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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

基本信息

批准号：
10398904
负责人：
Lydia E. Kavraki
金额：
$ 39.74万
依托单位：
RICE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10398904
关键词：
3-Dimensional 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 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 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 tool tumor user-friendly vaccine development web app

项目摘要

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.

项目总结