Research

The long-term research goal of Shukla group is to combine theory, computation, and experiments to develop quantitative models of biological phenomena relevant for health, energy and climate change. Under the broad umbrella of molecular engineering and sciences, our research program is focused on developing a platform for integrating novel evolution-based methods for understanding protein function, elucidating mechanistic insights to regulate plant growth and development in context of global climate change, and understanding complex manipulation of signaling networks in both plants and humans by small molecules. The answers to these questions would require integrating ideas from a wide range of disciplines tied together by a vision of “Dynamic” biology and its role in engineering products for human health, energy, and climate change. Click on the individual research topics below to learn more about these research directions in Shukla Group.

  • Development of novel computational methods

    Methods for efficient sampling, understanding and design of biological systems.

    We use distributed computing techniques and advanced Markov state models for obtaining mechanistic insights into bimolecular dynamics. We aim to further develop these methods and integrate them with stochastic dynamics models of cellular signaling networks. In the above projects, the central question that we are trying to answer is how protein function can be modulated to achieve a particular response such as closing of guard cells in plants and signaling in GPCRs. A unique perspective on these fundamental questions can be obtained using evolutionary biology, where the key aims are also (a) to understand how new protein functions emerge due to historical mutations and (b) how protein architecture shapes or was shaped by the evolutionary process. Ancestral gene resurrection involves identification of ancient proteins using computational phylogenetic methods and experimental characterization of ancestral proteins. A detailed understanding of the molecular evolution can help to explain the mechanistic basis of function in the modern-day proteins.

  • Cellular signaling proteins involved in human diseases

    Understanding conformational changes in GPCRs and Kinases for drug design and development.

    G protein-coupled receptors (GPCRs) and kinases are key cellular signaling proteins and exceedingly prominent drug targets, responsible for more than 30% of all marketable drugs. These proteins regulate a large variety of physiological processes by sensing diverse signals and transmitting the signal to downstream effectors. The challenge is to construct computational models that account for the effects of ligands on the energetic landscape of protein conformational states to predict how these will translate into specific biological outcomes. The goal of the project is to not only obtain the conformational dynamics viewpoint of protein function but also to develop allosteric modulators of these proteins.

  • Evolution of protein structure, function and regulation

    Computational ancestral gene resurrection of proteins

    Development of methodologies to understand the evolution of protein structure, function and the regulation of the its activity. In particular, we are interested in understanding the evolution of protein involved plant hormone perception, drug and antibiotic resistance.

  • Mechanistic Studies of Membrane Transporters

    Computational modeling of substrate transport mechanism and its regulation.

    Despite the critical environmental and ecological role played by plants, the fundamental understanding of the biological processes such as substrate transport in plants is hampered by the lack of structural information. For example, only 87 structures out of ∼8000 membrane protein structures in Protein Data Bank belong to plant membrane proteins (2016 data). Consequently, we do not know how to engineer transporters with enhanced Nutrient-use efficiency. Successful examples of biophysical investigations of plant proteins have been reported for photosynthesis and key plant hormone signaling pathways (e.g., Abscisic acid signaling for drought-resistance). Yet, in many other cases, protein structure-function relationships, descriptors of functional activity or signaling mechanisms are not available. Overall, understanding of biophysical mechanisms of membrane proteins even for model plants are less complete as compared to humans or any other species of high biological importance. Therefore, the design of proteins with a desired function is done primarily using trial-and-error. Computational approaches combined with molecular level biophysical experiments can provide this much-needed information. This proposal demonstrates how we can successfully implement this strategy for a food and agricultural research problem of high importance to society.

  • Plant-hormone perception and signaling

    Understanding how plants perceive chemicals and induce signaling response.

    Much as adrenaline coursing through our veins drives our body’s reactions to stress, the plant hormones are behind plants’ responses to stressful situations such as drought, biotic and abiotic stress.  However, the molecular picture of plant hormone perception and signaling remains elusive. Modern computational chemistry approaches could not only be used to obtain molecular description of these key processes in nature but also for the development of novel agrochemicals and plant mutants with desired traits.

  • Multiscale modeling of plant growth and development

    Integrating molecular models of plant growth with cellular, species and ecosystem level models

    Specific spatial or temporal scale models of plant growth and development are limited in their ability to provide a holistic view of plant growth and its interaction with the ecosystem. However, this limitation can be addressed by integrative, multi-scale modeling, which will allow: i) identification and explanation of hidden/unknown parameters that can be targeted for experimental study; ii) simulation to design an ideal plant capable of withstanding future environmental challenges; iii) synergy among expertise at different levels of plant biology, engineering, math and computation.