Nirav Modha

Mr. Nirav Modha

orcid

Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 December 2025 in Journal of Computational Biophysics and Chemistry
Verified
Journal Article
Closed access
NMNirav Modha
Nirav Modha
Emil AlexovEmil Alexov
Emil Alexov

30 September 2026 in Preprints.org
Verified
Preprint
Open access
PDF file
KBK. C. Bikash
K. C. Bikash
NMNirav Modha
Nirav Modha
PPPitambar Poudel
Pitambar Poudel
Shailesh Kumar PandayShailesh Kumar Panday
Shailesh Kumar Panday
Emil AlexovEmil Alexov
Emil Alexov
Monogenic disorders present a compelling target for drug discovery due to their clear, single-gene etiology. Yet, effective therapies remain scarce, largely because diverse mutation types scattered across a protein obstruct targeted drug design. In this study, we leverage MOGEDO, a curated database of monogenic disorders, to test whether different pathogenic genotypes within a single protein converge on a common molecular mechanism. By clustering MOGEDO proteins into eight functional families and analyzing representative proteins carrying both benign and pathogenic mutations, we integrated molecular dynamics, network analysis, and side-chain entropy metrics. Our findings reveal that distinct pathogenic mutations disrupt the structural and dynamic properties of the wild-type and benign protein through common pathways. By demonstrating that diverse disease-causing mutations manifest a unified pathogenic phenotype, this work provides a framework for designing broad-spectrum therapeutics that target common dysfunctional mechanisms rather than individual mutations.

Verified
Journal Article
Open access
PDF file
BKBikash Kc
Bikash Kc
NMNirav Modha
Nirav Modha
PPPitambar Poudel
Pitambar Poudel
Shailesh Kumar PandayShailesh Kumar Panday
Shailesh Kumar Panday
Emil AlexovEmil Alexov
Emil Alexov
Monogenic disorders present a compelling target for drug discovery due to their clear, single-gene etiology. Yet, effective therapies remain scarce, largely because diverse mutation types scattered across a protein obstruct targeted drug design. In this study, we leverage MOGEDO, a curated database of monogenic disorders, to test whether different pathogenic genotypes within a single protein converge on a common molecular mechanism. By clustering MOGEDO proteins into eight functional families and analyzing representative proteins carrying both benign and pathogenic mutations, we integrated molecular dynamics, network analysis, and side-chain entropy metrics. Our findings reveal that distinct pathogenic mutations disrupt the structural and dynamic properties of the wild-type and benign protein through common pathways. By demonstrating that diverse disease-causing mutations manifest a unified pathogenic phenotype, this work provides a framework for designing broad-spectrum therapeutics that target common putative intervention sites rather than individual mutations.