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 理解蛋白质-配体分子相互作用是理解蛋白质在细胞内作用的基础。 复杂的疾病，如癌症。例如，人们越来越感兴趣的是预测 基于肽的配体（例如，环状和磷酸化肽）以抑制或诱导靶向降解 高风险的癌症靶点。另一个有希望的例子是识别癌症的肿瘤相关抗原 免疫疗法的应用。这两个例子都涉及非常特殊的分子相互作用， 用于计算机辅助设计更好的癌症治疗方法，并强调癌症结构分析的必要性 research.他们还需要新的方法来解释蛋白质受体的不稳定性和可变性 参与了这些分子间的相互作用。该项目的目标是制定一个综合办法， 癌症研究中蛋白质-配体相互作用的结构建模和分析， 蛋白质组学工具包PROTEAN-CR。拟议的工具包将采用数据科学方法来解决这个问题 通过引入数据采集和聚合的方法，以及处理数据的算法进步， 受体不稳定性和建模驱动突变，耐药多态性，和翻译后 修改。PROTEAN-CR将在提供有意义的数据的同时， 分析学我们研究的长期目标是充分整合关于 蛋白质和配体以及结构分析用于癌症研究。PI将与合作者合作， 广泛的用户，从几乎没有编程经验的实验者到高级用户， 能够轻松编写大规模分析脚本，并将工具包与自己的计算管道集成。 核心假设是，统一的艾德数据科学启发的方法可以用来解决重大挑战 在癌症研究中蛋白质-配体相互作用的结构分析。第一个目标将包括 在癌症研究的对接研究中蛋白质的可降解性。具体的工作流程将用于生成 蛋白质构象（受体可变性）和创新的机器学习方法将被实施， 在蛋白质-配体复合物的更好评分上。第二个目标将侧重于将癌症变异性纳入 结构分析我们的目标是填补癌症变异的可用数据和结构性变异之间存在的差距。 分析肿瘤相关突变和蛋白质修饰的集合。最后，第三个目标将侧重于 定制、可解释性和可扩展性，其中将部署用户友好的方法来管理集合 蛋白质-配体复合物。PROTEAN-CR将专注于特定的癌症相关项目， 拥有广泛的合作者网络，能够根据 癌症研究团体的需求。

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

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.

• DOI: 10.1016/j.compbiomed.2021.104943
• 发表时间: 2021-12
• 期刊: Computers in biology and medicine
• 影响因子: 7.7
• 作者: Hall-Swan S;Devaurs D;Rigo MM;Antunes DA;Kavraki LE;Zanatta G
• 通讯作者: Zanatta G

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.