BBSRC-NSF/BIO: An AI-based domain classification platform for 200 million 3D-models of proteins to reveal protein evolution

BBSRC-NSF/BIO：基于人工智能的域分类平台，可用于 2 亿个蛋白质 3D 模型，以揭示蛋白质进化

• 批准号: BB/Y001117/1
• 负责人: Christine Orengo
• 金额: $ 34.21万
• 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: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY001117%2F1
• 关键词: BBSRC; NSF; BIO; AI; based

Proteins play a major role in most important processes in life, such as the digestion of nutrients, immune response, and cellular regulation. They are comprised of long polymers that fold into compact globular forms known as domains. Most proteins have at least two domains and some are composed of dozens. Domains tend to be associated with specific functions, although sometimes an important function will result from combining multiple domains. 3D structure data and models are particularly valuable for detecting the pockets and surface features linked to domain function. Determining the structure and orientations of the constituent domains is important for understanding the overall function of the protein and the dynamic conformational changes linked to that. Until recently, structural data for proteins was very sparse, with <1% of all known proteins experimentally characterised. Whilst structures can be predicted with reasonable accuracy when the structure of a close relative is known, for a significant proportion of proteins such data did not exist. Even for important organisms like humans or wheat, <50% of proteins had structural data accurate enough to understand the structural impacts of changes in the genes coding the proteins.This situation changed dramatically in 2021 when DeepMind's AlphaFold AI system succeeded in predicting protein structures of comparable quality to experimentally characterised proteins. In August 2022, DeepMind released >214 million protein structures for all known proteins. Whilst recent analyses showed that in some cases AlphaFold models are not accurate enough for detailed studies, largely because the data needed to make the prediction is still too sparse, the AlphaFold data still massively increases the amount of high-quality structural data available for understanding the mechanisms by which proteins function.Identifying constituent domains in a protein is not trivial. This project will exploit powerful AI technologies to more accurately predict domain boundaries. Preliminary studies are already showing significant improvements. We will apply multiple domain detection algorithms independently developed by two world-renowned protein domain classification teams (ECOD, CATH), both of whom have long track records in successfully automating domain detection. Their methods employ complementary strategies that can be combined to give a consensus prediction where agreement in assignments reflects higher confidence levels. Another major challenge will be coping with the scale of the data. Even allowing for a 50% loss due to poor model quality, the data represents a >200-fold increase in the data already classified in these evolutionary resources. An existing domain assignment and classification pipeline (3D-SCAFOLD) built to integrate experimental domain data from two resources (SCOP, CATH) will be re-engineered to incorporate ECOD (which is much more comprehensive than SCOP) and capture the vast predicted data from AlphaFold. This will require new and more efficient workflows that parallelise the processes. Furthermore, the pipeline will be more complex as additional steps will be necessary to determine the model quality and remove poor models. We will also adapt access to the webpages and APIs to allow users to request targeted subsets and perform more complex queries needed by the increase in the scale of the data.In addition, we expect that many large, more complex multidomain proteins will be very challenging, leading to discrepancies between the results provided by the different resources. We will hold workshops for the teams to agree on consensus assignments.To cope with the scale of the data, we will initially target proteins in pathogenic organisms, crops essential for food security, and protein families linked to human health and well-being, including enzyme families important for environmental remediation and the production of commercially valuable compounds.

蛋白质在生命中最重要的过程中发挥着重要作用，例如营养物质的消化、免疫反应和细胞调节。它们由长聚合物组成，折叠成紧凑的球状形式，称为域。大多数蛋白质至少有两个结构域，有些由数十个结构域组成。域往往与特定功能相关，尽管有时组合多个域会产生重要的功能。 3D 结构数据和模型对于检测与域功能相关的口袋和表面特征特别有价值。确定组成域的结构和方向对于理解蛋白质的整体功能以及与之相关的动态构象变化非常重要。直到最近，蛋白质的结构数据还非常稀疏，只有不到 1% 的已知蛋白质经过实验表征。虽然当近亲的结构已知时，可以以合理的精度预测结构，但对于很大一部分蛋白质来说，这样的数据并不存在。即使对于人类或小麦等重要生物体，<50% 的蛋白质也具有足够准确的结构数据，足以了解编码蛋白质的基因变化的结构影响。这种情况在 2021 年发生了巨大变化，当时 DeepMind 的 AlphaFold AI 系统成功预测了蛋白质的蛋白质结构。与实验表征的蛋白质的质量相当。 2022 年 8 月，DeepMind 发布了所有已知蛋白质的超过 2.14 亿个蛋白质结构。虽然最近的分析表明，在某些情况下 AlphaFold 模型对于详细研究来说不够准确，很大程度上是因为进行预测所需的数据仍然太稀疏，但 AlphaFold 数据仍然大大增加了可用于理解结构的高质量结构数据的数量。蛋白质发挥作用的机制。识别蛋白质的组成域并非易事。该项目将利用强大的人工智能技术来更准确地预测领域边界。初步研究已经显示出显着的改进。我们将应用两个世界著名的蛋白质域分类团队（ECOD、CATH）独立开发的多个域检测算法，这两个团队在成功实现域检测自动化方面拥有长期的成功记录。他们的方法采用互补策略，这些策略可以组合起来给出一致的预测，其中分配的一致性反映了更高的置信水平。另一个主要挑战是应对数据规模。即使考虑到由于模型质量差而导致 50% 的损失，这些数据也代表这些进化资源中已分类的数据增加了 200 倍以上。将重新设计现有的域分配和分类管道 (3D-SCAFOLD)，用于集成来自两个资源（SCOP、CATH）的实验域数据，以纳入 ECOD（比 SCOP 更全面）并捕获来自阿尔法折叠。这将需要新的、更高效的工作流程来并行处理。此外，管道将更加复杂，因为需要额外的步骤来确定模型质量并删除不良模型。我们还将调整对网页和 API 的访问，以允许用户请求目标子集并执行数据规模增加所需的更复杂的查询。此外，我们预计许多大型、更复杂的多域蛋白质将非常具有挑战性，导致不同资源提供的结果之间存在差异。我们将为各团队举办研讨会，以就共识任务达成一致。为了应对数据规模，我们将首先针对病原生物、对粮食安全至关重要的作物以及与人类健康和福祉相关的蛋白质家族中的蛋白质，包括对于环境修复和具有商业价值的化合物的生产非常重要的酶家族。