New Developments of Large-scale Automatic Protein Function Prediction using Graphical Learning Techniques

生命所需的大部分细胞活动都是由蛋白质进行的，其中一些已经被广泛研究多年。确切地知道这些分子在何时、何地以及如何起作用，对于医学和生物技术的应用是至关重要的。不幸的是，这种高级应用所需的细节水平只适用于典型细胞中的一小部分蛋白质;对于其中许多蛋白质，我们有一些关于其生物学的合理线索。此外，还有很大一部分我们几乎无法与我们对生物学的理解联系起来，即使我们相信它们存在。例如，在人类细胞中，这些蛋白质约占蛋白质的40%。显然，通过实验测试所有蛋白质以在最精细的细节水平上描述它们的功能是非常具有挑战性的。计算机程序可以通过利用已知的实验数据和一些蛋白质特征可以用于识别一些经过充分研究的功能单元的事实来帮助缩小检测的数量。随着时间的推移，托换算法变得越来越先进，但一些独立的研究表明，这一领域仍有很大的改进空间。阻碍进展的一个明显瓶颈是，目前所有的方法都分别解决蛋白质做什么以及在什么情况下做什么的问题。然而，有明确的证据表明，蛋白质在特定的细胞区室中进行分子活动，并与其他生物合作伙伴。拟议的项目建立在蛋白质功能预测的成功工作的基础上，以扩大我们的工具的范围和准确性。这些已经利用了存储在公共数据库中的大量异质实验数据，这些数据可以提供关于感兴趣的蛋白质的信息，例如，关于其与其他特征蛋白质的进化关系，以及与其物理相互作用或与其共调控的其他蛋白质的信息。这些不同来源的信息然后通过一些最流行的机器学习方法结合起来，这些方法在过去曾成功地应用于许多其他领域，如游戏，语音识别和电子邮件垃圾邮件过滤。在这里，我们试图更好地利用我们的系统中已经包含的信息，引入其他生物数据类型，以及探索新的和更智能的方法来组合它们。我们将利用我们的专业知识，为蛋白质结构和功能预测提供可靠和用户友好的在线工具，以便实验人员只需一台PC和一个标准的Web浏览器就可以轻松地使用和分析新的程序和预测。

A large fraction of the cellular activities required for life are carried out by proteins, some of which have been extensively studied over the years. Knowing exactly what these molecules do, when, where and how has been instrumental for medical and biotechnological use. Unfortunately the required level of details for such advanced applications is only available for a tiny fraction of the proteins in a typical cell; for many of them we have some reasonable clues about their biological. Moreover, there is also a substantial portion that we can barely link to our understanding of biology, even though we are confident that they exist. In human cells, for instance, these represent approximately 40% of the proteins.It is clearly very challenging to experimentally test all the proteins in order to describe their function at the finest level of details. Computer programs can help narrow down the number of assays to run by leveraging on known experimental data and on the fact that some protein features can be used to recognise some well-studied functional units. The underpinning algorithms have become more and more advanced over time, but a number of independent studies have shown that there is still a lot of room for improvement in this field. One clear bottleneck that hampers progress is that all current methods address separately the questions of what proteins do and in which context. However, there is clear evidence that proteins carry out molecular activities in specific cellular compartments and in concert with other biological partners.The proposed project builds on successful previous work on protein function prediction to expand the scope and accuracy of our tools. These already make use of a wide array of heterogeneous experimental data stored in public databases, which can give information about the protein of interest in terms of its evolutionary relationships to other characterized proteins, as well as of the other proteins it physically interacts with or it is co-regulated with, for instance. These diverse sources of information are then combined through some of the most popular machine-learning methods, which were successfully applied in the past in many other areas such as game-playing, speech recognition and e-mail spam filtering.Here we seek to make better use of the information already included in our system, to introduce additional biological data types, as well as to explore new and smarter ways of combining them. We will exploit our expertise in providing reliable and user-friendly online tools for protein structure and function prediction so that the new programs and predictions can be easily used and analyzed by experimentalists for their own research with just a PC and a standard web browser.