Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins

Abstract

Protein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast Saccharomyces cerevisiae as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at http://plast.bii.a-star.edu.sg.

Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis

“…Unsupervised clustering of subcellular localization patterns identified colocalized proteins (Handfield et al, 2013;Loo et al, 2014) and a classification approach assessed a mixture of spatial patterns to identify changes in protein localization in a microchemostat array (Dé nervaud et al, 2013). However, while these approaches are information-rich, they have not attempted to computationally assign a specific localization for each protein and thus are of limited use to biologists.…”

High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures

“…Loo and colleagues have previously developed computational methods to automatically construct phenotypic profiles from large numbers of unbiased and quantitative descriptors (or features) of cellular phenotypes based on microscopy images of cells (Loo et al 2007 ; Laksameethanasan et al 2013 ). These profiles were used to distinguish large numbers of compounds with different targets/mechanisms (Loo et al 2007 ) or proteins involved in different biological processes (Loo et al 2014 ). Here, we present a study that uses similar phenotypic profiling methods to screen for a compact set of phenotypic features that are predictive of in vivo PTC toxicity of xenobiotic compounds.…”

Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence

“…These functions allow us to detect, for example, the number of γH2AX objects/foci or the average intensity of γH2AX at the chromosomal region. Some of these phenotypic features were previously used to build assays for predicting nephrotoxicity (Su et al 2016 ), cellular sensitivity to cytotoxic agents (Loo et al 2017 ), drug targets/mechanisms (Loo et al 2007 ), or protein functions (Loo et al 2014 ). Thus, our current phenotypic feature set may also be discriminative enough to predict pulmonotoxicity.…”