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this paper, we examine the relationship between the complexity and

Protein families are a rich source of information; sequence conservation and sequence correlation are two of the main properties that can be derived from the analysis of multiple sequence alignments. Sequence conservation is related to the direct evolutionary pressure to retain the chemical characteristics of some positions in order to maintain a given function. Sequence correlation is attributed to the small sequence adjustments needed to maintain protein stability against constant mutational drift. Here, we showed that sequence conservation and correlation were each frequently informative enough to detect incorrectly folded proteins. Furthermore, combining conservation, correlation, and polarity, we achieved an almost perfect discrimination between native and incorrectly folded proteins. Thus, we made use of this information for threading by evaluating the models suggested by a threading method according to the degree of proximity of the corresponding correlated, conserved, and apolar residues. The results showed that the fold recognition capacity of a given threading approach could be improved almost fourfold by selecting the alignments that score best under the three different sequencebased approaches.

Finding the 3-D geometry or tertiary structure of an arbitrary protein is vital to understanding the functionality of that protein. The prediction of this structure, known as the protein folding problem, is very difficult and has been labeled one of the "grand challenge problems" for the scientific community. We report here on further work to determine tertiary structures via genetic algorithms. We build on work done first by Unger and Moult using a simplified protein model but improve on the application of GAs to this model. We show, using the same simplified model, that the genetic algorithm indeed appears effective for determining tertiary structure with far fewer computational steps than first reported. 1. INTRODUCTION In this paper we explore the applicability of a standard GA approach to the problem of protein structure prediction. This section presents the problem of protein structure prediction in detail. For the reader with a basic understanding of biochemistry and the natur...

The three-dimensional structure of proteins has been a subject of intense study for several decades. A common way to simplify these complex structures is to consider restrictions on the local mainchain

this paper is to use this information implicitly as a fold recognition tool, although the information also has applications for studies of evolution and protein folding. About 20% of the possible substitutions in the H3P2 matrix are so rare that they do not occur in the Pair Database. Substitutions for which no data are observed receive values of-9.0 in the matrix. Out of 882 elements, there are 198 such values. Notice the difference between calculating a 7 x 3 x 2 x 7 x 3 (5-dimensional) matrix and calculating, for example, a 7 x 7, a 3 x 3, and a 2 x 2 matrix and summing their information values to get the substitution score. If residue type, secondary structure and burial class were distributed independently in the Pair Database, the summed values of the 3 smaller matrices would be proportional to the values in the larger matrix, but these distributions are not independent. Residues type is correlated with solvent accessibility and with secondary structure class. The H3P2 matrix contains the substitution values for the environment descriptions with all their interdependencies taken into account. If the descriptions are assumed to be independent and the substitution values for each description simply summed, many substitutions will receive erroneously high and low scores. An example is given in the Introduction. 3.3 Fold recognition performance of the H3P2 matrix

Abstract- A problem of immense importance in computational biology is the determination of the functional conformations of protein molecules. With the advent of faster computers, it is now possible to use rules to search conformation space for protein structures that have minimal free energy. This paper surveys work done in the last ve years using evolutionary search algorithms to nd low energy protein conformations. In particular, a detailed description is included of some work recently started at the National Cancer Institute, which uses evolution strategies. 1Introduction Biological organisms contain thousands of di erenttypes of proteins. Proteins are responsible for transporting small molecules (e.g., hemoglobin transports O2 in the bloodstream), catalyzing biological functions, providing structure to collagen and skin, regulating hormones, and many other functions. Each protein is a sequence of amino acids, bound into linear chains, that adopts a speci c folded three-dimensional shape. Each shape provides valuable clues to the protein's function. Indeed, this information is vital to the design of new drugs capable of combating disease. Regrettably, ascertaining the shape of a protein is a di cult, expensive task which explains why few proteins have been categorized in this regard. Virtual protein models, created on computers, may provide a cost effective solution to accurate prediction of protein shapes. Unfortunately, the protein folding problem|trying to predict the structure of a protein given only the protein's sequence of amino acids|is a combinatorial optimization problem, which so far has eluded solution because of the exponential number of potential solutions. The purpose of this paper is two-fold. Previous papers have surveyed the use of evolutionary algorithms (EAs) in protein folding problems, but they have been written primarily from the perspective of biophysicists|

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Sites are microenvironments within a biomolecular structure, distinguished by their structural or functional role. A site can be defined by a three-dimensional location, and a local neighborhood around this location in which the structure or function exists. We have developed a computer system to facilitate structural analysis (both qualitative and quantitative) of biomolecular sites. Our system automatically examines the spatial distributions of biophysical and biochemical properties, and reports those regions within a site where the distribution of these properties differs significantly from control nonsites. The properties range from simple atom-based characteristics such as charge to polypeptide-based characteristics such as type of secondary structure. Our analysis of sites uses nonsites as controls, providing a baseline for the quantitative assessment of the significance of the features that are uncovered. In this paper, we use radial distributions of properties to study three well-known sites (the binding sites for calcium, the milieu of disulfide bridges, and the serine protease active site). We demonstrate that the system automatically finds many of the previously described features of these sites, and augments these features with some new details. In some cases, we can not confirm the statistical significance of previously reported features. Our results demonstrate that analysis of protein structure is sensitive to assumptions about background distributions, and that these distributions should be considered explicitly during structural analyses.

A statistical analysis was performed to determine to what extent an amino acid determines the identity of its neighbors and to what extent this is determined by the structural environment. Log-linear analysis was used to discriminate chance occurrence from statistically meaningful correlations. The classification of structures was arbitrary, but was also tested for significance.A list of statistically significant interaction types was selected and then ranked according to apparent importance for applications such as protein design. This showed that, in general, nonlocal, throughspace interactions were more important than those between residues near in the protein sequence. The highest ranked nonlocal interactions involved residues in b-sheet structures. Of the local interactions, those between residues i and i 1 2 were the most important in both a-helices and b-strands. Some surprisingly strong correlations were discovered within b-sheets between residues and sites sequentially near to their bridging partners. The results have a clear bearing on protein engineering studies, but also have implications for the construction of knowledge-based force fields. Proteins 32:175--189, 1998. r 1998 Wiley-Liss, Inc. Key words: pairwise statistics; secondary structure; nonlocal interactions