Next Article in Journal
Titanium Surface Analysis after Instrumentation with Different Burs Simulating the Implantoplasty Technique: A Pilot In Vitro Experimental Study
Next Article in Special Issue
MULTI-NETVIS: Visual Analytics for Multivariate Network
Previous Article in Journal
Computer-Aided Surgical Simulation through Digital Dynamic 3D Skeletal Segments for Correcting Torsional Deformities of the Lower Limbs in Children with Cerebral Palsy
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

PNMAVis: Visual Analysis Tool of Protein Normal Mode for Understanding Cavity Dynamics

1
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2
The Key Laboratory for Software Engineering of Hebei Province, Qinhuangdao 066004, China
3
Engineering Training Center, Yanshan University, Qinhuangdao 066004, China
4
School of Medicine and Pharmacy, Ocean University of China, Qingdao 266005, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(15), 7919; https://doi.org/10.3390/app12157919
Submission received: 9 July 2022 / Revised: 4 August 2022 / Accepted: 5 August 2022 / Published: 7 August 2022
(This article belongs to the Special Issue Multidimensional Data Visualization: Methods and Applications)

Abstract

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.
Keywords: cavity dynamics; normal mode analysis; visualization; allosteric site cavity dynamics; normal mode analysis; visualization; allosteric site

Share and Cite

MDPI and ACS Style

Guo, D.; Feng, L.; Zhang, T.; Guo, Y.; Wang, Y.; Xu, X. PNMAVis: Visual Analysis Tool of Protein Normal Mode for Understanding Cavity Dynamics. Appl. Sci. 2022, 12, 7919. https://doi.org/10.3390/app12157919

AMA Style

Guo D, Feng L, Zhang T, Guo Y, Wang Y, Xu X. PNMAVis: Visual Analysis Tool of Protein Normal Mode for Understanding Cavity Dynamics. Applied Sciences. 2022; 12(15):7919. https://doi.org/10.3390/app12157919

Chicago/Turabian Style

Guo, Dongliang, Li Feng, Taoxiang Zhang, Yaoyao Guo, Yanfen Wang, and Ximing Xu. 2022. "PNMAVis: Visual Analysis Tool of Protein Normal Mode for Understanding Cavity Dynamics" Applied Sciences 12, no. 15: 7919. https://doi.org/10.3390/app12157919

APA Style

Guo, D., Feng, L., Zhang, T., Guo, Y., Wang, Y., & Xu, X. (2022). PNMAVis: Visual Analysis Tool of Protein Normal Mode for Understanding Cavity Dynamics. Applied Sciences, 12(15), 7919. https://doi.org/10.3390/app12157919

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop