Deciphering Protein drug Targets for Mucormycosis: A Computational Approach

A serious public health issue worldwide, Mucormycosis is a potentially fatal fungal infection that is especially dangerous for people with weakened immune systems. Developing successful treatment plans requires identifying possible protein targets for therapeutic action against the substances causing Mucormycosis. Here, we describe a methodical approach to in silico protein target discovery in Mucorcircinelloides, the causative agent of Mucormycosis. To find candidate proteins necessary for these infections' survival and pathogenicity, we analyzed proteomic data using subtractive proteomics approach aided by bioinformatics tools and databases. To rank possible therapeutic targets, we used sequence analysis, protein-protein interaction prediction, and functional annotation.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2025-04-12
DOI
https://doi.org/10.36948/ijfmr.2025.v07i02.41250
Primary Topic
Antifungal resistance and susceptibility
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deciphering Protein drug Targets for Mucormycosis: A Computational Approach

Anubhuti Jha, Ranjana Sahu, Monika Nirmalkar
International Journal For Multidisciplinary Research
Antifungal resistance and susceptibility
article

Deciphering Protein drug Targets for Mucormycosis: A Computational Approach

Anubhuti Jha, Ranjana Sahu, Monika Nirmalkar
article en

Abstract

A serious public health issue worldwide, Mucormycosis is a potentially fatal fungal infection that is especially dangerous for people with weakened immune systems. Developing successful treatment plans requires identifying possible protein targets for therapeutic action against the substances causing Mucormycosis. Here, we describe a methodical approach to in silico protein target discovery in Mucorcircinelloides, the causative agent of Mucormycosis. To find candidate proteins necessary for these infections' survival and pathogenicity, we analyzed proteomic data using subtractive proteomics approach aided by bioinformatics tools and databases. To rank possible therapeutic targets, we used sequence analysis, protein-protein interaction prediction, and functional annotation.

International Journal For Multidisciplinary ResearchVol. 7(2)
Openalex Percentile: Top 90%
Antifungal resistance and susceptibility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.