(2je) Permeability, Energetics and Kinetics of Photosynthetic Metabolites across Synthetic Microcompartments

Research Interests: Computers today are roughly 3-fold more powerful than those a decade ago. This dramatic period of growth, from the post industrial revolution era through today, has led to significant advancement in multiscale modeling, which is a cutting-edge field that combines physics-based algorithms to predict complex biological behavior. This view of the cellular organization is now possible due to the advent of exascale computing infrastructure, and the development of GPU-resident molecular simulations, high-fidelity continuum models and machine learning.

Conventionally, multiscale simulations embody the theoretical framework to predict biological system behavior which is often challenging to determine experimentally., Now, observations from such simulations are combined with experimental findings, enabling us to probe deeper into the structure-function relationship behind complex biomolecular processes. Thus, there is a need of such integrative modeling strategies, which have already proven to have immense impact on human health - developing novel biomaterials, observing host-pathogen interaction to explain disease progression, targeted drug delivery for therapeutic or biosensing application, molecular hyperthermia for organ cryo-preservation. Success in biomedical space - have encouraged investigators to develop and apply these predictive models in building a circular economy for sustainable energy applications as well.

Towards these diverse applications of national interest, as a computational scientist, I have experience working on multi million atom molecular simulations of synthetic sub-cellular microcompartments in cyanobacteria. The insights gained from MD simulations on metabolite permeability help towards the goal of repurposing these natural compartments to engineer improvements in photosynthesis, as a cargo vesicle for targeted drug-delivery and similar bioenergy and sustainability applications.

Also in the sustainability space, my research in the area of plant-based biomaterials, specifically the plant secondary cell wall has explained the behavior of cell wall polymers (cellulose, hemicellulose and lignin) at different moisture levels. This work has shed light on the mechanistic understanding of diffusive processes within the cell wall, which eventually leads to moisture induced glass transition. Such observations help refine the existing strategies for wood protection treatment and biomass conversion, which collectively represent a significant renewable resource towards the national interest.

In the similar context of plant-based biomaterials, often to improve the efficiency of chemo-mechanical processes of biomass conversion, it is imperative to determine the thermodynamics and kinetics of biomass recalcitrance. Using principles of statistical mechanics and physical chemistry, binding free energies calculated from MD simulations for different organosolv-based separation of lignocellulosic feedstock help to identify organic solvents, a first step in lignin valorization strategies. Here, physics based multiscale simulations, combined with pilot experimental studies and techno economic models can help optimize the relationship between economic growth and environmental impact, a critical gap in scientific research today.

Switching the lens of the "computational microscope" now towards biomedical applications has resulted in significant advancements - which bear the potential to have a great impact on human health. This success has created new and exciting opportunities in the field. Towards this, my efforts have focused on integrative modeling, an area of rapid methodological development, wherein, atom-resolved structures of biological systems are determined by merging data from multiple experimental sources with physics and informatics approaches. These elegant fitting, learning, and inferencing methodologies, capitalizing on molecular simulations, have been successful in resolving a range of biomolecular complexes. One such recent success is towards the high-resolution cryo-EM structure of ChAdOx1 nCoV-19 viral vector (AZD1222/Vaxzevria), popularly known as AstraZeneca’s COVID-19 vaccine (PDB: 7RD1). Here, the computational method of molecular dynamics flexible fitting (MDFF) was combined with single particle cryo-EM experiments, to resolve the structure of the viral shell at 3.07 Å. Subsequently, brownian dynamics simulation and surface plasmon resonance (SPR) experiments provided the mechanistic and functional insights into the interaction of human platelet factor protein (PF-4) with ChAdox1. This led to the biological discovery explaining the occurrence of a rare but potentially life-threatening blood-clotting disorder clinically referred to as thrombosis with thrombocytopenia syndrome (TTS). Similar opportunities prevail in understanding polysaccharide arrangement in a fungal cell wall, which contribute towards virulence and design of targeted antifungal therapy. Another area which stands to benefit from multiscale modeling is molecular hyperthermia, where biocompatible nanomaterials, with engineered thermophysical properties, are pivotal for rapid phase-transition during rewarming of vitrified tissue, later to be used for organ transplant surgeries.

Here, I have listed a few examples where multiscale computational modeling and simulation provide a unique strategy for challenges encountered in health and sustainability applications. This field is gaining traction due to its ability to provide insights into complex behavior of biological systems, where there is scope for both bottom-up and top-down approaches. Physics-based simulations combined with artificial intelligence are increasingly becoming complementary to experimental approaches, to answer critical questions related to health, climate and energy securities.

Teaching Interests: My academic training was in Mechanical Engineering, specializing in the area of thermodynamics (statistical mechanics), kinetics, heat transfer, fluid mechanics and applied mathematics. My postdoctoral research experience has focussed on molecular simulations, which embodies concepts from classical physics, physical chemistry, organic chemistry, biochemistry, bioinformatics, structural biology and scientific computing. Due to this diverse range of experience, I can teach both undergraduate and graduate courses in mechanical, chemical or bioengineering.

In addition, as an adjunct lecturer in Mechanical Engineering, I taught an undergraduate course on thermodynamics. Prior to that, I had multiple opportunities as a teaching assistant for both undergraduate and graduate courses in the area of thermal sciences. Here, I list the courses classified under the undergraduate and graduate curriculum, that I will be interested to offer if presented with the opportunity.

Undergraduate: Thermodynamics and Chemical Kinetics, Physical Chemistry, General Chemistry, Heat Transfer and Fluid Flow, Mathematical Analysis, Scientific Programming, Finite Element, Biomechanics, Biocomputation (includes Bioinformatics).

Graduate: Statistical Mechanics, Thermodynamics and Chemical Kinetics, Nanoscale Thermal Physics, Computational Biophysics, Finite Element, Transport Phenomena, Multiscale modeling in Biology, Computational Chemistry.

Abstract: Carboxysomes are bacterial microcompartments (BMCs), found in cyanobacteria that locally concentrate carbon dioxide to improve the efficiency of the enzyme RuBisCO, a key step of the photosynthetic carbon fixation by the Calvin-Benson-Bassham cycle. The carboxysome shell encapsulates the enzymes RuBisCO and Carbonic Anhydrase, where the latter converts water soluble bicarbonate to carbon dioxide, increasing the local concentration of carbon dioxide for RuBisCO inside the carboxysome. In addition to CO₂ and bicarbonate, other metabolites such as oxygen, 3-phosphoglyceric acid and ribulose-1,5-bisphosphate also need to permeate through the carboxysome shell to efficiently perform carbon fixation. Direct quantification of permeability of these chemical compounds across the carboxysome currently remains a challenge and is a critical information for the design of synthetic BMCs in photosynthesis applications. In this work, we use a high resolution cryo-EM structure of a synthetic beta-carboxysome shell and perform unbiased all-atom molecular simulations, to determine the metabolite permeability across this synthetic shell. Contrary to prior hypotheses, we find that the synthetic carboxysome shell itself is not selectively permeable to CO₂ over bicarbonate, with the metabolite permeabilities estimated around 0.01 cm/s. This is 100x slower compared to CO₂ permeability across phospholipid bilayers and 10x faster when compared against bicarbonate transport across cellular membranes.

That being said, computing a one-dimensional free-energy profile along the central pore of the BMC hexamer (BMC-H) protein using enhanced sampling, does indicate metabolite transport selectivity, where anionic carbonaceous compounds are favorable to transport through the positively gated central pore, while the transport of gaseous molecules remain unfavorable along this reaction path. So then why is permeability calculated from metabolite transition events observed in equilibrium molecular simulations within the same order of magnitude?

To answer this question, we compute the two-dimensional free energy surface along the central pore and radial axes of the BMC-H protein. Indeed, the two-dimensional free energy reveals insight into alternate pathways. Here, the anionic charged metabolites favor transport through the central pore of the BMC-H protein, with a free energy change of -2 kcal/mol. However, gaseous molecules prefer to leak through the interfaces between monomeric protein chains in a single hexamer unit or between multimers, in addition to the central pores present in the synthetic shell.

Subsequently, having gained insight into metabolite permeability and transport energetics, we ask how does a carboxysome concentrate CO₂ to compensate for the inefficiencies of endogenous RuBisCO, specifically slow turnover rate and photorespiration? To answer that we perform Brownian Dynamics simulations using Atomic Resolution Brownian Dynamics (ARBD), to estimate the mean first passage time for carbon dioxide diffusion in a RuBisCO crowded in-silico carboxysome. The first passage time provides insights into the rate of CO₂ interacting with the RuBisCO enzyme, present inside carboxysome. The results suggest that while carbon dioxide can leak from the shell at around 0.01 cm/s, there is a high propensity for the gas molecule to encounter a RuBisCO protein, before permeating out of the shell.

Hence, using such detailed multiscale molecular simulations, we provide mechanistic insights on the permeability, energetics (free-energy) and kinetics to fix atmospheric CO₂ by the Calvin-Benson-Bassham cycle. Such results help design and engineer cellular sub-compartments for not only sustainable energy applications, but also present opportunities in metabolic, chemical and biomedical space.