BACKGROUND: Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. RESULTS: In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. CONCLUSION: MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/ .

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: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Article Free Access Share on The E-CELL project: towards integrative simulation of cellular processes Authors: Masaru Tomita Laboratory for Bioinformatics and Department of Environmental Informatics Laboratory for Bioinformatics and Department of Environmental InformaticsView Profile , Kenta Hashimoto Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Koichi Takahashi Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Yuri Matsuzaki Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Ryo Matsushima Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Katsuyuki Yugi Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Fumihiko Miyoshi Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Hisako Nakano Laboratory for Bioinformatics and Department of Policy Management, Keio University, 5322 Endo, Fujisawa, 252-8520 Japan Laboratory for Bioinformatics and Department of Policy Management, Keio University, 5322 Endo, Fujisawa, 252-8520 JapanView Profile , Yusuke Saito Laboratory for Bioinformatics and Department of Environmental Informatics Laboratory for Bioinformatics and Department of Environmental InformaticsView Profile , S. Shimizu Laboratory for Bioinformatics and Department of Zoology, Cambridge University, CB2 3EJ, UK Laboratory for Bioinformatics and Department of Zoology, Cambridge University, CB2 3EJ, UKView Profile , Yoichi Nakayama Laboratory for Bioinformatics and Department of Environmental Informatics Laboratory for Bioinformatics and Department of Environmental InformaticsView Profile Authors Info & Claims RECOMB '00: Proceedings of the fourth annual international conference on Computational molecular biologyApril 2000 Pages 290–298https://doi.org/10.1145/332306.332567Published:08 April 2000Publication History 1citation569DownloadsMetricsTotal Citations1Total Downloads569Last 12 Months13Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

MOTIVATION: Genome sequencing projects and further systematic functional analyses of complete gene sets are producing an unprecedented mass of molecular information for a wide range of model organisms. This provides us with a detailed account of the cell with which we may begin to build models for simulating intracellular molecular processes to predict the dynamic behavior of living cells. Previous work in biochemical and genetic simulation has isolated well-characterized pathways for detailed analysis, but methods for building integrative models of the cell that incorporate gene regulation, metabolism and signaling have not been established. We, therefore, were motivated to develop a software environment for building such integrative models based on gene sets, and running simulations to conduct experiments in silico. RESULTS: E-CELL, a modeling and simulation environment for biochemical and genetic processes, has been developed. The E-CELL system allows a user to define functions of proteins, protein-protein interactions, protein-DNA interactions, regulation of gene expression and other features of cellular metabolism, as a set of reaction rules. E-CELL simulates cell behavior by numerically integrating the differential equations described implicitly in these reaction rules. The user can observe, through a computer display, dynamic changes in concentrations of proteins, protein complexes and other chemical compounds in the cell. Using this software, we constructed a model of a hypothetical cell with only 127 genes sufficient for transcription, translation, energy production and phospholipid synthesis. Most of the genes are taken from Mycoplasma genitalium, the organism having the smallest known chromosome, whose complete 580 kb genome sequence was determined at TIGR in 1995. We discuss future applications of the E-CELL system with special respect to genome engineering. AVAILABILITY: The E-CELL software is available upon request. SUPPLEMENTARY INFORMATION: The complete list of rules of the developed cell model with kinetic parameters can be obtained via our web site at: http://e-cell.org/.