Clustering Of Protein Sequences Research Articles

Large scale clustering of protein sequences with FORCE -A layout based heuristic for weighted cluster editing

BackgroundDetecting groups of functionally related proteins from their amino acid sequence alone has been a long-standing challenge in computational genome research. Several clustering approaches, following different strategies, have been published to attack this problem. Today, new sequencing technologies provide huge amounts of sequence data that has to be efficiently clustered with constant or increased accuracy, at increased speed.ResultsWe advocate that the model of weighted cluster editing, also known as transitive graph projection is well-suited to protein clustering. We present the FORCE heuristic that is based on transitive graph projection and clusters arbitrary sets of objects, given pairwise similarity measures. In particular, we apply FORCE to the problem of protein clustering and show that it outperforms the most popular existing clustering tools (Spectral clustering, TribeMCL, GeneRAGE, Hierarchical clustering, and Affinity Propagation). Furthermore, we show that FORCE is able to handle huge datasets by calculating clusters for all 192 187 prokaryotic protein sequences (66 organisms) obtained from the COG database. Finally, FORCE is integrated into the corynebacterial reference database CoryneRegNet.ConclusionFORCE is an applicable alternative to existing clustering algorithms. Its theoretical foundation, weighted cluster editing, can outperform other clustering paradigms on protein homology clustering. FORCE is open source and implemented in Java. The software, including the source code, the clustering results for COG and CoryneRegNet, and all evaluation datasets are available at .

CLUSS: clustering of protein sequences based on a new similarity measure.

BackgroundThe rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important. The challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as proteins with different domain structures, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-free algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families. In the rest of the paper, we use the term "phylogenetic" in the sense of "relatedness of biological functions".ResultsTo show the effectiveness of CLUSS, we performed an extensive clustering on COG database. To demonstrate its ability to deal with hard-to-align sequences, we tested it on the GH2 family. In addition, we carried out experimental comparisons of CLUSS with a variety of mainstream algorithms. These comparisons were made on hard-to-align and easy-to-align protein sequences. The results of these experiments show the superiority of CLUSS in yielding clusters of proteins with similar functional activity.ConclusionWe have developed an effective method and tool for clustering protein sequences to meet the needs of biologists in terms of phylogenetic analysis and prediction of biological functions. Compared to existing clustering methods, CLUSS more accurately highlights the functional characteristics of the clustered families. It provides biologists with a new and plausible instrument for the analysis of protein sequences, especially those that cause problems for the alignment-dependent algorithms.

A maximum likelihood approximation method for Dirichlet's parameter estimation

Dirichlet distributions are natural choices to analyse data described by frequencies or proportions since they are the simplest known distributions for such data apart from the uniform distribution. They are often used whenever proportions are involved, for example, in text-mining, image analysis, biology or as a prior of a multinomial distribution in Bayesian statistics. As the Dirichlet distribution belongs to the exponential family, its parameters can be easily inferred by maximum likelihood. Parameter estimation is usually performed with the Newton–Raphson algorithm after an initialisation step using either the moments or Ronning's methods. However this initialisation can result in parameters that lie outside the admissible region. A simple and very efficient alternative based on a maximum likelihood approximation is presented. The advantages of the presented method compared to two other methods are demonstrated on synthetic data sets as well as for a practical biological problem: the clustering of protein sequences based on their amino acid compositions.

Ancestry of neuronal monoamine transporters in the Metazoa

Selective Na(+)-dependent re-uptake of biogenic monoamines at mammalian nerve synapses is accomplished by three types of solute-linked carrier family 6 (SLC6) membrane transporter with high affinity for serotonin (SERTs), dopamine (DATs) and norepinephrine (NETs). An additional SLC6 monoamine transporter (OAT), is responsible for the selective uptake of the phenolamines octopamine and tyramine by insect neurons. We have characterized a similar high-affinity phenoloamine transporter expressed in the CNS of the earthworm Lumbricus terrestris. Phylogenetic analysis of its protein sequence clusters it with both arthropod phenolamine and chordate catecholamine transporters. To clarify the relationships among metazoan monoamine transporters we identified representatives in the major branches of metazoan evolution by polymerase chain reaction (PCR)-amplifying conserved cDNA fragments from isolated nervous tissue and by analyzing available genomic data. Analysis of conserved motifs in the sequence data suggest that the presumed common ancestor of modern-day Bilateria expressed at least three functionally distinct monoamine transporters in its nervous system: a SERT currently found throughout bilaterian phyla, a DAT now restricted in distribution to protostome invertebrates and echinoderms and a third monoamine transporter (MAT), widely represented in contemporary Bilateria, that is selective for catecholamines and/or phenolamines. Chordate DATs, NETs, epinephrine transporters (ETs) and arthropod and annelid OATs all belong to the MAT clade. Contemporary invertebrate and chordate DATs belong to different SLC6 clades. Furthermore, the genes for dopamine and norepinephrine transporters of vertebrates are paralogous, apparently having arisen through duplication of an invertebrate MAT gene after the loss of an invertebrate-type DAT gene in a basal protochordate.

SEQOPTICS: a protein sequence clustering system

BackgroundProtein sequence clustering has been widely used as a part of the analysis of protein structure and function. In most cases single linkage or graph-based clustering algorithms have been applied. OPTICS (Ordering Points To Identify the Clustering Structure) is an attractive approach due to its emphasis on visualization of results and support for interactive work, e.g., in choosing parameters. However, OPTICS has not been used, as far as we know, for protein sequence clustering.ResultsIn this paper, a system of clustering proteins, SEQOPTICS (SEQuence clustering with OPTICS) is demonstrated. The system is implemented with Smith-Waterman as protein distance measurement and OPTICS at its core to perform protein sequence clustering. SEQOPTICS is tested with four data sets from different data sources. Visualization of the sequence clustering structure is demonstrated as well.ConclusionThe system was evaluated by comparison with other existing methods. Analysis of the results demonstrates that SEQOPTICS performs better based on some evaluation criteria including Jaccard coefficient, Precision, and Recall. It is a promising protein sequence clustering method with future possible improvement on parallel computing and other protein distance measurements.

Efficient median based clustering and classification techniques for protein sequences

In this paper, an efficient K-medians clustering (unsupervised) algorithm for prototype selection and Supervised K-medians (SKM) classification technique for protein sequences are presented. For sequence data sets, a median string/sequence can be used as the cluster/group representative. In K-medians clustering technique, a desired number of clusters, K, each represented by a median string/sequence, is generated and these median sequences are used as prototypes for classifying the new/test sequence whereas in SKM classification technique, median sequence in each group/class of labelled protein sequences is determined and the set of median sequences is used as prototypes for classification purpose. It is found that the K-medians clustering technique outperforms the leader based technique and also SKM classification technique performs better than that of motifs based approach for the data sets used. We further use a simple technique to reduce time and space requirements during protein sequence clustering and classification. During training and testing phase, the similarity score value between a pair of sequences is determined by selecting a portion of the sequence instead of the entire sequence. It is like selecting a subset of features for sequence data sets. The experimental results of the proposed method on K-medians, SKM and Nearest Neighbour Classifier (NNC) techniques show that the Classification Accuracy (CA) using the prototypes generated/used does not degrade much but the training and testing time are reduced significantly. Thus the experimental results indicate that the similarity score does not need to be calculated by considering the entire length of the sequence for achieving a good CA. Even space requirement is reduced during both training and classification.

Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.

Large Scale Protein Sequence Clustering - Not Solved But Solvable

Protein sequence clustering is one of the oldest problems addressed in the field of computational biology. Back in the 60s, when the first protein sequence database was published as printed version, Margaret Dayhoff defined the basic principles of this discipline with only a small number of sequences at hand. With up to a million sequences available in public databases nowadays and several well known methods for automatic grouping of proteins into somehow biologically meaningful families, subfamilies and superfamilies, the problem seems to be satisfactorily solved. Nevertheless, apart from the problem of handling such a huge amount of data, several pitfalls have emerged since Dayhoffs times: databases fill up as fast as genomes are sequenced and a great many of these sequences are fragmental or disappear again when identified as being transcripts of wrongly predicted genes or hypothetical products of pseudogenes. This article first reviews the different approaches developed during the last decades. These insights will then be used to point out possible challenges waiting in the future.

Spectral clustering of protein sequences

An important problem in genomics is automatically clustering homologous proteins when only sequence information is available. Most methods for clustering proteins are local, and are based on simply thresholding a measure related to sequence distance. We first show how locality limits the performance of such methods by analysing the distribution of distances between protein sequences. We then present a global method based on spectral clustering and provide theoretical justification of why it will have a remarkable improvement over local methods. We extensively tested our method and compared its performance with other local methods on several subsets of the SCOP (Structural Classification of Proteins) database, a gold standard for protein structure classification. We consistently observed that, the number of clusters that we obtain for a given set of proteins is close to the number of superfamilies in that set; there are fewer singletons; and the method correctly groups most remote homologs. In our experiments, the quality of the clusters as quantified by a measure that combines sensitivity and specificity was consistently better [on average, improvements were 84% over hierarchical clustering, 34% over Connected Component Analysis (CCA) (similar to GeneRAGE) and 72% over another global method, TribeMCL].

Exploiting homogeneity in protein sequence clusters for construction of protein family hierarchies

In the field of proteomics, protein hierarchies based on sequence analysis have been extensively applied to automate the annotations of new proteins and facilitate the discovery and analysis of protein families. However, the presence of ambiguous similarities in large databases increases the difficulty of delivering protein family hierarchies with favorable sensitivity and specificity. This work develops the HomoClust algorithm that exploits the homogeneity of protein sequences in generating protein family hierarchies. HomoClust improves the clustering quality of traditional hierarchical clustering algorithms by adopting different clustering mechanisms for different levels of sequence similarity. With considering homogeneity detection during clustering process, HomoClust increases the sensitivity of protein clusters without a drop in high specificity.

Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks

BackgroundThe sequencing of the human genome has enabled us to access a comprehensive list of genes (both experimental and predicted) for further analysis. While a majority of the approximately 30000 known and predicted human coding genes are characterized and have been assigned at least one function, there remains a fair number of genes (about 12000) for which no annotation has been made. The recent sequencing of other genomes has provided us with a huge amount of auxiliary sequence data which could help in the characterization of the human genes. Clustering these sequences into families is one of the first steps to perform comparative studies across several genomes.ResultsHere we report a novel clustering algorithm (CLUGEN) that has been used to cluster sequences of experimentally verified and predicted proteins from all sequenced genomes using a novel distance metric which is a neural network score between a pair of protein sequences. This distance metric is based on the pairwise sequence similarity score and the similarity between their domain structures. The distance metric is the probability that a pair of protein sequences are of the same Interpro family/domain, which facilitates the modelling of transitive homology closure to detect remote homologues. The hierarchical average clustering method is applied with the new distance metric.ConclusionBenchmarking studies of our algorithm versus those reported in the literature shows that our algorithm provides clustering results with lower false positive and false negative rates. The clustering algorithm is applied to cluster several eukaryotic genomes and several dozens of prokaryotic genomes.

An Algorithm to Classify Amino Acid Sequences into Protein Groups of Bothrops jararacussu Venomous Gland

An algorithm for automatic clustering of database protein sequences from Bothrops jararacussu venomous gland, according to sequence similarities of their domains, is described. The program was written in C and Perl languages. This algorithm compares a domain with each ORF protein sequence in the database. Each nucleotide FASTA sequence generates six ORFs. As a result, the user has a list containing all sequences found in a specific domain and a display of the sequence, domain and number of hits. The algorithm lists only the sequences that present a minimum similarity of 30 hits and the best alignment. This limit was considered appropriate. The algorithm is available in the Internet (www.compbionet.org.br/cgi-domains/homesnake) and it can quickly and accurately organizes large database into classes.

Large scale hierarchical clustering of protein sequences

BackgroundSearching a biological sequence database with a query sequence looking for homologues has become a routine operation in computational biology. In spite of the high degree of sophistication of currently available search routines it is still virtually impossible to identify quickly and clearly a group of sequences that a given query sequence belongs to.ResultsWe report on our developments in grouping all known protein sequences hierarchically into superfamily and family clusters. Our graph-based algorithms take into account the topology of the sequence space induced by the data itself to construct a biologically meaningful partitioning. We have applied our clustering procedures to a non-redundant set of about 1,000,000 sequences resulting in a hierarchical clustering which is being made available for querying and browsing at .ConclusionsComparisons with other widely used clustering methods on various data sets show the abilities and strengths of our clustering methods in producing a biologically meaningful grouping of protein sequences.

Super paramagnetic clustering of protein sequences

BackgroundDetection of sequence homologues represents a challenging task that is important for the discovery of protein families and the reliable application of automatic annotation methods. The presence of domains in protein families of diverse function, inhomogeneity and different sizes of protein families create considerable difficulties for the application of published clustering methods.ResultsOur work analyses the Super Paramagnetic Clustering (SPC) and its extension, global SPC (gSPC) algorithm. These algorithms cluster input data based on a method that is analogous to the treatment of an inhomogeneous ferromagnet in physics. For the SwissProt and SCOP databases we show that the gSPC improves the specificity and sensitivity of clustering over the original SPC and Markov Cluster algorithm (TRIBE-MCL) up to 30%. The three algorithms provided similar results for the MIPS FunCat 1.3 annotation of four bacterial genomes, Bacillus subtilis, Helicobacter pylori, Listeria innocua and Listeria monocytogenes. However, the gSPC covered about 12% more sequences compared to the other methods. The SPC algorithm was programmed in house using C++ and it is available at . The FunCat annotation is available at .ConclusionThe gSPC calculated to a higher accuracy or covered a larger number of sequences than the TRIBE-MCL algorithm. Thus it is a useful approach for automatic detection of protein families and unsupervised annotation of full genomes.

Cluster-C, an algorithm for the large-scale clustering of protein sequences based on the extraction of maximal cliques

Although the characterization of proteins cannot solely rely upon sequence similarity, it has been widely proved that all-vs-all massive sequence comparisons may be an effective approach and a good basis for the prediction of biochemical functions or for the delineation of common shared properties. The program Cluster-C presented here enables a stand-alone and efficient construction of protein families within whole proteomes. The algorithm, which is based on the detection of cliques, ensures a high level of connectivity within the clusters. As opposed to the single transitive linkage method, Cluster-C allows a large number of sequences to be classified in such a way that the multidomain proteins do not produce a chain-grouping effect resulting in meaningless clusters. Moreover, some proteins can be present in several different but relevant clusters, which is of help in the determination of their functional domains. In the present analysis we used the Z-value, an evaluation of the significance of the similarity score, as the criterion for connecting sequences (the user can freely define the threshold of the similarity criterion). The clusters built with a rather low threshold ( Z=14) include more than 97% of the sequences and are consistent with known protein families and PROSITE patterns.

Graph-based clustering for finding distant relationships in a large set of protein sequences

Clustering of protein sequences is widely used for the functional characterization of proteins. However, it is still not easy to cluster distantly-related proteins, which have only regional similarity among their sequences. It is therefore necessary to develop an algorithm for clustering such distantly-related proteins. We have developed a time and space efficient clustering algorithm. It uses a graph representation where its vertices and edges denote proteins and their sequence similarities above a certain cutoff score, respectively. It repeatedly partitions the graph by removing edges that have small weights, which correspond to low sequence similarities. To find the appropriate partitions, we introduce a score combining the normalized cut and a locally minimal cut capacities. Our method is applied to the entire 40,703 human proteins in SWISS-PROT and TrEMBL. The resulting clusters shows a 76% recall (20,529 proteins) of the 26,917 classified by InterPro. It also finds relationships not found by other clustering methods. The complete result of our algorithm for all the human proteins in SWISS-PROT and TrEMBL, and other supplementary information are available at http://motif.ics.es.osaka-u.ac.jp/Ncut-KL/

Clustering protein sequence and structure space with infinite Gaussian mixture models.

We describe a novel approach to the problem of automatically clustering protein sequences and discovering protein families, subfamilies etc., based on the theory of infinite Gaussian mixtures models. This method allows the data itself to dictate how many mixture components are required to model it, and provides a measure of the probability that two proteins belong to the same cluster. We illustrate our methods with application to three data sets: globin sequences, globin sequences with known three-dimensional structures and G-protein coupled receptor sequences. The consistency of the clusters indicate that our method is producing biologically meaningful results, which provide a very good indication of the underlying families and subfamilies. With the inclusion of secondary structure and residue solvent accessibility information, we obtain a classification of sequences of known structure which both reflects and extends their SCOP classifications. A supplementray web site containing larger versions of the figures is available at http://public.kgi.edu/approximately wid/PSB04/index.html

Characterization of a divergent non-classical MHC class I gene in sharks.

Sharks are the most ancient group of vertebrates known to possess members of the major histocompatibility complex (MHC) gene family. For this reason, sharks provide a unique opportunity to gain insight into the evolution of the vertebrate immune system through comparative analysis. Two genes encoding proteins related to the MHC class I gene family were isolated from splenic cDNA derived from spiny dogfish shark ( Squalus acanthias). The genes have been designated MhcSqac-UAA*01 and MhcSqac-UAA*NC1. Comparative analysis demonstrates that the Sqac-UAA*01 protein sequence clusters with classical MHC class I of several shark species and has structural elements common to most classical MHC class I molecules. In contrast, Sqac-UAA*NC1 is highly divergent from all vertebrate classical MHC class I proteins, including the Sqac-UAA *01 sequence and those of other shark species. Although Sqac-UAA*NC1 is clearly related to the MHC class I gene family, no orthologous genes from other species were identified due to the high degree of sequence divergence. In fact, the Sqac NC1 protein sequence is the most divergent MHC class-I-like protein identified thus far in any shark species. This high degree of divergence is similar in magnitude to some of the MHC class-I-related genes found in mammals, such as MICA or CD1. These data support the existence of a class of highly divergent non-classical MHC class I genes in the most primitive vertebrates known to possess homologues of the MHC and other components of the adaptive immune system.

Coverage of protein sequence space by current structural genomics targets.

By its purest definition the ultimate goal of structural genomics (SG) is the determination of the structures of all proteins encoded by genomes. Most of these will be obtained by homology modeling using the structures of a set of target proteins for experimental determination. Thanks to the open exchange of SG target information, we are able to analyze the sequences of the current target list to evaluate the extent of its coverage of protein sequence space. The presence of homologous sequences currently either in the Protein Data Bank (PDB) or among SG targets has been determined for each of the protein sequences in several organisms. In this way we are able to evaluate the coverage by existing or targeted structural data for the non-membranous parts of entire proteomes. For small bacterial proteomes such as that of H. influenzae almost all proteins have homologous sequences among SG targets or in the PDB. There is significantly lower coverage for more complex organisms, such as C. elegans. We have mapped the SG target list onto the ProtoMap clustering of protein sequences. Clusters occupied by SG targets represent over 150,000 protein sequences, which is approximately 44% of the total protein sequences classified by ProtoMap. The mapping of SG targets also enables an evaluation of the degree of overlap within the target list. An SG target typically occupies a ProtoMap cluster with more than six other homologous targets.

Gclust: Genome-Wide Clustering of Protein Sequences for Identification of Photosynthesis-Related Genes Resulting from Massive Horizontal Gene Transfer

Use of whole genomic data in genome phylogeny is an alternative to the conventional sequence-based phylogeny. In sequence-based phylogeny, single-copy genes with well-defined orthologues in every species are used. This excludes the use of many physiologically important genes in multigene families. The genes that are present in limited taxa are also excluded. The use of all sequence data in genome comparison is expected to alleviate bias due to the use of such limited data. There are different ways of whole genome comparison. One is the use of orthologues that are, for example, defined by bidirectional best hit. Another is the use of homologue groups [2]. I reported in a previous GIW meeting an attempt of whole genome comparison of six species of cyanobacteria and a green plant, as well as non-photosynthetic organisms [3].