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

Lit-Hsin Loo,Yi-Ling Tung,Danai Laksameethanasan,Christian Von Mering

doi:10.1371/journal.pcbi.1003504

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.

Highlights

Proteins have to be localized at the appropriate subcellular compartments to perform their functions
Many proteins have complex or subtle differences in their localization patterns that can be hardly represented by these categories
We show that our tool can identify proteins located at the same subcellular regions more accurately than existing categorization-based methods

Summary

Introduction

Proteins have to be localized at the appropriate subcellular compartments to perform their functions. Automated image processing algorithms have been useful in extracting quantitative descriptors for protein localization patterns, the resulting descriptors are often being converted back into these discrete categories using supervised classification or unsupervised clustering methods [9,10,11,12,13]. Image alignment We noticed non-zero lateral offsets between the DIC and fluorescence images (both GFP and DAPI) in the UCSF yeast GFP dataset These offsets might be due to misalignment of image acquisition instruments. We estimated the displacement dx,dy between the binary cell mask and a GFP image using the standard cross-correlation algorithm:

Methods

Results

Discussion

Conclusion