Abstract
The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein’s sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyperLOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.
Highlights
Aberrant protein sub-cellular localisation has been implicated in numerous diseases, including cancers [1], obesity [2], and multiple others [3]
We demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches
Motivated by the need for novelty detection methods which quantify the uncertainty in the number of clusters and the assignments of proteins to each cluster, we developed Novelty T-Augmented Gaussian Mixture (TAGM)
Summary
Aberrant protein sub-cellular localisation has been implicated in numerous diseases, including cancers [1], obesity [2], and multiple others [3]. Proteins are compartmentalised into sub-cellular niches, including organelles, subcellular structures, liquid phase droplets and protein complexes These compartments ensure that the biochemical conditions for proteins to function correctly are met, and that they are in the proximity of interaction partners [6]. A common approach to map the global sub-cellular localisation of proteins is to couple gentle cell lysis with high-accuracy mass spectrometry (MS) [4, 7,8,9]. These methods are designed to yield fractions differentially enriched in the subcellular compartments rather than purifying the compartments into individual fractions. These spatial proteomics approaches aim to interrogate the greatest number of sub-cellular niches possible by relying upon rigorous data analysis and interpretation [10, 11]
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