Improve predictions of structure/function by Protein

描述（由申请人提供）： PredictProtein（PP）是第一个用于蛋白质结构预测的互联网服务器，于1992年在EMBL上线。从那时起，它也是使用最广泛的结构预测服务器。自1999年以来，PredictProtein在没有财政公共支持的情况下在哥伦比亚大学运行。有限的CPU资源使我们无法应用当前最好的方法;有限的人力资源使我们无法使分子生物学家更容易获得结果。PP在两个方面与大多数其他资源不同。首先，它尝试将各种工具合并到一个报表中。 其次，许多方法是PP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解决方案，以改善PredictProtein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、结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评估这些方法，例如，在该概率水平下信号肽预测应该优先于膜预测。我们的主要重点将是改善膜螺旋蛋白的预测，开发预测β-膜蛋白的方法，并使用结构预测更准确地推断功能信息。改进膜预测已经变得特别紧迫，因为最近解决的高分辨率结构显示，所有现有的方法都被高估了。我们希望现有的和新的方法相结合，并细化各自的路线使用将大大提高预测精度。为了预测β-膜蛋白，我们希望探索一种基于神经网络和类似系统的新型预测方法与类似马尔可夫模型的组合，该模型在这些蛋白质中实现了所观察到的语法。作为一个特殊的例子，使用结构信息，以提高推断功能的可靠性，我们建议调查酶活性的保护。

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.