From Bench Science to Data-Driven Drug Discovery: An Interdisciplinary Research Journey 

Research Synopsis

My doctoral research focused on the molecular regulation of apoptosis in cancer cells, with particular emphasis on death receptor signaling, mitochondrial control of cell death, and cross-talk with survival pathways. Using biochemical, molecular, and cell-based approaches, this work examined how cancer cells evade programmed cell death despite activation of pro-apoptotic signals.

A major component of this research investigated TRAIL (TNF-related apoptosis-inducing ligand)–induced apoptosis in leukemic and breast cancer cells. These studies identified Protein Kinase C (PKC) and MAPK signaling as key negative regulators of apoptosis, demonstrating that although TRAIL activates upstream caspases and BID cleavage, PKC-dependent signaling blocks critical mitochondrial events—including Bax translocation, cytochrome c release, and caspase-9 activation—thereby preventing apoptotic cell death. This work provided early mechanistic insight into TRAIL resistance, a significant challenge in cancer therapy.

Extending these findings, the research further showed that PKC-mediated activation of the MAPK pathway antagonizes TRAIL-induced apoptosis downstream of death receptor signaling in breast cancer cells, highlighting how survival pathways modulate mitochondrial commitment to apoptosis in tumor cells.

In parallel, this body of work also examined PARP-1–mediated DNA damage responses in cancer cells, elucidating how PARP activity influences apoptosis and cell fate following genotoxic stress. These findings contributed to understanding how DNA repair mechanisms intersect with apoptotic signaling, informing later therapeutic strategies that exploit vulnerabilities in apoptotic and DNA repair pathways.

Together, these studies establish a cohesive mechanistic framework describing how cancer cells integrate death receptor signals, mitochondrial checkpoints, and survival pathways to regulate apoptosis, with broad implications for the development of targeted cancer therapies.

Publications

Sarker, M., Ruiz-Ruiz, C., & López-Rivas, A. (2001). Activation of protein kinase C inhibits TRAIL-induced caspase activation, mitochondrial events and apoptosis in a human leukemic cell line. Cell Death & Differentiation, 8(9), 912–920. https://doi.org/10.1038/sj.cdd.4400883

Sarker, M., Ruiz-Ruiz, C., Robledo, G., & López-Rivas, A. (2002). Stimulation of the mitogen-activated protein kinase pathway antagonizes TRAIL-induced apoptosis downstream of BID cleavage in human breast cancer MCF-7 cells. Oncogene, 21, 4323–4327. https://doi.org/10.1038/sj.onc.1205523

Sarker, M., Ruiz-Ruiz, C., & López-Rivas, A. (2002). PARP-1 modulates the effectiveness of p53-mediated DNA damage response. Oncogene, 21, 8845–8855. https://doi.org/10.1038/sj.onc.1205959

Research Synopsis

This peer-reviewed study from my postdoctoral research at the National Institutes of Health (NIH) examines the earliest molecular events in death receptor–mediated apoptosis.

The work identifies SPOTS (Signaling Protein Oligomeric Transduction Structures) as novel plasma membrane–associated signaling platforms that form following Fas (CD95) receptor activation, prior to receptor internalization and mitochondrial involvement. Using confocal microscopy, live-cell imaging, and biochemical approaches, the study shows that SPOTS formation requires FADD recruitment but occurs independently of downstream caspase activity.

Importantly, disease-associated Fas mutations in Autoimmune Lymphoproliferative Syndrome (ALPS) disrupt SPOTS formation, linking defective receptor clustering to impaired apoptotic signaling. These findings reveal a previously unrecognized membrane-based amplification step in death receptor signaling, with implications for immune regulation, cancer biology, and therapeutic targeting of apoptotic pathways.

Reference 

Siegel, R. M., Muppidi, J. R., Sarker, M., Lobito, A., Jen, M., Martin, D., Straus, S. E., & Lenardo, M. J. (2004). SPOTS: Signaling protein oligomeric transduction structures are early mediators of death receptor–induced apoptosis at the plasma membrane. The Journal of Cell Biology, 167(4), 735–744. https://doi.org/10.1083/jcb.200406101

Research Synopsis

This body of peer-reviewed work focuses on the development, curation, and analysis of large-scale biological databases to enable systems-level understanding of human proteins, their interactions, and disease relevance. The research integrates proteomics, bioinformatics, and literature-based curation to transform fragmented experimental data into structured, accessible knowledge resources for the scientific community.

Key contributions include the Plasma Proteome Database (PPD), developed as part of the HUPO Plasma Proteome Project, which provides comprehensive annotation of thousands of plasma proteins, including isoforms, post-translational modifications, SNPs, tissue expression, and disease associations. This resource supports biomarker discovery and translational proteomics by enabling systematic exploration of the human plasma proteome.

In parallel, this work contributed to the Human Protein Reference Database (HPRD) and large-scale analyses of the human protein interactome, integrating curated protein–protein interaction data to reveal global properties of cellular networks. These studies established foundational interaction maps and comparative analyses across species, supporting network-based approaches to understanding disease mechanisms.

Together, these contributions advanced early systems biology frameworks, demonstrating how curated biological databases and interaction networks can be leveraged to study human disease, guide experimental design, and support data-driven biomedical research.

References 

1. Muthusamy, B., Hanumanthu, G., Suresh, S., Rekha, B., Srinivas, D., Karthick, L., … Sarker, M., & Pandey, A. (2005). Plasma Proteome Database as a resource for proteomics research. Proteomics, 5(13), 3531–3536. https://doi.org/10.1002/pmic.200401335

2. Peri, S., Navarro, J. D., Amanchy, R., Kristiansen, T. Z., Jonnalagadda, C. K., Surendranath, V., … Sarker, M., & Pandey, A. (2003). Development of the Human Protein Reference Database as an initial platform for approaching systems biology in humans. Genome Research, 13(10), 2363–2371.
   https://doi.org/10.1101/gr.1239303

3. Rual, J.-F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., … Sarker, M., & Vidal, M. (2005). Towards a proteome-scale map of the human protein–protein interaction network. Nature, 437, 1173–1178. https://doi.org/10.1038/nature04209

4. Pandey, A., & Sarker, M. (2006). Human protein interaction networks. In Protein–Protein Interactions: Methods and Applications (pp. xx–xx). Humana Press. https://link.springer.com/chapter/10.1007/978-1-59745-113-3_14

Research Synopsis

This collection of peer-reviewed studies reflects my work in computational biology and translational informatics applied to infectious-disease drug discovery, with an emphasis on tuberculosis (Mycobacterium tuberculosis) and Chagas disease (Trypanosoma cruzi).

Across these papers, I combine pathway/genome-scale biology, cheminformatics, and machine learning to:

• identify metabolic vulnerabilities and potential antimicrobial targets through in silico pathway analysis,

• build and evaluate predictive models that prioritize compounds and targets using biological + chemical features, and

• translate methods into practical tools (e.g., TB Mobile/TB Mobile 2.0) that support searching, visualization, and decision-making for TB drug discovery.

Together, these studies show how computational methods can connect biological networks + chemical space + real-world usability to accelerate hypothesis generation and early-stage discovery for high-burden infectious diseases.

References 

1. Chandra, N., Padiadpu, J., & Sarker, M. (2011). In silico pathway analysis predicts metabolites that are potential antimicrobial targets. Journal of Computer Science & Systems Biology. https://doi.org/10.4172/jcsb.1000071

2. Ekins, S., Freundlich, J. S., Choi, I., Sarker, M., & Talcott, C. (2011). Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery. Trends in Microbiology, 19(2), 65–74. https://doi.org/10.1016/j.tim.2010.10.005

3. Keseler, I. M., Bonavides-Martínez, C., Collado-Vides, J., Gama-Castro, S., Gunsalus, R. P., Johnson, D. A., … Karp, P. D. (2011). EcoCyc: A comprehensive database of Escherichia coli biology. Nucleic Acids Research, 39(Database issue), D583–D590. https://doi.org/10.1093/nar/gkr335

4. Sarker, M., Talcott, C., Madrid, P., Chopra, S., Bunin, B. A., Lamichhane, G., Freundlich, J. S., & Ekins, S. (2012). Combining cheminformatics methods and pathway analysis to identify molecules with whole-cell activity against Mycobacterium tuberculosis. Pharmaceutical Research, 29(12), 3324–3337.
   https://doi.org/10.1007/s11095-013-1172-7

5. Sarker, M., Talcott, C., & Galande, A. (2013). In silico systems biology approaches for the identification of antimicrobial targets. In In Silico Models for Drug Discovery (Methods in Molecular Biology, Vol. 993). https://doi.org/10.1007/978-1-62703-342-8_2

6. Sarker, M., Reynolds, R. C., Freundlich, J. S., & Ekins, S. (2013). TB Mobile: A mobile app for anti-tuberculosis molecules with known targets. Journal of Cheminformatics, 5, Article 13. https://doi.org/10.1186/1758-2946-5-13

7. Ponder, E. L., Freundlich, J. S., Sarker, M., & Ekins, S. (2014). Computational models for neglected diseases: Gaps and opportunities. Pharmaceutical Research, 31(2), 271–277. https://doi.org/10.1007/s11095-013-1205-2

8. Sarker, M., Freundlich, J. S., Reynolds, R. C., & Ekins, S. (2014). TB Mobile 2.0: New target prediction and visualization tools incorporating open-source molecular fingerprints. Journal of Cheminformatics, 6, Article 38. https://doi.org/10.1186/s13321-014-0038-2

9. Ekins, S., Freundlich, J. S., Reynolds, R. C., Sarker, M., & others. (2015). Machine learning models and Pathway Genome Data Base for Trypanosoma cruzi drug discovery. PLOS Neglected Tropical Diseases, 9(6), e0003878. https://doi.org/10.1371/journal.pntd.0003878

10. Ekins, S., Freundlich, J. S., Sarker, M., & others. (2015). Combining metabolite-based pharmacophores with Bayesian machine learning models for Mycobacterium tuberculosis drug discovery. PLOS ONE, 10(4), e0126692. https://doi.org/10.1371/journal.pone.0126692

1. Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery, PLOS ONE 2015 (PMID: 26517557)

2. Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery, PLOS NTD 2015 (PMID: 26114876)

3. New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0, Journal of Cheminformatics 2014 (PMID: 25302078)

4. Computational Models for Neglected Diseases: Gaps and Opportunities, Pharmaceutical Research 2014 (PMID: 23990313)

5. TB mobile: a mobile app for anti-tuberculosis molecules with known targets, Journal of Cheminformatics 2013 (PMID: 23497706)

6. Combining Cheminformatics Methods and Pathway Analysis to Identify Molecules with Whole-Cell Activity Against Mycobacterium Tuberculosis, Pharmaceutical Research 2012 (PMID: 22477069)

7. In Silico Pathway Analysis Predicts Metabolites That Are Potential Antimicrobial Targets, J Comput Sci Syst Biol 2011 (ResearchGate)

8. Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery, Trends Microbiol. 2011 (PMID: 21129975)

9. EcoCyc: a comprehensive database of Escherichia coli biology, Nucleic Acids Res. 2011 (PMID: 21097882)

10. Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets, Nat Genet. 2006 (PMID: 16501559)

11. Human protein reference database--2006 update, Nucleic Acids Res 2006 (PMID: 16381900)

12. Plasma Proteome Database as a resource for proteomics research, Proteomics 2005 (PMID: 16041672)

13. SPOTS: signaling protein oligomeric transduction structures are early mediators of death receptor-induced apoptosis at the plasma membrane, J Cell Biol. 2004 (PMID: 15557123)

14. Stimulation of the mitogen-activated protein kinase pathway antagonizes TRAIL-induced apoptosis downstream of BID cleavage in human breast cancer MCF-7 cells, Oncogene 2002 (PMID: 12082620)

15. PARP-1 modifies the effectiveness of p53-mediated DNA damage response, Oncogene 2002 (PMID: 11850828)

16. Activation of protein kinase C inhibits TRAIL-induced caspases activation, mitochondrial events and apoptosis in a human leukemic T cell line, Cell Death Differ. 2001 (PMID: 11313719)

1. In Silico Systems Biology Approaches for the Identification of Antimicrobial Targets, Springer, Methods in Molecular Biology Volume 993, 2013, pp 13-30 2013 (PMID: 23568461)

2. Plasma Proteome Database, Proteomics of human body fluids Thongboonkerd, V. Editor (Humana Press,Totowa, New Jersey, USA) 2006 (http://www.springer.com/us/book/9781588296573)