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Metachromatic ground substance of normal aortic media at different ages in Chinese subjects

MUMTAZUDDIN AHMED M, MOHIUDDIN A

Med J Malaya, 1963 Mar;17:199-208.
PMID: 13936588
Fulltext Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

Ferdous N, Reza MN, Hossain MU, Mahmud S, Napis S, Chowdhury K, et al.

PLoS One, 2023;18(6):e0287179.
PMID: 37352252 DOI: 10.1371/journal.pone.0287179

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 (Mpro) 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-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro 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 Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. 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 Mpro. 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 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.
Fulltext Investigating the role of hypothetical protein (AAB33144.1) in HIV-1 virus pathogenicity: A comparative study with FDA-Approved inhibitor compounds through In silico analysis and molecular docking

Hossain MI, Asha AT, Hossain MA, Mahmud S, Chowdhury K, Mohiuddin RB, et al.

Heliyon, 2024 Jan 15;10(1):e23183.
PMID: 38163140 DOI: 10.1016/j.heliyon.2023.e23183

AIM AND OBJECTIVE: Due to the a lot of unexplored proteins in HIV-1, this research aimed to explore the functional roles of a hypothetical protein (AAB33144.1) that might play a key role in HIV-1 pathogenicity.
METHODS: The homologous protein was identified along with building and validating the 3D structure by searching several bioinformatics tools.
RESULTS: Retroviral aspartyl protease and retropepsin like functional domains and motifs, folding pattern (cupredoxins), and subcellular localization in cytoplasmic membrane were determined as biological activity. Besides, the functional annotation revealed that the chosen hypothetical protein possessed protease-like activity. To validate our generated protein 3D structure, molecular docking was performed with five compounds where nelfinavir showed (-8.2 kcal/mol) best binding affinity against HXB2 viral protease (PDB ID: 7SJX) and main protease (PDB ID: 4EYR) protein.
CONCLUSIONS: This study suggests that the annotated hypothetical protein related to protease action, which may be useful in viral genetics and drug discovery.

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