PROTEAN-CR: Proteomics Toolkit for Ensemble Analysis in Cancer Research

PROTEAN-CR：用于癌症研究中整体分析的蛋白质组学工具包

• 批准号: 10188196
• 负责人: Lydia E. Kavraki
• 金额: $ 40.21万
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188196
• 关键词: Accounting; Address; Adopted; Algorithms; Binding; Biological; Cancer Center; Cancer Diagnostics; Cellular immunotherapy; Clinical Research; Collaborations; Communities; Complement; Complex; Computer software; Computer-Aided Design; Computers; Country; Custom; Cyclic Peptides; Data; Data Aggregation; Data Analytics; Data Science; Databases; Development; Dimensions; Disease; Docking; Drug resistance; Estrogen Receptors; Evolution; Funding; Genetic Polymorphism; Goals; Greek; HLA Antigens; Intuition; Letters; Ligand Binding; Ligands; Malignant Neoplasms; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Molecular Structure; Mutation; Mythology; Nature; Occupations; Peptides; Phosphopeptides; Phosphorylated Peptide; Play; Post-Translational Protein Processing; Process; Protein Analysis; Protein Conformation; Proteins; Proteomics; Public Health; RNA Editing; Research; Research Personnel; Resources; Role; Running; Sampling; Structural Models; Structure; System; T-Lymphocyte; The Cancer Genome Atlas; Tumor Antigens; Variant; Visualization; Work; anticancer research; base; cancer immunotherapy; cancer therapy; computational pipelines; computerized tools; data acquisition; design; driver mutation; drug discovery; experience; flexibility; improved; in silico; innovation; interest; machine learning method; malignant breast neoplasm; melanoma; multi-scale modeling; novel diagnostics; novel therapeutics; peptide based vaccine; programs; prototype; receptor; scale up; screening; three dimensional structure; tool; tumor; user-friendly; vaccine development; web app

Project Summary Understanding protein–ligand molecular interactions is fundamental to understanding the role of proteins in complex diseases such as cancer. For instance, there is growing interest in predicting the binding modes of peptide-based ligands (e.g., cyclic and phosphorylated peptides) to inhibit or induce targeted degradation of high-profile cancer targets. Another promising example is the identification of tumor-associated antigens for cancer immunotherapy applications. Both examples involve very specific molecular interactions, provide opportunities for computer-aided design of better cancer treatments, and highlight the need for structural analyses in cancer research. They also require new methods that account for the flexibility and variability of the protein receptors involved in these molecular interactions. The objective of this project is to develop an integrated approach to the structural modeling and analysis of protein–ligand interactions in cancer research that will be implemented in the proteomics toolkit PROTEAN-CR. The proposed toolkit will adopt a data-science approach to the problem by introducing approaches for data acquisition and aggregation, as well as algorithmic advances for handling receptor flexibility and for modeling driver mutations, drug-resistance polymorphisms, and post-translational modifications. PROTEAN-CR will streamline running structural analyses at scale while providing meaningful data analytics. The long-term goal of our research is to fully integrate three-dimensional structural information about proteins and ligands and structural analysis into cancer research. The PIs will work with collaborators to target a wide range of users, from experimentalists with little to no programming experience, to advanced users who are comfortable scripting large-scale analyses and integrating the toolkit with their own computational pipeline. The central hypothesis is that a unified data-science-inspired approach can be used to address major challenges in structural analysis of protein–ligand interactions in cancer research at scale. The first aim will incorporate protein flexibility in docking studies for cancer research. Specific workflows will be used to generate ensembles of protein conformations (receptor flexibility) and innovative machine learning methods will be implemented aiming at a better scoring of protein–ligand complexes. The second aim will focus on including cancer variability into structural analysis. We aim to fill the gap that exists between available data on cancer variants and the structural analysis of ensembles of tumor-associated mutations and protein modifications. Finally, the third aim will focus on customization, interpretability and scalability, where user-friendly methods will be deployed to manage ensembles of protein-ligand complexes. PROTEAN-CR will be developed focusing on specific cancer-related projects, and with a broad network of collaborators, enabling the design, implementation and evolution of the tool according to the needs of the cancer research community.

项目摘要 了解蛋白质-配体分子相互作用是理解蛋白质在 复杂的疾病，如癌症。例如，人们对预测分子的结合模式越来越感兴趣 基于多肽的配体(例如，环肽和磷酸化肽)，以抑制或诱导靶向降解 高度支持file癌症靶点。另一个有希望的例子是肿瘤相关抗原的fi阳离子。 免疫疗法的应用。这两个例子都涉及非常特殊的fic分子相互作用，提供了机会 用于更好的癌症治疗的计算机辅助设计，并强调癌症结构分析的必要性 研究。他们还需要新的方法来解释蛋白质受体的fl灵活性和可变性。 参与这些分子间的相互作用。这个项目的目标是开发一种综合的方法来处理 癌症研究中蛋白质-配体相互作用的结构建模和分析，将在 蛋白质组学工具包Protean-CR。拟议的工具包将采用数据科学的方法来解决这个问题 通过介绍数据获取和聚合的方法以及处理的算法进步 受体fl的灵活性，并用于模拟驱动突变、耐药性多态和翻译后 莫迪fi阳离子。Protean-CR将简化大规模运行的结构分析，同时提供有意义的数据 分析。我们研究的长期目标是完全集成关于 将蛋白质、配体和结构分析应用于癌症研究。PI将与合作者合作，以 广泛的用户，从几乎没有编程经验的实验者到高级用户 编写大规模分析的脚本，并将工具包与自己的计算管道集成在一起。 中心假设是，可以使用一种受数据科学启发的统一fi方法来应对主要挑战 在大规模癌症研究中蛋白质-配体相互作用的结构分析。fi第一个目标将包括 蛋白质fl在癌症研究对接研究中的灵活性。SPECIfic WorkflOWS将用于生成 蛋白质构象(受体fl灵活性)和创新的机器学习方法将针对 蛋白质-配体复合体的得分更高。第二个目标将专注于将癌症变异性纳入 结构分析。我们的目标是消除现有的癌症变异数据和结构数据之间存在的差距(fiall 肿瘤相关突变和蛋白质Modifi阳离子的集合分析。最后，第三个目标将集中在 定制化、可解释性和可伸缩性，将部署用户友好的方法来管理合奏 蛋白质-配体复合体。Protean-CR将专注于特定fic癌症相关项目的开发，以及 拥有广泛的协作者网络，支持工具的设计、实施和发展 癌症研究界的需求。