Protein structures are flexible, changing their shapes not only upon substrate binding, but also during evolution as a collective effect of mutations, deletions and insertions. A new generation of protein structure comparison algorithms allows for such flexibility; they go beyond identifying the largest common part between two proteins and find hinge regions and patterns of flexibility in protein families. Here we present a Flexible Structural Neighborhood (FSN), a database of structural neighbors of proteins deposited in PDB as seen by a flexible protein structure alignment program FATCAT, developed previously in our group. The database, searchable by a protein PDB code, provides lists of proteins with statistically significant structural similarity and on lower menu levels provides detailed alignments, interactive superposition of structures and positions of hinges that were identified in the comparison. While superficially similar to other structural protein alignment resources, FSN provides a unique resource to study not only protein structural similarity, but also how protein structures change. FSN is available from a server http://fatcat.burnham.org/fatcat/struct_neighbor and by direct links from the PDB database.

Background

Protein structure analysis and comparison are major challenges in structural bioinformatics. Despite the existence of many tools and algorithms, very few of them have managed to capture the intuitive understanding of protein structures developed in structural biology, especially in the context of rapid database searches. Such intuitions could help speed up similarity searches and make it easier to understand the results of such analyses.

Results

We developed a TOPS++FATCAT algorithm that uses an intuitive description of the proteins' structures as captured in the popular TOPS diagrams to limit the search space of the aligned fragment pairs (AFPs) in the flexible alignment of protein structures performed by the FATCAT algorithm. The TOPS++FATCAT algorithm is faster than FATCAT by more than an order of magnitude with a minimal cost in classification and alignment accuracy. For beta-rich proteins its accuracy is better than FATCAT, because the TOPS+ strings models contains important information of the parallel and anti-parallel hydrogen-bond patterns between the beta-strand SSEs (Secondary Structural Elements). We show that the TOPS++FATCAT errors, rare as they are, can be clearly linked to oversimplifications of the TOPS diagrams and can be corrected by the development of more precise secondary structure element definitions.

Software availability

The benchmark analysis results and the compressed archive of the TOPS++FATCAT program for Linux platform can be downloaded from the following web site: http://fatcat.burnham.org/TOPS/ CONCLUSION: TOPS++FATCAT provides FATCAT accuracy and insights into protein structural changes at a speed comparable to sequence alignments, opening up a possibility of interactive protein structure similarity searches.

Background

Apoptosis, one of the main types of programmed cell death, is regulated and performed by a complex protein network. Studies in model organisms, mostly in the nematode Caenorhabditis elegans, identified a relatively simple apoptotic network consisting of only a few proteins. However, analysis of several recently sequenced invertebrate genomes, ranging from the cnidarian sea anemone Nematostella vectensis, representing one of the morphologically simplest metazoans, to the deuterostomes sea urchin and amphioxus, contradicts the current paradigm of a simple ancestral network that expanded in vertebrates.

Results

Here we show that the apoptosome-forming CED-4/Apaf-1 protein, present in single copy in vertebrate, nematode, and insect genomes, had multiple paralogs in the cnidarian-bilaterian ancestor. Different members of this ancestral Apaf-1 family led to the extant proteins in nematodes/insects and in deuterostomes, explaining significant functional differences between proteins that until now were believed to be orthologous. Similarly, the evolution of the Bcl-2 and caspase protein families appears surprisingly complex and apparently included significant gene loss in nematodes and insects and expansions in deuterostomes.

Conclusion

The emerging picture of the evolution of the apoptosis network is one of a succession of lineage-specific expansions and losses, which combined with the limited number of 'apoptotic' protein families, resulted in apparent similarities between networks in different organisms that mask an underlying complex evolutionary history. Similar results are beginning to surface for other regulatory networks, contradicting the intuitive notion that regulatory networks evolved in a linear way, from simple to complex.

POSA (Partial Order Structure Alignment), available at http://posa.godziklab.org, is a server for multiple protein structure alignment introduced in 2005 (Ye,Y. and Godzik,A. (2005) Multiple flexible structure alignment using partial order graphs. Bioinformatics, 21, 2362-2369). It is free and open to all users, and there is no login requirement, albeit there is an option to register and store results in individual, password-protected directories. In the updated POSA server described here, we introduce two significant improvements. First is an interface allowing the user to provide additional information by defining segments that anchor the alignment in one or more input structures. This interface allows users to take advantage of their intuition and biological insights to improve the alignment and guide it toward a biologically relevant solution. The second improvement is an interactive visualization with options that allow the user to view all superposed structures in one window (a typical solution for visualizing results of multiple structure alignments) or view them individually in a series of synchronized windows with extensive, user-controlled visualization options. The user can rotate structure(s) in any of the windows and study similarities or differences between structures clearly visible in individual windows.

Background

Defining blocks forming the global protein structure on the basis of local structural regularity is a very fruitful idea, extensively used in description, and prediction of structure from only sequence information. Over many years the secondary structure elements were used as available building blocks with great success. Specially prepared sets of possible structural motifs can be used to describe similarity between very distant, non-homologous proteins. The reason for utilizing the structural information in the description of proteins is straightforward. Structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate.

Results

Here we provide a new fragment library for Local Structure Segment (LSS) prediction called FRAGlib which is integrated with a previously described segment alignment algorithm SEA. A joined FRAGlib/SEA server provides easy access to both algorithms, allowing a one stop alignment service using a novel approach to protein sequence alignment based on a network matching approach. The FRAGlib used as secondary structure prediction achieves only 73% accuracy in Q3 measure, but when combined with the SEA alignment, it achieves a significant improvement in pairwise sequence alignment quality, as compared to previous SEA implementation and other public alignment algorithms. The FRAGlib algorithm takes approximately 2 min. to search over FRAGlib database for a typical query protein with 500 residues. The SEA service align two typical proteins within circa approximately 5 min. All supplementary materials (detailed results of all the benchmarks, the list of test proteins and the whole fragments library) are available for download on-line at http://ffas.ljcrf.edu/darman/results/.

Conclusions

The joined FRAGlib/SEA server will be a valuable tool both for molecular biologists working on protein sequence analysis and for bioinformaticians developing computational methods of structure prediction and alignment of proteins.

Background

Regulation in protein networks often utilizes specialized domains that 'join' (or 'connect') the network through specific protein-protein interactions. The innate immune system, which provides a first and, in many species, the only line of defense against microbial and viral pathogens, is regulated in this way. Amphioxus (Branchiostoma floridae), whose genome was recently sequenced, occupies a unique position in the evolution of innate immunity, having diverged within the chordate lineage prior to the emergence of the adaptive immune system in vertebrates.

Results

The repertoire of several families of innate immunity proteins is expanded in amphioxus compared to both vertebrates and protostome invertebrates. Part of this expansion consists of genes encoding proteins with unusual domain architectures, which often contain both upstream receptor and downstream activator domains, suggesting a potential role for direct connections (shortcuts) that bypass usual signal transduction pathways.

Conclusion

Domain rearrangements can potentially alter the topology of protein-protein interaction (and regulatory) networks. The extent of such arrangements in the innate immune network of amphioxus suggests that domain shuffling, which is an important mechanism in the evolution of multidomain proteins, has also shaped the development of immune systems.

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.