William R. Pearson

Rapid and sensitive sequence comparison with FASTP and FASTA.

The FASTA program can search the NBRF protein sequence library (2.5 million residues) in less than 20 min on an IBM-PC microcomputer and unambiguously detect proteins that shared a common ancestor billions of years in the past. FASTA is both fast and selective because it initially considers only amino acid identities. Its sensitivity is increased not only by using the PAM250 matrix to score and rescore regions with large numbers of identities but also by joining initial regions. The results of searches with FASTA compare favorably with results using NWS-based programs that are 100 times slower. FASTA is slightly less sensitive but considerably more selective. It is not clear that NWS-based programs would be more successful in finding distantly related members of the G-protein-coupled receptor family. The joining step by FASTA to calculate the initn score is especially useful for sequences that share regions of sequence similarity that are separated by variable-length loops. FASTP and FASTA were designed to identify protein sequences that have descended from a common ancestor, and they have proved very useful for this task. In many cases, a FASTA sequence search will result in a list of high scoring library sequences that are homologous to the query sequence, or the search will result in a list of sequences with similarity scores that cannot be distinguished from the bulk of the library. In either case, the question of whether there are sequences in the library that are clearly related to the query sequence has been answered unambiguously. Unfortunately, the results often will not be so clear-cut, and careful analysis of similarity scores, statistical significance, the actual aligned residues, and the biological context are required. In the course of analyzing the G-protein-coupled receptor family, several proteins were found that, because of a high initn score and a low init1 score that increased almost 2-fold with optimization, appeared to be members of this family which were not previously recognized. RDF2 analysis showed borderline z values, and only a careful examination of the sequence alignments that focused on the conserved residues provided convincing evidence that the high scores were fortuitous. As sequence comparison methods become more powerful by becoming more sensitive, they become more likely to mislead, and even greater care is required.

Current Protocols in Bioinformatics

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Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms.

The sensitivity and selectivity of the FASTA and the Smith-Waterman protein sequence comparison algorithms were evaluated using the superfamily classification provided in the National Biomedical Research Foundation/Protein Identification Resource (PIR) protein sequence database. Sequences from each of the 34 superfamilies in the PIR database with 20 or more members were compared against the protein sequence database. The similarity scores of the related and unrelated sequences were determined using either the FASTA program or the Smith-Waterman local similarity algorithm. These two sets of similarity scores were used to evaluate the ability of the two comparison algorithms to identify distantly related protein sequences. The FASTA program using the ktup = 2 sensitivity setting performed as well as the Smith-Waterman algorithm for 19 of the 34 superfamilies. Increasing the sensitivity by setting ktup = 1 allowed FASTA to perform as well as Smith-Waterman on an additional 7 superfamilies. The rigorous Smith-Waterman method performed better than FASTA with ktup = 1 on 8 superfamilies, including the globins, immunoglobulin variable regions, calmodulins, and plastocyanins. Several strategies for improving the sensitivity of FASTA were examined. The greatest improvement in sensitivity was achieved by optimizing a band around the best initial region found for every library sequence. For every superfamily except the globins and immunoglobulin variable regions, this strategy was as sensitive as a full Smith-Waterman. For some sequences, additional sensitivity was achieved by including conserved but nonidentical residues in the lookup table used to identify the initial region.

The genome of Cryptosporidium hominis

Cryptosporidium species cause acute gastroenteritis and diarrhoea worldwide. They are members of the Apicomplexa—protozoan pathogens that invade host cells by using a specialized apical complex and are usually transmitted by an invertebrate vector or intermediate host. In contrast to other Apicomplexans, Cryptosporidium is transmitted by ingestion of oocysts and completes its life cycle in a single host. No therapy is available, and control focuses on eliminating oocysts in water supplies. Two species, C. hominis and C. parvum, which differ in host range, genotype and pathogenicity, are most relevant to humans. C. hominis is restricted to humans, whereas C. parvum also infects other mammals. Here we describe the eight-chromosome ∼9.2-million-base genome of C. hominis. The complement of C. hominis protein-coding genes shows a striking concordance with the requirements imposed by the environmental niches the parasite inhabits. Energy metabolism is largely from glycolysis. Both aerobic and anaerobic metabolisms are available, the former requiring an alternative electron transport system in a simplified mitochondrion. Biosynthesis capabilities are limited, explaining an extensive array of transporters. Evidence of an apicoplast is absent, but genes associated with apical complex organelles are present. C. hominis and C. parvum exhibit very similar gene complements, and phenotypic differences between these parasites must be due to subtle sequence divergence.

Nomenclature for Mammalian Soluble Glutathione Transferases

The nomenclature for human soluble glutathione transferases (GSTs) is extended to include new members of the GST superfamily that have been discovered, sequenced, and shown to be expressed. The GST nomenclature is based on primary structure similarities and the division of GSTs into classes of more closely related sequences. The classes are designated by the names of the Greek letters: Alpha, Mu, Pi, etc., abbreviated in Roman capitals: A, M, P, and so on. (The Greek characters should not be used.) Class members are distinguished by Arabic numerals and the native dimeric protein structures are named according to their subunit composition (e.g., GST A1-2 is the enzyme composed of subunits 1 and 2 in the Alpha class). Soluble GSTs from other mammalian species can be classified in the same manner as the human enzymes, and this chapter presents the application of the nomenclature to the rat and mouse GSTs.

Getting More from Less Algorithms for Rapid Protein Identification with Multiple Short Peptide Sequences

We describe two novel sequence similarity search algorithms, FASTS and FASTF, that use multiple short peptide sequences to identify homologous sequences in protein or DNA databases. FASTS searches with peptide sequences of unknown order, as obtained by mass spectrometry-based sequencing, evaluating all possible arrangements of the peptides. FASTF searches with mixed peptide sequences, as generated by Edman sequencing of unseparated mixtures of peptides. FASTF deconvolutes the mixture, using a greedy heuristic that allows rapid identification of high scoring alignments while reducing the total number of explored alternatives. Both algorithms use the heuristic FASTA comparison strategy to accelerate the search but use alignment probability, rather than similarity score, as the criterion for alignment optimality. Statistical estimates are calculated using an empirical correction to a theoretical probability. These calculated estimates were accurate within a factor of 10 for FASTS and 1000 for FASTF on our test dataset. FASTS requires only 15–20 total residues in three or four peptides to robustly identify homologues sharing 50% or greater protein sequence identity. FASTF requires about 25% more sequence data than FASTS for equivalent sensitivity, but additional sequence data are usually available from mixed Edman experiments. Thus, both algorithms can identify homologues that diverged 100 to 500 million years ago, allowing proteomic identification from organisms whose genomes have not been sequenced.

Sensitivity and selectivity in protein structure comparison

Seven protein structure comparison methods and two sequence comparison programs were evaluated on their ability to detect either protein homologs or domains with the same topology (fold) as defined by the CATH structure database. The structure alignment programs Dali, Structal, Combinatorial Extension (CE), VAST, and Matras were tested along with SGM and PRIDE, which calculate a structural distance between two domains without aligning them. We also tested two sequence alignment programs, SSEARCH and PSI‐BLAST. Depending upon the level of selectivity and error model, structure alignment programs can detect roughly twice as many homologous domains in CATH as sequence alignment programs. Dali finds the most homologs, 321–533 of 1120 possible true positives (28.7%–45.7%), at an error rate of 0.1 errors per query (EPQ), whereas PSI‐BLAST finds 365 true positives (32.6%), regardless of the error model. At an EPQ of 1.0, Dali finds 42%–70% of possible homologs, whereas Matras finds 49%–57%; PSI‐BLAST finds 36.9%. However, Dali achieves >84% coverage before the first error for half of the families tested. Dali and PSI‐BLAST find 9.2% and 5.2%, respectively, of the 7056 possible topology pairs at an EPQ of 0.1 and 19.5, and 5.9% at an EPQ of 1.0. Most statistical significance estimates reported by the structural alignment programs overestimate the significance of an alignment by orders of magnitude when compared with the actual distribution of errors. These results help quantify the statistical distinction between analogous and homologous structures, and provide a benchmark for structure comparison statistics.

Aligning two sequences within a specified diagonal band

We describe an algorithm for aligning two sequences within a diagonal band that requires only O(NW) computation time and O(N) space, where N is the length of the shorter of the two sequences and W is the width of the band. The basic algorithm can be used to calculate either local or global alignment scores. Local alignments are produced by finding the beginning and end of a best local alignment in the band, and then applying the global alignment algorithm between those points. This algorithm has been incorporated into the FASTA program package, where it has decreased the amount of memory required to calculate local alignments from O(NW) to O(N) and decreased the time required to calculate optimized scores for every sequence in a protein sequence database by 40%. On computers with limited memory, such as the IBM-PC, this improvement both allows longer sequences to be aligned and allows optimization within wider bands, which can include longer gaps.

The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes

Understanding which are the catalytic residues in an enzyme and what function they perform is crucial to many biology studies, particularly those leading to new therapeutics and enzyme design. The original version of the Catalytic Site Atlas (CSA) (http://www.ebi.ac.uk/thornton-srv/databases/CSA) published in 2004, which catalogs the residues involved in enzyme catalysis in experimentally determined protein structures, had only 177 curated entries and employed a simplistic approach to expanding these annotations to homologous enzyme structures. Here we present a new version of the CSA (CSA 2.0), which greatly expands the number of both curated (968) and automatically annotated catalytic sites in enzyme structures, utilizing a new method for annotation transfer. The curated entries are used, along with the variation in residue type from the sequence comparison, to generate 3D templates of the catalytic sites, which in turn can be used to find catalytic sites in new structures. To ease the transfer of CSA annotations to other resources a new ontology has been developed: the Enzyme Mechanism Ontology, which has permitted the transfer of annotations to Mechanism, Annotation and Classification in Enzymes (MACiE) and UniProt Knowledge Base (UniProtKB) resources. The CSA database schema has been re-designed and both the CSA data and search capabilities are presented in a new modern web interface.

Dynamic programming algorithms for biological sequence comparison

Efficient dynamic programming algorithms are available for a broad class of protein and DNA sequence comparison problems. These algorithms require computer time proportional to the product of the lengths of the two sequences being compared [O(N2)] but require memory space proportional only to the sum of these lengths [O(N)]. Although the requirement for O(N2) time limits use of the algorithms to the largest computers when searching protein and DNA sequence databases, many other applications of these algorithms, such as calculation of distances for evolutionary trees and comparison of a new sequence to a library of sequence profiles, are well within the capabilities of desktop computers. In particular, the results of library searches with rapid searching programs, such as FASTA or BLAST, should be confirmed by performing a rigorous optimal alignment. Whereas rapid methods do not overlook significant sequence similarities, FASTA limits the number of gaps that can be inserted into an alignment, so that a rigorous alignment may extend the alignment substantially in some cases. BLAST does not allow gaps in the local regions that it reports; a calculation that allows gaps is very likely to extend the alignment substantially. Although a Monte Carlo evaluation of the statistical significance of a similarity score with a rigorous algorithm is much slower than the heuristic approach used by the RDF2 program, the dynamic programming approach should take less than 1 hr on a 386-based PC or desktop Unix workstation. For descriptive purposes, we have limited our discussion to methods for calculating similarity scores and distances that use gap penalties of the form g = rk. Nevertheless, programs for the more general case (g = q+rk) are readily available. Versions of these programs that run either on Unix workstations, IBM-PC class computers, or the Macintosh can be obtained from either of the authors.