Research

Pandey et. al. arXiv 2026

Foldability-Evolvability Paradigm

As a group we are intrigued by the complex relationship between protein folding and evolutionary adaptability. While traditional studies focus primarily on protein folding mechanisms, we take a unique detour by linking the folding landscape directly to the biophysical fitness landscape. We aim to understand how the physical properties of proteins—such as stability, flexibility, and folding kinetics—interact with evolutionary pressures to shape protein function and adaptability. By integrating insights from protein dynamics and evolutionary biology, we are interested in uncovering the principles that govern protein evolution.

Resistome Modelling and Evolution-Informed Drug Discovery

We are probing into the drug-“resistome”—the complete set of drug resistance genes and the associated systems scale evolutionary mechanisms leading to the emergence of resistant phenotypes.

Bacterial Evolution and Antibiotic Resistance

We are taking a multi-scale “Evolution-informed” approach to understand the complex problem of “Antibiotic-resistance”. This is an “Evolutionary” problem as the resistance is the result of “evolutionary-escape” variants arising because of drug induced “selection”. Using computer simulations, experimental evolution and molecular studies we are attempting to draft an “anti-evolution” approach whereby the “Escape routes” could be constrained and drugs could be made “Evolution-proof”. We are currently focusing on folate-pathway proteins and how they regulate the evolutionary dynamics of resistance and shape up the biophysical fitness landscape.

Evolutionary Dynamics in Apicomplexan Systems

Very recently we have started investigating the evolutionary dynamics of apicomplexan parasites, such as Toxoplasma and Plasmodium species, respectively causing toxoplasmosis and malaria. We want to understand how these parasites evolve in response to drug pressure and immune system challenges. To start off we are focusing on the folate pathway proteins viz. Pf-DHFR and DHPS and their evolutionary-systems scale contributions in shaping the fitness landscape. Going forward we will take a more super-pathway level approach to understanding the sensitivities in the metabolic graph architecture and come up with systems and evolution informed drugs which would constrain apicomplexan evolvability.

Network Constraints and Systems-Level Evolvability

We study how molecular perturbations propagate and shape cellular fitness, robustness, and evolutionary adaptation. We ask how the position of our target/candidate protein within metabolic and regulatory networks determines the consequences of mutation, inhibition, and environmental stress. Using kinetic modelling, network analysis, perturbation studies, and experimental validation, we map how cells redistribute flux, exploit compensatory routes, and maintain function under selective pressure. A central goal is to connect molecular biophysics with network architecture—linking changes in protein function, dynamics, and evolvability to pathway-level behavior and ultimately to cellular fitness. This provides a framework for understanding antimicrobial resistance, adaptive rewiring, and the rational design of evolution-informed therapeutic combinations.

Sanyal et.al. Biochem.Biophys.Res.Commun. 2025

Protein Biophysics to Translational Therapeutics

Our group integrates structural biology, computational modeling, and biophysical characterization to guide the rational design of next-generation therapeutics, with applications in antibody–drug conjugate (ADC)-based cancer immunotherapy and antimicrobial resistance.

A central focus is MUC1-C, an oncoprotein that is aberrantly expressed in many carcinomas. We map the binding interfaces of therapeutic antibodies on the MUC1-C extracellular SEA domain, including antibodies developed with collaborators at Brigham and Women's Hospital, Harvard Medical School. We also build glycosylated, membrane-bound models of MUC1-C and study them with molecular dynamics simulations to understand its structure, stability, and conformational dynamics in a native-like environment. These data are complemented by biophysical characterization of the protein's folding and conformational behavior.

Finally, we use evolutionary conservation and sequence-variation analysis across species to identify functionally constrained epitopes. This information informs the refinement of antibody specificity and the design of more effective ADCs. By linking molecular-level insight to therapeutic design, we aim to develop more selective and potent biologics against cancer and drug-resistant pathogens.

Evolutionary Dynamics of Neurodegeneration

We examine the evolutionary dynamics underlying neurodegenerative diseases. By studying genetic mutations and protein misfolding, we aim to uncover new therapeutic strategies to slow or prevent neurodegeneration. Protein misfolding and aggregation has been linked as one of the primary causative factors for multiple neurodegenerative diseases including Parkinsonism, Alzheimer’s disease and Amyotrophic Lateral Sclerosis (ALS). Aggregation of human cytosolic Cu–Zn superoxide dismutase (SOD1) is implicated in the motor neuron disease, amyotrophic lateral sclerosis (ALS). Exploring SOD1’s evolutionary history we revealed that the bulk of the evolutionarily co-varying residues are localized in the loop regions and positioned differentially depending upon the metal residence and concomitant steric restrictions of the loops. Presently we are working to understand the evolutionary selection rules that led to the emergence of metal coordination sites and local-folds/substructures in SOD system and how that has impacted the foldabaility and multiscale evolvability.

Shape of protein universe

Exploring the shape of protein universe - Evolution informed Design

We aim to explore the protein-fold space and design evolution inspired “synthetic (multi)functional proteins”. Deep mutational analyses give a glimpse into the vast landscape of alternative-conformational possibilities barring the small subset which nature has explored in 3.9 billion years of evolution. Often two interesting properties for two different proteins are adjacently located on the sequence space but are separated by large barriers which evolution has not crossed. Understanding the topography of sequence space can help design artificial protein with more than one function. We aim to map the vast diversity of protein structures and their evolutionary trajectories. By studying the "protein fold space," we seek to understand how different protein folds and functions have evolved, providing a comprehensive view of the relationships between protein structure, function, and evolution. We want to understand the shape of the protein-universe and its underlying design principles and evolutionary constraints, to guide the development of novel proteins with desired functions.

Evolution and Systems Informed Pipeline for Drug Target Identification

We have developed a systems-and -evolution guided pipeline to understand the mechanisms of actions of potential drug candidates. Elucidating intracellular drug targets has been a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. We developed a hierarchic workflow to narrow in on specific targets based on analysis of metabolomics data and rescue experiments. We analyze global metabolomics utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets and identify the drug-target using experiments. As interest in ‘white-box’ machine learning methods continues to grow, we demonstrate how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows in general.

§ Methodologies:

Computational Biophysics, Experimental Biophysics, Experimental Evolution, Machine Learning and Language Models, Systems Biology

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