Identification of allosteric and orthosteric ligand regulation sites using protein structure prediction.

我的主要工作重点将是在Sternberg博士的实验室开发新的蛋白质结构预测计算方法，以期应用这些工具来支持Mann博士实验室的实验工作。曼恩博士正在研究半胱氨酸反应性小分子，这种小分子可以与蛋白质结合，从而充当生物功能的探针。这些蛋白质的结构可能无法通过实验解决，这就需要建立模型。目前存在多种公开实现的蛋白质结构预测算法。这些方法在一个称为结构预测关键评估（CASP）的盲测试中进行评估。传统上，基于序列同源性的算法，旨在识别模板和模型之间的进化序列相似性，表现最好。一个使用这种方法的流行服务器Phyre已经在Sternberg博士的实验室中建立和维护。由于实验蛋白不能保证与已知结构相关，因此我建议建立一个Phyre的补充，以便在没有同源性假设的情况下进行建模。方法我想从以下两个思路开始我对蛋白质结构预测算法的研究。线程联系。这是一种相对较新的蛋白质结构预测方法，在最近几轮CASP中取得了成功。该方法的主要思想是预测蛋白质的接触图——相互接触的残基——作为预测其结构的代理。采用动态规划方法对模板和目标接触矩阵的特征向量进行对齐。多序列比对（MSA）的蛋白质，其同源物是用来确定接触矩阵。这是将这种方法应用于没有足够同源物的蛋白质的限制。我正计划研究一种不需要MSA作为输入的变体。高斯混合模型。这些是用来提供一个基于概率的分数，以确定某种形状的蛋白质片段在自然界中出现的可能性。这些分数可以纳入动态规划对准过程中使用的接触线程。结果新的蛋白质结构预测方法不太可能像传统的模板建模一样准确，因为目标具有接近的已知同源物。如果这种新方法比现有的方法在具有很少或没有已知同源物的蛋白质上表现得更好，那么它将被认为是成功的。性能将在一组具有代表性的PDB结构上进行评估，这些结构将从训练数据中排除，以及在CASP数据集上进行评估。新方法将作为网络服务提供给公众使用。它将用于为上述实验研究建立蛋白质模型。现有的变构和正构结合位点的方法将用于识别配体结合区域。

The main focus on my work will be developing novel computational methods of protein structure prediction in Dr Sternberg's lab, with the view to apply those tools to support the experimental work of Dr Mann's lab. Dr Mann is researching cystein reactive small molecules that can bind to proteins, thus acting as probes of biological function. The proteins may not have their structures solved experimentally, which brings out the need for modelling.There currently exist multiple algorithms for protein structure prediction with publicly available implementations. These methods are evaluated in a blind test called Critical Assessment of Structure Prediction (CASP). Traditionally, algorithms based on sequence homology, that aim to identify evolutionary sequence similarity between the template and the model, have performed best. A popular server that uses this approach, Phyre, has been built and maintained in Dr Sternberg's lab. Since the experimental proteins are not guaranteed to be related to well known structures, I am proposing to build a supplement to Phyre that would allow modelling without the homology assumption.MethodsI would like to start my research of protein structure prediction algorithms by investigating the following two ideas.1. Contact threading. This is a relatively new method of protein structure prediction that has demonstrated success in the recent rounds of CASP. The main idea of the method is predicting the contact map of a protein - the residues that are in contact with each other - as a proxy to predicting its structure. Eigenvectors of template's and target's contact matrices are aligned with dynamical programming. Multiple sequence alignment (MSA) of a protein to its homologues is used to determine the contact matrices. This is a limitation for applying this method to proteins that do not have enough homologues. I am planning to research a variant that does not require an MSA as its input.2. Gaussian Mixture Models. These are used to provide a probability based score of how likely a protein fragment of a certain shape is to occur in nature. These scores can be incorporated into dynamical programming alignment procedure that is used in contact threading.ResultsIt is unlikely that the new method of protein structure prediction will be as accurate as traditional template modelling for targets that have close well known homologues. The new method will be judged a success if it performs better than the existing methods on proteins that have few or none well known homologues. Performance will be evaluated on a representative set of PDB structures that will be excluded from the training data, as well as on CASP data set.The new method will be made available for public use as a web service. It will be used to model proteins for the experimental research outlined above. Existing methods for allosteric and orthosteric binding sites will be used to identify ligand binding regions.