Research

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.

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.

· 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). We demystified the folding/aggregation landscape of SOD1 and decoded the conformational communication made by the two metal pockets by deploying a unique application of statistical cluster analysis based on Fourier Transform Infrared spectroscopic readouts. Combining experimental and theoretical frameworks using representative ALS disease mutants, we developed a ‘co-factor derived membrane association model’ wherein mutational stress closer to the Zn (but not to the Cu) pocket is responsible for membrane association-mediated toxic aggregation and survival time scale after ALS diagnosis. 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.

· Protein Dynamics and Evolutionary Systems for Therapeutic Innovation

An ongoing work with Brigham Women’s Hospital and Harvard Medical School focusses on the critical intersection of protein structural dynamics and underlying systems-scale evolutionary dynamics to enhance therapeutic innovation.

In immuno-oncology oncoproteins represent critical targets due to their role in signalling proliferation and survival within carcinomas. Recent advances in understanding the structural dynamics of these oncoproteins have paved the way for the development of novel therapeutic agents. Concurrently, in endocrinology, the structural dynamics of Hormone binding proteins play a pivotal role in accurately determining free hormone levels, which are essential for diagnosing and managing hormonal disorders.

With our multi-scale approaches we are elucidating the conformational changes and interactions of these proteins. This integrative approach will enhance our understanding of their function and regulation and help develop systems-informed therapeutic strategies in both cancer and hormonal disorders. We deploy multiscale systems modelling to bridge molecular dynamics with clinical phenotypes, ultimately aiming to advance personalized medicine and therapeutic precision.

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.

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.

§ Methodologies:

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

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