Benjamin Dominik Maier, Borgthor Petursson, Alessandro Lussana, Evangelia Petsalaki
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Data-driven extraction of human kinase-substrate relationships from omics datasets.
Phosphorylation forms an important part of the signalling system that cells use for decision making and regulation of processes such as cell division and differentiation. In human, >90% of identified phosphosites don't have annotations regarding the relevant upstream kinase. At the same time around 30% of kinases (as annotated in Uniprot) have no known target. This knowledge gap stresses the need to make large scale, data-driven computational predictions. In this study, we have created a machine learning-based model to derive a probabilistic kinase-substrate network from omics datasets. Our methodology displays improved performance compared to other state-of-the-art kinase-substrate prediction methods and provides predictions for more kinases. Importantly, it better captures new experimentally-identified kinase-substrate relationships. It can therefore allow the improved prioritisation of kinase-substrate pairs for illuminating the dark human cell signalling space. Our model is integrated into a web server, SELPHI2.0, to allow unbiased analysis of phosphoproteomics data, facilitating the design of downstream experiments to uncover mechanisms of signal transduction across conditions and cellular contexts.
期刊介绍:
The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action.
The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data.
Scope:
-Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights
-Novel experimental and computational technologies
-Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes
-Pathway and network analyses of signaling that focus on the roles of post-translational modifications
-Studies of proteome dynamics and quality controls, and their roles in disease
-Studies of evolutionary processes effecting proteome dynamics, quality and regulation
-Chemical proteomics, including mechanisms of drug action
-Proteomics of the immune system and antigen presentation/recognition
-Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease
-Clinical and translational studies of human diseases
-Metabolomics to understand functional connections between genes, proteins and phenotypes