ECOD: Large scale classification of predicted and experimental protein structures

ECOD：预测和实验蛋白质结构的大规模分类

基本信息

批准号：
10659763
负责人：
Richard Dustin Schaeffer
金额：
$ 34.44万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10659763
关键词：
3-Dimensional Acceleration Adopted Amino Acid Sequence Artificial Intelligence Base Sequence Benchmarking Biochemical Biological Biological Assay Cataloging Classification Code Collaborations Communities Computing Methodologies Data Data Set Data Sources Databases Dedications Deposition Development Disease Electron Microscopy European Evolution Family Future Genetic Growth Homologous Gene Human Infrastructure Laboratories Lead Manuals Membrane Methods Modeling Molecular Biology NMR Spectroscopy Pathogenicity Pathogenicity Island Peptide Signal Sequences Periodicals Property Protein Family Proteins Science Sequence Homology Site Structure System Techniques Tertiary Protein Structure Testing Time Universities Update Vibrio Virulence Factors Work Workload X-Ray Crystallography candidate identification caspase 14 comparative genomics computational pipelines data quality database schema deep learning experimental study human disease human pathogen improved learning network model organism novel pathogen pathogenic bacteria protein data bank protein function protein structure protein structure prediction structural biology success three dimensional structure tool web interface web page web site

项目摘要

Project Summary Classification of protein domains have historically served to contextualize the 3D structural data collectively generated by experimental structure determination methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. Our database, Evolutionary Classification of protein Domains (ECOD), has served the biological community for seven years cataloguing evolutionary relationships between domains from experimental structures. The recent advent of high-accuracy structure prediction methods, such as AlphaFold (AF) and RoseTTAFold (RF), and the consequent release of 1 million predicted structures in AlphaFold Database (AFDB) heralds a paradigm shift in structural biology and domain classification. The rate of structure deposition is expected to jump between a hundred to a thousand- fold. We propose to take advantage of this revolution and transform ECOD into a comprehensive classification of the entire protein university using sequence, structure, and functional evidence. By simultaneously classifying experimental and predicted structures of proteins from model organisms and human pathogens, our classification will help the scientific community to critically evaluate structure models and utilize the evolutionary information to discover and experimentally characterize protein function. Classifying AF models challenges the ECOD pipeline by a 50-fold increase in the workload and by the significant fraction of non-globular and low-quality regions in the models. Thus, our first Aim is to upgrade ECOD’s infrastructure and develop methods to identify single domains from AF models and to integrate sequence, structure, and functional site similarities into our automatic classification. Compared to the current ECOD workflow that relies on human experts for structure-and- function-based classification, these improvements will drastically decrease the need for manual curation and will allow us to achieve our second Aim, i.e., classifying domains of over 1 million released AF models into ECOD via a combination of computational pipelines and minimal manual efforts (0.25%  1% cases). Utilizing the deluge of AF models, the new automatic pipeline, and expertise of human curators, we expect both to significantly improve ECOD and to evaluate the quality of AF models by (1) covering all known protein families in Pfam, (2) confirming remote homology via evolutionary intermediates, (3) comparing evolutionarily related experimental and predicted structures, and (4) resolving errors and inconsistency through periodic quality checks. Finally, we will take the lead in making functional discoveries for biomedically important proteins classified by ECOD in our third Aim, studying virulence factors (VFs) in bacterial pathogens modelled by AFDB or studied by our experimental collaborators, the Orth lab. Fast evolving VFs were a challenge for structure prediction or functional inference by sequence. We will identify candidate VFs in two dozen bacterial pathogens, obtain their structure models, and infer their function using similarities to known proteins in structure and functional sites. Promising hypotheses will be tested experimentally in the Orth lab through biochemical and genetic assays.

项目概要蛋白质结构域的分类历来用于将 3D 结构数据集中起来实验结构测定方法，如X射线晶体学、核磁共振波谱、我们的数据库，蛋白质结构域的进化分类（ECOD），已服务于生物学。社区七年来从实验结构中对领域之间的进化关系进行了分类。最近出现的高精度结构预测方法，例如 AlphaFold (AF) 和 RoseTTAFold (RF)，以及随后在 AlphaFold 数据库 (AFDB) 中发布了 100 万个预测结构，预示着结构领域的范式转变生物学和领域分类的结构沉积率预计将在一百到一千之间跳跃。我们建议利用这场革命，将 ECOD 转变为综合分类。通过同时对实验进行分类，使用序列、结构和功能证据来分析整个蛋白质大学。并预测来自模式生物和人类病原体的蛋白质结构，我们的分类将有助于科学社区批判性地评估结构模型并利用进化信息来发现和实验表征蛋白质功能。对 AF 模型进行分类对 ECOD 流程提出了挑战，工作量增加了 50 倍，并且大部分因此，我们的首要目标是升级 ECOD 的基础设施并进行开发。从 AF 模型中识别单个域并整合序列、结构和功能位点相似性的方法与当前依赖人类专家进行结构和-的ECOD工作流程相比。基于功能的分类，这些改进将大大减少手动管理的需要，并使我们能够实现我们的第二个目标，即通过组合将超过 100 万个已发布的 AF 模型的域分类为 ECOD 利用大量的 AF 模型，新的计算流程和最少的手动工作（0.25% - 1% 的情况）。自动管道和人类策展人的专业知识，我们期望能够显着改善 ECOD 并评估通过 (1) 涵盖 Pfam 中所有已知的蛋白质家族，(2) 通过进化确认远程同源性来确保 AF 模型的质量中间体，（3）比较进化相关的实验结构和预测结构，以及（4）解决错误和最后，我们将通过定期质量检查来率先进行功能发现。我们的第三个目标是研究细菌病原体中的毒力因子 (VF)，通过 ECOD 分类的生物医学重要蛋白质由 AFDB 建模或由我们的实验合作者研究的快速发展的 VF 对我们来说是一个挑战。我们将通过序列识别候选 VF，获得它们的结构模型，并利用与已知蛋白质在结构和功能位点上的相似性来推断它们的功能。有希望的假设将在奥尔斯实验室通过生化和遗传分析进行实验测试。