title: Distributions of experimental protein structures on coarse-grained free energy landscapes.
abstract: Predicting conformational changes of proteins is needed in order to fully comprehend functional mechanisms. With the large number of available structures in sets of related proteins, it is now possible to directly visualize the clusters of conformations and their conformational transitions through the use of principal component analysis. The most striking observation about the distributions of the structures along the principal components is their highly non-uniform distributions. In this work, we use principal component analysis of experimental structures of 50 diverse proteins to extract the most important directions of their motions, sample structures along these directions, and estimate their free energy landscapes by combining knowledge-based potentials and entropy computed from elastic network models. When these resulting motions are visualized upon their coarse-grained free energy landscapes, the basis for conformational pathways becomes readily apparent. Using three well-studied proteins, T4 lysozyme, serum albumin, and sarco-endoplasmic reticular Ca(2+) adenosine triphosphatase (SERCA), as examples, we show that such free energy landscapes of conformational changes provide meaningful insights into the functional dynamics and suggest transition pathways between different conformational states. As a further example, we also show that Monte Carlo simulations on the coarse-grained landscape of HIV-1 protease can directly yield pathways for force-driven conformational changes.
authors.affiliation: Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA.
authors.forename: Kannan
authors.initials: K
authors.lastname: Sankar
authors.affiliation: Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA.
authors.forename: Robert L
authors.initials: RL
authors.lastname: Jernigan
mesh_terms: D017122:Bacteriophage T4 / Q000201:enzymology; D016333:HIV Protease / Q000737:chemistry* / Q000378:metabolism; D006801:Humans; D056004:Molecular Dynamics Simulation; D009010:Monte Carlo Method; D009113:Muramidase / Q000737:chemistry* / Q000378:metabolism; D011487:Protein Conformation; D053498:Sarcoplasmic Reticulum Calcium-Transporting ATPases / Q000737:chemistry* / Q000378:metabolism; D012709:Serum Albumin / Q000737:chemistry*
chemical_list: D012709:Serum Albumin; D009113:Muramidase; D016333:HIV Protease; C514803:p16 protease, Human immunodeficiency virus 1; D053498:Sarcoplasmic Reticulum Calcium-Transporting ATPases