Ronnie Mondal, PhD
Ex-President, VT GPSS | Python Developer | Computational Chemist
PhD scientist and strategy professional working in research proposal development at Tulane University, providing key support to faculty across academic disciplines, with a primary focus on the School of Science and Engineering, in the strategic preparation and submission of competitive federal and foundation grant proposals for research funding. Extensive expertise in data analytics, Python software development, and complex quantitative modeling strengthens the technical rigor of proposals and advances university-wide research initiatives. Proven ability to lead high-stakes workstreams, develop actionable strategies, and build cross-disciplinary partnerships that maximize institutional success.
Holds a PhD in Chemistry from Virginia Tech and previously served as President of the Virginia Tech Graduate and Professional Student Senate (VT GPSS). Currently driving cross-disciplinary research strategy and proposal workstreams, with a long-term focus on higher education governance, institutional policy, and research administration. Open to executive leadership opportunities, strategic advisory roles, and collaborative initiatives at the intersection of science, quantitative data, and academic policy.
Publications
PhD Dissertation - Modeling the Role of Water in Protein Structure and Function
Water is a solvent with high static dielectric constant, substantial dipole moment, significant electronic polarizability, and a dynamic hydrogen-bonding network. As such, water plays a pivotal role in protein structure and function, the molecular mechanisms of which remain challenging to determine. This dissertation develops and applies computational frameworks to rigorously integrate water effects on protein structure and function. We first examine how stronger protein–water interactions in the classical AMOEBA force field produce more realistic deformation behavior in Collagen Mimetic Peptides (CMPs). In particular, our simulations of (PPG)n CMPs under physiological conditions capture the experimental observation that shorter CMPs deform more strongly than longer ones. To better characterize these CMPs, we developed HeliXplore, an open-source Python package for analyzing single- and multi-strand deformations. We further report that the length-dependent deformation we observe is associated with anisotropic translational and rotational relaxation dynamics of surrounding water molecules. Overall, we show that AMOEBA offers a substantial improvement over traditional force fields that often underestimate protein–water interactions. We next investigate the diffusion of solvated sodium ions through voltage-gated sodium channels (Navs). To do so, we develop a Continuous-Time Random Walk (CTRW) model that describes microscopic diffusion as a function of spatial and temporal disorder at the molecular scale. In addition, we establish a framework that incorporates this molecular disorder using information from polarizable MD simulations, enabling a consistent bottom-up approach. Using the CTRW model, we show that increased temporal disorder of the ion can accelerate diffusion in disordered media. More broadly, our results suggest that structural dynamics may help explain how Navs achieve both high selectivity, through strong sodium binding, and rapid ion diffusion across the membrane.
HeliXplore: A Python package for analyzing multi-strand helix deformations. (Software Release)
Software release on Zenodo and GitHub. HeliXplore is an open-source Python tool for quantitative analysis of deformations in multi-strand helices, using molecular dynamics trajectories.
Using AMOEBA to Uncover Deformation and Hydration Anisotropy in Collagen-Mimetic Peptides
Collagen is the primary constituent of the supramolecular fibers found in connective tissues. To be able to predict collagen’s properties, we need rigorous characterization at the molecular scale. In this work, we focus on collagen-mimetic peptides (CMPs) that are composed of short sequences of (PPG) tripeptides folded into a triple helix. We use molecular dynamics simulations with the AMOEBA polarizable force field to model these CMPs at physiological temperature. We show that AMOEBA captures length-dependent fraying of the triple helix, consistent with experimental observations. We also introduce new metrics to quantify the deformation of the triple helix and hydration dynamics. This enables a systematic quantification of CMPs under a variety of conditions, which was missing in the field. We apply our approach to mutated CMPs and CMPs in the presence of d-glucose. We find that translational diffusion anisotropy is a molecular signature of CMP structural integrity.
Tuning Polyacrylate Composition to Recognize and Modulate Fluorescent Proteins
Molecular definition is usually regarded as a prerequisite to achieve protein recognition and functional modulation, particularly for macromolecular interactions. Herein, we report that polymers with specific combinations of monomers arranged into random sequences [random hetero oligomers (RHOs)] can selectively bind to a model protein. Using green fluorescent protein (GFP) as a target, polyacrylates were developed that bound with nanomolar affinity and enhanced fluorescence by >100%. Purification of the polymerization product revealed subpopulations of compositions with distinct affinities and selectivity for GFP over a competing protein. Experimental and computational binding analyses confirmed that there are distinct RHO–GFP interactions, which are influenced by RHO chemical composition. These findings show that sequence-defined structures are not a prerequisite for selective protein recognition. Synthetic polymers can instead serve as scalable, tunable platforms for molecular recognition—representing a significant leap towards next-generation sensing, therapeutic, responsive, and catalytic materials in domains previously dominated by biologics or complex peptide scaffolds.
Kinetics of calcite nucleation onto sulfated chitosan derivatives and implications for water–polysaccharide interactions during crystallization of sparingly soluble salts
Anionic macromolecules are found at sites of CaCO3 biomineralization in diverse organisms, but their roles in crystallization are not well-understood. We prepared a series of sulfated chitosan derivatives with varied positions and degrees of sulfation, DS(SO3–), and measured calcite nucleation rate onto these materials. Fitting the classical nucleation theory model to the kinetic data reveals the interfacial free energy of the calcite–polysaccharide–solution system, γnet, is lowest for nonsulfated controls and increases with DS(SO3–). The kinetic prefactor also increases with DS(SO3–). Simulations of Ca2+–H2O–chitosan systems show greater water structuring around sulfate groups compared to uncharged substituents, independent of sulfate location. Ca2+–SO3– interactions are solvent-separated by distances that are inversely correlated with DS(SO3–) of the polysaccharide. The simulations also predict SO3– and NH3+ groups affect the solvation waters and HCO3– ions associated with Ca2+. Integrating the experimental and computational evidence suggests sulfate groups influence nucleation by increasing the difficulty of displacing near-surface water, thereby increasing γnet. By correlating γnet and net charge per monosaccharide for diverse polysaccharides, we suggest the solvent-separated interactions of functional groups with Ca2+ influence thermodynamic and kinetic components to crystallization by similar solvent-dominated processes. The findings reiterate the importance of establishing water structure and properties at macromolecule–solution interfaces.
Dynamics accelerate the kinetics of ion diffusion through channels: Continuous-time random walk models beyond the mean field approximation
Ion channels are proteins that play a significant role in physiological processes, including neuronal excitability and signal transduction. However, the precise mechanisms by which these proteins facilitate ion diffusion through cell membranes are not well understood. This is because experimental techniques to characterize ion channel activity operate on a time scale too large to understand the role of the various protein conformations on diffusion. Meanwhile, computational approaches operate on a time scale too short to rationalize the observed behavior at the microscopic scale. In this paper, we present a continuous-time random walk model that aims to bridge the scales between the atomistic models of ion channels and the experimental measurement of their conductance. We show how diffusion slows down in complex systems by using 3D lattices that map out the pore geometry of two channels: Nav1.7 and gramicidin. We also introduce spatial and dynamic site disorder to account for system heterogeneity beyond the mean field approximation. Computed diffusion coefficients show that an increase in spatial disorder slows down diffusion kinetics, while dynamic disorder has the opposite effect. Our results imply that microscopic or phenomenological models based on the potential of mean force data overlook the functional importance of protein dynamics on ion diffusion through channels.