IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences.
IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences.
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DOI：
10.1093/nar/gkv236
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发表时间：
2015-07-01
影响因子：
14.9
通讯作者：
Roche DB
中科院分区：
文献类型：
--
作者：
McGuffin LJ;Atkins JD;Salehe BR;Shuid AN;Roche DB
IntFOLD是一个独立的Web服务器，集成了我们领先的结构和功能预测方法。该服务器提供了一个简单的统一界面，旨在使生命科学家更容易访问复杂的蛋白质建模数据。服务器Web界面设计直观，并集成了一组复杂的定量数据，因此3D建模结果可以在单个页面上查看，并由非专业建模人员一目了然地解释。唯一需要输入到服务器的是目标蛋白质的氨基酸序列。在这里，我们描述了主要的性能和用户界面更新的服务器，其中包括一个集成的管道的方法：三级结构预测，全球和本地的3D模型质量评估，疾病预测，结构域预测，功能预测和蛋白质-配体相互作用的建模。该服务器已在众多CASP（蛋白质结构预测技术的关键评估）实验中进行了独立验证，并通过CAMEO（连续自动模型评估）项目进行了持续评估。IntFOLD服务器可从以下网址获得：http://www.reading.ac.uk/bioinf/IntFOLD/
IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions. The server has been independently validated during numerous CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being continuously evaluated by the CAMEO (Continuous Automated Model Evaluation) project. The IntFOLD server is available at: http://www.reading.ac.uk/bioinf/IntFOLD/
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影响因子：
14.9
作者：
Sillitoe I;Cuff AL;Dessailly BH;Dawson NL;Furnham N;Lee D;Lees JG;Lewis TE;Studer RA;Rentzsch R;Yeats C;Thornton JM;Orengo CA
通讯作者：
Orengo CA
影响因子：
2.9
作者：
Huang, Yuanpeng J.;Mao, Binchen;Aramini, James M.;Montelione, Gaetano T.
通讯作者：
Montelione, Gaetano T.
影响因子：
2.9
作者：
Kryshtafovych, Andriy;Fidelis, Krzysztof;Tramontano, Anna
通讯作者：
Tramontano, Anna
影响因子：
14.9
作者：
Roche DB;Buenavista MT;Tetchner SJ;McGuffin LJ
通讯作者：
McGuffin LJ
影响因子：
2.9
作者：
Schmidt T;Haas J;Gallo Cassarino T;Schwede T
通讯作者：
Schwede T