Computational biology uses tools from different fields, including computer science, to solve the challenges in the field of molecular biology and engages in the interpretation, classification and understanding of biological datasets. These datasets may involve RNA, DNA and protein sequences, comparison of genes and genomes of organisms, prediction of protein structures and creation of models for biological systems and molecules. Among these, building models for the biological molecules, especially proteins, is of great importance.
Proteins have different functions as chemical receptors, messengers, catalysts, etc. Most of these tasks are achieved via conformational changes in the proteins. For some proteins, this change in conformation is compared to the opening and closing of doors. The proteins that undergo this open-close conformational change are called “hinge proteins” because they act as if they have relative “hinge joints” between the opening and closing bodies of the protein.
Since the conformational change in hinge proteins involves movement of different protein parts, we modeled proteins as structures understood by rigidity theory. While we hypothesized that this might lead to the identification of “hinge proteins”, we found that current rigidity theory models do not retain enough information to predict protein motion. In particular, rigidity theory does not model steric hindrance. Computational experiments indicate that the feasible motions identified by the rigidity theory analysis may actually result in collisions between atoms.
This research was initially motivated by our successful analysis of mechanical models after which the proteins are modeled. Incorporating steric hindrance into our current protein model will most likely provide the additional information that we are missing. This future work will be challenging, because, even in the mechanical world, algorithms for detecting collisions are computationally expensive.