Comparative homology agreement search: An effective combination of homology-search methods

Abstract

Many methods have been developed to search for homologous members of a protein family in databases, and the reliability of results and conclusions may be compromised if only one method is used, neglecting the others. Here we introduce a general scheme for combining such methods. Based on this scheme, we implemented a tool called comparative homology agreement search (CHASE) that integrates different search strategies to obtain a combined ''E value.'' Our results show that a consensus method integrating distinct strategies easily outperforms any of its component algorithms. More specifically, an evaluation based on the Structural Classification of Proteins database reveals that, on average, a coverage of 47% can be obtained in searches for distantly related homologues (i.e., members of the same superfamily but not the same family, which is a very difficult task), accepting only 10 false positives, whereas the individual methods obtain a coverage of 28 -38%.S equence-homology search algorithms are important computational tools in molecular biology. There exist at least three general classes of techniques used in searches for protein homologues, namely pairwise sequence comparisons such as basic local alignment search tool (BLAST), profile-based searches such as HMMSEARCH, and motif-or pattern-based analyses such as pattern-hit-initiated BLAST (PHI-BLAST) (1-5).In a pairwise search, a query sequence is compared to any database sequence, yielding a confidence estimate that is supposed to indicate the probability of finding a comparably similar sequence (of the same size) in a database of random sequences. The comparison is done for every sequence in the database, and the sequences with highest confidence (''hits'') are reported. The most popular pairwise-search tool is BLAST (1).Simple profile searches make use of position-specific scoring statistics and are usually more sensitive than pairwise comparisons. The introduction of hidden Markov models (HMMs) seems to provide a firmer statistical basis for profile search. The majority of currently available profile tools use HMMs (for example, the HMMER package) (6).Kinship between protein sequences can also lead to (and thus be recognized by) the occurrence of particular amino acid motifs (also known as patterns, signatures, or fingerprints) that were conserved throughout the evolution of the protein family in question and are believed to correlate with specific structural features and function. Motif analysis, therefore, can also be used for identifying new members of a protein family (7-10). Motifs are the backbone of homology-search methods such as PHI-BLAST (5). In contrast to profiles, motifs are usually short, they include a short stretch of very specific amino acids deemed relevant for function, and they are denoted by specific regular expressions.In this study, we will show that the overall performance of homology searches can be improved if these methods are combined appropriately. The combination of methods is an advanced form of a metastudy. Important medical...

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