The more proteins diveins in sequence, the more difficult it becomes for bioinformatics to infer similarities of protein function and structure from sequence. The precise thresholds used in automated genome annotations depend on the particular aspect of protein function transferred by homology. Here, we presented the first large-scale analysis of the relation between sequence similarity and identity in subcellular localization.

A protein sequence folds into a unique threedimensional structure. Interestingly, this one-toone correspondence is no longer valid when all proteins are considered. The size of the protein

ABSTRACT Current analyses of protein sequence/structure relationships have focused on expected similarity relationships for structurally similar proteins. To survey and explore the basis of these relationships, we present a general sequence/ structure map that covers all combinations of similarity/dissimilarity relationships and provide novel energetic analyses of these relationships. To aid our analysis, we divide protein relationships into four categories: expected/unexpected similarity (S and S? ) and expected/unexpected dissimilarity (D and D? ) relationships. In the expected similarity region S, we show that trends in the sequence/structure relation can be derived based on the requirement of protein stability and the energetics of sequence and structural changes. Specifically, we derive a formula relating sequence and structural deviations to a parameter characterizing protein stiffness; the formula fits the data reasonably well. We suggest that the absence of data in region S? (high structural but low sequence similarity) is due to unfavorable energetics. In contrast to region S, region D? (high sequence but low structural similarity) is well-represented by proteins that can accommodate large structural changes. Our analyses indicate that there are several categories of similarity relationships and that protein energetics provide a basis for understanding these relationships.

The Conserved Key Amino Acid Positions DataBase (CKAAPs DB) provides access to an analysis of structurally similar proteins with dissimilar sequences where key residues within a common fold are identified. CKAAPs may be important in protein folding and structural stability and function, and hence useful for protein engineering studies. This paper provides an update to the initial report of CKAAPs DB [Li et al. (2001) Nucleic Acids Res., 29, 329--331]. CKAAPs DB contains CKAAPs for the representative set of polypeptide chains derived from the CE and FSSP databases, as well as subdomains (conserved regions of the order of 100 residues within a domain) identified by CE. The new version now offers different perspectives on the CKAAPs. First, CKAAPs are mapped onto their respective Protein Data Bank (PDB) structures rendered by Molscript, providing a spatial context for the CKAAPs. Secondly, CKAAPs may be highlighted within a structure-based sequence alignment, as well as secondary structure alignment. Thirdly, the resulting sequence homologs from the structure alignment may be viewed in alignments colorized based on identities and property groups using Mview. New search capabilities have also been provided for searching by keyword combinations, PDB IDs, EC numbers, GI numbers, LocusLink ID, taxonomy, gene ontology and pathways. A new custom CKAAPs analysis interface has been implemented where a user may change the criteria for inclusion of chains, initiate CKAAPs analysis and retrieve results. CKAAPs DB is accessible through the web at http://ckaaps.sdsc.edu/. Plain text analysis results are available by FTP at ftp://ftp.sdsc.edu/pub/ sdsc/biology/ckaap.

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We consider the problem of similarity searches on protein databases based on both sequence and structure information simultaneously. Our program extracts feature vectors from both the sequence and structure components of the proteins. These feature vectors are then combined and indexed using a novel multi-dimensional index structure. For a given query, we employ this index structure to find candidate matches from the database. We develop a new method for computing the statistical significance of these candidates. The candidates with high significance are then aligned to the query protein using the Smith-Waterman technique to find the optimal alignment. The experimental results show that our method can classify up to 97 % of the superfamilies and up to 100 % of the classes correctly according to the SCOP classification. Our method is up to 37 times faster than CTSS, a recent structure search technique, combined with Smith-Waterman technique for sequences.

Research article Improving protein structure similarity searches using domain boundaries based on conserved sequence information

Abstract: Non-independent evolution of amino acid sites has become a noticeable limitation of most methods aimed at identifying selective constraints at functionally important amino acid sites or protein regions. The need for a generalised framework to account for non-independence of amino acid sites has fuelled the design and development of new mathematical models and computational tools centred on resolving this problem. Molecular coevolution is one of the most active areas of research, with an increasing rate of new models and methods being developed everyday. Both parametric and nonparametric methods have been developed to account for correlated variability of amino acid sites. These methods have been utilised for detecting phylogenetic, functional and structural coevolution as well as to identify surfaces of amino acid sites involved in protein-protein interactions. Here we discuss and briefly describe these methods, and identify their advantages and limitations.

Measuring in a quantitative, statistical sense the degree to which structural and functional information can be ``transferred' ' between pairs of related protein sequences at various levels of similarity is an essential prerequisite for robust genome annotation. To this end, we performed pairwise sequence, structure and function comparisons on 30,000 pairs of protein domains with known structure and function. Our domain pairs, which are constructed according to the SCOP fold classi®cation, range in similarity from just sharing a fold, to being nearly identical. Our results show that traditional scores for sequence and structure similarity have the same basic exponential relationship as observed previously, with structural divergence, measured in RMS, being exponentially related to sequence divergence, measured in percent identity. However, as the scale of our survey is much larger than any previous investigations, our results have greater statistical weight and precision. We have been able to express the relationship of sequence and structure similarity using

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