Evolutionary and structural feedback on selection of sequences for comparative analysis of proteins
ABSTRACT It has been noted that slowly evolving protein residues have two properties: (a) they tend to cluster in the native fold, and (b) they delineate functional surfaces-parts of the surface through which the protein interacts with other proteins or small ligands. Herein, we demonstrate that the two are coupled sufficiently strongly that one effect, when observed, statistically implies the other. Detection of both can be accomplished in multiple sequence alignment related methods by the careful selection of relevant sequences. For the demonstration, we use two sets of protein families: a small set of diverse proteins with diverse functional surfaces, and a large set of homodimerizing enzymes. A practical outcome of our considerations is a simple prescriptive rule for the selection of homologous sequences for the comparative analysis of proteins: in order to optimize the detection of (potentially unknown) functional surfaces, it is sufficient to select sequences in such a way that the residues observed at any level of evolutionary divergence, as implied by the alignment, cluster on the folded protein.
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ABSTRACT: Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details.08/2013; 8(11):e201308005. DOI:10.5936/csbj.201308005
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ABSTRACT: When comparing sequences of similar proteins, two kinds of questions can be asked, and the related two kinds of inference made. First, one may ask to what degree they are similar, and then, how they differ. In the first case one may tentatively conclude that the conserved elements common to all sequences are of central and common importance to the protein's function. In the latter case the regions of specialization may be discriminative of the function or binding partners across subfamilies of related proteins. Experimental efforts - mutagenesis or pharmacological intervention - can then be pointed in either direction, depending on the context of the study. Cube simplifies this process for users that already have their favorite sets of sequences, and helps them collate the information by visualization of the conservation and specialization scores on the sequence and on the structure, and by spreadsheet tabulation. All information can be visualized on the spot, or downloaded for reference and later inspection. http://eopsf.org/cube.PLoS ONE 11/2013; 8(11):e79480. DOI:10.1371/journal.pone.0079480 · 3.53 Impact Factor
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ABSTRACT: Structural genomics projects have solved many new structures with unknown functions. One strategy to investigate the function of a structure is to computationally find the functionally important residues or regions on it. Therefore, the development of functional region prediction methods has become an important research subject. An effective approach is to use a method employing structural and evolutionary information, such as the evolutionary trace (ET) method. ET ranks the residues of a protein structure by calculating the scores for relative evolutionary importance, and locates functionally important sites by identifying spatial clusters of highly ranked residues. After ET was developed, numerous ET-like methods were subsequently reported, and many of them are in practical use, although they require certain conditions. In this mini review, we first introduce the remaining problems and the recent improvements in the methods using structural and evolutionary information. We then summarize the recent developments of the methods. Finally, we conclude by describing possible extensions of the evolution- and structure-based methods.08/2013; 8:e201308007. DOI:10.5936/csbj.201308007