Combining structures and proteomic data to study protein pocket binding potential
Supervision
Pedro Beltrao
ETHZ, 1st Supervisor
Ben Lehner
SANGER, 2nd Supervisor
Objectives
The project aims to integrate protein structure prediction methods with proteomics data on accessibility to enhance the prediction of protein conformational variations. This will further the understanding of protein allosteric regulation, the effects of mutations, and the discovery of new drug binding sites.
Methodology
Compile existing proteomics data on protein accessibility measurements by mining publicly available proteomics datasets for assays that report on protein accessibility such as limited proteolysis assays. Test and improve the use of neural network models for sequence-based structure predictions for the objective of predicting multiple conformations. Combing proteomics and structure prediction methods to determine the most plausible conformations present in given cellular state.
Required skills
The candidate should have experience in bioinformatics and proteomics data analysis, with knowledge of protein structure and sequence-based prediction methods. Skills in machine learning and data integration are desirable, along with proficiency in Python or similar programming languages.
Expected Results
A method to predict the most plausible structural conformations of proteins in a given cellular state. An atlas of protein structure conformations that are supported by experimental proteomics evidence.
Planned Secondments
Host: SANGER (B. Lehner), Duration: 2 Months; When: Year 1, Goal: Prediction of small proteins that can lock target proteins in specific conformations.
Host: IRB (P. Aloy), Duration: 1 Month, When: Year2, Goal: Prediction of molecules that could bind cryptic pockets found in different conformations.
Host: IMB (K. Luck), Duration: 1 Month; When: Year 3, Goal: Learning on how to predict structures for linear-motif interactions.
Enrolment in doctoral programs
Federal Institute of Technology Zurich
References
1 Akdel et al. A structural biology community assessment of AlphaFold2 applications NSMB 2022 (DOI:10.1038/s41594-
022-00849-w)
2 Barrio-Hernandez et al. Network expansion of genetic associations defines a pleiotropy map of human cell biology Nature
Genetics 2023 (DOI: 10.1038/s41588-023-01327-9)
3 Barrio-Hernandez et al. Clustering predicted structures at the scale of the known protein universe Nature 2023 (DOI:
10.1038/s41586-023-06510-w)