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

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-pro?le cancer targets. Another promising example is the identi?cation of tumor-associated antigens for cancer immunotherapy applications.

Both examples involve very speci?c 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 ?exibility 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 ?exibility and for modeling driver mutations, drug-resistance polymorphisms, and post-translational modi?cations.

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 uni?ed data-science-inspired approach can be used to address major challenges in structural analysis of protein?ligand interactions in cancer research at scale. The ?rst aim will incorporate protein ?exibility in docking studies for cancer research.

Speci?c work?ows will be used to generate ensembles of protein conformations (receptor ?exibility) 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 ?ll the gap that exists between available data on cancer variants and the structural analysis of ensembles of tumor-associated mutations and protein modi?cations.

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 speci?c 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.