Our group focuses on developing algorithms to bridge between computer science and the life sciences, in particular, biophysics. Our work falls in newly-coined areas such as computational structural biology, structural bioinformatics, computational biophysics, molecular design, and molecular structure prediction. Our long-standing goal is to design efficient and accurate algorithms through which to simulate and understand how molecules behave in cells.
Some important problems we are working on include how protein chains and protein complexes morph their shapes to dock on one another or other molecules and perform a needed biological function. Even more interesting problems encompass the area of molecular design, where the focus is on designing de-novo molecules with specific behavior that we can control.
In particular, our interests lie in developing physical-based algorithms to compute, characterize, and analyze biologically-relevant motions in dynamic organic molecules such as proteins and in large molecular complexes. An objective of this research is to obtain a comprehensive and accurate view of functionally-relevant motions in proteins. Our research interleaves efficient computer algorithms inspired in robotics and computational geometry with realistic molecular modeling and application of statistical mechanics to molecular systems.
Our long-standing goal is to develop a unified interdisciplinary framework that blends computation and biophysics to efficiently represent and understand structural changes in large biomolecular machines. Achieving this goal can improve our understanding of how inter-molecular associations determine the behavior of cellular or engineered molecular machines. This is an important step towards using computation to assist in the process of designing drugs and drug-delivery templates and even the construction of novel functional materials.