PNMAVis: Visual Analysis Tool of Protein Normal Mode for Understanding Cavity Dynamics - Dongliang Guo; Li Feng; Taoxiang Zhang; Yaoyao Guo; Yanfen Wang; Ximing Xu (Applied Sciences, 2022)
Molecular cavities play a critical role in our understanding of molecular phenomena. Recently, a number of works on the visual analysis of protein cavity dynamics have been developed to allow experts and users to interactively research dynamic cavity data. However, previous explorations are limited to studying cavity-lining amino acids and they lack a consideration of the impact of the key amino acids, which are far away from the cavity but have an important impact on the cavity. When studying protein amino acids, biochemists use normal mode decomposition to analyze protein changes on a time scale. However, the high-dimensional parameter space generated via decomposition is too large to be analyzed in detail. We present a novel approach that combines cavity characterization and normal mode analysis (NMA) for cavity dynamics analysis to reduce and explore this vast space through interactive visualization. PNMAVis can analyze whether direct factors (cavity-lining amino acids) or indirect factors (key amino acids) affect cavity changes, through multiple linked 2D and 3D views. The visual analysis method we proposed is based on close cooperation with domain experts, aiming to meet their needs to explore the relationship between cavity stability and cavity-lining amino acids fluctuations and key amino acids fluctuations as much as possible, and also to help domain experts identify potential allosteric residues. The effectiveness of our new method is demonstrated by the case study conducted by cooperative protein experts on a biological field case and an open normal mode data set.