Welcome to the Structural Systems Biology Group at ASU's Biodesign Institute !

The unified theme of our research is to combine rigorous statistical mechanical methodologies with state-of-the-art computational approaches for capturing large-scale structural changes with atomistic precision. The program spans three independent, yet mutually synergistic themes.

A list of our articles can found on Abhi's Google Scholar profile

Design principles of biological energy transduction

The annual energy demand for sustaining life on Earth has scaled up to ~15 TW, and continues to grow sharply. In contrast, the yearly average solar power striking earth’s surface remains constant at ~170 W/m2. As an expected consequence, the pursuit of efficient energy-capture and storage strategies has become a holy grail of today’s scientific endeavors.

Harnessing most of the available solar power, photosynthetic organisms have been optimized by over two billion years of evolution into highly efficient energy-harvesting machines that surpass man-made solar devices in robustness, adaptation to environmental stress, and efficiency of energy conversion. Remarkably, higher life forms utilize an analogous network of motor proteins to regulate the energy conversion pathways in mitochondrial respiration. Inspired by the ubiquity of bioenergetic protein complexes in sustaining life on the biosphere, the mission of my research is to invoke state of the art multiscale methodologies and bioinformatics approaches for discovering the evolutionary design principles of biological energy transfer. This study brings to light a couple of cutting-edge biomedical applications, namely, determination of the molecular origins of cellular ageing and programmed cell death, and creation of a novel computer-aided pipeline pertaining to intricate pathology of the respiratory network.

Bioinspired designs of artificial motors

In an attempt to port the properties of biological motors in abiological systems we investigate linear motors composed of organic macrocycles. In particular, chemical substituents of the motors are computationally modified to allow the molecular movements optimally utilize the input chemical energy. As an exemplary investigation, it took us over 5 years we have determined the structure, function and inchworm motion of motors composed of the so called cyanostar macrocycles. Now, together with A. Flood and J. Jeppesen we are optimizing the motor’s energy efficiency. We are combining Brownian Dynamics simulations with Machine Learning approches to develop a search, simulation and learning protocol to accelerate the engineering of functionally-efficient artificial motors.

Flexible-fitting tools for refining low-resolution crystallographic and electron microscopy data

X-ray crystallography remains the most dominant method for solving atomic structures. However, for relatively large systems, the availability of only medium-to-low-resolution diffraction data often limits the determination of all-atom details. We have developed a flexible fitting-based real space refinement approach, xMDFF, for determining structures from such low-resolution crystallographic data. Even during the testing phases, xMDFF solved the structure of a voltage sensor protein, Ci-VSP. The refinement enabled resolution of search models 6 Å away from the target data even with maps as coarse as 7 Å, a feat very rare in the 100 years of X-ray crystallography. Resorting to the application of chemically accurate force fields, xMDFF allows uniquely the use of macromolecular structure determination strategies for resolving small molecule crystals. Finally, since xMDFF very naturally addresses whole-molecule disorder, we are currently employing it with to decrypt low-resolution diffraction patterns from X-ray free electron laser data of membrane proteins. In recent years, however, cryo-EM has evolved into one of the most effective structure determination tools rivaling X-ray crystallography, and also reaching 3-5 Å resolutions. Taking advantage of this overlapping resolution limits, we have successfully modified our low-resolution crystallographic tools into ones for addressing high-resolution cryo-EM data. Termed resolution-exchange MDFF, these novel cryo-EM tools have applied to validate the structure of TRP channels, and determine the latest human proteasome model.