Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

Journal article

The steady-state localisation of proteins provides vital insight into their function. These localisations are context speci c with proteins translocating between di erent subcellular niches upon perturbation of the subcellular environment. Di erential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic in- sight of subcellular protein dynamics. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein di erentially localises upon cellular perturbation. Extensive simula- tion studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to several datasets recovers well- studied translocations. In an application to cytomegalovirus infection, we obtain insights into the rewiring of the host proteome. Integration of other high-throughput datasets allows us to provide the functional context of these data

Authors: Crook, OM; Davies, CTR; Breckels, LM; Christopher, JA; Gatto, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Communications
• Volume: 13
• Issue: 1
• Pages: 5948-5948
• Article number: 5948
• Publication date: 2022-10-10
• DOI: 10.1038/s41467-022-33570-9
• EISSN: 2041-1723
• ISSN: 2041-1723
• Language: English
• Keywords: Biology; Differential (mechanical device); Proteomics; Computer science; Physics; Biochemistry; Computational biology