Understanding what causes proteins to change shape and how the resulting shape influences function will expedite the design of more narrowly focused drugs and therapies. Shape alterations are often the result of flexibility changes in a set of localized neighborhoods that may or may not act in concert. Computational models have been developed to predict flexibility changes under varying empirical parameters. In this paper, we tackle a significant challenge facing scientists when analyzing outputs of a computational model, namely how to identify, examine, compare, and group interesting neighborhoods of proteins under different parameter sets. This is a difficult task since comparisons over protein subunits that comprise diverse neighborhoods are often too complex to characterize with a simple metric and too numerous to analyze manually. Here, we present a series of novel visual analytics approaches toward addressing this task. User scenarios illustrate the utility of these approaches and feedback from domain experts confirms their effectiveness.
展开▼
