Enhanced protein domain discovery using taxonomy

WIV, a protein domain found in a wide number of arthropod viruses, which probably facilitates infection

The most powerful approach to detect distant homologues of a protein is based on structure prediction and comparison. Yet this approach is still inapplicable to many viral proteins. Therefore, we applied a powerful sequence-based procedure to identify distant homologues of viral proteins. It relies on three principles: (1) traces of sequence similarity can persist beyond the significance cutoff of homology detection programmes; (2) candidate homologues can be identified among proteins with weak sequence similarity to the query by using 'contextual' information, e.g. taxonomy or type of host infected; (3) these candidate homologues can be validated using highly sensitive profile-profile comparison. As a test case, this approach was applied to a protein without known homologues, encoded by ORF4 of Lake Sinai viruses (which infect bees). We discovered that the ORF4 protein contains a domain that has homologues in proteins from >20 taxa of viruses infecting arthropods. We called this domain 'widespread, intriguing, versatile' (WIV), because it is found in proteins with a wide variety of functions and within varied domain contexts. For example, WIV is found in the NSs protein of tospoviruses, a global threat to food security, which infect plants as well as their arthropod vectors; in the RNA2 ORF1-encoded protein of chronic bee paralysis virus, a widespread virus of bees; and in various proteins of cypoviruses, which infect the silkworm Bombyx mori. Structural modelling with AlphaFold indicated that the WIV domain has a previously unknown fold, and bibliographical evidence suggests that it facilitates infection of arthropods.

Genome-wide survey of remote homologues for protein domain superfamilies of known structure reveals unequal distribution across structural classes

Domains are the basic building blocks of proteins which can combine to give rise to different domain architectures. Annotation of domains in a sequence is the first step towards understanding the biological function. Since there are a limited number of folds and evolutionarily related proteins have a similar structure, function can be inferred through remote homology. Computational sequence searches were performed for remote homologues on genomes of around ∼160 000 different organisms, starting from nearly 11 000 superfamily queries of known structure. Case studies revealed that most of the associated domains are involved in the same biological process. Using all the proteins predicted to have at least one structural domain, a coverage of 61% of Pfam families was achieved which is higher than the existing methods (43.36% by SIFTS). Taxonomic analysis of the proteins revealed 493 superfamilies in all the major kingdoms of life and a few lateral gene transfers between viruses and cellular organisms. The distribution of remote homologues across different classes, folds and superfamilies was studied and reveals that sequences are unequally distributed across structural classes. Finally, domain architectures were computed for the homologues and these data were compiled for each superfamily and organism.

BLASTGrabber: a bioinformatic tool for visualization, analysis and sequence selection of massive BLAST data

Background

Advances in sequencing efficiency have vastly increased the sizes of biological sequence databases, including many thousands of genome-sequenced species. The BLAST algorithm remains the main search engine for retrieving sequence information, and must consequently handle data on an unprecedented scale. This has been possible due to high-performance computers and parallel processing. However, the raw BLAST output from contemporary searches involving thousands of queries becomes ill-suited for direct human processing. Few programs attempt to directly visualize and interpret BLAST output; those that do often provide a mere basic structuring of BLAST data.

Results

Here we present a bioinformatics application named BLASTGrabber suitable for high-throughput sequencing analysis. BLASTGrabber, being implemented as a Java application, is OS-independent and includes a user friendly graphical user interface. Text or XML-formatted BLAST output files can be directly imported, displayed and categorized based on BLAST statistics. Query names and FASTA headers can be analysed by text-mining. In addition to visualizing sequence alignments, BLAST data can be ordered as an interactive taxonomy tree. All modes of analysis support selection, export and storage of data. A Java interface-based plugin structure facilitates the addition of customized third party functionality.

Conclusion

The BLASTGrabber application introduces new ways of visualizing and analysing massive BLAST output data by integrating taxonomy identification, text mining capabilities and generic multi-dimensional rendering of BLAST hits. The program aims at a non-expert audience in terms of computer skills; the combination of new functionalities makes the program flexible and useful for a broad range of operations.

Pfam: the protein families database

Pfam, available via servers in the UK (http://pfam.sanger.ac.uk/) and the USA (http://pfam.janelia.org/), is a widely used database of protein families, containing 14 831 manually curated entries in the current release, version 27.0. Since the last update article 2 years ago, we have generated 1182 new families and maintained sequence coverage of the UniProt Knowledgebase (UniProtKB) at nearly 80%, despite a 50% increase in the size of the underlying sequence database. Since our 2012 article describing Pfam, we have also undertaken a comprehensive review of the features that are provided by Pfam over and above the basic family data. For each feature, we determined the relevance, computational burden, usage statistics and the functionality of the feature in a website context. As a consequence of this review, we have removed some features, enhanced others and developed new ones to meet the changing demands of computational biology. Here, we describe the changes to Pfam content. Notably, we now provide family alignments based on four different representative proteome sequence data sets and a new interactive DNA search interface. We also discuss the mapping between Pfam and known 3D structures.

Fitting hidden Markov models of protein domains to a target species: application to Plasmodium falciparum

BACKGROUND

Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains.

RESULTS

Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values.

CONCLUSION

We show that the new approaches allow identification of several domain families previously absent in the P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/~terrapon/HMMfit/

Lineage-specific expansion of DNA-binding transcription factor families

DNA-binding domains (DBDs) are essential components of sequence-specific transcription factors (TFs). We have investigated the distribution of all known DBDs in more than 500 completely sequenced genomes from the three major superkingdoms (Bacteria, Archaea and Eukaryota) and documented conserved and specific DBD occurrence in diverse taxonomic lineages. By combining DBD occurrence in different species with taxonomic information, we have developed an automatic method for inferring the origins of DBD families and their specific combinations with other protein families in TFs. We found only three out of 131 (2%) DBD families shared by the three superkingdoms.

Genomic repertoires of DNA-binding transcription factors across the tree of life

Sequence-specific transcription factors (TFs) are important to genetic regulation in all organisms because they recognize and directly bind to regulatory regions on DNA. Here, we survey and summarize the TF resources available. We outline the organisms for which TF annotation is provided, and discuss the criteria and methods used to annotate TFs by different databases. By using genomic TF repertoires from ∼700 genomes across the tree of life, covering Bacteria, Archaea and Eukaryota, we review TF abundance with respect to the number of genes, as well as their structural complexity in diverse lineages. While typical eukaryotic TFs are longer than the average eukaryotic proteins, the inverse is true for prokaryotes. Only in eukaryotes does the same family of DNA-binding domain (DBD) occur multiple times within one polypeptide chain. This potentially increases the length and diversity of DNA-recognition sequence by reusing DBDs from the same family. We examined the increase in TF abundance with the number of genes in genomes, using the largest set of prokaryotic and eukaryotic genomes to date. As pointed out before, prokaryotic TFs increase faster than linearly. We further observe a similar relationship in eukaryotic genomes with a slower increase in TFs.

The 20 years of PROSITE

PROSITE consists of documentation entries describing protein domains, families and functional sites, as well as associated patterns and profiles to identify them. It is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. In this article, we describe the implementation of a new method to assign a status to pattern matches, the new PROSITE web page and a new approach to improve the specificity and sensitivity of PROSITE methods. The latest version of PROSITE (release 20.19 of 11 September 2007) contains 1319 patterns, 745 profiles and 764 ProRules. Over the past 2 years, about 200 domains have been added, and now 53% of UniProtKB/Swiss-Prot entries (release 54.2 of 11 September 2007) have a PROSITE match. PROSITE is available on the web at: http://www.expasy.org/prosite/.

Identification of homologs in insignificant blast hits by exploiting extrinsic gene properties

BACKGROUND

Homology is a key concept in both evolutionary biology and genomics. Detection of homology is crucial in fields like the functional annotation of protein sequences and the identification of taxon specific genes. Basic homology searches are still frequently performed by pairwise search methods such as BLAST. Vast improvements have been made in the identification of homologous proteins by using more advanced methods that use sequence profiles. However additional improvement could be made by exploiting sources of genomic information other than the primary sequence or tertiary structure.

RESULTS

We test the hypothesis that extrinsic gene properties gene length and gene order can be of help in differentiating spurious sequence similarity from homology in the gray zone. Sharing gene order and similarity in size dramatically increase the chance of a query-hit pair being homologous: gray zone query-hit pairs of similar size and with conserved gene order are homologous in 99% of all cases, while for query-hit pairs without gene order conservation and with different sizes this is only 55%.

CONCLUSION

We have shown that using gene length and gene order drastically improves the detection of homologs within the BLAST gray zone. Our findings suggest that the use of such extrinsic gene properties can also improve the performance of homology detection by more advanced methods, and our study thereby underscores the importance of true data integration for fully exploiting genomic information.

DOMAC: an accurate, hybrid protein domain prediction server

Protein domain prediction is important for protein structure prediction, structure determination, function annotation, mutagenesis analysis and protein engineering. Here we describe an accurate protein domain prediction server (DOMAC) combining both template-based and ab initio methods. The preliminary version of the server was ranked among the top domain prediction servers in the seventh edition of Critical Assessment of Techniques for Protein Structure Prediction (CASP7), 2006. DOMAC server and datasets are available at: http://www.bioinfotool.org/domac.html

DOUTfinder--identification of distant domain outliers using subsignificant sequence similarity

DOUTfinder is a web-based tool facilitating protein domain detection among related protein sequences in the twilight zone of sequence similarity. The sequence set required for this analysis can be provided by the user or will be collected using PSI-BLAST if a single sequence is given as an input. The obtained sequence family is analyzed for known Pfam and SMART domains, and the thereby identified subsignificant domain similarities are evaluated further. Domains with several subthreshold hits in the query set are ranked based on a sum-score function and likely homologous domains are suggested according to established cut-offs. By providing a post-filtering procedure for subsignificant domain hits DOUTfinder allows the detection of non-trivial domain relationships and can thereby lead to new insights into the function and evolution of distantly related sequence families. DOUTfinder is available at .

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.

Results

Using default settings, SAM was overall more sensitive. SAM's model estimation was superior, while HMMER's model scoring was more accurate. Critical features for model building were then analysed by comparing the two packages' algorithmic choices and parameters. The weighting between prior probabilities and multiple alignment counts held the primary explanation why SAM's model building was superior. Our analysis suggests that HMMER gives too much weight to the sequence counts. SAM's emission prior probabilities were also shown to be more sensitive. The relative sequence weighting schemes are different in the two packages but performed equivalently.

Conclusion

SAM model estimation was more sensitive, while HMMER model scoring was more accurate. By combining the best algorithmic features from both packages the accuracy was substantially improved compared to their default performance.

'Conserved hypothetical' proteins: prioritization of targets for experimental study

Comparative genomics shows that a substantial fraction of the genes in sequenced genomes encodes 'conserved hypothetical' proteins, i.e. those that are found in organisms from several phylogenetic lineages but have not been functionally characterized. Here, we briefly discuss recent progress in functional characterization of prokaryotic 'conserved hypothetical' proteins and the possible criteria for prioritizing targets for experimental study. Based on these criteria, the chief one being wide phyletic spread, we offer two 'top 10' lists of highly attractive targets. The first list consists of proteins for which biochemical activity could be predicted with reasonable confidence but the biological function was predicted only in general terms, if at all ('known unknowns'). The second list includes proteins for which there is no prediction of biochemical activity, even if, for some, general biological clues exist ('unknown unknowns'). The experimental characterization of these and other 'conserved hypothetical' proteins is expected to reveal new, crucial aspects of microbial biology and could also lead to better functional prediction for medically relevant human homologs.