phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. the focus of phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. phyre2 replaces phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous snps (nssnps)) for a user's protein sequence. users are guided through results by a simple interface at a level of detail they determine. this protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. a range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. the server is available at http://www.sbg. bio.ic.ac.uk/phyre2. a typical structure prediction will be returned between 30 min and 2 h after submission.

Determining the structure and function of a novel protein is a cornerstone of many aspects of modern biology. Over the past decades, a number of computational tools for structure prediction have been developed. It is critical that the biological community is aware of such tools and is able to interpret their results in an informed way. This protocol provides a guide to interpreting the output of structure prediction servers in general and one such tool in particular, the protein homology/analogy recognition engine (Phyre). New profile-profile matching algorithms have improved structure prediction considerably in recent years. Although the performance of Phyre is typical of many structure prediction systems using such algorithms, all these systems can reliably detect up to twice as many remote homologies as standard sequence-profile searching. Phyre is widely used by the biological community, with 4150 submissions per day, and provides a simple interface to results. Phyre takes 30 min to predict the structure of a 250-residue protein. INTRODUCTIONAt present, over six million unique protein sequences have been deposited in the public databases, and this number is growing rapidly (http://www.ncbi.nlm.nih.gov/RefSeq/). Meanwhile, despite the progress of high-throughput structural genomics initiatives, just over 50,000 protein structures have so far been experimentally determined. This enormous disparity between the number of sequences and structures has driven research toward computational methods for predicting protein structure from sequence. Computational methods grounded in simulation of the folding process using only the sequence itself as input (the so-called ab initio or de novo approaches) have been pursued for decades and are showing some progress 1 . However, in general, these methods are either computationally intractable or show poor performance on everything except the smallest proteins (o100 amino acids) 1 .The most successful general approach for predicting the structure of proteins involves the detection of homologs of known three-dimensional (3D) structure-the so-called template-based homology modeling or fold-recognition. These methods rely on the observation that the number of folds in nature appears to be limited and that many different remotely homologous protein sequences adopt remarkably similar structures 2 . Thus, given a protein sequence of interest, one may compare this sequence with the sequences of proteins with experimentally determined structures. If a homolog can be found, an alignment of the two sequences can be generated and used directly to build a 3D model of the sequence of interest. The practical applications of protein structure prediction are many and varied, including guiding the development of functional hypotheses about hypothetical proteins 3 , improving phasing signals in crystallography 4 , selecting sites for mutagenesis 5 and the rational design of drugs 6 .Every 2 years an international blind trial of protein structure prediction techniques is held (Critical Assessmen...

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Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools.

Summary Alternative splicing (AS) generates vast transcriptomic and proteomic complexity. However, which of the myriad of detected AS events provide important biological functions is not well understood. Here, we define the largest program of functionally coordinated, neural-regulated AS described to date in mammals. Relative to all other types of AS within this program, 3-15 nucleotide ‘microexons’ display the most striking evolutionary conservation and switch-like regulation. These microexons modulate the function of interaction domains of proteins involved in neurogenesis. Most neural microexons are regulated by the neuronal-specific splicing factor nSR100/SRRM4, through its binding to adjacent intronic enhancer motifs. Neural microexons are frequently misregulated in the brains of individuals with autism spectrum disorder, and this misregulation is associated with reduced levels of nSR100. The results thus reveal a highly conserved program of dynamic microexon regulation associated with the remodeling of protein interaction networks during neurogenesis, the misregulation of which is linked to autism.

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CAPRI is a communitywide experiment to assess the capacity of protein-docking methods to predict protein-protein interactions. Nineteen groups participated in rounds 1 and 2 of CAPRI and submitted blind structure predictions for seven protein-protein complexes based on the known structure of the component proteins. The predictions were compared to the unpublished X-ray structures of the complexes. We describe here the motivations for launching CAPRI, the rules that we applied to select targets and run the experiment, and some conclusions that can already be drawn. The results stress the need for new scoring functions and for methods handling the conformation changes that were observed in some of the target systems. CAPRI has already been a powerful drive for the community of computational biologists who development docking algorithms. We hope that this issue of Proteins will also be of interest to the community of structural biologists, which we call upon to provide new targets for future rounds of CAPRI, and to all molecular biologists who view protein-protein recognition as an essential process. Proteins 2003;52: 2-9.

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