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PubMed2004

VKCDB: voltage-gated potassium channel database.

BACKGROUND: The family of voltage-gated potassium channels comprises a functionally diverse group of membrane proteins. They help maintain and regulate the potassium ion-based component of the membrane potential and are thus central to many critical physiological processes. VKCDB (Voltage-gated potassium [K] Channel DataBase) is a database of structural and functional data on these channels. It is designed as a resource for research on the molecular basis of voltage-gated potassium channel function. DESCRIPTION: Voltage-gated potassium channel sequences were identified by using BLASTP to search GENBANK and SWISSPROT. Annotations for all voltage-gated potassium channels were selectively parsed and integrated into VKCDB. Electrophysiological and pharmacological data for the channels were collected from published journal articles. Transmembrane domain predictions by TMHMM and PHD are included for each VKCDB entry. Multiple sequence alignments of conserved domains of channels of the four Kv families and the KCNQ family are also included. Currently VKCDB contains 346 channel entries. It can be browsed and searched using a set of functionally relevant categories. Protein sequences can also be searched using a local BLAST engine. CONCLUSIONS: VKCDB is a resource for comparative studies of voltage-gated potassium channels. The methods used to construct VKCDB are general; they can be used to create specialized databases for other protein families. VKCDB is accessible at http://vkcdb.biology.ualberta.ca.

Animals↗
PubMed2004

Pegasys: software for executing and integrating analyses of biological sequences.

BACKGROUND: We present Pegasys--a flexible, modular and customizable software system that facilitates the execution and data integration from heterogeneous biological sequence analysis tools. RESULTS: The Pegasys system includes numerous tools for pair-wise and multiple sequence alignment, ab initio gene prediction, RNA gene detection, masking repetitive sequences in genomic DNA as well as filters for database formatting and processing raw output from various analysis tools. We introduce a novel data structure for creating workflows of sequence analyses and a unified data model to store its results. The software allows users to dynamically create analysis workflows at run-time by manipulating a graphical user interface. All non-serial dependent analyses are executed in parallel on a compute cluster for efficiency of data generation. The uniform data model and backend relational database management system of Pegasys allow for results of heterogeneous programs included in the workflow to be integrated and exported into General Feature Format for further analyses in GFF-dependent tools, or GAME XML for import into the Apollo genome editor. The modularity of the design allows for new tools to be added to the system with little programmer overhead. The database application programming interface allows programmatic access to the data stored in the backend through SQL queries. CONCLUSIONS: The Pegasys system enables biologists and bioinformaticians to create and manage sequence analysis workflows. The software is released under the Open Source GNU General Public License. All source code and documentation is available for download at http://bioinformatics.ubc.ca/pegasys/.

Computational Biology↗
PubMed2005

Species-specific analysis of protein sequence motifs using mutual information.

BACKGROUND: Protein sequence motifs are by definition short fragments of conserved amino acids, often associated with a specific function. Accordingly protein sequence profiles derived from multiple sequence alignments provide an alternative description of functional motifs characterizing families of related sequences. Such profiles conveniently reflect functional necessities by pointing out proximity at conserved sequence positions as well as depicting distances at variable positions. Discovering significant conservation characteristics within the variable positions of profiles mirrors group-specific and, in particular, evolutionary features of the underlying sequences. RESULTS: We describe the tool PROfile analysis based on Mutual Information (PROMI) that enables comparative analysis of user-classified protein sequences. PROMI is implemented as a web service using Perl and R as well as other publicly available packages and tools on the server-side. On the client-side platform-independence is achieved by generally applied internet delivery standards. As one possible application analysis of the zinc finger C2H2-type protein domain is introduced to illustrate the functionality of the tool. CONCLUSION: The web service PROMI should assist researchers to detect evolutionary correlations in protein profiles of defined biological sequences. It is available at http://promi.mpimp-golm.mpg.de where additional documentation can be found.

Amino Acid Motifs↗
PubMed2005

JACOP: a simple and robust method for the automated classification of protein sequences with modular architecture.

BACKGROUND: Whole-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). RESULTS: Here 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. CONCLUSION: The 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.

Algorithms↗
PubMed2005

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence.

BACKGROUND: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of Cbeta atoms in other residues within a sphere around the Cbeta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. RESULTS: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either "contacted" or "non-contacted", the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. CONCLUSION: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary protein sequence and higher order consecutive protein structural and functional properties.

Algorithms↗
PubMed2006

Novel knowledge-based mean force potential at the profile level.

BACKGROUND: The development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials. RESULTS: The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. As a result, the protein sequences are represented as sequences of binary profiles rather than sequences of amino acids. Similar to the knowledge-based potentials at the residue level, a class of novel potentials at the profile level is introduced. We develop four types of profile-level statistical potentials including distance-dependent, contact, Phi/Psi dihedral angle and accessible surface statistical potentials. These potentials are first evaluated by the fold assessment between the correct and incorrect models generated by comparative modeling from our own and other groups. They are then used to recognize the native structures from well-constructed decoy sets. Experimental results show that all the knowledge-base mean force potentials at the profile level outperform those at the residue level. Significant improvements are obtained for the distance-dependent and accessible surface potentials (5-6%). The contact and Phi/Psi dihedral angle potential only get a slight improvement (1-2%). Decoy set evaluation results show that the distance-dependent profile-level potentials even outperform other atom-level potentials. We also demonstrate that profile-level statistical potentials can improve the performance of threading. CONCLUSION: The knowledge-base mean force potentials at the profile level can provide better discriminatory ability than those at the residue level, so they will be useful for protein structure prediction and model refinement.

Algorithms↗
PubMed2006

Genome BLAST distance phylogenies inferred from whole plastid and whole mitochondrion genome sequences.

BACKGROUND: Phylogenetic methods which do not rely on multiple sequence alignments are important tools in inferring trees directly from completely sequenced genomes. Here, we extend the recently described Genome BLAST Distance Phylogeny (GBDP) strategy to compute phylogenetic trees from all completely sequenced plastid genomes currently available and from a selection of mitochondrial genomes representing the major eukaryotic lineages. BLASTN, TBLASTX, or combinations of both are used to locate high-scoring segment pairs (HSPs) between two sequences from which pairwise similarities and distances are computed in different ways resulting in a total of 96 GBDP variants. The suitability of these distance formulae for phylogeny reconstruction is directly estimated by computing a recently described measure of "treelikeness", the so-called delta value, from the respective distance matrices. Additionally, we compare the trees inferred from these matrices using UPGMA, NJ, BIONJ, FastME, or STC, respectively, with the NCBI taxonomy tree of the taxa under study. RESULTS: Our results indicate that, at this taxonomic level, plastid genomes are much more valuable for inferring phylogenies than are mitochondrial genomes, and that distances based on breakpoints are of little use. Distances based on the proportion of "matched" HSP length to average genome length were best for tree estimation. Additionally we found that using TBLASTX instead of BLASTN and, particularly, combining TBLASTX and BLASTN leads to a small but significant increase in accuracy. Other factors do not significantly affect the phylogenetic outcome. The BIONJ algorithm results in phylogenies most in accordance with the current NCBI taxonomy, with NJ and FastME performing insignificantly worse, and STC performing as well if applied to high quality distance matrices. delta values are found to be a reliable predictor of phylogenetic accuracy. CONCLUSION: Using the most treelike distance matrices, as judged by their delta values, distance methods are able to recover all major plant lineages, and are more in accordance with Apicomplexa organelles being derived from "green" plastids than from plastids of the "red" type. GBDP-like methods can be used to reliably infer phylogenies from different kinds of genomic data. A framework is established to further develop and improve such methods. delta values are a topology-independent tool of general use for the development and assessment of distance methods for phylogenetic inference.

Algorithms↗
PubMed2006

Predicting residue-wise contact orders in proteins by support vector regression.

BACKGROUND: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. RESULTS: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. CONCLUSION: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Algorithms↗
PubMed2006

XRate: a fast prototyping, training and annotation tool for phylo-grammars.

BACKGROUND: Recent years have seen the emergence of genome annotation methods based on the phylo-grammar, a probabilistic model combining continuous-time Markov chains and stochastic grammars. Previously, phylo-grammars have required considerable effort to implement, limiting their adoption by computational biologists. RESULTS: We have developed an open source software tool, xrate, for working with reversible, irreversible or parametric substitution models combined with stochastic context-free grammars. xrate efficiently estimates maximum-likelihood parameters and phylogenetic trees using a novel "phylo-EM" algorithm that we describe. The grammar is specified in an external configuration file, allowing users to design new grammars, estimate rate parameters from training data and annotate multiple sequence alignments without the need to recompile code from source. We have used xrate to measure codon substitution rates and predict protein and RNA secondary structures. CONCLUSION: Our results demonstrate that xrate estimates biologically meaningful rates and makes predictions whose accuracy is comparable to that of more specialized tools.

Algorithms↗
PubMed2006

Integrating protein structures and precomputed genealogies in the Magnum database: examples with cellular retinoid binding proteins.

BACKGROUND: When accurate models for the divergent evolution of protein sequences are integrated with complementary biological information, such as folded protein structures, analyses of the combined data often lead to new hypotheses about molecular physiology. This represents an excellent example of how bioinformatics can be used to guide experimental research. However, progress in this direction has been slowed by the lack of a publicly available resource suitable for general use. RESULTS: The precomputed Magnum database offers a solution to this problem for ca. 1,800 full-length protein families with at least one crystal structure. The Magnum deliverables include 1) multiple sequence alignments, 2) mapping of alignment sites to crystal structure sites, 3) phylogenetic trees, 4) inferred ancestral sequences at internal tree nodes, and 5) amino acid replacements along tree branches. Comprehensive evaluations revealed that the automated procedures used to construct Magnum produced accurate models of how proteins divergently evolve, or genealogies, and correctly integrated these with the structural data. To demonstrate Magnum's capabilities, we asked for amino acid replacements requiring three nucleotide substitutions, located at internal protein structure sites, and occurring on short phylogenetic tree branches. In the cellular retinoid binding protein family a site that potentially modulates ligand binding affinity was discovered. Recruitment of cellular retinol binding protein to function as a lens crystallin in the diurnal gecko afforded another opportunity to showcase the predictive value of a browsable database containing branch replacement patterns integrated with protein structures. CONCLUSION: We integrated two areas of protein science, evolution and structure, on a large scale and created a precomputed database, known as Magnum, which is the first freely available resource of its kind. Magnum provides evolutionary and structural bioinformatics resources that are useful for identifying experimentally testable hypotheses about the molecular basis of protein behaviors and functions, as illustrated with the examples from the cellular retinoid binding proteins.

Amino Acid Sequence↗
PubMed2006

Identification of new members of hydrophobin family using primary structure analysis.

BACKGROUND: Hydrophobins are fungal proteins that can turn into amphipathic membranes at hydrophilic/hydrophobic interfaces by self-assembly. The assemblages by Class I hydrophobins are extremely stable and possess the remarkable ability to change the polarity of the surface. One of its most important industrial applications is its usage as paint. Without detailed knowledge of the 3D structure and self-assembly principles of hydrophobins, it is difficult to make significant progress in furthering its research. RESULTS: In order to provide useful information to hydrophobin researchers, we analyzed primary structure of hydrophobins to gain more insight about these proteins. In this paper, we presented an in-depth primary sequence analysis using batch BLAST search of the database, sequence filtering by programming and motif finding by MEME. We used batch BLAST to find similar sequences in the NCBI nr database. Then we used MEME to find out motifs. Based on the newly found motifs and the well-known C-CC-C-C-CC-C pattern we used MAST to search the entire nr database. At the end, domain search and phylogenetic analysis were conducted to confirm the result. After searching the nr database with the new PSSM-format motifs identified by MEME, many sequences from various species were found by MAST. Filtering process by pattern, domain and length left 9 qualified candidates. CONCLUSION: All of 9 newly identified potential hydrophobins possess the common pattern and hydrophobin domain. From the multiple sequence alignment result, we can see that some of them are grouped very close to other known hydrophobins, which means their phylogenetic relationship is very close and it is highly plausible that they are indeed hydrophobin proteins.

Amino Acid Sequence↗
PubMed2004

Reconstruction of ancestral protein sequences and its applications.

BACKGROUND: Modern-day proteins were selected during long evolutionary history as descendants of ancient life forms. In silico reconstruction of such ancestral protein sequences facilitates our understanding of evolutionary processes, protein classification and biological function. Additionally, reconstructed ancestral protein sequences could serve to fill in sequence space thus aiding remote homology inference. RESULTS: We developed ANCESCON, a package for distance-based phylogenetic inference and reconstruction of ancestral protein sequences that takes into account the observed variation of evolutionary rates between positions that more precisely describes the evolution of protein families. To improve the accuracy of evolutionary distance estimation and ancestral sequence reconstruction, two approaches are proposed to estimate position-specific evolutionary rates. Comparisons show that at large evolutionary distances our method gives more accurate ancestral sequence reconstruction than PAML, PHYLIP and PAUP*. We apply the reconstructed ancestral sequences to homology inference and functional site prediction. We show that the usage of hypothetical ancestors together with the present day sequences improves profile-based sequence similarity searches; and that ancestral sequence reconstruction methods can be used to predict positions with functional specificity. CONCLUSIONS: As a computational tool to reconstruct ancestral protein sequences from a given multiple sequence alignment, ANCESCON shows high accuracy in tests and helps detection of remote homologs and prediction of functional sites. ANCESCON is freely available for non-commercial use. Pre-compiled versions for several platforms can be downloaded from ftp://iole.swmed.edu/pub/ANCESCON/.

Amino Acid Sequence↗
PubMed2006

Molecular phylogenetics and comparative modeling of HEN1, a methyltransferase involved in plant microRNA biogenesis.

BACKGROUND: Recently, HEN1 protein from Arabidopsis thaliana was discovered as an essential enzyme in plant microRNA (miRNA) biogenesis. HEN1 transfers a methyl group from S-adenosylmethionine to the 2'-OH or 3'-OH group of the last nucleotide of miRNA/miRNA* duplexes produced by the nuclease Dicer. Previously it was found that HEN1 possesses a Rossmann-fold methyltransferase (RFM) domain and a long N-terminal extension including a putative double-stranded RNA-binding motif (DSRM). However, little is known about the details of the structure and the mechanism of action of this enzyme, and about its phylogenetic origin. RESULTS: Extensive database searches were carried out to identify orthologs and close paralogs of HEN1. Based on the multiple sequence alignment a phylogenetic tree of the HEN1 family was constructed. The fold-recognition approach was used to identify related methyltransferases with experimentally solved structures and to guide the homology modeling of the HEN1 catalytic domain. Additionally, we identified a La-like predicted RNA binding domain located C-terminally to the DSRM domain and a domain with a peptide prolyl cis/trans isomerase (PPIase) fold, but without the conserved PPIase active site, located N-terminally to the catalytic domain. CONCLUSION: The bioinformatics analysis revealed that the catalytic domain of HEN1 is not closely related to any known RNA:2'-OH methyltransferases (e.g. to the RrmJ/fibrillarin superfamily), but rather to small-molecule methyltransferases. The structural model was used as a platform to identify the putative active site and substrate-binding residues of HEN and to propose its mechanism of action.

Amino Acid Sequence↗
PubMed2006

Phylogenetic analysis of the tenascin gene family: evidence of origin early in the chordate lineage.

BACKGROUND: Tenascins are a family of glycoproteins found primarily in the extracellular matrix of embryos where they help to regulate cell proliferation, adhesion and migration. In order to learn more about their origins and relationships to each other, as well as to clarify the nomenclature used to describe them, the tenascin genes of the urochordate Ciona intestinalis, the pufferfish Tetraodon nigroviridis and Takifugu rubripes and the frog Xenopus tropicalis were identified and their gene organization and predicted protein products compared with the previously characterized tenascins of amniotes. RESULTS: A single tenascin gene was identified in the genome of C. intestinalis that encodes a polypeptide with domain features common to all vertebrate tenascins. Both pufferfish genomes encode five tenascin genes: two tenascin-C paralogs, a tenascin-R with domain organization identical to mammalian and avian tenascin-R, a small tenascin-X with previously undescribed GK repeats, and a tenascin-W. Four tenascin genes corresponding to tenascin-C, tenascin-R, tenascin-X and tenascin-W were also identified in the X. tropicalis genome. Multiple sequence alignment reveals that differences in the size of tenascin-W from various vertebrate classes can be explained by duplications of specific fibronectin type III domains. The duplicated domains are encoded on single exons and contain putative integrin-binding motifs. A phylogenetic tree based on the predicted amino acid sequences of the fibrinogen-related domains demonstrates that tenascin-C and tenascin-R are the most closely related vertebrate tenascins, with the most conserved repeat and domain organization. Taking all lines of evidence together, the data show that the tenascins referred to as tenascin-Y and tenascin-N are actually members of the tenascin-X and tenascin-W gene families, respectively. CONCLUSION: The presence of a tenascin gene in urochordates but not other invertebrate phyla suggests that tenascins may be specific to chordates. Later genomic duplication events led to the appearance of four family members in vertebrates: tenascin-C, tenascin-R, tenascin-W and tenascin-X.

Animals↗
PubMed2005

The domain architecture of large guanine nucleotide exchange factors for the small GTP-binding protein Arf.

BACKGROUND: Small G proteins, which are essential regulators of multiple cellular functions, are activated by guanine nucleotide exchange factors (GEFs) that stimulate the exchange of the tightly bound GDP nucleotide by GTP. The catalytic domain responsible for nucleotide exchange is in general associated with non-catalytic domains that define the spatio-temporal conditions of activation. In the case of small G proteins of the Arf subfamily, which are major regulators of membrane trafficking, GEFs form a heterogeneous family whose only common characteristic is the well-characterized Sec7 catalytic domain. In contrast, the function of non-catalytic domains and how they regulate/cooperate with the catalytic domain is essentially unknown. RESULTS: Based on Sec7-containing sequences from fully-annotated eukaryotic genomes, including our annotation of these sequences from Paramecium, we have investigated the domain architecture of large ArfGEFs of the BIG and GBF subfamilies, which are involved in Golgi traffic. Multiple sequence alignments combined with the analysis of predicted secondary structures, non-structured regions and splicing patterns, identifies five novel non-catalytic structural domains which are common to both subfamilies, revealing that they share a conserved modular organization. We also report a novel ArfGEF subfamily with a domain organization so far unique to alveolates, which we name TBS (TBC-Sec7). CONCLUSION: Our analysis unifies the BIG and GBF subfamilies into a higher order subfamily, which, together with their being the only subfamilies common to all eukaryotes, suggests that they descend from a common ancestor from which species-specific ArfGEFs have subsequently evolved. Our identification of a conserved modular architecture provides a background for future functional investigation of non-catalytic domains.

ADP-Ribosylation Factors↗
PubMed2006

Pfarao: a web application for protein family analysis customized for cytoskeletal and motor proteins (CyMoBase).

BACKGROUND: Annotation of protein sequences of eukaryotic organisms is crucial for the understanding of their function in the cell. Manual annotation is still by far the most accurate way to correctly predict genes. The classification of protein sequences, their phylogenetic relation and the assignment of function involves information from various sources. This often leads to a collection of heterogeneous data, which is hard to track. Cytoskeletal and motor proteins consist of large and diverse superfamilies comprising up to several dozen members per organism. Up to date there is no integrated tool available to assist in the manual large-scale comparative genomic analysis of protein families. DESCRIPTION: Pfarao (Protein Family Application for Retrieval, Analysis and Organisation) is a database driven online working environment for the analysis of manually annotated protein sequences and their relationship. Currently, the system can store and interrelate a wide range of information about protein sequences, species, phylogenetic relations and sequencing projects as well as links to literature and domain predictions. Sequences can be imported from multiple sequence alignments that are generated during the annotation process. A web interface allows to conveniently browse the database and to compile tabular and graphical summaries of its content. CONCLUSION: We implemented a protein sequence-centric web application to store, organize, interrelate, and present heterogeneous data that is generated in manual genome annotation and comparative genomics. The application has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase) but can easily be adapted for any protein.

Amino Acid Sequence↗
PubMed2006

Phylogenomic analysis of the GIY-YIG nuclease superfamily.

BACKGROUND: The GIY-YIG domain was initially identified in homing endonucleases and later in other selfish mobile genetic elements (including restriction enzymes and non-LTR retrotransposons) and in enzymes involved in DNA repair and recombination. However, to date no systematic search for novel members of the GIY-YIG superfamily or comparative analysis of these enzymes has been reported. RESULTS: We carried out database searches to identify all members of known GIY-YIG nuclease families. Multiple sequence alignments together with predicted secondary structures of identified families were represented as Hidden Markov Models (HMM) and compared by the HHsearch method to the uncharacterized protein families gathered in the COG, KOG, and PFAM databases. This analysis allowed for extending the GIY-YIG superfamily to include members of COG3680 and a number of proteins not classified in COGs and to predict that these proteins may function as nucleases, potentially involved in DNA recombination and/or repair. Finally, all old and new members of the GIY-YIG superfamily were compared and analyzed to infer the phylogenetic tree. CONCLUSION: An evolutionary classification of the GIY-YIG superfamily is presented for the very first time, along with the structural annotation of all (sub)families. It provides a comprehensive picture of sequence-structure-function relationships in this superfamily of nucleases, which will help to design experiments to study the mechanism of action of known members (especially the uncharacterized ones) and will facilitate the prediction of function for the newly discovered ones.

Archaeal Proteins↗
PubMed2006

Analysis of an optimal hidden Markov model for secondary structure prediction.

BACKGROUND: Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models. RESULTS: Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model alpha-helices, 12 that model coil and 9 that model beta-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%. CONCLUSION: The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.

Computational Biology↗