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Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template-based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template-based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use at https://tbm.cluspro.org , is demonstrated by predicting the protein-protein targets of rounds 38 to 45 of CAPRI.  相似文献   

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Gene-based tests of association can increase the power of a genome-wide association study by aggregating multiple independent effects across a gene or locus into a single stronger signal. Recent gene-based tests have distinct approaches to selecting which variants to aggregate within a locus, modeling the effects of linkage disequilibrium, representing fractional allele counts from imputation, and managing permutation tests for p-values. Implementing these tests in a single, efficient framework has great practical value. Fast ASsociation Tests (Fast) addresses this need by implementing leading gene-based association tests together with conventional SNP-based univariate tests and providing a consolidated, easily interpreted report. Fast scales readily to genome-wide SNP data with millions of SNPs and tens of thousands of individuals, provides implementations that are orders of magnitude faster than original literature reports, and provides a unified framework for performing several gene based association tests concurrently and efficiently on the same data. Availability: https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Home  相似文献   

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Using Japanese literature, we created a consolidated list of host records of butterflies in Japan. The list used the host records described in eight major illustrated reference books, two checklists, and 14 other pieces of literature. The presence of larvae on plants, the observation of larvae eating plants or insects in the field were considered as host records. We collected all species recorded in Japan. Scientific, family, and Japanese names of butterflies were consolidated using the BINRAN database (http://binran.lepimages.jp/). Scientific and Japanese names of host plants were based on the YList database (http://ylist.info/). If scientific names of host plants were not found in YList, we used scientific names based on The Plant List (http://www.theplantlist.org/). Family names of host plants were based on the Catalogue of Life database (http://www.catalogueoflife.org/). Scientific, family, and Japanese names of host insects were based on the MOKUROKU database (http://konchudb.agr.agr.kyushu-u.ac.jp/mokuroku/) for Hymenoptera and the catalogue of the Paraneoptera of Japan published by the Entomological Society of Japan for Hemiptera. We also provided the references of each host record and the original names described in the referred literature. Two datasets, HostDB and ReferenceDB, were created to include 3600 records of butterfly larval hosts in Japan, along with scientific and Japanese names of each species and a literature list. These datasets will be useful for basic and applied biological studies of butterflies. Data files are stored in the Ecological Research Data Archives (http://db.cger.nies.go.jp/JaLTER/ER_DataPapers/) and available from http://hostbj.lepumus.net/. These datasets are published under the Creative Commons License Attribution-ShareAlike 4.0 (CC BY-SA, https://creativecommons.org/licenses/by-sa/4.0/).  相似文献   

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The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) is a free, one-stop web service for protein bioinformatic analysis. It currently offers 34 interconnected external and in-house tools, whose functionality covers sequence similarity searching, alignment construction, detection of sequence features, structure prediction, and sequence classification. This breadth has made the Toolkit an important resource for experimental biology and for teaching bioinformatic inquiry. Recently, we replaced the first version of the Toolkit, which was released in 2005 and had served around 2.5 million queries, with an entirely new version, focusing on improved features for the comprehensive analysis of proteins, as well as on promoting teaching. For instance, our popular remote homology detection server, HHpred, now allows pairwise comparison of two sequences or alignments and offers additional profile HMMs for several model organisms and domain databases. Here, we introduce the new version of our Toolkit and its application to the analysis of proteins.  相似文献   

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