Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.

ABSTRACT: Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell line

SUBMITTER: Zhao S 

PROVIDER: S-EPMC10810365 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature