18-BBSRC-NSF/BIO : CIBR:Implementing an explicit phylogenetic framework for large-scale protein sequence annotation

蛋白质是执行我们基因组编码指令的主要分子机器。蛋白质最终塑造我们的细胞、组织、器官和身体对周围环境的反应，要么直接（如肌肉收缩），要么通过它们的功能输出（如沿树突产生神经冲动或动作电位的电信号）。因此，了解每种蛋白质的功能作用对许多科学领域的研究和发展至关重要，特别是生物学、医学和应用生物技术。下一代测序技术吞吐量的快速增长具有重要的影响，因为我们对生物体基因组进行测序并确定其编码的蛋白质的能力远远超过了我们通过实验表征蛋白质功能的能力。因此，对于每一个功能特征的蛋白质，现在有成千上万的蛋白质永远不会被实验表征。分子生物学越来越依赖于我们对相关序列进行计算分组的能力，以及将少数实验表征的蛋白质的功能注释转移到那些相关但尚未表征的蛋白质上的能力。有关蛋白质的知识已被收集并存储在公共数据库中，如UniProt，这是一个世界领先的蛋白质序列和功能资源。目前，UniProt中有超过1.5亿个序列，每两年翻一番。因此，开发新的可靠的计算方法来推断蛋白质功能是至关重要的，这些方法可以扩展到数十亿个序列。我们的目标是实现一个包含进化信息的注释系统，允许相应调整注释传输的级别，同时确保注释的可扩展性和速度满足当前和未来的需求。这个新的注释系统将整合目前用于生产世界级资源的两种现有方法中最具创新性的功能。基因本体（GO）联盟开发了一种软件，用于沿着系统发育树的特定分支对GO注释的增益和损失进行明确的进化建模，并将其应用于推断实验中未表征的蛋白质的GO注释。UniProt开发了UniRule系统，该系统应用注释“规则”，将蛋白质家族和结构域的信息（来自InterPro资源）与一系列其他类型的信息（如分类）相结合，以做出更精确和信息丰富的注释。我们的目标是创建一个融合了这两种方法的下一代大规模注释系统，并在UniProt资源中实现该注释系统，从而提高数据库中功能注释的质量，造福科学界。我们提出了实现这一目标的三个具体目标：(1)将现有的unirrule规则转换为明确的进化模型；(2)集成软件以将进化模型（TreeGrafter）应用到UniProt注释管道中；(3)开发软件以持续管理其他注释类型和蛋白质家族的新进化模型。结果将是一个基于明确进化原理的注释管道，这将使UniProt和GO管理过程之间的信息无缝共享，并大大提高公共数据库中推断蛋白质注释的准确性、全全性和信息性。

Proteins are the primary molecular machines that perform the instructions encoded in our genomes. Proteins ultimately shape the response of our cells, tissues, organs, and bodies to the surrounding environment, either directly (e.g. muscle contraction) or through their functional outputs (e.g. the electrical signals along the dendrites to produce a nerve impulse or action potential). Therefore, understanding the functional role(s) performed by each protein is critical to research and development in many areas of science, particularly biology, medicine and applied biotechnology. The rapid increase in throughput of next-generation sequencing technologies has important ramifications, in that our ability to sequence an organism's genome and determine the proteins it encodes far out paces our ability to experimentally characterise the function of a protein. Thus, for every functionally characterised protein, there are now many thousands of proteins that will never be experimentally characterised. Molecular biology increasingly relies on our ability to computationally group related sequences and to transfer functional annotations from the few experimentally characterised proteins, to those related, yet uncharacterised, proteins. Knowledge on proteins has been collected and stored in public databases like UniProt, a world-leading resource on protein sequences and function. Currently, there are over 150 million sequences in UniProt, with the number doubling every two years. Therefore, it is crucial to develop new and reliable computational methods for inferring protein function that can be scaled to billions of sequences. We aim to implement an annotation system that incorporates evolutionary information, permitting the level of annotation transfer to be tuned accordingly, while also ensuring scalability and speed of annotation that meets current and future demands. This new annotation system will integrate the most innovative features present in two pre-existing methods that are currently used in producing world-class resources. The Gene Ontology (GO) Consortium has developed software for explicit evolutionary modelling of GO annotation gain and loss along specific branches of phylogenetic trees, and has applied it to inferring GO annotations for experimentally uncharacterised proteins. UniProt has developed the UniRule system that applies annotation "rules" that combines information on protein families and domains (from the InterPro resource), with a range of other types of information like taxonomy, to make more precise and informative annotations. Our goal is to create a next-generation, large-scale annotation system that merges the two approaches, and to implement this annotation system in the UniProt resource, thereby increasing the quality of functional annotations in the database for the benefit of the scientific community. We propose three specific aims to achieve this goal: (1) convert existing UniRule rules into explicit evolutionary models, (2) integrate software to apply the evolutionary models (TreeGrafter) into the UniProt annotation pipeline, and (3) develop software for ongoing curation of new evolutionary models of additional annotation types and protein families. The result will be an annotation pipeline based on explicit evolutionary principles, which will enable seamless sharing of information between the UniProt and GO curation processes, and substantially improve the accuracy, comprehensiveness and informativeness of inferred protein annotations in public databases.