Protein structures

Proteins are very complicated molecules. With 20 different amino acids that can be arranged in any order to make a polypeptide of up to thousands of amino acids long, their potential for variety is extraordinary. This variety allows proteins to function as exquisitely specific enzymes that compose a cell's metabolism. An E. coli bacterium , one of the most simple biological organisms, has over a 1000 different proteins working at various times to catalyze the necessary reactions to sustain life.

Interdisciplinary Applied Mathematics, 2010

IEEE Signal Processing Magazine, 2004

The tertiary structure of the protein is defined as the folding of polypeptide chains to assume a compact, three-dimensional structure. This structure is referred to as the protein's tertiary structure. .

Journal of Molecular Biology, 1990

The relation between amino acid sequence and local structure in proteins is investigated. The local structures considered are either the four classes of secondary structure (H, E, T and C) or four classes of local conformations defned using measures of conformational similarity based on distances between C ~ atoms. The classes are obtained by applying an automatic clustering procedure to short polypeptide fragments of uniform length from a database of 75 known protein structures. The thrust of our investigation consists of systematically searching the database for simple amino acid patterns of the type Gly-X-Ala-X-X-Val, where X denotes an arbitrary residue. Patterns that are nearly always associated with the same structure are retained. Finding many such associations, we then evaluate by a statistical approach how many among them are non-random and compare the results for different definitions of local structure. A similar comparison is made for the predictive value of retained associations, which is assessed using an internal test based on dividing the database into "learning" and "test" subsets. While we find that local structures defined by conformational similarity are not superior to secondary structure for prediction purposes, they help us gain insight into the factors that influence the predictive value of derived associations. A major conclusion is that the number of retained associations is in large excess over the number expected from a random correlation between sequence and structure, irrespective of how local conformation is defined. However, only a very small number of these associations can be earmarked as reliable using statistical criteria, due to the limited size of the database. We find, for instance, that the pattern Ala-Ala-X-X-Lys reliably characterizes helix, and the pattern Val-X-VaI-X-X-X-AIa reliably characterizes extended structure and r-strand. The possibility is discussed that these and other reliable associations correspond to regions of the polypeptide chain whose conformations are locally determined and that these regions may play a role in folding.

Proteins: Structure, Function, and Genetics, 1997

Knowledge of amino acid composition, alone, is verified here to be sufficient for recognizing the structural class, ␣, ␤, ␣ϩ␤, or ␣/␤ of a given protein with an accuracy of 81%. This is supported by results from exhaustive enumerations of all conformations for all sequences of simple, compact lattice models consisting of two types (hydrophobic and polar) of residues. Different compositions exhibit strong affinities for certain folds. Within the limits of validity of the lattice models, two factors appear to determine the choice of particular folds: 1) the coordination numbers of individual sites and 2) the size and geometry of non-bonded clusters. These two properties, collectively termed the distribution of nonbonded contacts, are quantitatively assessed by an eigenvalue analysis of the so-called Kirchhoff or adjacency matrices obtained by considering the non-bonded interactions on a lattice. The analysis permits the identification of conformations that possess the same distribution of non-bonded contacts. Furthermore, some distributions of non-bonded contacts are favored entropically, due to their high degeneracies. Thus, a competition between enthalpic and entropic effects is effective in determining the choice of a distribution for a given composition. Based on these findings, an analysis of non-bonded contacts in protein structures was made. The analysis shows that proteins belonging to the four distinct folding classes exhibit significant differences in their distributions of non-bonded contacts, which more directly explains the success in predicting structural class from amino acid composition.

Protein secondary structure prediction is one of the hot topics of bioinformatics and computational biology. In this article we present a new method to predict secondary structure of proteins. PSSMs of proteins are used to generate pseudo image of proteins. These protein images are used to extract digital image features. Digital image features vectors used for similarity analysis. We believe that PSSM pseudo digital images of proteins could help us to represent protein global intrinsic information in order find globally similar proteins and use these similar proteins during prediction. Highest prediction accuracy for Q3 recorded as 72.1% by using the system. Beside the high accuracy, this method allows us to shorten computational time for predicting secondary structure of proteins.