DbClustal: rapid and reliable global multiple alignments of protein sequences detected by database searches

DbClustal addresses the important problem of the automatic multiple alignment of the top scoring full-length sequences detected by a database homology search. By combining the advantages of both local and global alignment algorithms into a single system, DbClustal is able to provide accurate global alignments of highly divergent, complex sequence sets. Local alignment information is incorporated into a ClustalW global alignment in the form of a list of anchor points between pairs of sequences. The method is demonstrated using anchors supplied by the Blast post-processing program, Ballast. The rapidity and reliability of DbClustal have been demonstrated using the recently annotated Pyrococcus abyssi proteome where the number of alignments with totally misaligned sequences was reduced from 20% to <2%. A web site has been implemented proposing BlastP database searches with automatic alignment of the top hits by DbClustal.     

Assessment of a rigorous transitive profile based search method to detect remotely similar proteins

Profile-based sequence search procedures are commonly employed to detect remote relationships between proteins. We provide an assessment of a Cascade PSI-BLAST protocol that rigorously employs intermediate sequences in detecting remote relationships between proteins. In this approach we detect using PSI-BLAST, which involves multiple rounds of iteration, an initial set of homologues for a protein in a 'first generation' search by querying a database. We propagate a 'second generation' search in the database, involving multiple runs of PSI-BLAST using each of the homologues identified in the previous generation as queries to recognize homologues not detected earlier. This non-directed search process can be viewed as an iteration of iterations that is continued to detect further homologues until no new hits are detectable. We present an assessment of the coverage of this 'cascaded' intermediate sequence search on diverse folds and find that searches for up to three generations detect most known homologues of a query. Our assessments show that this approach appears to perform better than the traditional use of PSI-BLAST by detecting 15% more relationships within a family and 35% more relationships within a superfamily. We show that such searches can be performed on generalized sequence databases and non-trivial relationships between proteins can be detected effectively. Such a propagation of searches maximizes the chances of detecting distant homologies by effectively scanning protein "fold space".     

Assessment of a Rigorous Transitive Profile Based Search Method to Detect Remotely Similar Proteins

Abstract

Profile-based sequence search procedures are commonly employed to detect remote relationships between proteins. We provide an assessment of a Cascade PSI-BLAST protocol that rigorously employs intermediate sequences in detecting remote relationships between proteins. In this approach we detect using PSI-BLAST, which involves multiple rounds of iteration, an initial set of homologues for a protein in a ‘first generation’ search by querying a database. We propagate a ‘second generation’ search in the database, involving multiple runs of PSI-BLAST using each of the homologues identified in the previous generation as queries to recognize homologues not detected earlier. This non-directed search process can be viewed as an iteration of iterations that is continued to detect further homologues until no new hits are detectable. We present an assessment of the coverage of this ‘cascaded’ intermediate sequence search on diverse folds and find that searches for up to three generations detect most known homologues of a query. Our assessments show that this approach appears to perform better than the traditional use of PSI-BLAST by detecting 15% more relationships within a family and 35% more relationships within a superfamily. We show that such searches can be performed on generalized sequence databases and non-trivial relationships between proteins can be detected effectively. Such a propagation of searches maximizes the chances of detecting distant homologies by effectively scanning protein “fold space”.     

Motifer, a search tool for finding amino acid sequence patterns from nucleotide sequence databases.

Motifer is a software tool able to find directly in nucleotide databases very distant homologues to an amino acid query sequence. It focuses searches on a specific amino acid pattern, scoring the matching and intervening residues as specified by the user. The program has been developed for searching databases of expressed sequence tags (ESTs), but it is also well suited to search genomic sequences. The query sequence can be a variable pattern with alternative amino acids or gaps and the sequences searched can contain introns or sequencing errors with accompanying frame shifts. Other features include options to generate a searchable output, set the maximal sequencing error frequency, limit searches to given species, or exclude already known matches. Motifer can find sequence homologues that other search algorithms would deem unrelated or would not find because of sequencing errors or a too large number of other homologues. The ability of Motifer to find relatives to a given sequence is exemplified by searches for members of the transforming growth factor-beta family and for proteins containing a WW-domain. The functions aimed at enhancing EST searches are illustrated by the 'in silico' cloning of a novel cytochrome P450 enzyme.     

Filling-in Void and Sparse Regions in Protein Sequence Space by Protein-Like Artificial Sequences Enables Remarkable Enhancement in Remote Homology Detection Capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like “linker” sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be “plugged-into” routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold.     

Probabilistic alignment detects remote homology in a pair of protein sequences without homologous sequence information

Dynamic programming (DP) and its heuristic algorithms are the most fundamental methods for similarity searches of amino acid sequences. Their detection power has been improved by including supplemental information, such as homologous sequences in the profile method. Here, we describe a method, probabilistic alignment (PA), that gives improved detection power, but similarly to the original DP, uses only a pair of amino acid sequences. Receiver operating characteristic (ROC) analysis demonstrated that the PA method is far superior to BLAST, and that its sensitivity and selectivity approach to those of PSI-BLAST. Particularly for orphan proteins having few homologues in the database, PA exhibits much better performance than PSI-BLAST. On the basis of this observation, we applied the PA method to a homology search of two orphan proteins, Latexin and Resuscitation-promoting factor domain. Their molecular functions have been described based on structural similarities, but sequence homologues have not been identified by PSI-BLAST. PA successfully detected sequence homologues for the two proteins and confirmed that the observed structural similarities are the result of an evolutional relationship.     

Strategies for the effective identification of remotely related sequences in multiple PSSM search approach

Searches using position specific scoring matrices (PSSMs) have been commonly used in remote homology detection procedures such as PSI-BLAST and RPS-BLAST. A PSSM is generated typically using one of the sequences of a family as the reference sequence. In the case of PSI-BLAST searches the reference sequence is same as the query. Recently we have shown that searches against the database of multiple family-profiles, with each one of the members of the family used as a reference sequence, are more effective than searches against the classical database of single family-profiles. Despite relatively a better overall performance when compared with common sequence-profile matching procedures, searches against the multiple family-profiles database result in a few false positives and false negatives. Here we show that profile length and divergence of sequences used in the construction of a PSSM have major influence on the performance of multiple profile based search approach. We also identify that a simple parameter defined by the number of PSSMs corresponding to a family that is hit, for a query, divided by the total number of PSSMs in the family can distinguish effectively the true positives from the false positives in the multiple profiles search approach.     

SAND, a new protein family: from nucleic acid to protein structure and function prediction

As a result of genome, EST and cDNA sequencing projects, there are huge numbers of predicted and/or partially characterised protein sequences compared with a relatively small number of proteins with experimentally determined function and structure. Thus, there is a considerable attention focused on the accurate prediction of gene function and structure from sequence by using bioinformatics. In the course of our analysis of genomic sequence from Fugu rubripes, we identified a novel gene, SAND, with significant sequence identity to hypothetical proteins predicted in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans, a Drosophila melanogaster gene, and mouse and human cDNAs. Here we identify a further SAND homologue in human and Arabidopsis thaliana by use of standard computational tools. We describe the genomic organisation of SAND in these evolutionarily divergent species and identify sequence homologues from EST database searches confirming the expression of SAND in over 20 different eukaryotes. We confirm the expression of two different SAND paralogues in mammals and determine expression of one SAND in other vertebrates and eukaryotes. Furthermore, we predict structural properties of SAND, and characterise conserved sequence motifs in this protein family.     

Strategies for searching sequence databases

We provide a detailed overview of the choices inherent in performing a sequence database search, including the choice of algorithm, substitution matrix and gap model. Each of these choices has implications that can be described as restrictions on the underlying model of sequence evolution, the expected degree of divergence between the query sequence and the database sequences (if one uses an evolutionary based matrix), as well as the sensitivity and selectivity of the search. We conclude with a series of recommendations for researchers performing these searches based on our experience and literature studies.     

Statistical assignment of DNA sequences using Bayesian phylogenetics

We provide a new automated statistical method for DNA barcoding based on a Bayesian phylogenetic analysis. The method is based on automated database sequence retrieval, alignment, and phylogenetic analysis using a custom-built program for Bayesian phylogenetic analysis. We show on real data that the method outperforms Blast searches as a measure of confidence and can help eliminate 80% of all false assignment based on best Blast hit. However, the most important advance of the method is that it provides statistically meaningful measures of confidence. We apply the method to a re-analysis of previously published ancient DNA data and show that, with high statistical confidence, most of the published sequences are in fact of Neanderthal origin. However, there are several cases of chimeric sequences that are comprised of a combination of both Neanderthal and modern human DNA.     

序列同源性分析软件Blast的WEB界面构建及其应用

基于局域网(Intranet)内的PC/Linux服务器, 构建了序列同源性分析软件Blast的WEB界面. 局域网内的所有计算机均可通过WEB方式访问该服务器进行公共数据库和自建数据库的查询,具有保密、高效、免费的优点,能够满足实验室和研究院所的大规模、快速数据分析任务.     

Dirofilaria immitis: identification of a novel ligand-gated ion channel-related polypeptide

We have isolated a cDNA from Dirofilaria immitis that encodes a predicted ion channel subunit, Di-LGR-1. Secondary structure predictions and database searches reveal that Di-LGR-1 is distantly related to ligand-gated anion channels, such as the GABA(A) receptors, though there are marked differences in the sequences of the putative channel forming regions. Di-LGR-1 has 52% sequence identity to the Caenorhabditis elegans predicted polypeptide, T27A1.4: neighbour-joining trees show that these two polypeptides are the most divergent members of the nematode ligand-gated anion channel family. No close homologues are present in vertebrates, suggesting that their function may be specific to nematodes. RNAi experiments using a fragment of T27A1.4 with C. elegans failed to reveal any obvious phenotype, so the function of these channels remains unknown.     

Serial BLAST searching

MOTIVATION: The translating BLAST algorithms are powerful tools for finding protein-coding genes because they identify amino acid similarities in nucleotide sequences. Unfortunately, these kinds of searches are computationally intensive and often represent bottlenecks in sequence analysis pipelines. Tuning parameters for speed can make the searches much faster, but one risks losing low-scoring alignments. However, high scoring alignments are relatively resistant to such changes in parameters, and this fact makes it possible to use a serial strategy where a fast, insensitive search is used to pre-screen a database for similar sequences, and a slow, sensitive search is used to produce the sequence alignments. RESULTS: Serial BLAST searches improve both the speed and sensitivity.     

The HSSP database of protein structure-sequence alignments.

HSSP is a derived database merging structural (3-D) and sequence (1-D) information. For each protein of known 3-D structure from the Protein Data Bank (PDB), the database has a multiple sequence alignment of all available homologues and a sequence profile characteristic of the family. The list of homologues is the result of a database search in SwissProt using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). The database is updated frequently. The listed homologues are very likely to have the same 3-D structure as the PDB protein to which they have been aligned. As a result, the database is not only a database of aligned sequence families, but also a database of implied secondary and tertiary structures covering 29% of all SwissProt-stored sequences.     

The HSSP database of protein structure-sequence alignments.

HSSP is a derived database merging structural three dimensional (3-D) and sequence one dimensional(1-D) information. For each protein of known 3-D structure from the Protein Data Bank (PDB), the database has a multiple sequence alignment of all available homologues and a sequence profile characteristic of the family. The list of homologues is the result of a database search in Swissprot using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). The database is updated frequently. The listed homologues are very likely to have the same 3-D structure as the PDB protein to which they have been aligned. As a result, the database is not only a database of aligned sequence families, but also a database of implied secondary and tertiary structures covering 27% of all Swissprot-stored sequences.     

Preprocessing of tandem mass spectrometric data to support automatic protein identification

Liquid chromatography tandem mass spectrometry is a major tool for identifying proteins. The fragment spectra of peptides can be interpreted automatically in conjunction with a sequence database search. With the development of powerful automatic search engines, research now focuses on optimizing the result returned from database searches. We present a series of preprocessing steps for fragment spectra to increase the accuracy and specificity of automatic database searches. After processing, the correct amino acid sequences from the database can be related better to the fragment spectra. This increases the sensitivity and reliability of protein identifications, especially with very large genomic databanks, and can be important for the systematic characterization of post-translational modifications.     

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.     

Similarity graphing and enzyme-reaction database: methods to detect sequence regions of importance for recognition of chemical structures

We developed a new method which searches sequence segments responsible for the recognition of a given chemical structure. These segments are detected as those locally conserved among a sequence to be analyzed (target sequence) and a set of sequences (reference sequences). Reference sequences are the sequences of functionally related proteins, ligands of which contain a common chemical substructure in their molecular structures. 'Similarity graphing' cuts target sequences into segments, aligns them with reference sequence pairwise, calculates the degree of similarity for each alignment, and shows graphically cumulative similarity values on target sequence. Any locally conserved regions, short or long in length and weak or strong in similarity, are detected at their optimal conditions by adjusting three parameters. The 'enzyme-reaction database' contains chemical structures and their related enzymes. When a chemical substructure is input into the database, sequences of the enzymes related to the input substructure are systematically searched from the NBRF sequence database and output as reference sequences. Examples of analysis using similarity graphing in combination with the enzyme-reaction database showed a great potentiality in the systematic analysis of the relationships between sequences and molecular recognitions for protein engineering.     

The HSSP database of protein structure-sequence alignments and family profiles.

HSSP (http: //www.sander.embl-ebi.ac.uk/hssp/) is a derived database merging structure (3-D) and sequence (1-D) information. For each protein of known 3D structure from the Protein Data Bank (PDB), we provide a multiple sequence alignment of putative homologues and a sequence profile characteristic of the protein family, centered on the known structure. The list of homologues is the result of an iterative database search in SWISS-PROT using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). The database is updated frequently. The listed putative homologues are very likely to have the same 3D structure as the PDB protein to which they have been aligned. As a result, the database not only provides aligned sequence families, but also implies secondary and tertiary structures covering 33% of all sequences in SWISS-PROT.     

IMPALA: matching a protein sequence against a collection of PSI-BLAST-constructed position-specific score matrices

MOTIVATION: Many studies have shown that database searches using position-specific score matrices (PSSMs) or profiles as queries are more effective at identifying distant protein relationships than are searches that use simple sequences as queries. One popular program for constructing a PSSM and comparing it with a database of sequences is Position-Specific Iterated BLAST (PSI-BLAST). RESULTS: This paper describes a new software package, IMPALA, designed for the complementary procedure of comparing a single query sequence with a database of PSI-BLAST-generated PSSMs. We illustrate the use of IMPALA to search a database of PSSMs for protein folds, and one for protein domains involved in signal transduction. IMPALA's sensitivity to distant biological relationships is very similar to that of PSI-BLAST. However, IMPALA employs a more refined analysis of statistical significance and, unlike PSI-BLAST, guarantees the output of the optimal local alignment by using the rigorous Smith-Waterman algorithm. Also, it is considerably faster when run with a large database of PSSMs than is BLAST or PSI-BLAST when run against the complete non-redundant protein database.