From an early age I was drawn to the natural world — spending time outdoors, observing wildlife, and wondering how living things worked at levels invisible to the naked eye. That childhood fascination with nature’s complexity grew alongside a deepening interest in science, eventually leading me to pursue chemistry and biochemistry as a way to understand the molecular logic underlying biological systems. I was particularly captivated by the idea that organisms, through millions of years of evolution, had already solved many of the problems that medicine and pharmacology are still working to address.
Reading Venomous: How Earth’s Deadliest Creatures Mastered Biochemistry by Christie Wilcox crystallized this perspective for me. Wilcox’s exploration of how venomous animals evolved extraordinarily precise biochemical tools — proteases, ion channel blockers, receptor-targeted peptides — that are now being harnessed to treat human disease made clear that nature is not just an inspiration but an active partner in drug discovery. That book, alongside my doctoral research into secreted proteolytic enzymes in Bacillus subtilis, has shaped my conviction that the most promising therapeutic molecules may already exist in snake venoms, fungal bioactive compounds, and plant metabolites — waiting to be identified by the right combination of proteomics, mass spectrometry, and biochemical characterization.
PhD Thesis Research
Comparative N-terminomic analysis of extracellular protease substrates in B. subtilis
Secreted proteins from Bacillus subtilis were analyzed to identify novel proteolytic modifications using a comparative N-terminomics approach. By examining the wild-type strain alongside a mutant lacking all seven major extracellular proteases (Δ7), 139 previously documented and 129 novel proteolytic sites were identified across 46 proteins. Three polysaccharide-degrading hydrolases were identified with novel cleavages separating their catalytic domains from carbohydrate-binding modules, revealing a previously undescribed mechanism of proteolytic enzyme remodeling in the B. subtilis secretome.
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N-terminal guanidinylation workflow for secretome N-terminomics
Developed and applied a COFRADIC-like N-terminomics workflow using 1H-pyrazole-1-carboxamidine (HPCA) to guanidinylate free primary amines on secreted B. subtilis proteins prior to tryptic digestion. Unlike SMTA amidination, HPCA labeling converts lysine ε-amines to homoarginine, preserving trypsin cleavage sites and improving peptide coverage. Following labeling, proteins were digested with trypsin, fractionated by HpHRPLC into 12 fractions, and analyzed by nanoLC-MS/MS on an Orbitrap Fusion Lumos. Workflow validation achieved 92% labeling efficiency at known signal peptidase cleavage sites.
Comparative secretome profiling of wild-type vs. Δ7 protease-deficient B. subtilis
Performed a quantitative comparative proteomics study between B. subtilis strain 3610 and its Δ7 derivative lacking all seven major extracellular proteases (AprE, NprE, Bpr, Epr, Mpr, Vpr, WprA). LFQ using Proteome Discoverer 2.5 and Sequest HT identified 1,174 proteins representing 28% of the proteome. Student’s t-test analysis of 69 consistently observed secreted proteins identified 30 with statistically significant abundance differences between strains.
Identification of novel proteolytic sites & protease substrate mapping
129 novel proteolytic cleavage sites were identified across 46 proteins. Of these, 47 WT-exclusive sites implicated 17 proteins as substrates of the seven extracellular proteases. Domain-separating cleavages were identified in AmyE, EglS, and Abn2, where proteolysis separated catalytic glycoside hydrolase domains from carbohydrate-binding modules. Structural homologs YncM and YrpD (96% sequence identity) displayed conserved cleavage motifs, providing a candidate sequence for protease specificity analysis.
Novel cleavage site discoveryProtease substrate identificationDomain architecture analysisAlphaFold / PDB
Future Research Interests
Where I want to go next
Building on my doctoral foundation in microbial proteomics, I am eager to pursue two interconnected research directions. Click either panel to explore further.
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Therapeutic Discovery from Natural Sources
Identifying bioactive compounds with drug potential
Nature has evolved an extraordinary diversity of proteins, metabolites, and bioactive compounds with precise biological activities. Select a natural source below to explore research directions and supporting literature.
🐍Venom proteins→
🍄Fungal bioactive compounds→
🔗BioID & protein interactome mapping→
🚧 Under Construction🧬
Biomarker Identification via Mass Spectrometry Imaging
Spatially resolved molecular mapping in patient tissues
Mass spectrometry imaging enables untargeted, spatially resolved detection of proteins, peptides, lipids, and metabolites directly within tissue sections — preserving the anatomical context of molecular distributions for disease biomarker discovery.
Content coming soon
Therapeutic Discovery from Natural Sources
Venom Proteins
Venom proteomics & therapeutic discovery
Overview
Animal venoms are highly complex mixtures of biologically active proteins, peptides, and non-protein molecules — effectively constituting vast natural libraries of pharmacologically active, receptor-targeted compounds pre-optimized over millions of years of evolution. Snake venoms alone encompass diverse protein families including serine proteases, metalloproteases, phospholipases A2, three-finger toxins, and disintegrins, many exhibiting exquisite target selectivity that makes them compelling leads for therapeutic development. The venomics approach — integrating proteomics, transcriptomics, and high-resolution mass spectrometry — has now been applied to over 200 snake species, revealing an enormously rich pharmacological resource that remains largely untapped. The same experimental toolkit developed during my doctoral work maps directly onto established venomics workflows.
Serine protease; cleaves fibrinogen to promote clot dissolution
References
1Abd El-Aziz, T.M., Soares, A.G., & Stockand, J.D. (2020). Advances in venomics: Modern separation techniques and mass spectrometry. Journal of Chromatography B, 1160, 122352.
2Escoubas, P., & King, G.F. (2009). Venomics as a drug discovery platform. Expert Review of Proteomics, 6(3), 221–224.
3Tan, C.H., Tan, K.Y., & Tan, N.H. (2019). A protein decomplexation strategy in snake venom proteomics. In Functional Proteomics: Methods and Protocols (vol. 1871). Humana Press.
Therapeutic Discovery from Natural Sources
Fungal Bioactive Compounds
Fungal bioactive compound discovery
Overview
Fungi represent one of the most chemically prolific kingdoms in nature, producing a remarkable diversity of bioactive compounds — including antibiotics, immunosuppressants, statins, antifungals, alkaloids, terpenoids, polysaccharides, and bioactive proteins — many of which have already transformed medicine and remain far from fully characterized. Edible and medicinal mushrooms such as Hericium erinaceus, Ganoderma sp., and Lentinula edodes are known to produce relevant molecules with demonstrated antioxidant, anti-inflammatory, antimicrobial, antidiabetic, and anticancer properties, while genera such as Penicillium, Aspergillus, and Streptomyces continue to yield clinically relevant low-molecular-weight bioactive compounds. Critically, only a small fraction of described fungal species has ever been screened for drug potential, and the integration of modern multi-omics approaches is now opening systematic routes to discovering novel bioactive compounds. My doctoral expertise in LC-MS/MS-based protein and compound identification provides a direct methodological bridge to this opportunity.
References
1Kaushik, R. (2025). Mushrooms as nutritional powerhouses: A review of their bioactive compounds, health benefits, and value-added products. Foods, 14(5), 741. https://doi.org/10.3390/foods14050741
2Sharma, K.K. (2022). Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics. Applied Microbiology and Biotechnology, 106(9), 3465–3488. https://doi.org/10.1007/s00253-022-11945-8
3Role of fungal metabolites in pharmaceuticals, human health, and agriculture. In Fungi and Fungal Metabolites for the Improvement of Human and Animal Life, Nutrition and Health. Elsevier. https://doi.org/10.1016/B978-0-323-95241-5.00005-8
Therapeutic Discovery from Natural Sources
BioID & Interactome Mapping
Identifying drug candidate interactors using BioID & mass spectrometry
Overview
Once a bioactive compound is identified as a candidate therapeutic, a critical next step is understanding the full network of proteins it engages — both its intended targets and off-target interactors — within a living cellular context. Protein–protein interactions (PPIs) are essential for signal transduction, transcriptional regulation, and metabolism, and their perturbation is directly linked to diseases including cancer, neurodegeneration, and infectious disease. Two complementary mass spectrometry-based approaches are particularly powerful for mapping these interaction networks.
BioID: proximity-dependent biotinylation
BioID fuses a promiscuous mutant biotin ligase (BirA*) to a protein of interest — such as a drug target or candidate therapeutic. When expressed in living cells and supplied with biotin, BirA* covalently biotinylates all proteins within a ~10 nm radius, capturing even weak, transient, or insoluble interactions that conventional pull-down approaches miss. Biotinylated proteins are then purified under stringent conditions via streptavidin affinity chromatography and identified by nanoLC-MS/MS. Because labeling is triggered by biotin addition, BioID also enables temporal resolution — allowing researchers to track how an interactome changes in response to drug treatment, cellular stress, or changes in localization. Improved variants including BioID2 and TurboID offer reduced size and faster labeling kinetics, further expanding the approach’s utility in drug mechanism studies.
MS-based PPI mapping: complementary strategies
Beyond BioID, a suite of MS-based techniques offers orthogonal views of drug candidate interaction networks. Affinity purification–mass spectrometry (AP-MS) captures stable protein complexes through antibody or tag-based pulldowns, while cross-linking MS (XL-MS) provides structural information on interaction interfaces by covalently capturing protein pairs in close proximity before digestion. Size exclusion chromatography–MS (SEC-MS) profiles native protein complexes without chemical perturbation, and thermal proteome profiling (TPP) identifies target engagement by measuring shifts in protein thermal stability upon drug binding across the proteome. Together these approaches provide a comprehensive picture of how a candidate therapeutic engages the cellular proteome, informing both mechanism of action and potential off-target liability.
References
1Roux, K.J., Kim, D.I., Burke, B., & May, D.G. (2018). BioID: A screen for protein–protein interactions. Current Protocols in Protein Science, 91, 19.23.1–19.23.15. https://doi.org/10.1002/cpps.51
2Liu, X., Abad, L., Chatterjee, L., Cristea, I.M., & Varjosalo, M. (2024). Mapping protein–protein interactions by mass spectrometry. Mass Spectrometry Reviews, 45(1), 69–106. https://doi.org/10.1002/mas.21887
Future Research Interest
Biomarker identification via mass spectrometry imaging
Overview
Mass spectrometry imaging (MSI) is an emerging technique that enables untargeted, spatially resolved detection of proteins, peptides, lipids, and metabolites directly within tissue sections — without the need for labels or antibodies. Unlike conventional proteomics, MSI preserves the anatomical context of molecular distributions, allowing researchers to correlate biochemical signatures with specific tissue regions, cell types, or pathological features. Platforms such as MALDI-MSI and DESI-MSI are increasingly being applied to clinical biomarker discovery in cancer, neurodegeneration, and inflammatory disease. My background in mass spectrometry-based proteomics, sample preparation, and database-driven peptide identification positions me to contribute meaningfully to MSI workflows.
Key research questions
Which spatially localized protein or peptide signatures in patient tissues correlate with disease state, stage, or treatment response?
Can proteolytic processing events — such as signal peptide cleavage or propeptide removal — be detected and mapped spatially using MSI?
How can N-terminomics sample preparation principles improve detection of low-abundance biomarker peptides in complex tissue matrices?
What MSI-derived molecular signatures can serve as diagnostic or prognostic biomarkers in oncology or metabolic disease?
References
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Technical Skills
Laboratory expertise
Skills extracted directly from doctoral thesis research and validated through hands-on experimental application.
⚗️Mass Spectrometry & Instrumentation
Orbitrap Fusion Lumos Tribrid MSLTQ Ion Trap (Thermo Finnigan)ESI-Q-TOFElectrospray Ionization (ESI)nanoLC-MS/MSData-Dependent Acquisition (DDA)Data-Independent Acquisition (DIA)Collision-Induced Dissociation (CID)High Energy CID (HCD)Tandem MS (MS/MS)Top-down proteomicsBottom-up proteomicsb1 ion detection & stabilization
