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: 10615697
• 负责人: Lydia E. Kavraki
• 金额: $ 38.36万
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615697
• 关键词: 3-Dimensional; Acceleration; 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; Cyclic Peptides; Data; Data Aggregation; Data Analytics; Data Science; Databases; Development; Dimensions; Disease; Docking; Drug resistance; Estrogen Receptors; Evolution; Friends; Funding; Genetic Polymorphism; Goals; Greek; HLA Antigens; Intuition; Letters; Ligand Binding; Ligands; Malignant Neoplasms; Methodology; Methods; Modeling; Molecular; Molecular Conformation; 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; Software Tools; Structural Models; System; T-Lymphocyte; The Cancer Genome Atlas; Tumor Antigens; Variant; Visualization; Work; anticancer research; cancer immunotherapy; cancer therapy; computational pipelines; computerized tools; data acquisition; data integration; 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; tool; tumor; user-friendly; vaccine development; web app; web server

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 癌症等复杂疾病。例如，预测的结合模式越来越兴趣 基于肽的配体（例如环状和磷酸化肽）抑制或影响靶向降解的降解 高蛋白癌靶标。另一个有希望的例子是鉴定癌症相关的抗原 免疫疗法应用。这两个示例都涉及非常具体的分子相互作用，提供了机会 用于计算机辅助设计更好的癌症治疗，并强调对癌症进行结构分析的需求 研究。他们还需要新的方法来解释蛋白质接收器的灵活性和可变性 参与这些分子相互作用。该项目的目的是开发一种综合方法 癌症研究中蛋白质 - 配体相互作用的结构建模和分析将在 蛋白质组学工具包蛋白质。拟议的工具包将采用数据科学方法来解决问题 通过介绍数据获取和聚合的方法，以及处理算法的进步 受体的功能和建模驱动器突变，抗药性多态性和翻译后 修改。 Protean-CR将在提供有意义的数据的同时简化大规模运行结构分析 分析。我们研究的长期目标是完全整合有关有关的三维结构信息 蛋白质和配体以及对癌症研究的结构分析。 PI将与合作者合作以定位 从几乎没有编程经验的实验者到高级用户的广泛用户 可以舒适地脚本脚本进行大规模分析，并将工具包与自己的计算管道集成在一起。 中心假设是一种统一的数据科学风格的方法可用于应对主要挑战 在癌症研究中蛋白质与配体相互作用的结构分析中。第一个目标将纳入 癌症研究对接研究中的蛋白质灵活性。特定的工作流将用于生成 将实施蛋白质构象（受体的敏感性）和创新的机器学习方法 在蛋白质 - 配合物配合物的更好评分下。第二个目标将集中于将癌症变异性纳入其中 结构分析。我们的目的是填补有关癌症变体的可用数据和结构的差距 分析肿瘤相关突变和蛋白质修饰的综合。最后，第三个目标将重点放在 自定义，可解释性和可扩展性，将部署用户友好的方法来管理合奏 蛋白质配体复合物。 Protean-CR将集中于与癌症相关的特定项目，以及 通过广泛的合作者网络，可以根据该工具的设计，实施和演变 癌症研究界的需求。

项目成果

3pHLA-score improves structure-based peptide-HLA binding affinity prediction.

• DOI: 10.1038/s41598-022-14526-x
• 发表时间: 2022-06-24
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Conev, Anja;Devaurs, Didier;Rigo, Mauricio Menegatti;Antunes, Dinler Amaral;Kavraki, Lydia E.
• 通讯作者: Kavraki, Lydia E.

Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving Peptide-Targets From BCG Vaccine and SARS-CoV-2.

• DOI: 10.3389/fimmu.2021.812176
• 发表时间: 2021
• 期刊: Frontiers in immunology
• 影响因子: 7.3
• 作者: Tarabini RF;Rigo MM;Faustino Fonseca A;Rubin F;Bellé R;Kavraki LE;Ferreto TC;Amaral Antunes D;de Souza APD
• 通讯作者: de Souza APD

Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules.

• DOI: 10.1093/pnasnexus/pgac124
• 发表时间: 2022-07
• 期刊: PNAS NEXUS
• 作者: Jackson, Kyle R.;Antunes, Dinler A.;Talukder, Amjad H.;Maleki, Ariana R.;Amagai, Kano;Salmon, Avery;Katailiha, Arjun S.;Chiu, Yulun;Fasoulis, Romanos;Rigo, Mauricio Menegatti;Abella, Jayvee R.;Melendez, Brenda D.;Li, Fenge;Sun, Yimo;Sonnemann, Heather M.;Belousov, Vladislav;Frenkel, Felix;Justesen, Sune;Makaju, Aman;Liu, Yang;Horn, David;Lopez-Ferrer, Daniel;Huhmer, Andreas F.;Hwu, Patrick;Roszik, Jason;Hawke, David;Kavraki, Lydia E.;Lizee, Gregory
• 通讯作者: Lizee, Gregory

Machine learning models in the prediction of drug metabolism: challenges and future perspectives.

• DOI: 10.1080/17425255.2021.1998454
• 发表时间: 2021-11
• 期刊: Expert opinion on drug metabolism & toxicology
• 影响因子: 4.3
• 作者: Litsa EE;Das P;Kavraki LE
• 通讯作者: Kavraki LE

HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors.

• DOI: 10.1016/j.isci.2023.108613
• 发表时间: 2024-01-19
• 期刊: ISCIENCE
• 影响因子: 5.8
• 作者: Conev, Anja;Fasoulis, Romanos;Hall-Swan, Sarah;Ferreira, Rodrigo;Kavraki, Lydia E.
• 通讯作者: Kavraki, Lydia E.