Predictions of structure/function by PredictProtein

描述(由申请人提供)： Prodicate Protein(PP)是第一个用于蛋白质结构预测的互联网服务器，它于1992年在EMBL上线。从那时起，它也一直是使用最广泛的结构预测服务器。自1999年以来，前独裁蛋白质在哥伦比亚大学没有财政和公共支持的情况下运行。有限的CPU资源阻碍了我们应用当前最好的方法；有限的人力资源阻碍了我们让分子生物学家更容易地获得结果。PP与大多数其他资源在两个方面不同。首先，它尝试将各种工具合并到一个报告中。其次，许多方法是PP所独有的，例如用于预测二级结构、溶剂可及性和跨膜螺旋的PHD和PROF方法。在这里，我们提出了各种技术和科学的解决方案，以改善前决定蛋白的功能。(1)技术解决方案涉及工作和数据处理、数据库更新、用户界面、网页布局、成果展示以及直接链接原始资源。(2)方法的系统组合需要在相同的任务上并行地评估这些方法，例如，信号肽预测应以何种概率水平凌驾于膜预测之上。我们的主要重点将是改进膜螺旋蛋白的预测，开发预测β膜蛋白的方法，以及使用结构预测来更准确地推断功能信息。最近解决的高分辨率结构表明，所有现有的方法都被高估了，因此改进膜预测变得尤为紧迫。我们希望，现有方法和新方法的结合，以及对各自使用的调整的改进，将大大提高预测精度。为了预测β-膜蛋白，我们希望探索基于神经网络和类似系统的新预测方法与实现这些蛋白质中观察到的语法的马尔科夫模型的组合。作为利用结构信息提高推断功能可靠性的一个特殊例子，我们建议研究酶活性的保守性。

DESCRIPTION (provided by applicant): PredictProtein (PP) was the first Internet server for protein structure prediction when it went online in 1992 at EMBL. Ever since it has also been the most widely used structure prediction server. Since 1999, PredictProtein runs without financial public support at Columbia University. Limited CPU resources, prevent us from applying the best current methods; limited human resources prevent us from making the results more readily available to molecular biologists. PP differs from most other resources in two ways. Firstly, it tries merging a variety of tools into one single report. Secondly, a number of methods are unique to PP, e.g. the PHD and PROF methods for predictions of secondary structure, solvent accessibility, and transmembrane helices. Here, we propose a variety of technical and scientific solutions improving the functionality of PredictProtein. (1) The technical solutions address job and data handling, database update, user interface, web page layout, presentation of results, and directly linking original resources. (2) The systematic combination of methods requires evaluating these in parallel on identical tasks, e.g., at which level of probability should a signal peptide prediction override the membrane prediction. Our major focus will be on improving predictions for membrane helical proteins, developing methods predicting beta-membrane proteins, and on using structure predictions to more accurately infer functional information. Improving membrane predictions has become particularly urgent, since the recently solved high-resolution structures revealed that all existing methods were over-estimated. We hope that a combination of existing and new methods and a refinement of the respective alignments used will considerably improve prediction accuracy. To predict beta-membrane proteins, we want to explore a combination of novel prediction methods based on neural networks and similar systems with a Markovian-like model that implements the observed grammar in these proteins. As a particular example for using structural information to improve the reliability of inferring function, we propose to investigate the conservation of enzymatic activity.