Health aspects
Machine learning
Molecular dynamics
Proteases
Online Access:Get full text
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Summary:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (M.sup.pro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure-activity relationship (CSAR) model to find substructures that leads to to anti-M.sup.pro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe M.sup.pro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set's modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for M.sup.pro inhibition. Finally, the predictive model is made publicly accessible as a web-app named M.sup.pro pred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 M.sup.pro . Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to M.sup.pro . Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 M.sup.pro . The web-app can be accessed from
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0287179