GenDiS: Genomic Distribution of protein structural domain Superfamilies

Peptides encoded by noncoding genes: challenges and perspectives

In recent years, noncoding gene (NCG) translation events have been frequently discovered. The resultant peptides, as novel findings in the life sciences, perform unexpected functions of increasingly recognized importance in many fundamental biological and pathological processes. The emergence of these novel peptides, in turn, has advanced the field of genomics while indispensably aiding living organisms. The peptides from NCGs serve as important links between extracellular stimuli and intracellular adjustment mechanisms. These peptides are also important entry points for further exploration of the mysteries of life that may trigger a new round of revolutionary biotechnological discoveries. Insights into NCG-derived peptides will assist in understanding the secrets of life and the causes of diseases, and will also open up new paths to the treatment of diseases such as cancer. Here, a critical review is presented on the action modes and biological functions of the peptides encoded by NCGs. The challenges and future trends in searching for and studying NCG peptides are also critically discussed.

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.

Genome-wide survey of putative RNA-binding proteins encoded in the human proteome

RNA-binding proteins (RBPs) are involved in various post-transcriptional gene regulatory processes and are also functionally important members of the ribosome and the spliceosome. However, RBPs and their interactions with RNA are less well-studied in comparison to DNA-binding proteins. We have classified the existing RBP structures, available in complexes with RNA and RNA/DNA hybrids, into different structural families and created Hidden Markov Models (HMMs). These structure-centric family HMMs, along with the sequence-centric family HMMs, were used as a primary database to systematically search the human proteome for the presence of putative RBPs. We have found more than 2600 gene products with RBP signatures in humans, of which around 28% are likely to bind to RNA but not DNA, whereas 9% might bind to both RNA and DNA. 11% of them do not contain an explicit functional annotation yet. Nearly 30% of the putative RBPs are exclusively nuclear, 15% have known disease associations and around 30% are enzymes. Around 40% of the proteins identified in this study are novel and have not been reported by recent large-scale studies on human RBPs.

Improved performance of sequence search approaches in remote homology detection

The protein sequence space is vast and diverse, spanning across different families. Biologically meaningful relationships exist between proteins at superfamily level. However, it is highly challenging to establish convincing relationships at the superfamily level by means of simple sequence searches. It is necessary to design a rigorous sequence search strategy to establish remote homology relationships and achieve high coverage. We have used iterative profile-based methods, along with constraints of sequence motifs, to specify search directions. We address the importance of multiple start points (queries) to achieve high coverage at protein superfamily level. We have devised strategies to employ a structural regime to search sequence space with good specificity and sensitivity. We employ two well-known sequence search methods, PSI-BLAST and PHI-BLAST, with multiple queries and multiple patterns to enhance homologue identification at the structural superfamily level. The study suggests that multiple queries improve sensitivity, while a pattern-constrained iterative sequence search becomes stringent at the initial stages, thereby driving the search in a specific direction and also achieves high coverage. This data mining approach has been applied to the entire structural superfamily database.

Comparing proteins by their internal dynamics: exploring structure-function relationships beyond static structural alignments

The growing interest for comparing protein internal dynamics owes much to the realisation that protein function can be accompanied or assisted by structural fluctuations and conformational changes. Analogously to the case of functional structural elements, those aspects of protein flexibility and dynamics that are functionally oriented should be subject to evolutionary conservation. Accordingly, dynamics-based protein comparisons or alignments could be used to detect protein relationships that are more elusive to sequence and structural alignments. Here we provide an account of the progress that has been made in recent years towards developing and applying general methods for comparing proteins in terms of their internal dynamics and advance the understanding of the structure-function relationship.

A clip domain serine protease (cSP) from the Chinese mitten crab Eriocheir sinensis: cDNA characterization and mRNA expression

Clip domain serine protease (cSP), characterized by conserved clip domains, is a new serine protease family identified mainly in arthropod, and plays important roles in development and immunity. In the present study, the full-length cDNA of a cSP (designated EscSP) was cloned from Chinese mitten crab Eriocheir sinensis by expressed sequence tags (ESTs) and PCR techniques. The 1380 bp EscSP cDNA contained a 1152 bp open reading frame (ORF) encoding a putative cSP of 383 amino acids, a 5'-untranslated region (UTR) of 54 bp, and a 3'-UTR of 174 bp. Multiple sequence alignment presented twelve conserved cysteine residues and a canonical catalytic triad (His(185), Asp(235) and Ser(332)) critical for the fundamental structure and function of EscSP. Two types of cSP domains, the clip domain and tryp_spc domain, were identified in the deduced amino acids sequence of EscSP. The conservation characteristics and similarities with previously known cSPs indicated that EscSP was a member of the large cSP family. The mRNA expression of EscSP in different tissues and the temporal expression in haemocytes challenged by Listonella anguillarum were measured by real-time RT-PCR. EscSP mRNA transcripts could be detected in all examined tissues, and were higher expressed in muscle than that in hepatopancreas, gill, gonad, haemocytes and heart. The EscSP mRNA expression in haemocytes was up-regulated after L. anguillarum challenge and peaked at 2 h (4.96 fold, P < 0.05) and 12 h (9.90 fold, P < 0.05). Its expression pattern was similar to prophenoloxidase (EsproPO), one of the components of crab proPO system found in our previous report. These results implied that EscSP was involved in the processes of host-pathogen interaction probably as one of the proPO system members.

Genome-wide survey of prokaryotic serine proteases: analysis of distribution and domain architectures of five serine protease families in prokaryotes

Background

Serine proteases are one of the most abundant groups of proteolytic enzymes found in all the kingdoms of life. While studies have established significant roles for many prokaryotic serine proteases in several physiological processes, such as those associated with metabolism, cell signalling, defense response and development, functional associations for a large number of prokaryotic serine proteases are relatively unknown. Current analysis is aimed at understanding the distribution and probable biological functions of the select serine proteases encoded in representative prokaryotic organisms.

Results

A total of 966 putative serine proteases, belonging to five families, were identified in the 91 prokaryotic genomes using various sensitive sequence search techniques. Phylogenetic analysis reveals several species-specific clusters of serine proteases suggesting their possible involvement in organism-specific functions. Atypical phylogenetic associations suggest an important role for lateral gene transfer events in facilitating the widespread distribution of the serine proteases in the prokaryotes. Domain organisations of the gene products were analysed, employing sensitive sequence search methods, to infer their probable biological functions. Trypsin, subtilisin and Lon protease families account for a significant proportion of the multi-domain representatives, while the D-Ala-D-Ala carboxypeptidase and the Clp protease families are mostly single-domain polypeptides in prokaryotes. Regulatory domains for protein interaction, signalling, pathogenesis, cell adhesion etc. were found tethered to the serine protease domains. Some domain combinations (such as S1-PDZ; LON-AAA-S16 etc.) were found to be widespread in the prokaryotic lineages suggesting a critical role in prokaryotes.

Conclusion

Domain architectures of many serine proteases and their homologues identified in prokaryotes are very different from those observed in eukaryotes, suggesting distinct roles for serine proteases in prokaryotes. Many domain combinations were found unique to specific prokaryotic species, suggesting functional specialisation in various cellular and physiological processes.

CUSP: an algorithm to distinguish structurally conserved and unconserved regions in protein domain alignments and its application in the study of large length variations

BACKGROUND

Distantly related proteins adopt and retain similar structural scaffolds despite length variations that could be as much as two-fold in some protein superfamilies. In this paper, we describe an analysis of indel regions that accommodate length variations amongst related proteins. We have developed an algorithm CUSP, to examine multi-membered PASS2 superfamily alignments to identify indel regions in an automated manner. Further, we have used the method to characterize the length, structural type and biochemical features of indels in related protein domains.

RESULTS

CUSP, examines protein domain structural alignments to distinguish regions of conserved structure common to related proteins from structurally unconserved regions that vary in length and type of structure. On a non-redundant dataset of 353 domain superfamily alignments from PASS2, we find that 'length- deviant' protein superfamilies show > 30% length variation from their average domain length. 60% of additional lengths that occur in indels are short-length structures (< 5 residues) while 6% of indels are > 15 residues in length. Structural types in indels also show class-specific trends.

CONCLUSION

The extent of length variation varies across different superfamilies and indels show class-specific trends for preferred lengths and structural types. Such indels of different lengths even within a single protein domain superfamily could have structural and functional consequences that drive their selection, underlying their importance in similarity detection and computational modelling. The availability of systematic algorithms, like CUSP, should enable decision making in a domain superfamily-specific manner.

Enhanced function annotations for Drosophila serine proteases: a case study for systematic annotation of multi-member gene families

Systematically annotating function of enzymes that belong to large protein families encoded in a single eukaryotic genome is a very challenging task. We carried out such an exercise to annotate function for serine-protease family of the trypsin fold in Drosophila melanogaster, with an emphasis on annotating serine-protease homologues (SPHs) that may have lost their catalytic function. Our approach involves data mining and data integration to provide function annotations for 190 Drosophila gene products containing serine-protease-like domains, of which 35 are SPHs. This was accomplished by analysis of structure-function relationships, gene-expression profiles, large-scale protein-protein interaction data, literature mining and bioinformatic tools. We introduce functional residue clustering (FRC), a method that performs hierarchical clustering of sequences using properties of functionally important residues and utilizes correlation co-efficient as a quantitative similarity measure to transfer in vivo substrate specificities to proteases. We show that the efficiency of transfer of substrate-specificity information using this method is generally high. FRC was also applied on Drosophila proteases to assign putative competitive inhibitor relationships (CIRs). Microarray gene-expression data were utilized to uncover a large-scale and dual involvement of proteases in development and in immune response. We found specific recruitment of SPHs and proteases with CLIP domains in immune response, suggesting evolution of a new function for SPHs. We also suggest existence of separate downstream protease cascades for immune response against bacterial/fungal infections and parasite/parasitoid infections. We verify quality of our annotations using information from RNAi screens and other evidence types. Utilization of such multi-fold approaches results in 10-fold increase of function annotation for Drosophila serine proteases and demonstrates value in increasing annotations in multiple genomes.

A machine learning approach for the identification of odorant binding proteins from sequence-derived properties

Background

Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins.

Results

In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC) in conjunction with multiple physicochemical properties of amino acids to predict odorant-binding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively).

Conclusion

Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information.

Genome-wide Survey of Prokaryotic O-protein Phosphatases

Complex and diverse signal transduction circuits are responsible for the efficient functioning of cellular network. Protein kinases and O-protein phosphatases are primarily responsible for propagating such stimuli within a eukaryotic cell. However, there is limited understanding of O-protein phosphatases in the prokaryotic genomes. The availability of complete genome sequence information for several prokaryotes permits a genome-wide survey of O-protein phosphatases. The distribution of the various protein phosphatase families has been observed to be mosaic, with the exception of the members of the phospho protein family P (PPP), which is consistent with previous studies. The PPP family is ubiquitous in the prokaryotic world and undergoes the highest sequence divergence within a genome amongst phosphatases studied. The co-occurrence of low molecular mass tyrosine phosphatase (LMWPc) and PPP domain in a single polypeptide suggests that the protein present in Archaeoglobus fulgidus might represent the progenitor for all protein phosphatases. The curation of data on prokaryotic protein phosphatases provides a convenient framework for the analysis of domain architectures and for characterising structural and functional properties of this important family of signalling proteins.

DIAL: a web-based server for the automatic identification of structural domains in proteins

DIAL is a web server for the automatic identification of structural domains given the 3D coordinates of a protein. Delineation of the structural domains and their exact boundaries are the starting points for the better realization of distantly related members of the domain families, for the rational design of the experiments and for clearer understanding of the biological function. The current server can examine crystallographic multiple chains and provide structural domain solutions that can also describe domain swapping events. The server can be accessed from . The Supplementary data can be accessed from .