Sequence Comparison Methods Research Articles

Modeling sequence evolution with kernel methods

We model the evolution of biological and linguistic sequences by comparing their statistical properties. This comparison is performed by means of efficiently computable kernel functions, that take two sequences as an input and return a measure of statistical similarity between them. We show how the use of such kernels allows to reconstruct the phylogenetic trees of primates based on the mitochondrial DNA (mtDNA) of existing animals, and the phylogenetic tree of Indo-European and other languages based on sample documents from existing languages. Kernel methods provide a convenient framework for many pattern analysis tasks, and recent advances have been focused on efficient methods for sequence comparison and analysis. While a large toolbox of algorithms has been developed to analyze data by using kernels, in this paper we demonstrate their use in combination with standard phylogenetic reconstruction algorithms and visualization methods.

Patterns of recombination in turnip mosaic virus genomic sequences indicate hotspots of recombination

Potyviruses have variable single-stranded RNA genomes and many show clear evidence of recombination. This report studied the distribution of recombination sites in the genomes of 92 isolates of the potyvirus Turnip mosaic virus (TuMV); 42 came from the international gene sequence databases and an additional 50 complete genomic sequences were generated from field samples collected in Europe and Asia. The sequences were examined for evidence of recombination using seven different sequence comparison methods and the exact position of each site was confirmed by sequence composition analysis. Recombination sites were found throughout the genomes, except in the small 6K1 protein gene, and only 24 of the genomes (26%) showed no evidence of recombination. Statistically significant clusters of recombination sites were found in the P1 gene and in the CI/6K2/VPg gene region. Most recombination sites were bordered by an upstream (5') region of GC-rich and downstream (3') region of AU-rich sequence of a similar length. Correlations between the presence and type of recombination site and provenance, host type and phylogenetic relationships are discussed, as is the role of recombination in TuMV evolution.

Structural and functional insights into Mimivirus ORFans

BackgroundMimivirus isolated from A. polyphaga is the largest virus discovered so far. It is unique among all the viruses in having genes related to translation, DNA repair and replication which bear close homology to eukaryotic genes. Nevertheless, only a small fraction of the proteins (33%) encoded in this genome has been assigned a function. Furthermore, a large fraction of the unassigned protein sequences bear no sequence similarity to proteins from other genomes. These sequences are referred to as ORFans. Because of their lack of sequence similarity to other proteins, they can not be assigned putative functions using standard sequence comparison methods. As part of our genome-wide computational efforts aimed at characterizing Mimivirus ORFans, we have applied fold-recognition methods to predict the structure of these ORFans and further functions were derived based on conservation of functionally important residues in sequence-template alignments.ResultsUsing fold recognition, we have identified highly confident computational 3D structural assignments for 21 Mimivirus ORFans. In addition, highly confident functional predictions for 6 of these ORFans were derived by analyzing the conservation of functional motifs between the predicted structures and proteins of known function. This analysis allowed us to classify these 6 previously unannotated ORFans into their specific protein families: carboxylesterase/thioesterase, metal-dependent deacetylase, P-loop kinases, 3-methyladenine DNA glycosylase, BTB domain and eukaryotic translation initiation factor eIF4E.ConclusionUsing stringent fold recognition criteria we have assigned three-dimensional structures for 21 of the ORFans encoded in the Mimivirus genome. Further, based on the 3D models and an analysis of the conservation of functionally important residues and motifs, we were able to derive functional attributes for 6 of the ORFans. Our computational identification of important functional sites in these ORFans can be the basis for a subsequent experimental verification of our predictions. Further computational and experimental studies are required to elucidate the 3D structures and functions of the remaining Mimivirus ORFans.

Parallel strategies for the local biological sequence alignment in a cluster of workstations

Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison is a basic operation in DNA sequencing projects, and most sequence comparison methods currently in use are based on heuristics, which are faster but offer no guarantees of producing the best alignments possible. In order to alleviate this problem, Smith–Waterman proposed an algorithm. This algorithm obtains the best local alignments but at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments involving three strategies to run the Smith–Waterman algorithm in a cluster of workstations using a Distributed Shared Memory System. Our results on an eight-machine cluster presented very good speed-up and indicate that impressive improvements can be achieved depending on the strategy used. In addition, we present a number of theoretical remarks concerning how to reduce the amount of memory used.

A Protein Classification Benchmark collection for machine learning

Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection () was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It is primarily meant for method developers and users interested in comparing methods under standardized conditions. The collection contains datasets of sequences and structures, and each set is subdivided into positive/negative, training/test sets in several ways. There is a total of 6405 classification tasks, 3297 on protein sequences, 3095 on protein structures and 10 on protein coding regions in DNA. Typical tasks include the classification of structural domains in the SCOP and CATH databases based on their sequences or structures, as well as various functional and taxonomic classification problems. In the case of hierarchical classification schemes, the classification tasks can be defined at various levels of the hierarchy (such as classes, folds, superfamilies, etc.). For each dataset there are distance matrices available that contain all vs. all comparison of the data, based on various sequence or structure comparison methods, as well as a set of classification performance measures computed with various classifier algorithms.

A combined bioinformatics and chemoinformatics approach for the development of new antiparasitic drugs

A modern concept for the development of novel antiparasitic drugs is the combination of bioinformatics and chemoinformatics approaches. This covers, for example, the identification of target proteins serving as molecular points of attack for parasiticides—the idea is that, owing to some essential role, inhibition of a target protein should eradicate the parasite. To prevent toxicity problems for vertebrate host organisms, it is advantageous that these proteins show significant differences from their vertebrate counterparts. In the present work, we identified potential target proteins in parasitic nematodes ( Ascaris suum, Brugia malayi, and Haemonchus contortus) and arthropods ( Boophilus microplus and Rhipicephalus appendiculatus) using bioinformatic sequence comparison methods on expressed sequence tags. Interesting target proteins (e.g., S-adenosyl- l-methionine synthetase) were characterized in detail by subjecting them to in-depth bioinformatic analysis. S-Adenosyl- l-methionine synthetase was also used to elucidate chemoinformatics approaches like homology modeling and docking, which represent appropriate methods for generating valuable data for the development of new drug candidates.

Spectral distortion measures for biological sequence comparisons and database searching

In bioinformatics and computational biology, methods for biological sequence comparison play the most important role for the interpretation of complex nucleotide and protein data such as the inference of relationships between genes, proteins and species; and the discovery of novel protein structures and functions. This type of inference is derived by sequence similarity matching on the databases of biological sequences. As many entire genomes have being determined at a rapid rate, computational methods for comparing genomic and protein sequences will be more essential for probing the complexity of genes, genomes, and molecular machines. In this paper we introduce a pattern-comparison algorithm, which is based on the mathematical concepts of linear predictive coding and its cepstral-distortion measures for the analyses of both DNA and protein sequences. The results obtained from several experiments on real datasets have shown the effectiveness of the proposed approach.

A tale of two ferredoxins: sequence similarity and structural differences

BackgroundSequence similarity between proteins is usually considered a reliable indicator of homology. Pyruvate-ferredoxin oxidoreductase and quinol-fumarate reductase contain ferredoxin domains that bind [Fe-S] clusters and are involved in electron transport. Profile-based methods for sequence comparison, such as PSI-BLAST and HMMer, suggest statistically significant similarity between these domains.ResultsThe sequence similarity between these ferredoxin domains resides in the area of the [Fe-S] cluster-binding sites. Although overall folds of these ferredoxins bear no obvious similarity, the regions of sequence similarity display a remarkable local structural similarity. These short regions with pronounced sequence motifs are incorporated in completely different structural environments. In pyruvate-ferredoxin oxidoreductase (bacterial ferredoxin), the hydrophobic core of the domain is completed by two β-hairpins, whereas in quinol-fumarate reductase (α-helical ferredoxin), the cluster-binding motifs are part of a larger all-α-helical globin-like fold core.ConclusionFunctionally meaningful sequence similarity may sometimes be reflected only in local structural similarity, but not in global fold similarity. If detected and used naively, such similarities may lead to incorrect fold predictions.

Knock-out of SO1377 gene, which encodes the member of a conserved hypothetical bacterial protein family COG2268, results in alteration of iron metabolism, increased spontaneous mutation and hydrogen peroxide sensitivity in Shewanella oneidensis MR-1

BackgroundShewanella oneidensis MR-1 is a facultative, gram-negative bacterium capable of coupling the oxidation of organic carbon to a wide range of electron acceptors such as oxygen, nitrate and metals, and has potential for bioremediation of heavy metal contaminated sites. The complete 5-Mb genome of S. oneidensis MR-1 was sequenced and standard sequence-comparison methods revealed approximately 42% of the MR-1 genome encodes proteins of unknown function. Defining the functions of hypothetical proteins is a great challenge and may need a systems approach. In this study, by using integrated approaches including whole genomic microarray and proteomics, we examined knockout effects of the gene encoding SO1377 (gi24372955), a member of the conserved, hypothetical, bacterial protein family COG2268 (Clusters of Orthologous Group) in bacterium Shewanella oneidensis MR-1, under various physiological conditions.ResultsCompared with the wild-type strain, growth assays showed that the deletion mutant had a decreased growth rate when cultured aerobically, but not affected under anaerobic conditions. Whole-genome expression (RNA and protein) profiles revealed numerous gene and protein expression changes relative to the wild-type control, including some involved in iron metabolism, oxidative damage protection and respiratory electron transfer, e. g. complex IV of the respiration chain. Although total intracellular iron levels remained unchanged, whole-cell electron paramagnetic resonance (EPR) demonstrated that the level of free iron in mutant cells was 3 times less than that of the wild-type strain. Siderophore excretion in the mutant also decreased in iron-depleted medium. The mutant was more sensitive to hydrogen peroxide and gave rise to 100 times more colonies resistant to gentamicin or kanamycin.ConclusionOur results showed that the knock-out of SO1377 gene had pleiotropic effects and suggested that SO1377 may play a role in iron homeostasis and oxidative damage protection in S. oneidensis MR-1.

Handbook of genome research: genomics, proteomics, metabolomics, bioinformatics, ethical and legal issues

Handbook of genome research: genomics, proteomics, metabolomics, bioinformatics, ethical and legal issues

Identification and in silico analysis of a new group of double-histone fold-containing proteins

The double-histone fold is a rare protein fold in which two consecutive regions characterized by the typical structure of histones assemble together, thus giving a histone pseudodimer. Previously, this fold was found in a few prokaryotic histones and in the regulatory region of guanine-nucleotide exchange factors of the Sos family. Standard methods of sequence comparison did not allow us to find new proteins containing a histone pseudodimer, as previously reported (Sondermann et al. 2003). However, a deeper investigation of protein sequences showed that the two histone folds included in Sos proteins share significant sequence similarity with nucleosomal histones. On the basis of this observation, we applied a specific strategy of sequence-homology search, which led to the identification of a new group of histone pseudodimers in Cca3 and proteins similar to Cca3 (Cca3S). A homology model of the histone pseudodimer included in rat Cca3 was constructed. A subsequent structure-function relationship study revealed that the histone pseudodimers included in Cca3 and Cca3S proteins, but not those present in Sos proteins, could retain the ability of mediating protein-DNA interactions, and could consequently act as DNA-binding modules.

A new dot plot-based algorithm for genomes sequences comparison: A preliminary study

To have efficient data mining systems, we need powerful algorithms to extract and mine the data. In the case of genomes data mining system, the algorithms search for genomes/proteins that share similar properties. Proteins that have a significant biological relationship to one another often share only isolated regions of sequence similarity. When identifying relationships of this nature, the ability to find local regions of optimal similarity is advantageous over global alignments that optimize the overall alignment of two entire sequences. The paper describes a new method for genome sequence comparison. This algorithm can be used in a genomes data mining system. It provides a good theoretical improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives. The method is based on the popular progressive approach, the dot plot method, but avoids the most serious pitfalls caused by the greedy nature of this technique. The new approach pre-processes a data set of all pair-wise alignments between the sequences. This provides a library of alignment information that can be used to guide the comparison. The algorithm is based on the similar segment method, i.e. having n similar identities in window of size L. The paper presents some results about the termination and correctness of the algorithm and how to include this algorithm into other comparison algorithms. The paper introduces the mechanism to create random sequences. These data will be our main benchmarks for comparing our algorithms.

Statistical evaluation of pairwise protein sequence comparison with the Bayesian bootstrap

Protein sequence comparison methods are routinely used to infer the intricate network of evolutionary relationships found within the rapidly growing library of protein sequences, and thereby to predict the structure and function of uncharacterized proteins. In the present study, we detail an improved statistical benchmark of pairwise protein sequence comparison algorithms. We use bootstrap resampling techniques to determine standard statistical errors and to estimate the confidence of our conclusions. We show that the underlying structure within benchmark databases causes Efron's standard, non-parametric bootstrap to be biased. Consequently, the standard bootstrap underpredicts average performance when used in the context of evaluating sequence comparison methods. We have developed, as an alternative, an unbiased statistical evaluation based on the Bayesian bootstrap, a resampling method operationally similar to the standard bootstrap. We apply our analysis to the comparative study of amino acid substitution matrix families and find that using modern matrices results in a small, but statistically significant improvement in remote homology detection compared with the classic PAM and BLOSUM matrices. The sequence sets and code for performing these analyses are available from http://compbio.berkeley.edu/. brenner@compbio.berkeley.edu.

Does secondary structure determine tertiary structure in proteins?

Is highly approximate knowledge of a protein's backbone structure sufficient to successfully identify its family, superfamily, and tertiary fold? To explore this question, backbone dihedral angles were extracted from the known three-dimensional structure of 2,439 proteins and mapped into 36 labeled, 60 degrees x 60 degrees bins, called mesostates. Using this coarse-grained mapping, protein conformation can be approximated by a linear sequence of mesostates. These linear strings can then be aligned and assessed by conventional sequence-comparison methods. We report that the mesostate sequence is sufficient to recognize a protein's family, superfamily, and fold with good fidelity.

Prediction of Functional Class of the SARS Coronavirus Proteins by a Statistical Learning Method

The complete genome of severe acute respiratory syndrome coronavirus (SARS-CoV) reveals the existence of putative proteins unique to SARS-CoV. Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi .

PSI-BLAST-ISS: an intermediate sequence search tool for estimation of the position-specific alignment reliability

BackgroundProtein sequence alignments have become indispensable for virtually any evolutionary, structural or functional study involving proteins. Modern sequence search and comparison methods combined with rapidly increasing sequence data often can reliably match even distantly related proteins that share little sequence similarity. However, even highly significant matches generally may have incorrectly aligned regions. Therefore when exact residue correspondence is used to transfer biological information from one aligned sequence to another, it is critical to know which alignment regions are reliable and which may contain alignment errors.ResultsPSI-BLAST-ISS is a standalone Unix-based tool designed to delineate reliable regions of sequence alignments as well as to suggest potential variants in unreliable regions. The region-specific reliability is assessed by producing multiple sequence alignments in different sequence contexts followed by the analysis of the consistency of alignment variants. The PSI-BLAST-ISS output enables the user to simultaneously analyze alignment reliability between query and multiple homologous sequences. In addition, PSI-BLAST-ISS can be used to detect distantly related homologous proteins. The software is freely available at: .ConclusionPSI-BLAST-ISS is an effective reliability assessment tool that can be useful in applications such as comparative modelling or analysis of individual sequence regions. It favorably compares with the existing similar software both in the performance and functional features.

Reliable Classification and Recombination Analysis of Porcine Endogenous Retroviruses

Prevention of cross-species infection with porcine endogenous retroviruses (PERV) is crucial for xenotransplantation. Previous studies described the potential risk of infection for the PERV gamma1 subfamilies A, B and C. Replication competent PERV gamma1 proviruses designated to a particular subfamily and hybrid viruses originating from retroviral recombination events between the subfamilies were observed. Future pig genome sequencing projects will reveal multiple novel PERV proviruses from additional breeds and animals. Evaluation of these viral genomes has to be carried out to assess the potential risk of retroviral cross-species infection. In this study, we tested common sequence comparison methods for the classification of PERV sequences and the detection of hybrid clones. The examination of the polymorphic nucleotide positions was found to be the most suitable procedure. We describe a fast and simple method using bioinformatic software tools which can also be applied to analogous analyses of other viral genomes.

Efficient recognition of protein fold at low sequence identity by conservative application of Psi-BLAST: validation

A substantial fraction of protein sequences derived from genomic analyses is currently classified as representing 'hypothetical proteins of unknown function'. In part, this reflects the limitations of methods for comparison of sequences with very low identity. We evaluated the effectiveness of a Psi-BLAST search strategy to identify proteins of similar fold at low sequence identity. Psi-BLAST searches for structurally characterized low-sequence-identity matches were carried out on a set of over 300 proteins of known structure. Searches were conducted in NCBI's non-redundant database and were limited to three rounds. Some 614 potential homologs with 25% or lower sequence identity to 166 members of the search set were obtained. Disregarding the expect value, level of sequence identity and span of alignment, correspondence of fold between the target and potential homolog was found in more than 95% of the Psi-BLAST matches. Restrictions on expect value or span of alignment improved the false positive rate at the expense of eliminating many true homologs. Approximately three-quarters of the putative homologs obtained by three rounds of Psi-BLAST revealed no significant sequence similarity to the target protein upon direct sequence comparison by BLAST, and therefore could not be found by a conventional search. Although three rounds of Psi-BLAST identified many more homologs than a standard BLAST search, most homologs were undetected. It appears that more than 80% of all homologs to a target protein may be characterized by a lack of significant sequence similarity. We suggest that conservative use of Psi-BLAST has the potential to propose experimentally testable functions for the majority of proteins currently annotated as 'hypothetical proteins of unknown function'.

A structure-based method for protein sequence alignment

With the continuing rapid growth of protein sequence data, protein sequence comparison methods have become the most widely used tools of bioinformatics. Among these methods are those that use position-specific scoring matrices (PSSMs) to describe protein families. PSSMs can capture information about conserved patterns within families, which can be used to increase the sensitivity of searches for related sequences. Certain types of structural information, however, are not generally captured by PSSM search methods. Here we introduce a program, Structure-based ALignment TOol (SALTO), that aligns protein query sequences to PSSMs using rules for placing and scoring gaps that are consistent with the conserved regions of domain alignments from NCBI's Conserved Domain Database. In most cases, the alignment scores obtained using the local alignment version follow an extreme value distribution. SALTO's performance in finding related sequences and producing accurate alignments is similar to or better than that of IMPALA; one advantage of SALTO is that it imposes an explicit gapping model on each protein family. A stand-alone version of the program that can generate global or local alignments is available by ftp distribution (ftp://ftp.ncbi.nih.gov/pub/SALTO/), and has been incorporated to Cn3D structure/alignment viewer. bryant@ncbi.nlm.nih.gov.

The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis

The CATH database of protein domain structures (http://www.biochem.ucl.ac.uk/bsm/cath/) currently contains 43 229 domains classified into 1467 superfamilies and 5107 sequence families. Each structural family is expanded with sequence relatives from GenBank and completed genomes, using a variety of efficient sequence search protocols and reliable thresholds. This extended CATH protein family database contains 616 470 domain sequences classified into 23 876 sequence families. This results in the significant expansion of the CATH HMM model library to include models built from the CATH sequence relatives, giving a 10% increase in coverage for detecting remote homologues. An improved Dictionary of Homologous superfamilies (DHS) (http://www.biochem.ucl.ac.uk/bsm/dhs/) containing specific sequence, structural and functional information for each superfamily in CATH considerably assists manual validation of homologues. Information on sequence relatives in CATH superfamilies, GenBank and completed genomes is presented in the CATH associated DHS and Gene3D resources. Domain partnership information can be obtained from Gene3D (http://www.biochem.ucl.ac.uk/bsm/cath/Gene3D/). A new CATH server has been implemented (http://www.biochem.ucl.ac.uk/cgi-bin/cath/CathServer.pl) providing automatic classification of newly determined sequences and structures using a suite of rapid sequence and structure comparison methods. The statistical significance of matches is assessed and links are provided to the putative superfamily or fold group to which the query sequence or structure is assigned.