Functional characterization of Genlisea aurea (S8E8K3) protein

This work predicts the functions of Genlisea aurea (S8E8K3) protein. Identification of corresponding proteins, conserved domains, functions and pedigree tree of target and corresponding proteins was obtained. Uniprot database, Basic Local Alignment Search Tool (BLAST) and Clustal Omega were used in this study. Results indicated that proteins from Sesamum indicum (XP011073982.1), Erythranthe guttate (XP012836557.1), Handroanthus impetiginosus (PIN05468.1) and Olea europaea var. sylvestris (XP022874946.1) showed 91%, 90%, 89% and 87% similarity, respectively, to S8E8K3. Model proteins all possessed WD40 domain. All model proteins functioned as ribonucleoprotein and phylogenetic tree showed that all proteins had eukaryotic origin. Therefore, S8E8K3 is and performs the role of a ribonucleoprotein

Functional characterization of Genlisea aurea (S8E8K3) protein

This work predicts the functions of Genlisea aurea (S8E8K3) protein. Identification of corresponding proteins, conserved domains, functions and pedigree tree of target and corresponding proteins was obtained. Uniprot database, Basic Local Alignment Search Tool (BLAST) and Clustal Omega were used in this study. Results indicated that proteins from Sesamum indicum (XP011073982.1), Erythranthe guttate (XP012836557.1), Handroanthus impetiginosus (PIN05468.1) and Olea europaea var. sylvestris (XP022874946.1) showed 91%, 90%, 89% and 87% similarity, respectively, to S8E8K3. Model proteins all possessed WD40 domain. All model proteins functioned as ribonucleoprotein and phylogenetic tree showed that all proteins had eukaryotic origin. Therefore, S8E8K3 is and performs the role of a ribonucleoprotein

Towards a career in bioinformatics

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation from 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 9-11, 2009 at Biopolis, Singapore. InCoB has actively engaged researchers from the area of life sciences, systems biology and clinicians, to facilitate greater synergy between these groups. To encourage bioinformatics students and new researchers, tutorials and student symposium, the Singapore Symposium on Computational Biology (SYMBIO) were organized, along with the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and the Clinical Bioinformatics (CBAS) Symposium. However, to many students and young researchers, pursuing a career in a multi-disciplinary area such as bioinformatics poses a Himalayan challenge. A collection to tips is presented here to provide signposts on the road to a career in bioinformatics. An overview of the application of bioinformatics to traditional and emerging areas, published in this supplement, is also presented to provide possible future avenues of bioinformatics investigation. A case study on the application of e-learning tools in undergraduate bioinformatics curriculum provides information on how to go impart targeted education, to sustain bioinformatics in the Asia-Pacific region. The next InCoB is scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010

Multiple graph regularized protein domain ranking

Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG- Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an ob- jective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.Comment: 21 page

Domain-based approaches to understanding phylogeny and orthology

Domain-based approaches are used in phylogenetic reconstruction and functional identification. Two groups of ionotropic glutamate receptors (iGluR???s) were identified with the topology of the binding core and pore-loop of the eukaryotic iGluR???s. Group 1 has a potassium-like selectivity filter and Group 2 is most closely related to eukaryotic iGluR???s. The relationship among them was investigated in this research. Then, the domain complexity of proteins was analysed on a comprehensive basis. Our results showed that bacterial and archaeal proteins are as complex as eukaryotic proteins in domain abundance, but more promiscuous. Proteins emerged in early stage are also more promiscuous, but with low domain abundance. The possible application of protein comparison based on domain content was also suggested in this research and could be used to help the identification of function and orthology. Therefore, domain-based approaches are proved to be useful in many areas of proteome research, including functional annotation, evolutionary illustration, and protein-protein network construction

FACT: Functional annotation transfer between proteins with similar feature architectures

<p>Abstract</p> <p>Background</p> <p>The increasing number of sequenced genomes provides the basis for exploring the genetic and functional diversity within the tree of life. Only a tiny fraction of the encoded proteins undergoes a thorough experimental characterization. For the remainder, bioinformatics annotation tools are the only means to infer their function. Exploiting significant sequence similarities to already characterized proteins, commonly taken as evidence for homology, is the prevalent method to deduce functional equivalence. Such methods fail when homologs are too diverged, or when they have assumed a different function. Finally, due to convergent evolution, functional equivalence is not necessarily linked to common ancestry. Therefore complementary approaches are required to identify functional equivalents.</p> <p>Results</p> <p>We present the <b>F</b>eature <b>A</b>rchitecture <b>C</b>omparison <b>T</b>ool <url>http://www.cibiv.at/FACT</url> to search for functionally equivalent proteins. FACT uses the similarity between feature architectures of two proteins, i.e., the arrangements of functional domains, secondary structure elements and compositional properties, as a proxy for their functional equivalence. A scoring function measures feature architecture similarities, which enables searching for functional equivalents in entire proteomes. Our evaluation of 9,570 EC classified enzymes revealed that FACT, using the full feature, set outperformed the existing architecture-based approaches by identifying significantly more functional equivalents as highest scoring proteins. We show that FACT can identify functional equivalents that share no significant sequence similarity. However, when the highest scoring protein of FACT is also the protein with the highest local sequence similarity, it is in 99% of the cases functionally equivalent to the query. We demonstrate the versatility of FACT by identifying a missing link in the yeast glutathione metabolism and also by searching for the human GolgA5 equivalent in <it>Trypanosoma brucei</it>.</p> <p>Conclusions</p> <p>FACT facilitates a quick and sensitive search for functionally equivalent proteins in entire proteomes. FACT is complementary to approaches using sequence similarity to identify proteins with the same function. Thus, FACT is particularly useful when functional equivalents need to be identified in evolutionarily distant species, or when functional equivalents are not homologous. The most reliable annotation transfers, however, are achieved when feature architecture similarity and sequence similarity are jointly taken into account.</p

ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

<p>Abstract</p> <p>Background</p> <p>The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.</p> <p>Results</p> <p>In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure <it>d_ij</it>by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (<it>i</it>, <it>j</it>), if their context <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i1"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>i</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> and <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i2"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>j</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing <it>d_ij</it>by a factor learned from the context <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i3"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>i</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> and <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i4"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>j</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula>.</p> <p>Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new <b>S</b>upervised learned <b>Dis</b>similarity measure, we update the <b>Pro</b>tein <b>H</b>ierarchial <b>Cont</b>ext <b>C</b>oherently in an iterative algorithm--<b>ProDis-ContSHC</b>.</p> <p>We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.</p> <p>Conclusions</p> <p>Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature.</p

Evolution of protein domain architectures

This chapter reviews current research on how protein domain architectures evolve. We begin by summarizing work on the phylogenetic distribution of proteins, as this will directly impact which domain architectures can be formed in different species. Studies relating domain family size to occurrence have shown that they generally follow power law distributions, both within genomes and larger evolutionary groups. These findings were subsequently extended to multi-domain architectures. Genome evolution models that have been suggested to explain the shape of these distributions are reviewed, as well as evidence for selective pressure to expand certain domain families more than others. Each domain has an intrinsic combinatorial propensity, and the effects of this have been studied using measures of domain versatility or promiscuity. Next, we study the principles of protein domain architecture evolution and how these have been inferred from distributions of extant domain arrangements. Following this, we review inferences of ancestral domain architecture and the conclusions concerning domain architecture evolution mechanisms that can be drawn from these. Finally, we examine whether all known cases of a given domain architecture can be assumed to have a single common origin (monophyly) or have evolved convergently (polyphyly). We end by a discussion of some available tools for computational analysis or exploitation of protein domain architectures and their evolution

Protein Function Prediction using Phylogenomics, Domain Architecture Analysis, Data Integration, and Lexical Scoring

“As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally.” (Radivojac, Clark, Oron, et al. 2013) With this goal, three new protein function annotation tools were developed, which produce trustworthy and concise protein annotations, are easy to obtain and install, and are capable of processing large sets of proteins with reasonable computational resource demands. Especially for high throughput analysis e.g. on genome scale, these tools improve over existing tools both in ease of use and accuracy. They are dubbed: • Automated Assignment of Human Readable Descriptions (AHRD) (github.com/groupschoof/AHRD; Hallab, Klee, Srinivas, and Schoof 2014), • AHRD on gene clusters, and • Phylogenetic predictions of Gene Ontology (GO) terms with specific calibrations (PhyloFun v2). “AHRD” assigns human readable descriptions (HRDs) to query proteins and was developed to mimic the decision making process of an expert curator. To this end it processes the descriptions of reference proteins obtained by searching selected databases with BLAST (Altschul, Madden, Schaffer, et al. 1997). Here, the trust a user puts into results found in each of these databases can be weighted separately. In the next step the descriptions of the found homologous proteins are filtered, removing accessions, species information, and finally discarding uninformative candidate descriptions like e.g. “putative protein”. Afterwards a dictionary of meaningful words is constructed from those found in the remaining candidates. In this, another filter is applied to ignore words, not conveying information like e.g. the word “protein” itself. In a lexical approach each word is assigned a score based on its frequency in all candidate descriptions, the sequence alignment quality associated with the candidate reference proteins, and finally the already mentioned trust put into the database the reference was obtained from. Subsequently each candidate description is assigned a score, which is computed from the respective scores of the meaningful words contained in that candidate. Also incorporated into this score is the description’s frequency among all regarded candidates. In the final step the highest scoring description is assigned to the query protein. The performance of this lexical algorithm, implemented in “AHRD”, was subsequently compared with that of competitive methods, which were Blast2GO and “best Blast”, where the latter “best Blast” simply passes the description of the best scoring hit to the query protein. To enable this comparison of performance, and in lack of a robust evaluation procedure, a new method to measure the accuracy of textual human readable protein descriptions was developed and applied with success. In this, the accuracy of each assigned competitive description was inferred with the frequently used “F-measure”, the harmonic mean of precision and recall, which we computed regarding meaningful words appearing in both the reference and the assigned descriptions as true positives. The results showed that “AHRD” not only outperforms its competitors by far, but also is very robust and thus does not require its users to use carefully selected parameters. In fact, AHRD’s robustness was demonstrated through cross validation and use of three different reference sets. The second annotation tool “AHRD on gene clusters” uses conserved protein domains from the InterPro database (Apweiler, Attwood, Bairoch, et al. 2000) to annotate clusters of homologous proteins. In a first step the domains found in each cluster are filtered, such that only the most informative are retained. For example are family descriptions discarded, if more detailed sub-family descriptions are also found annotated to members of the cluster. Subsequently, the most frequent candidate description is assigned, favoring those of type “family” over “domain”. Finally the third tool “PhyloFun (v2)” was developed to annotate large sets of query proteins with terms from the Gene Ontology. This work focussed on extending the “Belief propagation” (Pearl 1988) algorithm implemented in the “Sifter” annotation tool (Engelhardt, Jordan, Muratore, and Brenner 2005; Engelhardt, Jordan, Srouji, and Brenner 2011). Jöcker had developed a phylogenetic pipeline generating the input that was fed into the Sifter program. This pipeline executes stringent sequence similarity searches in a database of selected reference proteins, and reconstruct a phylogenetic tree from the found orthologs and inparalogs. This tree is than used by the Sifter program and interpreted as a “Bayesian Network” into which the GO term annotations of the homologous reference proteins are fed as “diagnostic evidence” (Pearl 1988). Subsequently the current strength of belief, the probability of this evidence being also the true state of ancestral tree nodes, is then spread recursively through the tree towards its root, and then vice versa towards the tips. These, of course, include the query protein, which in the final step is annotated with those GO terms that have the strongest belief. Note that during this recursive belief propagation a given GO term’s annotation probability depends on both the length of the currently processed branch, as well as the type of evolutionary event that took place. This event can be one of “speciation” or “duplication”, such that function mutation becomes more likely on longer branches and particularly after “duplication” events. A particular goal in extending this algorithm was to base the annotation probability of a given GO term not on a preconceived model of function evolution among homologous proteins as implemented in Sifter, but instead to compute these GO term annotation probabilities based on empirical measurements. To achieve this, calibrations were computed for each GO term separately, and reference proteins annotated with a given GO term were investigated such that the probability of function loss could be assessed empirically for decreasing sequence homology among related proteins. A second goal was to overcome errors in the identification of the type of evolutionary events. These errors arose from missing knowledge in terms of true species trees, which, in version 1 of the PhyloFun pipeline, are compared with the actual protein trees in order to tell “duplication” from “speciation” events (Zmasek and Eddy 2001). As reliable reference species trees are sparse or in many cases not available, the part of the algorithm incorporating the type of evolutionary event was discarded. Finally, the third goal postulated for the development of PhyloFun’s version 2 was to enable easy installation, usage, and calibration on latest available knowledge. This was motivated by observations made during the application of the first version of PhyloFun, in which maintaining the knowledge-base was almost not feasible. This obstacle was overcome in version 2 of PhyloFun by obtaining required reference data directly from publicly available databases. The accuracy and performance of the new PhyloFun version 2 was assessed and compared with selected competitive methods. These were chosen based on their widespread usage, as well as their applicability on large sets of query proteins without them surpassing reasonable time and computational resource requirements. The measurement of each method’s performance was carried out on a “gold standard”, obtained from the Uniprot/Swissprot public database (Boeckmann, Bairoch, Apweiler, et al. 2003), of 1000 selected reference proteins, all of which had GO term annotations made by expert curators and mostly based on experimental verifications. Subsequently the performance assessment was executed with a slightly modified version of the “Critical Assessment of Function Annotation experiment (CAFA)” experiment (Radivojac, Clark, Oron, et al. 2013). CAFA compares the performance of different protein function annotation tools on a worldwide scale using a provided set of reference proteins. In this, the predictions the competitors deliver are evaluated using the already introduced “F-measure”. Our performance evaluation of PhyloFun’s protein annotations interestingly showed that PhyloFun outperformed all of its competitors. Its use is recommended furthermore by the highly accurate phylogenetic trees the pipeline computes for each query and the found homologous reference proteins. In conclusion, three new premium tools addressing important matters in the computational prediction of protein function were developed and, in two cases, their performance assessed. Here, both AHRD and PhyloFun (v2) outperformed their competitors. Further arguments for the usage of all three tools are, that they are easy to install and use, as well as being reasonably resource demanding. Because of these results the publications of AHRD and PhyloFun (v2) are in preparation, even while AHRD already is applied by different researchers worldwide