3D-Gateway - Gateway to protein structure and function

3D-Gateway - 蛋白质结构和功能的门户

基本信息

批准号：
BB/S020144/1
负责人：
Christine Orengo
金额：
$ 37.37万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FS020144%2F1
关键词：
3D Gateway protein structure function

项目摘要

Proteins comprise long chains of organic molecules that fold into compact globular 3-dimensional structures. Knowing this structure can give very valuable insights into the clefts, pockets or other surface features important for binding other molecules in the cell eg small molecules or proteins. Knowledge of the structure is also essential for designing drugs that bind to these features and inhibit the protein and can also help in understanding whether mutations in the protein's residues affect its stability or function, leading to disease. Experimentally determining the structure can be challenging, which is why only a small percentage of known proteins (~145,000 out of 120 million) have been characterised. However, powerful computational methods have been developed that predict protein structures by inheriting structural information from evolutionary related proteins whose structures are known. These prediction techniques have been made even more powerful, recently, as new ways of exploiting the evolutionary data have been found that more accurately constrain contacts in the protein. Applying these techniques, structures can be predicted for a large proportion of uncharacterised proteins. For example, for human proteins about 5% of the structures are known but a further 88% can be modelled, some to very high accuracy, thereby providing important frameworks for designing drugs to treat human diseases. When inheriting structural data between distant relatives one has to be much more cautious and most prediction methods return a confidence score for the models produced. This project will build an infrastructure (3D-Beacons) that aggregates experimentally determined structures with predicted structures generated by groups applying different algorithms. This will be done for proteins from selected organisms relevant to food security and human health - some will be pathogenic bacteria that threaten humans or animals/crops. We will use this data to annotate proteins in the UniProt resource, widely used by more than 750,000 unique users each month. Since the prediction methods reside in many different labs, by pooling the data in this way we can significantly increase the number of proteins with structural data. In addition, combining models built by independent algorithms allows us to compare 3D-models to find which parts agree regardless of method and which parts vary between methods and are clearly harder to model. Therefore, we will use this aggregated data to research the best strategies for calculating model quality at each position in the protein.We will build web pages to display the known and predicted structures for a given protein. It can be difficult to determine the structure of the whole protein so, where appropriate, we will display both experimental and predicted structures, taking great care to label the structures with information on the source (eg method used) and reliability of the data (eg confidence).We will also use our 3D-Beacons infrastructure to aggregate information on known and predicted functional sites on the protein structure and display this data on web pages, together with information on source and confidence. The site data mapped onto structure will be particularly helpful for developing rules that allow us to gauge whether a protein with no experimental characterisation has the same function as an evolutionary related protein with experimental characterisation. Relatives sharing the same function should have the same key functional site residues. With these rules we will be able to provide structural and functional annotations for millions of proteins in UniProt. The new data will represent a tenfold or more increase in the number of UniProt sequences which have structural and functional site information. UniProt is also widely used by researchers in industry and thus this expansion in information will have a very significant impact.

蛋白质由长链有机分子组成，可折叠成紧凑的球状 3 维结构。了解这种结构可以为了解裂缝、口袋或其他表面特征提供非常有价值的见解，这些特征对于结合细胞中的其他分子（例如小分子或蛋白质）很重要。了解该结构对于设计与这些特征结合并抑制蛋白质的药物也至关重要，并且还可以帮助了解蛋白质残基的突变是否会影响其稳定性或功能，从而导致疾病。通过实验确定其结构可能具有挑战性，这就是为什么只有一小部分已知蛋白质（1.2 亿个蛋白质中的约 145,000 个）得到了表征。然而，已经开发出强大的计算方法，通过继承结构已知的进化相关蛋白质的结构信息来预测蛋白质结构。最近，随着发现利用进化数据的新方法可以更准确地限制蛋白质中的接触，这些预测技术变得更加强大。应用这些技术，可以预测大部分未表征的蛋白质的结构。例如，对于人类蛋白质，大约 5% 的结构是已知的，但另外 88% 的结构可以建模，其中一些精度非常高，从而为设计治疗人类疾病的药物提供重要框架。当继承远亲之间的结构数据时，人们必须更加谨慎，大多数预测方法都会返回所生成模型的置信度得分。该项目将构建一个基础设施（3D 信标），将实验确定的结构与应用不同算法的小组生成的预测结构聚合在一起。这将针对来自与粮食安全和人类健康相关的选定生物体的蛋白质进行，其中一些是威胁人类或动物/农作物的病原菌。我们将使用这些数据来注释 UniProt 资源中的蛋白质，该资源每月被超过 750,000 名唯一用户广泛使用。由于预测方法存在于许多不同的实验室，通过以这种方式汇集数据，我们可以显着增加具有结构数据的蛋白质的数量。此外，通过组合由独立算法构建的模型，我们可以比较 3D 模型，以找出哪些部分无论采用哪种方法都是一致的，哪些部分因方法而异并且显然更难建模。因此，我们将使用这些聚合数据来研究计算蛋白质中每个位置的模型质量的最佳策略。我们将构建网页来显示给定蛋白质的已知和预测结构。确定整个蛋白质的结构可能很困难，因此，在适当的情况下，我们将显示实验结构和预测结构，并非常小心地用有关来源（例如使用的方法）和数据可靠性（例如我们还将使用 3D 信标基础设施来聚合蛋白质结构上已知和预测的功能位点的信息，并将这些数据以及来源和置信度信息显示在网页上。映射到结构上的位点数据对于制定规则特别有帮助，这些规则使我们能够判断未经实验表征的蛋白质是否与具有实验表征的进化相关蛋白质具有相同的功能。具有相同功能的亲属应具有相同的关键功能位点残基。有了这些规则，我们将能够为 UniProt 中的数百万种蛋白质提供结构和功能注释。新数据将代表具有结构和功能位点信息的 UniProt 序列数量增加十倍或更多。 UniProt 也被工业研究人员广泛使用，因此信息的扩展将产生非常重大的影响。