An adaptation of Pfam profiles to predict protein sub-cellular localization in Gram positive bacteria

Genome-Wide Investigation of SBT Family Genes in Pineapple and Functional Analysis of AcoSBT1.12 in Floral Transition

SBT (Subtilisin-like serine protease), a clan of serine proteolytic enzymes, plays a versatile role in plant growth and defense. Although SBT family genes have been obtained from studies of dicots such as Arabidopsis, little is known about the potential functions of SBT in the monocots. In this study, 54 pineapple SBT genes (AcoSBTs) were divided into six subfamilies and then identified to be experienced strong purifying selective pressure and distributed on 25 chromosomes unevenly. Cis-acting element analysis indicated that almost all AcoSBTs promoters contain light-responsive elements. Further, the expression pattern via RNA-seq data showed that different AcoSBTs were preferentially expressed in different above-ground tissues. Transient expression in tobacco showed that AcoSBT1.12 was located in the plasma membrane. Moreover, Transgenic Arabidopsis ectopically overexpressing AcoSBT1.12 exhibited delayed flowering time. In addition, under the guidance of bioinformatic prediction, we found that AcoSBT1.12 could interact with AcoCWF19L, AcoPUF2, AcoCwfJL, Aco012905, and AcoSZF1 by yeast-two hybrid (Y2H). In summary, this study provided valuable information on pineapple SBT genes and illuminated the biological function of AcoSBT1.12 in floral transition.

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Sub-cellular localisation of proteins is critical to their function in all cellular processes; proteins localising to their intended micro-environment, e.g organelles, vesicles or macro-molecular complexes, will meet the interaction partners and biochemical conditions suitable to pursue their molecular function. Therefore, sound data and methods to reliably and systematically study protein localisation, and hence their mis-localisation and the disruption of protein trafficking, that are relied upon by the cell biology community, are essential. Here we present a method to infer protein localisation relying on the optimal integration of experimental mass spectrometry-based data and auxiliary sources, such as GO annotation, outputs from third-party software, protein-protein interactions or immunocytochemistry data. We found that the application of transfer learning algorithms across these diverse data sources considerably improves on the quantity and reliability of sub-cellular protein assignment, compared to single data classifiers previously applied to infer sub-cellular localisation using experimental data only. We show how our method does not compromise biologically relevant experimental-specific signal after integration with heterogeneous freely available third-party resources. The integration of different data sources is an important challenge in the data intensive world of biology and we anticipate the transfer learning methods presented here will prove useful to many areas of biology, to unify data obtained from different but complimentary sources.