Ab Initio Prediction of Protein Localization

就像在真实的地产中一样，在有机体中，蛋白质的位置很重要。了解位置为决定如何测试功能提供了基础。特定细胞产生的蛋白质赋予它特殊的特性。 同样，不同的亚细胞区室在细胞中发挥不同的功能。我们有不到4万种或目前已知的120万种蛋白质的功能的实验线索。该项目开发的工具将通过考虑蛋白质的结构，特别是有助于确定功能的表面成分，来预测蛋白质的定位。第一个任务将是开发使用神经网络来预测已知结构的蛋白质定位的方法。该方法将推广到蛋白质的未知结构的预测表面残留物，衍生的方法已经在手。将开发与位置相关的已知序列基序的数据库，并将其整合到预测方法中。定位图谱将有助于绘制关于任何蛋白质生命周期内功能的知识。定位是为蛋白质分配功能的一个很好的起点。该地图集也是分子生物学和信息学教学的工具。地图集和工具将在开发完成后通过服务器提供。

Just as in real estate, in an organism the location of a protein is important. Knowing the location provides a basis for deciding how to test for functionality. The proteins a specific cell makes give it a special character. Similarly, different sub-cellular compartments do different functions in the cell. We have experimental clues about function for fewer than 40,000 or the currently known 1.2 million proteins. The tools developed in this project will enable the predication of the localization of proteins by considering its structure, especially its surface components which should aid in determining function. The first task will be to develop methods using neural networks to predict localization for proteins of know structure. The method will be generalized to proteins of unknown structure by predicating surface residues, derivative of a method already in hand. A database of know sequence-motifs related to location will be developed and integrated into the prediction method. An atlas of localization will help map knowledge about function over the lifetime of any protein. Localization is an excellent starting point for assigning function to proteins. The atlas is also a vehicle for teaching both molecular biology and informatics. The atlas and tools will be available from servers as developed.