Biomaterial Database

Structural features that predict real-value fluctuations of globular proteins. 2012

Michal Jamroz, and Andrzej Kolinski, and Daisuke Kihara

Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warszawa, Poland.

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins.

UI	MeSH Term	Description	Entries
D011487	Protein Conformation	The characteristic 3-dimensional shape of a protein, including the secondary, supersecondary (motifs), tertiary (domains) and quaternary structure of the peptide chain. PROTEIN STRUCTURE, QUATERNARY describes the conformation assumed by multimeric proteins (aggregates of more than one polypeptide chain).	Conformation, Protein,Conformations, Protein,Protein Conformations
D011506	Proteins	Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.	Gene Products, Protein,Gene Proteins,Protein,Protein Gene Products,Proteins, Gene
D012044	Regression Analysis	Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.	Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D056004	Molecular Dynamics Simulation	A computer simulation developed to study the motion of molecules over a period of time.	Molecular Dynamics Simulations,Molecular Dynamics,Dynamic, Molecular,Dynamics Simulation, Molecular,Dynamics Simulations, Molecular,Dynamics, Molecular,Molecular Dynamic,Simulation, Molecular Dynamics,Simulations, Molecular Dynamics
D057927	Hydrophobic and Hydrophilic Interactions	The thermodynamic interaction between a substance and WATER.	Hydrophilic Interactions,Hydrophilic and Hydrophobic Interactions,Hydrophilicity,Hydrophobic Interactions,Hydrophobicity,Hydrophilic Interaction,Hydrophilicities,Hydrophobic Interaction,Hydrophobicities,Interaction, Hydrophilic,Interaction, Hydrophobic,Interactions, Hydrophilic,Interactions, Hydrophobic
D060388	Support Vector Machine	SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.	Support Vector Network,Machine, Support Vector,Machines, Support Vector,Network, Support Vector,Networks, Support Vector,Support Vector Machines,Support Vector Networks,Vector Machine, Support,Vector Machines, Support,Vector Network, Support,Vector Networks, Support
D030562	Databases, Protein	Databases containing information about PROTEINS such as AMINO ACID SEQUENCE; PROTEIN CONFORMATION; and other properties.	Amino Acid Sequence Databases,Databases, Amino Acid Sequence,Protein Databases,Protein Sequence Databases,SWISS-PROT,Protein Structure Databases,SwissProt,Database, Protein,Database, Protein Sequence,Database, Protein Structure,Databases, Protein Sequence,Databases, Protein Structure,Protein Database,Protein Sequence Database,Protein Structure Database,SWISS PROT,Sequence Database, Protein,Sequence Databases, Protein,Structure Database, Protein,Structure Databases, Protein