Hi, I'm Shobhit S. Chaturvedi

I am a postdoctoral researcher in the Department of Chemistry and Biochemistry at the University of California, Los Angeles, working under the mentorship of Dr. Anastassia N. Alexandrova. Building on my Ph.D. work at Michigan Technological University under Dr. Christo Z. Christov, where I explored enzyme reaction mechanisms through Molecular Dynamics (MD) and hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) simulations, I am now advancing my research by integrating these techniques with innovative method developments and Machine Learning to investigate the role of protein scaffolds in enzyme catalysis. My ultimate goal is to harness these insights to design enzymes with enhanced catalytic properties and novel functionalities. In my free time, I like to play cricket, listen to music, and read books.

EDUCATION & TRAINING

Postdoctoral Scholar in Chemistry & Biochemistry

University of California, Los Angles, USA, 2023 – Present.

Ph.D. & MS in Chemistry

Michigan Technological University, USA, 2018 – 2022.

BTech in Chemical Engineering

RTM Nagpur University, India, 2013 – 2017.

IntereSts

Enzyme Catalysis
Enzyme Design
Electrostatic Preorganization
Conformational Dynamics in Enzymes
Structure-Function Relationship in Enzymes

RESEARCH HIGHLIGHTS

Directed Evolution of Protoglobin Optimizes the Enzyme Electric Field

By tracking the evolving 3D electric field in protoglobin, we reveal how distinct field topologies can switch mechanistic pathways and enable efficient carbene transfer.

This discovery underscores the critical role of electric field dynamics in enzyme function and highlights how directed evolution exploits these fields to overcome reaction barriers, offering new strategies for rational enzyme design.

Adapted from: J. Am. Chem. Soc. 2024, 146, 24, 16670–16680.

Machine-Learning Prediction of Protein Function from the Portrait of Its Intramolecular Electric Field

We introduce a machine learning framework that directly interprets the diverse 3D electric fields in heme-iron oxidoreductases—encompassing monooxygenases, peroxidases, and catalases—to accurately predict enzyme function without additional protein-specific data.

By pinpointing key electric field components beyond the traditional Fe–O focus, our approach reveals signature electrostatic features essential for catalytic specificity and lays a new foundation for electrostatics-driven enzyme design.

Adapted from: J. Am. Chem. Soc. 2024, 146, 41, 28375–28383.

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