PrDOS is a server that predicts the disordered regions of a protein from its amino acid sequence (http://prdos.hgc.jp). The server accepts a single protein amino acid sequence, in either plain text or FASTA format. The prediction system is composed of two predictors: a predictor based on local amino acid sequence information and one based on template proteins. The server combines the results of the two predictors and returns a two-state prediction (order/disorder) and a disorder probability for each residue. The prediction results are sent by e-mail, and the server also provides a web-interface to check the results.

We present the Database of Disordered Protein Prediction (D²P²), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D²P² will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life.

Motivation: Intrinsically disordered regions in proteins have no unique stable structures without their partner molecules, thus these regions sometimes prevent high-quality structure determination. Furthermore, proteins with disordered regions are often involved in important biological processes, and the disordered regions are considered to play important roles in molecular interactions. Therefore, identifying disordered regions is important to obtain high-resolution structural information and to understand the functional aspects of these proteins. Results: We developed a new prediction method for disordered regions in proteins based on the meta approach and implemented a web-server for this prediction method named ‘metaPrDOS’. The method predicts the disorder tendency of each residue using support vector machines from the prediction results of the seven independent predictors. Evaluation of the meta approach was performed using the CASP7 prediction targets to avoid an overestimation due to the inclusion of proteins used in the training set of some component predictors. As a result, the meta approach achieved higher prediction accuracy than all methods participating in CASP7. Availability:http://prdos.hgc.jp/meta/ Contact:t-ishida@hgc.jp

DNA sequences are translated into protein coding sequences and then further assigned to protein families in metagenomic analyses, because of the need for sensitivity. However, huge amounts of sequence data create the problem that even general homology search analyses using BLASTX become difficult in terms of computational cost. We designed a new homology search algorithm that finds seed sequences based on the suffix arrays of a query and a database, and have implemented it as GHOSTX. GHOSTX achieved approximately 131–165 times acceleration over a BLASTX search at similar levels of sensitivity. GHOSTX is distributed under the BSD 2-clause license and is available for download at http://www.bi.cs.titech.ac.jp/ghostx/. Currently, sequencing technology continues to improve, and sequencers are increasingly producing larger and larger quantities of data. This explosion of sequence data makes computational analysis with contemporary tools more difficult. We offer this tool as a potential solution to this problem.

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of >97% strong scaling. Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock . Contact:akiyama@cs.titech.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online