New in protein structure and function annotation: Hotspots, single nucleotide polymorphisms and the 'Deep Web'

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation

MSAViewer:interactive JavaScript visualization of multiple sequence alignments

Summary: The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is ‘web ready’: written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components. Availability and Implementation: The MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected]

The BioJS article collection of open source components for biological data visualisation.

Data-driven research has gained momentum in the life sciences. Visualisation of these data is essential for quick generation of hypotheses and their translation into useful knowledge. BioJS is a new proposed standard for JavaScript-based components to visualise biological data. BioJS is an open source community project that to date provides 39 different components contributed by a global community. Here, we present the BioJS F1000Research collection series. A total of 12 components and a project status article are published in bulk. This collection does not intend to be an all-encompassing, comprehensive source of BioJS articles, but an initial set; future submissions from BioJS contributors are welcome

Alignment of Biological Sequences with Jalview

In this chapter, we introduce core functionality of the Jalview interactive platform for the creation, analysis, and publication of multiple sequence alignments. A workflow is described based on Jalview's core functions: from data import to figure generation, including import of alignment reliability scores from T-Coffee and use of Jalview from the command line. The accompanying notes provide background information on the underlying methods and discuss additional options for working with Jalview to perform multiple sequence alignment, functional site analysis, and publication of alignments on the web

BioJS: An open source standard for biological visualisation - its status in 2014

BioJS is a community-based standard and repository of functional components to represent biological information on the web. The development of BioJS has been prompted by the growing need for bioinformatics visualisation tools to be easily shared, reused and discovered. Its modular architecture makes it easy for users to find a specific functionality without needing to know how it has been built, while components can be extended or created for implementing new functionality. The BioJS community of developers currently provides a range of functionality that is open access and freely available. A registry has been set up that categorises and provides installation instructions and testing facilities at http://www.ebi.ac.uk/tools/biojs/. The source code for all components is available for ready use at https://github.com/biojs/biojs

LocTree3 prediction of localization

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3

Anatomy of BioJS, an open source community for the life sciences

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects

FluentDNA: Nucleotide Visualization of Whole Genomes, Annotations, and Alignments

Researchers seldom look at naked genome assemblies: instead the attributes of DNA sequences are mediated through statistics, annotations and high level summaries. Here we present software that visualizes the bare sequences of whole genome assemblies in a zoomable interface. This can assist in detection of chromosome architecture and contamination by the naked eye through changes in color patterns, in the absence of any other annotation. When available, annotations can be visualized alongside or on top of the naked sequence. Genome alignments can also be visualized, laying two genomes side by side in an alignment and highlighting their differences at nucleotide resolution. FluentDNA gives researchers direct visualization of whole genome assemblies, annotations and alignments, for quality control, hypothesis generation, and communicating results

Crystal structure of the Ego1-Ego2-Ego3 complex and its role in promoting Rag GTPase-dependent TORC1 signaling

The target of rapamycin complex 1 (TORC1) integrates various hormonal and nutrient signals to regulate cell growth, proliferation, and differentiation. Amino acid-dependent activation of TORC1 is mediated via the yeast EGO complex (EGOC) consisting of Gtr1, Gtr2, Ego1, and Ego3. Here, we identify the previously uncharacterized Ycr075w-a/Ego2 protein as an additional EGOC component that is required for the integrity and localization of the heterodimeric Gtr1-Gtr2 GTPases, equivalent to mammalian Rag GTPases. We also report the crystal structure of the Ego1-Ego2-Ego3 ternary complex (EGO-TC) at 2.4 Å resolution, in which Ego2 and Ego3 form a heterodimer flanked along one side by Ego1. Structural data also reveal the structural conservation of protein components between the yeast EGO-TC and the human Ragulator, which acts as a GEF for Rag GTPases. Interestingly, however, artificial tethering of Gtr1-Gtr2 to the vacuolar membrane is sufficient to activate TORC1 in response to amino acids even in the absence of the EGO-TC. Our structural and functional data therefore support a model in which the EGO-TC acts as a scaffold for Rag GTPases in TORC1 signaling