Molecular Engineering; Molecular Modeling and Simulations; Biophysics
Modern computational chemistry approaches have been increasingly used for obtaining molecular understanding of biological systems and the design of novel treatments for a variety of diseases. With the phenomenal advances in computational methods and hardware in the last decade, large systems and long timescale phenomena can be investigated now. However, there are still many scientific disciplines that have not benefited from these advances and ideas waiting to be integrated in the broad set of computational chemistry approaches.
Modern computational chemistry and chemical biology approaches are not only relevant for human health but also for the current grand challenges of climate change and alternative energy production.
The long-term research interest of Shukla Group is the combined use of theory, computation, and experiments to develop quantitative models of biological phenomena relevant for health, energy and environment.
The main goals of the projects in the lab are to develop 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.
Under this broad umbrella of computational molecular sciences, we integrate ideas from a wide range of disciplines to answer questions that are tied together by a vision of “Dynamic” biology and its role in engineering products for human health, energy and environment.
How can we design potent and selective drugs?
Monoamines in chocolate, and psychedelic drug LSD target the same protein in our body but induce very different physiological responses. Despite decades of research, molecular underpinnings of how the changes in the chemical structure of a drug bias the physiological outcome are not clear.
Specifically, we focus on a class of cellular signaling proteins called G-Protein Coupled Receptors, which are a family of membrane-bound alpha-helical proteins that are exceedingly prominent drug targets, responsible for at least 30% of all marketable drugs and half of the total market volume for pharmaceuticals. It is now known that different small molecules such as hormones, neurotransmitters etc. induce different changes in the protein structure, which leads to recruitment of different intracellular binding partners. However, the nature of these structural changes is not clear. A detailed mechanistic understanding of the manner in which known drugs bind this receptor and modulate GPCR conformational dynamics will provide us with key insights into the signaling process of these proteins, and can serve as a platform for the development of next generation therapeutics.
For example, roughly 70 million Americans suffer from chronic pain – more than from diabetes, heart disease and cancer combined (NIH fact sheet). Yet the fundamental causes of pain are still not completely understood, and an effective, safe means for treatment unclear. With nearly 7 million Americans abusing prescription painkillers in 2012 (including over 2 million addictions), a push from the sphere of basic research of pain toward clinical applications is now more pressing than ever. The most effective treatment of acute and chronic pain involves targeting the µ-opioid receptor (µ-OR), which belongs to the class of G-protein coupled receptor (GPCR) and plays a central role in the management of pain in the human body. The development of safe opioid analgesics is at the forefront of this ambition.
How can we design chemicals that make plants drought-resistant?
With the rising population and the changes in the global climate, the biggest challenge facing humanity will be to meet the future food and energy demands. Photosynthetic plants are the principal source of food and biofuels on planet. Much as adrenaline coursing through our veins drives our body’s reactions to stress, the plant hormones are behind plants’ responses to stressful situations. Despite its fundamental importance, much needs to be learned about how plants adapt to environmental stresses such as water and nutrient shortage, fluctuations in temperature, light, CO2 concentration etc.
Genetic and systems biology approaches have identified plant kinases and phosphatases as the key signaling enzymes involved in regulation of photosynthetic efficiency and response to external stresses. In humans, these enzymes are implicated in almost all forms of cancer and have been investigated extensively. However, the molecular understanding of these stress and energy signaling enzymes in plants 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 mutants and chemicals that can help plants survive the environmental stresses in hot and dry future.
How evolutionary insights can be used to understand protein structure and function?
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 stomatal openings in plants and signaling in GPCRs. A unique perspective on these fundamental questions can be obtained using evolutionary biology, where the key aims are (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 ancestral 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. Furthermore, there is a need for development and adoption of methodologies that connect the disparate scientific disciplines of evolutionary biology and molecular modeling & simulations to obtain functional understanding of the complex protein machinery.