ECPF: an efficient algorithm for expanding clustered protein families

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.

Since leucine-rich repeat (LRR) is known as a domain of protein specific to disease resistance genes, it is used to study the genes of rice with resistance to bacterial blight caused by Xanthomonas oryzae pv. oryzae. The CDS (coding domain sequence) and full-length cDNA sequences of the completely sequenced genes were obtained through the access from public databases of NCBI (National Center for Biotechnology Information) and KOME (Knowledge-based Oryza Molecular Biological Encyclopedia) and they were used to identify each LRR fragment in translated protein sequences through Pfam protein families database. Results showed that only Xa1, Xa21 and Xa26 genes were found to possess LRRs in their protein sequences. When bioinformatics and statistical methods were used for further comparative analysis of distribution and amino acid composition for these LRRs among Xa protein sequences, LRRs of Xa1 were loosely located after NB-ARC (nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4) in the protein sequences, whereas LRRs of Xa21 and Xa26 were densely located before NB-ARC or P-kinase (protein-kinase). It appeared that the first one or two LRRs of the gene Xa1 proteins in proximity to NB-ARC were distant from the other LRRs, but no similar phenomenon was observed within the protein sequences of the Xa21 and Xa26 genes. The LRRs of these three Xa genes mainly consisted of hydrophilic amino acids, with the highest frequency for leucine, followed by neutral serine. The LRRs of Xa1 protein sequences had different frequencies for some amino acids compared to LRRs of Xa21 and Xa26. These findings are useful in understanding of the structural variation of LRRs in protein sequences among Xa genes with resistance to bacterial blight of rice.

Marine sources have received great attention recently; research on marine-derived molecules has discovered new bioactive compounds with vital properties increasing their applicability as nutraceuticals in the food and supplement industries. Most notably Hippocrates, the “father of modern medicine”, is recordedas describing the therapeutic effects of various marine invertebrates and their constituents on human health.Astreias is an important marine of the family Asteriidae known for its variety of medicinal properties. Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the protein. The number of protein sequences that can be modeled, as well as the accuracy of the prediction, is increasing steadily because of the growth and number of known protein sequences and structures as well as improvements in the modeling software. It is currently possible to model, with useful accuracy. Significant parts of approximately one half of all known protein sequences. This research report deliver an innovative summary of bioinformatics study of Asterias rubens with emphasis on the current development and future directions, which shall provide tools and resources necessary to understand and uphold advances in this important field. The aim of the present study, 10 proteins of Asterias rubens were analysed using bioinformatics tools. Structural prediction and functional characterization of proteins of Asterias rubens were done using Expasy Protparm server, 3D structure was done using SWISS MODEL. The important enzymes present in Asterias rubens involved Reproductive function and proper growth and development of human body.

The Impact of Gene Expression Regulation on Evolution of Extracellular Signaling Pathways

BackgroundWhole-genome sequencing projects are rapidly producing an enormous number of new sequences. Consequently almost every family of proteins now contains hundreds of members. It has thus become necessary to develop tools, which classify protein sequences automatically and also quickly and reliably. The difficulty of this task is intimately linked to the mechanism by which protein sequences diverge, i.e. by simultaneous residue substitutions, insertions and/or deletions and whole domain reorganisations (duplications/swapping/fusion).ResultsHere we present a novel approach, which is based on random sampling of sub-sequences (probes) out of a set of input sequences. The probes are compared to the input sequences, after a normalisation step; the results are used to partition the input sequences into homogeneous groups of proteins. In addition, this method provides information on diagnostic parts of the proteins. The performance of this method is challenged by two data sets. The first one contains the sequences of prokaryotic lyases that could be arranged as a multiple sequence alignment. The second one contains all proteins from Swiss-Prot Release 36 with at least one Src homology 2 (SH2) domain – a classical example for proteins with modular architecture.ConclusionThe outcome of our method is robust, highly reproducible as shown using bootstrap and resampling validation procedures. The results are essentially coherent with the biology. This method depends solely on well-established publicly available software and algorithms.

The number of natural protein sequences is infinitely small as compared to the number of proteins theoretically possible. Thus, a huge number of protein sequences, defined as “never born proteins” or NBPs, have never been observed in nature. The study of the structural and functional properties of NBPs represents a way to improve our knowledge on the fundamental properties that make existing protein sequences so unique. Protein structure prediction tools combined with the use of large computing resources allow to tackle this problem. The study of NBPs requires the generation of a large library of non-natural protein sequences (105–107) and the prediction of their three-dimensional structure. On a single CPU it would require years to predict the structure of such a library of protein sequences. However, this is an embarrassingly parallel problem in which the same computation must be repeated several times and the use of grid infrastructures makes feasible to approach this problem in an acceptable time frame. Here we describe the set up of a simulation environment within the EUChinaGRID[1] infrastructure that allows non expert users to exploit grid resources for large-scale proteins structure prediction.

Performance Analysis of Fuzzy C-means (FCM) and Possibilistic C-means Algorithm (PCM) for Protein Sequence Clustering

The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl).

BackgroundBiological sequence repeats arranged in tandem patterns are widespread in DNA and proteins. While many software tools have been designed to detect DNA tandem repeats (TRs), useful algorithms for identifying protein TRs with varied levels of degeneracy are still needed.ResultsTo address limitations of current repeat identification methods, and to provide an efficient and flexible algorithm for the detection and analysis of TRs in protein sequences, we designed and implemented a new computational method called XSTREAM. Running time tests confirm the practicality of XSTREAM for analyses of multi-genome datasets. Each of the key capabilities of XSTREAM (e.g., merging, nesting, long-period detection, and TR architecture modeling) are demonstrated using anecdotal examples, and the utility of XSTREAM for identifying TR proteins was validated using data from a recently published paper.ConclusionWe show that XSTREAM is a practical and valuable tool for TR detection in protein and nucleotide sequences at the multi-genome scale, and an effective tool for modeling TR domains with diverse architectures and varied levels of degeneracy. Because of these useful features, XSTREAM has significant potential for the discovery of naturally-evolved modular proteins with applications for engineering novel biostructural and biomimetic materials, and identifying new vaccine and diagnostic targets.

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

Tandem repeats in proteins are patterns of residues repeated directly adjacent to each other. The evolution of these repeats can be assessed by using groups of homologous sequences, which can help pointing to events of unit duplication or deletion. High pressure in a protein family for variation of a given type of repeat might point to their function. Here, we propose the analysis of protein families to calculate protein short tandem repeats (pSTRs) in each protein sequence and assess their variability within the family in terms of number of units. To facilitate this analysis, we developed the pSTR tool, a method to analyze the evolution of protein short tandem repeats in a given protein family by pairwise comparisons between evolutionarily related protein sequences. We evaluated pSTR unit number variation in protein families of 12 complete metazoan proteomes. We hypothesize that families with more dynamic ensembles of repeats could reflect particular roles of these repeats in processes that require more adaptability.

In this paper, we present an unsupervised novel approach for protein sequences clustering by incorporating the functional domain information into the clustering process. In the proposed framework, the domain boundaries predicated by ProDom database are used to provide a better measurement in calculating the sequence similarity. In addition, we use an unsupervised clustering algorithm as the kernel that includes a hierarchical clustering in the first phase to pre-cluster the protein sequences, and a partitioning clustering in the second phase to refine the clustering results. More specifically, we perform the agglomerative hierarchical clustering on protein sequences in the first phase to obtain the initial clustering results for the subsequent partitioning clustering, and then, a profile Hidden Markove Model (HMM) is built for each cluster to represent the centroid of a cluster. In the second phase, the HMMs based k-means clustering is then performed to refine the cluster results as protein families. The experimental results show our model is effective and efficient in clustering protein families.

The Swiss-Prot protein knowledgebase and ExPASy: providing the plant community with high quality proteomic data and tools

BackgroundAs genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques. The gap between protein sequence and structure continues to enlarge rapidly as the throughput of genome sequencing techniques is much higher than that of protein structure determination techniques. Computational software tools for predicting protein structure and structural features from protein sequences are crucial to make use of this vast repository of protein resources.ResultsTo meet the need, we have developed a comprehensive MULTICOM toolbox consisting of a set of protein structure and structural feature prediction tools. These tools include secondary structure prediction, solvent accessibility prediction, disorder region prediction, domain boundary prediction, contact map prediction, disulfide bond prediction, beta-sheet topology prediction, fold recognition, multiple template combination and alignment, template-based tertiary structure modeling, protein model quality assessment, and mutation stability prediction.ConclusionsThese tools have been rigorously tested by many users in the last several years and/or during the last three rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7-9) from 2006 to 2010, achieving state-of-the-art or near performance. In order to facilitate bioinformatics research and technological development in the field, we have made the MULTICOM toolbox freely available as web services and/or software packages for academic use and scientific research. It is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.

At one point in the history of protein chemistry it was thought that—because of the vast number of possible sequences—proteins ought to exist in a countless number of forms befitting any conceivable function or structure. In fact, as the number of known amino acid sequences continues to mount, it is becoming abudantly clear that there is a practical limit to the number of types of protein structures that exist in living systems on Earth. Thus, although the number of possible sequences for 20 amino acids arranged randomly in strings of 350 units is a superastronomical 20350, certainly nowhere near that number of protein sequences has ever or will ever exist. Instead, a small number of genetically encoded protein structures has been expanded by the general route of “duplication and modification.” The duplications come in various degrees, from the very short to the supragenic or chromosomal. Postduplication modification mostly takes the form of base substitutions leading to amino acid replacement, and, theoretically, the history of any protein ought to be evident by appropriate comparison of the diverging sequences.