Thanks for visiting the Hwang Lab! Our areas of research are computational biophysics and molecular biomechanics. We perform computer simulation, modeling, and analysis, to study a range of biological systems and processes including molecular motors, immune systems, biofilaments, and macromolecular self-assembly. We also develop relevant computational and theoretical tools. Our big question is: How do biomolecules move, interact, and assemble, to drive life phenomena? Addressing these questions using computers is both fun and rewarding. Through a better understanding of how life works, our efforts will also help with developing therapeutic strategies for various diseases and potentially lead to bio-inspired engineering.
Modeling in Multiple Scales
We study a broad range of problems that are elements of biological phenomena at different length and time scales. These studies are closely related to our other projects including kinesins and T-cell receptors.
Kinesin is a motor protein that walks along the microtubule filament in the cell. There are different types of kinesins, specialized for tasks such as cargo transport or cell division. They exhibit impressive and diverse motility behaviors. Some kinesins walk like bipeds; others jump. The microtubule, kinesin's track, has polarity and its two ends are called plus and minus ends. Kinesins are unidirectional, walking towards either plus or minus ends, depending on the kinesin family. There are even kinesin families that depolymerize microtubules, like running on a bridge simultaneously as you break it by stomping. How do kinesins do all these?
We use various computational methods, mainly molecular dynamics simulation, to address this question. We study how kinesin processes its fuel molecule (adenosine triphosphate; ATP). Unlike a macroscopic gasoline engine where the energy is is obtained by burning gas, kinesin utilizes energies associated with binding of an ATP molecule (fuel injection), ATP hydrolysis (burning), and the release of hydrolysis products (exhaust) at different phases of its motility cycle. We also investigate how the chemical energy associated with processing ATP is converted to mechanical work, to generate a unidirectional step. Beyond a single kinesin molecule, how a team of kinesins work together to carry cargoes or organize microtubules, so called emergent behaviors, are also of interest.
T-cells are major defenders of our body against invaders as well as against cancerous or damaged cells. Millions of different T-cells move in our body for immune surveillance. T-cells have exquisite sensitivity, and fewer than 10 target peptides out of 100,000 present on the surface of antigen-presenting cell (APC) can be recognized by a T-cell. Mis-recognition of self-peptide can lead to autoimmune diseases. Due to its extreme sensitivity, T-cells can also be utilized to specifically target cancer cells (cancer immunotherapy). The main molecule on the surface of a T-cell recognizing antigens is the T-cell receptor (TCR). How a TCR works is not well understood. An emerging concept is that as a T-cell crawls over an APC, mechanical load applied to TCR enhances its sensitivity and the lifetime of the bond to its partner molecule on the surface of APC, the antigen peptide-bound major histocompatibility complex (pMHC). We use computer simulation to decipher the physical mechanisms of this process. Load-dependent conformational changes, dynamics at the TCR-pMHC interface, and their effect on the peptide antigen recognition, are being investigated.
Due to the rapidly advancing imaging technologies, increasingly large imaging datasets are produced 2-, 3-, and 4-dimensions (3 spatial dimensions plus time). Yet, information from bioimages are extracted largely by human eyes, usually assisted by mouse clicking on images to measure features. Such semi-manual methods become prohibitive as imaging data are becoming very large. Although a certain level of automation is possible for systems that exhibit relatively clear features, more often it is difficult to make the computer to recognize features in noisy images that human eyes (and brains) can easily do. Another challenge is image-based model building. For biomolecular structures, experiments such as x-ray crystallography, electron microscopy, and nuclear magnetic resonance, provide atomistic models so that we can perform molecular dynamics simulations. Can we use the imaging data to build structural models of, for example, the cytoskeletal network or the mitotic spindle, and perform meso-scale simulation? To achieve these goals, we are developing the Computer-Aided Feature Extraction (CAFE) program. Our present applications of CAFE include 2- and 3-dimensional filament networks (collagen and the microtubule), and zebrafish brain morphogenesis.