The cutting-edge field of multiscale modeling1–4 and integrative structural biology5–9 allows us to perform data- guided molecular simulations to study complex biological systems at multiple organizational levels. This approach has attracted increasing attention in recent years, as it allows the integration of a variety of experimental methods - to construct a more holistic view of the spatial organization within cells and tissues5. My research goal is to develop integrative modeling tools, to be able to build structural models of biomolecular assemblies using experimental data from either electron or fluorescence light microscopy, or spectroscopy; thereby accounting for the spatial heterogeneity while investigating the molecular forces driving biological function across different length and timescales. Since individual experimental methods only glimpse discrete biological length and timescales5, careful theoretical and computational strategies are necessary to towards mechanistic understanding of underlying biophysical and biochemical processes.
An example of such an approach is combining molecular dynamics with high-resolution structural data, from cryo- electron microscopy/tomography (cryo-EM/ET) and machine learning — to generate high-resolution atomic models10–14 .These high-resolution models of biomolecules or their assemblies are essential towards understanding of biological functions and presents opportunities for fundamental and applied research in areas of biomedical, chemical, physical, and clinical sciences. In the area of integrative modeling my scientific contributions has helped gain mechanistic insights into vaccine induced thrombotic thrombocytopenia as in the case of ChAdOx1 - human platelet 4 (PF4) interactions for the AstraZeneca COVID-19 vaccine15, metabolite permeation across bacterial microcompartment shells16, diffusion in crowded microcompartments16 and in plant17 and fungal18 cell walls, and determine the temperature dependent unfolding kinetics of protein inactivation19.
Figure 1 illustrates how different experimental methods can be integrated to perform data-guided molecular simulations to provide mechanistic insight into critical biological processes occurring inside or across the cell. In addition, thermodynamic properties derived from such molecular simulations can be used to build master equations to predict macroscopic pharmacokinetic behavior.
References:
(1) Eckmann, D. M.; Bradley, R. P.; Kandy, S. K.; Patil, K.; Janmey, P. A.; Radhakrishnan, R. Multiscale Modeling of Protein Membrane Interactions for Nanoparticle Targeting in Drug Delivery. Current Opinion in Structural Biology 2020, 64, 104–110, DOI: 10.1016/j.sbi.2020.06.023.
(2) Radhakrishnan, R. A Survey of Multiscale Modeling: Foundations, Historical Milestones, Current Status, and Future Prospects. AIChE Journal 2021, 67, e17026, DOI: 10.1002/aic.17026.
(3) Alber, M.; Buganza Tepole, A.; Cannon, W. R.; De, S.; Dura-Bernal, S.; Garikipati, K.; Karniadakis, G.; Lytton, W. W.; Perdikaris, P.; Petzold, L.; Kuhl, E. Integrating Machine Learning and Multiscale Modeling—Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences. npj Digit. Med. 2019, 2, 1–11, DOI: 10.1038/s41746-019-0193-y.
(4) Amaro, R. E.; Mulholland, A. J. Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures. Nat Rev Chem 2018, 2, 1–12, DOI: 10.1038/s41570-018-0148.
(5) McCafferty, C. L.; Klumpe, S.; Amaro, R. E.; Kukulski, W.; Collinson, L.; Engel, B. D. Integrating Cellular Electron Microscopy with Multimodal Data to Explore Biology across Space and Time. Cell 2024, 187, 563–584, DOI: 10.1016/j.cell.2024.01.005.
(6) Rout, M. P.; Sali, A. Principles for Integrative Structural Biology Studies. Cell 2019, 177, 1384–1403, DOI: 10. 1016/j.cell.2019.05.016.
(7) Nussinov, R.; Tsai, C.-J.; Shehu, A.; Jang, H. Computational Structural Biology: Successes, Future Directions, and Challenges. Molecules 2019, 24, 637, DOI: 10.3390/molecules24030637.
(8) Nussinov, R.; Liu, Y.; Zhang, W.; Jang, H. Cell Phenotypes Can Be Predicted from Propensities of Protein Con- formations. Current Opinion in Structural Biology 2023, 83, 102722, DOI: 10.1016/j.sbi.2023.102722.
(9) Frank, J.; Ourmazd, A. Continuous Changes in Structure Mapped by Manifold Embedding of Single-Particle Data in Cryo-EM. Methods 2016, 100, 61–67, DOI: 10.1016/j.ymeth.2016.02.007.
(10) Sarkar, D.; Lee, H.; Vant, J. W.; Turilli, M.; Vermaas, J. V.; Jha, S.; Singharoy, A. Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting. J. Chem. Inf. Model. 2023, DOI: 10.1021/acs.jcim.3c00350.
(11) Gupta, C.; Sarkar, D.; Tieleman, D. P.; Singharoy, A. The Ugly, Bad, and Good Stories of Large-Scale Biomolec- ular Simulations. Current Opinion in Structural Biology 2022, 73, 102338, DOI: 10.1016/j.sbi.2022.102338.
(12) Ziemianowicz, D. S.; Kosinski, J. New Opportunities in Integrative Structural Modeling. Current Opinion in Struc- tural Biology 2022, 77, 102488, DOI: 10.1016/j.sbi.2022.102488.
(13) Vant, J. W.; Sarkar, D.; Streitwieser, E.; Fiorin, G.; Skeel, R.; Vermaas, J. V.; Singharoy, A. Data-Guided Multi-Map Variables for Ensemble Refinement of Molecular Movies. J. Chem. Phys. 2020, 153, 214102, DOI: 10.1063/5. 0022433.
(14) Vant, J. W.; Sarkar, D.; Nguyen, J.; Baker, A. T.; Vermaas, J. V.; Singharoy, A. Exploring Cryo-Electron Microscopy with Molecular Dynamics. Biochemical Society Transactions 2022, 50, 569–581, DOI: 10.1042/BST20210485.
(15) Baker, A. T.*; Boyd, R. J.*; Sarkar, D.*; Teijeira-Crespo, A.*; Chan, C. K.*; Bates, E.; Waraich, K.; Vant, J.; Wilson, E.; Truong, C. D.; Lipka-Lloyd, M.; Fromme, P.; Vermaas, J.; Williams, D.; Machiesky, L.; Heurich, M.; Nagalo, B. M.; Coughlan, L.; Umlauf, S.; Chiu, P.-L.; Rizkallah, P. J.; Cohen, T. S.; Parker, A. L.; Singharoy, A.; Borad, M. J. ChAdOx1 Interacts with CAR and PF4 with Implications for Thrombosis with Thrombocytopenia Syndrome. Science Advances 2021, 7, eabl8213, DOI: 10.1126/sciadv.abl8213. (* = equal contribution)
(16) Sarkar, D.; Maffeo, C.; Sutter, M.; Aksimentiev, A.; Kerfeld, C.; Vermaas, J. Atomic View of Photosynthetic Metabo- lite Permeability Pathways and Confinement in Cyanobacterial Carboxysomes, 2024, DOI: 10.26434/chemrxiv- 2024-kbcdf.
(17) Sarkar, D.; Bu, L.; Jakes, J. E.; Zieba, J. K.; Kaufman, I. D.; Crowley, M. F.; Ciesielski, P. N.; Vermaas, J. V. Diffusion in Intact Secondary Cell Wall Models of Plants at Different Equilibrium Moisture Content. The Cell Surface 2023, 9, 100105, DOI: 10.1016/j.tcsw.2023.100105.
(18) Widanage, M. C. D.; Gautam, I.; Sarkar, D.; Mentink-Vigier, F.; Vermaas, J. V.; Fontaine, T.; Latgé, J.-P.; Wang, P.; Wang, T. Structural Remodeling of Fungal Cell Wall Promotes Resistance to Echinocandins, 2023, DOI: 10.1101/2023.08.09.552708.
(19) Sarkar, D.; Kang, P.; Nielsen, S. O.; Qin, Z. Non-Arrhenius Reaction-Diffusion Kinetics for Protein Inactivation over a Large Temperature Range. ACS Nano 2019, 13, 8669–8679, DOI: 10.1021/acsnano.9b00068.
Teaching Interests:
A prerequisite for scientific and independent thinking is the ability to learn the fundamental principles behind science and engineering. Once the fundamental knowledge is comprehended, then its application across many disciplines comes naturally. I believe that the initial step in achieving this goal is through a clear and effective teaching method that naturally invokes an acumen for logical reasoning, and thereby a taste for science. Such skills empower students to become competent citizens as well as competent professionals often motivating them to practice a career in a STEM-based field. However, there exists an intrinsic communication gap between a teacher and a student when introducing new concepts, methods, and information which often originates from mutual differences in their backgrounds and, sometimes, their academic expectations. To bridge this gap, my teaching strategies are transformational, and not necessarily transactional, ultimately ensuring that learning remains active.
I have nearly four years of experience as a teaching assistant in the Department of Mechanical and Aerospace Engineering, at the University of Texas at Arlington, where I assisted in courses in undergraduate and graduate-level thermodynamics, chemical kinetics, and heat transfer – conduction, convection, and radiation. Immediately after completing my doctoral degree, I was appointed by the Department as an Adjunct Lecturer to teach an undergraduate course on classical thermodynamics. My teaching experience combined with my academic training and research experience in biophysical and chemical sciences provides an excellent background to teach fundamental and applied courses such as thermodynamics, chemical kinetics, heat and mass transfer, statistical mechanics, computational physics, biophysics, physical chemistry, engineering mathematics, fluid mechanics, and scientific programming. While my academic education is in engineering, I have undertaken extensive training in applied mathematics at postgraduate level in physical and natural science – providing the necessary foundation required to teach physical and life science courses.
Also in my current role as a postdoc, I routinely mentor undergraduate research students in computational biochemistry, molecular biology and biophysics. Under my supervision they are encouraged to critically think science and they have presented their research in national conferences and published in peer-reviewed journals. In my experience, such early exposure while doing research has helped them to better plan the next steps of their young scientific career.
Google Scholar: https://scholar.google.com/citations?user=KSW1uFAAAAAJ&hl=en
Advisor: Dr. Josh Vermaas, MSU-DOE Plant Research Laboratory, East Lansing, Michigan