Overview Research
Shukla group is developing computational and experimental methods to enable the investigation of biological systems in atomistic detail. As we move from a static structural picture to a dynamic ensemble view of protein structure and function, novel computational and experimental paradigms are required for obtaining quantitative understanding of biological phenomena.
- Computational Methods Development. Develop AI/ML and molecular dynamics simulation based methods for mechanistic understanding and design of molecular systems.
- High-Throughout Experiments. Engineer proteins and chemicals using high-throughput experimental techniques in synergy with machine learning methods.
- Mechanistic understanding of molecular systems. Develop and employ computational and experimental tools to elucidate mechanistic principles that regulate function at the molecular scale. In particular, our group is focused on two specific problems of chemical perception and molecular transport.
Projects Research
Computational Methods
We are developing machine learning methods to address the challenges of conformational sampling, accuracy and interpretability of large scale simulation data from molecular simulations.
High-Throughput Experiments
We are employing high-throughput experimental methods to decipher sequence-structure-dynamics-function relationship for protein engineering and drug design.
Molecular Plant Biology
We employ computational and experimental techniques for interrogating protein dynamics to investigate regulation of plant proteins such as membrane transporters and hormone receptors to guide future crop innovation.
Molecular Medicine
Our group is interested in deciphering the mechanisms of regulation of protein function in human health with a particular focus on membrane transporter proteins and G-protein coupled receptors (GPCRs).
