Portrait of Paras Verma

Research profile

Paras Verma, Ph.D.

Computational biology of variants, proteoforms, and receptor mechanisms

I study how genetic background and post-transcriptional processing change protein functional landcape. For this, I link RNA-level variation to protein features and integrate structural models, omics datasets, and curated bioinformatics resources.

parasvcb@gmail.com ORCID GitHub LinkedIn

Ph.D., IISER Mohali, Shashi B. Pandit group

Current: Research Associate (Postdoc), Plaksha University

Department of Biological Systems (BioSystems) Engineering

Monika Sharma group

Plaksha University, Alpha, Sector 101, IT City Rd, Sahibzada Ajit Singh Nagar, Punjab 140306

Research focus Projects Publications Methods Training Life in motion

From population variation to receptor-level mechanism.

Many omics studies report variants, isoforms, genes, or differential events. I focus on the next question: whether those changes alter proteins, receptors, ligands, or membrane-facing regulatory systems in a way that can be tested structurally or functionally.

To do this, I am currently analyzing data at three different but complementary layers: how genetic variants are interpreted across populations and ethnic groups, how proteoforms affect cell-surface proteins, and how publicly deposited datasets from peer-reviewed studies can be reanalyzed to understand disease-specific state shifts (in context of ALS).

Questions
I am working to understand if and how ancestry-linked variants affect disease-relevant protein features, examining how post-transcriptional processing shape proteoforms using cell-surface proteins as a model system, and exploring how related molecular patterns may be reflected in inflammatory state shifts in ALS.
Approach
To do this, I am combining population-aware variant interpretation with exon- and transcript-level analysis and structural interpretation. I use a family of cell-surface proteins as a model system to examine how proteoform differences, including changes outside lipid-interacting regions, may influence receptor behavior and ligand interactions. This analysis helps generate hypotheses about how exon-level and proteoform variation may influence protein behavior and cellular state. I am examining this in the context of ALS by analyzing public transcriptomic datasets.