Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression.

ABSTRACT: As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming supp

SUBMITTER: Oom AL 

PROVIDER: S-EPMC8956815 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature