Yang, X., Da, Q., Qian, P., Veeravalli, B., Leong, T. W., Dai, L., Nordlund, P., Prabhu, N., Zhao, Z., & Zeng, Z. (2022). CETSA Feature Based Clustering for Protein Outlier Discovery by Protein-to-Protein Interaction Prediction. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/embc48229.2022.9871558
Abstract:
The Cellular Thermal Shift Assay (CETSA) is
a biophysical assay based on the principle of ligand-induced
thermal stabilization of target proteins. This technology has
revolutionized cell-based target engagement studies and has
been used as guidance for drug design. Although many applications
of CETSA data have been explored, the correlations
between CETSA data and protein-protein interactions (PPI)
have barely been touched. In this study, we conduct the first
exploration study applying CETSA data for PPI prediction.
We use a machine learning method, Decision Tree, to predict
PPI scores using proteins’ CETSA features. It shows promising
results that the predicted PPI scores closely match the groundtruth
PPI scores. Furthermore, for a small number of protein
pairs, whose PPI score predictions mismatch the ground truth,
we use iterative clustering strategy to gradually reduce the
number of these pairs. At the end of iterative clustering, the
remaining protein pairs may have some unusual properties and
are of scientific value for further biological investigation. Our
study has demonstrated that PPI is a brand-new application of
CETSA data. At the same time, it also manifests that CETSA
data can be used as a new data source for PPI exploration
study.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - Competitive Research Programme
Grant Reference no. : NRF-CRP22-2019-0003