Protein sequence similarity searches using patterns as seeds.

Protein families often are characterized by conserved sequence patterns or motifs. A researcher frequently wishes to evaluate the significance of a specific pattern within a protein, or to exploit knowledge of known motifs to aid the recognition of greatly diverged but homologous family members. To assist in these efforts, the pattern-hit initiated BLAST (PHI-BLAST) program described here takes as input both a protein sequence and a pattern of interest that it contains. PHI-BLAST searches a protein database for other instances of the input pattern, and uses those found as seeds for the construction of local alignments to the query sequence. The random distribution of PHI-BLAST alignment scores is studied analytically and empirically. In many instances, the program is able to detect statistically significant similarity between homologous proteins that are not recognizably related using traditional single-pass database search methods. PHI-BLAST is applied to the analysis of CED4-like cell death regulators, HS90-type ATPase domains, archaeal tRNA nucleotidyltransferases and archaeal homologs of DnaG-type DNA primases.     

Homology Induction: the use of machine learning to improve sequence similarity searches

Background  

The inference of homology between proteins is a key problem in molecular biology The current best approaches only identify ~50% of homologies (with a false positive rate set at 1/1000).     

Retrieval accuracy, statistical significance and compositional similarity in protein sequence database searches

Protein sequence database search programs may be evaluated both for their retrieval accuracy—the ability to separate meaningful from chance similarities—and for the accuracy of their statistical assessments of reported alignments. However, methods for improving statistical accuracy can degrade retrieval accuracy by discarding compositional evidence of sequence relatedness. This evidence may be preserved by combining essentially independent measures of alignment and compositional similarity into a unified measure of sequence similarity. A version of the BLAST protein database search program, modified to employ this new measure, outperforms the baseline program in both retrieval and statistical accuracy on ASTRAL, a SCOP-based test set.     

Improving protein structure similarity searches using domain boundaries based on conserved sequence information

Background  

The identification of protein domains plays an important role in protein structure comparison. Domain query size and composition are critical to structure similarity search algorithms such as the Vector Alignment Search Tool (VAST), the method employed for computing related protein structures in NCBI Entrez system. Currently, domains identified on the basis of structural compactness are used for VAST computations. In this study, we have investigated how alternative definitions of domains derived from conserved sequence alignments in the Conserved Domain Database (CDD) would affect the domain comparisons and structure similarity search performance of VAST.     

Intrinsic disorder in scaffold proteins: getting more from less

Regulation, recognition and cell signaling involve the coordinated actions of many players. Signaling scaffolds, with their ability to bring together proteins belonging to common and/or interlinked pathways, play crucial roles in orchestrating numerous events by coordinating specific interactions among signaling proteins. This review examines the roles of intrinsic disorder (ID) in signaling scaffold protein function. Several well-characterized scaffold proteins with structurally and functionally characterized ID regions are used here to illustrate the importance of ID for scaffolding function. These examples include scaffolds that are mostly disordered, only partially disordered or those in which the ID resides in a scaffold partner. Specific scaffolds discussed include RNase, voltage-activated potassium channels, axin, BRCA1, GSK-3beta, p53, Ste5, titin, Fus3, BRCA1, MAP2, D-AKAP2 and AKAP250. Among the mechanisms discussed are: molecular recognition features, fly-casting, ease of encounter complex formation, structural isolation of partners, modulation of interactions between bound partners, masking of intramolecular interaction sites, maximized interaction surface per residue, toleration of high evolutionary rates, binding site overlap, allosteric modification, palindromic binding, reduced constraints for alternative splicing, efficient regulation via posttranslational modification, efficient regulation via rapid degradation, protection of normally solvent-exposed sites, enhancing the plasticity of interaction and molecular crowding. We conclude that ID can enhance scaffold function by a diverse array of mechanisms. In other words, scaffold proteins utilize several ID-facilitated mechanisms to enhance function, and by doing so, get more functionality from less structure.     

Recent Hits Acquired by BLAST (ReHAB): A tool to identify new hits in sequence similarity searches

Background  

Sequence similarity searching is a powerful tool to help develop hypotheses in the quest to assign functional, structural and evolutionary information to DNA and protein sequences. As sequence databases continue to grow exponentially, it becomes increasingly important to repeat searches at frequent intervals, and similarity searches retrieve larger and larger sets of results. New and potentially significant results may be buried in a long list of previously obtained sequence hits from past searches.     

Shotgun genome sequence of a Yersinia enterocolitica isolate from the Philippines

The first shotgun genome sequence of a microbial pathogen from the Philippines is reported. Yersinia enterocolitica subsp. palearctica strain PhRBD_Ye1 is the first Y. enterocolitica strain sequenced from an animal source, swine, which is a natural source of yersiniosis. The closest phylogenetic match is a human clinical isolate from Germany.     

University Tech Transfer 2.0: Strategies for getting more innovation from public universities

Public universities in America saw steep declines in funding during the last major recession in the ‘80s. As jobs disappeared and the tax base shrunk, state and local governments tightened their belts and cutback support for higher education and research. But even as legislators clamped down in some areas, they created opportunities elsewhere. Then, as now, new pieces of legislation were enacted to stimulate economic development, including the Bayh-Dole Act enabling universities to own the intellectual property emanating from government-funded research. The intent was obvious—technological invention would become the engine of economic development. And the fastest way to realize a return on investment of both public and privately supported research was to foster direct collaboration between scientists and business and industry. This new model gave birth to technology transfer operations within hundreds of U.S. public research universities.

Some tech transfer organizations have had more success than others. The most recent survey conducted by the Association of University Technology Managers, indicates that 11 of its roughly 200 member universities account for more than half of all the revenues generated from university patents, in licensing agreements and royalties. Why are some more successful than others? And, more important given the current fiscal crisis, what will it take for others to become more effective? Cell Cycle talked to leaders at three of the country’s most successful public university tech transfer programs for insights. Download the article.     

Shotgun haplotyping: a novel method for surveying allelic sequence variation

Haplotypic sequences contain significantly more information than genotypes of genetic markers and are critical for studying disease association and genome evolution. Current methods for obtaining haplotypic sequences require the physical separation of alleles before sequencing, are time consuming and are not scaleable for large surveys of genetic variation. We have developed a novel method for acquiring haplotypic sequences from long PCR products using simple, high-throughput techniques. This method applies modified shotgun sequencing protocols to sequence both alleles concurrently, with read-pair information allowing the two alleles to be separated during sequence assembly. Although the haplotypic sequences can be assembled manually from the resultant data using pre-existing sequence assembly software, we have devised a novel heuristic algorithm to automate assembly and remove human error. We validated the approach on two long PCR products amplified from the human genome and confirmed the accuracy of our sequences against full-length clones of the same alleles. This method presents a simple high-throughput means to obtain full haplotypic sequences potentially up to 20 kb in length and is suitable for surveying genetic variation even in poorly-characterized genomes as it requires no prior information on sequence variation.     

BLAST PRINTS--alternative perspectives on sequence similarity.

SUMMARY: An implementation of BLAST for searching the PRINTS database is presented. The interface allows submission of either protein or DNA queries, and returns the familiar form of output, but modified by means of direct links both to the familial discriminators in PRINTS and to fingerprint profile visualization software. The server thus couples the rapidity of BLAST searching with the sensitivity of fingerprint diagnoses, providing alternative perspectives on a given query. AVAILABILITY: http://www.biochem.ucl. ac.uk/cgi-bin/wright/printsBLAST.cgi     

SS-Wrapper: a package of wrapper applications for similarity searches on Linux clusters

Background  

Large-scale sequence comparison is a powerful tool for biological inference in modern molecular biology. Comparing new sequences to those in annotated databases is a useful source of functional and structural information about these sequences. Using software such as the basic local alignment search tool (BLAST) or HMMPFAM to identify statistically significant matches between newly sequenced segments of genetic material and those in databases is an important task for most molecular biologists. Searching algorithms are intrinsically slow and data-intensive, especially in light of the rapid growth of biological sequence databases due to the emergence of high throughput DNA sequencing techniques. Thus, traditional bioinformatics tools are impractical on PCs and even on dedicated UNIX servers. To take advantage of larger databases and more reliable methods, high performance computation becomes necessary.     

Query-dependent banding (QDB) for faster RNA similarity searches

When searching sequence databases for RNAs, it is desirable to score both primary sequence and RNA secondary structure similarity. Covariance models (CMs) are probabilistic models well-suited for RNA similarity search applications. However, the computational complexity of CM dynamic programming alignment algorithms has limited their practical application. Here we describe an acceleration method called query-dependent banding (QDB), which uses the probabilistic query CM to precalculate regions of the dynamic programming lattice that have negligible probability, independently of the target database. We have implemented QDB in the freely available Infernal software package. QDB reduces the average case time complexity of CM alignment from LN^2.4 to LN^1.3 for a query RNA of N residues and a target database of L residues, resulting in a 4-fold speedup for typical RNA queries. Combined with other improvements to Infernal, including informative mixture Dirichlet priors on model parameters, benchmarks also show increased sensitivity and specificity resulting from improved parameterization.     

Improving the efficiency of dot-matrix similarity searches through use of an oligomer table.

Dot-matrix sequence similarity searches can be greatly speeded up through use of a table listing all locations of short oligomers in one of the sequences to find potential similarities with a second sequence. The algorithm described finds similarities between two sequences of lengths M and N, comparing L residues at a time, with an efficiency of L X M X N/(SK) where S is the alphabet size, and k is the length of the oligomer. For nucleic acids, in which S = 4, use of a tetranucleotide table results in an efficiency of L X M X N/256. The simplicity of the approach allows for a straightforward calculation of the level of similarities expected to be found for given search parameters. Furthermore, the storage required is minimal, allowing for even large sequences to be compared on small microcomputers. Theoretical considerations regarding the use of this search are discussed.     

Thyroid hormones signaling is getting more complex: STORMs are coming

T3 regulates many physiological and developmental processes by binding to thyroid hormone receptors (TRs). This induces a conformational change of DNA-bound TRs that releases corepressors in favor of coactivators. The associated chromatin modifications induce polymerase II recruitment. Mouse genetic studies clarified the respective contribution of each receptor isoform and revealed the important activity of unliganded TRs. They also confirm the paradoxical negative regulation of some promoters by liganded TRs. Recent advances place these molecular events in a broader context of extra- and intracellular regulation: control of ligand availability, changes in the cell sensitivity to T3, nongenomic effects, and cross talks with other signaling pathways contribute to increase the diversity and complexity of thyroid hormones signaling. A promising novel class of TRs synthetic ligands, called STORMs (selective TR modulators), might allow for tissue- and promoter-specific interventions.