A review on description dynamics and conformational changes of proteins using combination of principal component analysis and molecular dynamics simulation

Abstract

Understanding how proteins behave dynamically and undergo conformational changes is essential to comprehending their biological roles. This review article examines the potent tool of using Molecular Dynamics simulations in conjunction with Principal Component Analysis (PCA) to explore protein dynamics. Molecular dynamics data can be made easier to read by removing prominent patterns through the use of PCA, a sophisticated dimensionality reduction approach. Researchers can obtain critical insights into the fundamental principles governing protein function by using PCA on MD simulation data. We provide a systematic approach to PCA that includes data collection, input coordinate selection, and result interpretation. Protein collective movements and fundamental dynamics are made visible by PCA, which makes it possible to identify conformational substates that are crucial to function. By means of principal component analysis, scientists are able to observe and measure large-scale movements, like hinge bending and domain motions, as well as pinpoint areas of protein structural stiffness and flexibility. Moreover, PCA allows temporal separation, distinguishing slower global motions from faster local changes. A strong foundation for researching protein dynamics is provided by the combination of PCA and Molecular Dynamics simulations, which have applications in drug development and enhance our comprehension of intricate biological systems.

Keywords: Eigenvalue; Eigenvector; Essential dynamics; Molecular dynamics simulation; Principal component analysis.