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

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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.