Computational identification of protein-protein interactions

蛋白质是极其重要的生物分子。除了许多重要的结构作用外，它们还负责活细胞内的大部分活性生化功能和分子过程。几乎所有的蛋白质都是通过结合其他分子来作为生物系统的组成部分，并且大多数蛋白质都是作为“分子机器”或在优雅的“生产线”中协同工作的，例如信号传导途径，以执行复杂的生物功能。这些蛋白质相互作用在对抗外来蛋白质（如病毒感染）方面也很重要。大约60%的蛋白质参与某种蛋白质组装或“复合物”。这些蛋白质复合物在大多数细胞过程中发挥作用，现代生物学现在能够通过基因组和后基因组科学建立细胞蛋白质相互作用的连接部分列表。然而，在大多数情况下，我们并不了解各种蛋白质是如何特异性地识别它们的特定伴侣的。我们所知道的是，为了形成复合物，单个蛋白质必须与有限数量的特定伙伴接触（“结合”）。我们要研究的正是控制这种绑定“特异性”的规则。复合物中的结合是蛋白质三维结构中特定接触的结果。我们建议确定结合的关键区域（称为“接口”），将它们与非结合区域区分开来。推断界面区域内相互作用的强度可能有助于确定哪些氨基酸对结合最重要。为了实现我们计算识别蛋白质结合界面的目标，我们建议开发复杂的计算方法来描述界面上的进化与蛋白质上非相互作用位点的进化的不同之处。这些模型将在特定地点寻找进化的相关性。我们将检查从一系列相互作用和非相互作用蛋白质中获取的序列数据，以开发我们的复杂而严格的模型来解释这一进化过程。通过迭代改进和简化这个替代模型，我们将逐步提高我们区分相互作用和非相互作用位置的能力，使我们能够更好地识别相互作用的蛋白质和它们相互作用的特定界面。由此产生的模型将为研究生物系统提供一个强大的新计算工具，这在该领域迄今为止一直缺乏。通过使用建立在已建立的统计方法上的系统发育方法，我们将为这种类型的分析带来新的严格程度，并尽可能地利用我们序列中的信息。我们将应用该工具来研究酵母中的相互作用网络，识别新的潜在相互作用并识别实验方法中的错误。我们将与实验合作者一起确认这些计算推断，并进一步改进我们的模型。

Proteins are extremely important biological molecules. In addition to numerous vital structural roles, they are responsible for the majority of active biochemical functions and molecular processes within living cells. Nearly all proteins work as components of a biological system by binding other molecules, and most function in concert with others, as 'molecular machines' or in elegant 'production lines', such as signalling pathways, to carry out complex biological functions. These protein interactions are also important in combating foreign proteins, such as from a viral infection. Approximately 60% of proteins take part in some kind of protein assembly or 'complex'. These protein complexes play a role in the majority of cellular processes, and modern biology is now able to build the connecting parts list of cellular protein interactions via genomic and post-genomic science. However, in the majority of cases, we don't understand how the various protein specifically recognise their specific partners. What we do know is that in order to form complexes, individual proteins must make contact with ('bind') a limited number of specific partners. It is the rules that control this 'specificity' for binding that we propose to investigate. Binding in complexes is the result of specific contacts in the context of proteins' three-dimensional structures. We propose to determine the key regions for binding (termed 'interfaces'), distinguish them from non-binding regions. The strength of inferred interactions within the interface regions may help determine which amino acids are most important for binding. To achieve our goal of computationally identifying protein binding interfaces, we propose to develop sophisticated computational methods that describe how evolution at interfaces differs from that occurring at non-interacting site on proteins. These models will look for correlations in evolution at specific sites. We will examine sequence data taken from a range of interacting and non-interacting proteins to develop our a sophisticated and rigorous model to explain this evolutionary process. By iteratively improving and simplifying this substitution model we will progressively improve our ability to discriminate between interacting and non-interacting positions, enabling us to better identify both interacting proteins and the specific interfaces by which they interact. The resultant model will provide a powerful new computational tool for studying biological systems, which until now has been lacking in the field. By using phylogenetic methods that are founded on established statistical methodology, we will bring a new degree of rigour to this type of analysis and make the best possible use of information held within our sequences. We will apply the tool to investigate interaction networks in yeast, identifying new potential interactions and to identify errors in experimental methods. We will work with experimental collaborators to confirm these computational inferences and further improve our models.