KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions

Author summary Kinases are the enzymes responsible for controlling a large number of cell biological processes. They can regulate protein interactions, protein function, protein localization, and protein turnover by adding negatively charged phosphate groups to three amino acid types that make up proteins. Despite a large growth in knowledge about what sites on what proteins kinases can target, we still do not fully understand the exact relationship between every one of the hundreds of human protein kinases and the hundreds of thousands of possible amino acid targets within proteins. Instead, we can use algorithms to predict relationships and this has been an intense area of research and many such algorithms, based on different underlying features and mathematical approaches, have been published. Here, we create a method for making it easy to harness and compare multiple algorithms and keep these updated as new phosphorylation sites are observed. The outcome of this work is a computational pipeline, a current resource of predictions for three prediction algorithms for all know phosphorylation sites in the human proteome, and the results of comparing how different algorithms compare to each other and their inherent properties. Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown-predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, co-expression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms.

Automated summary

This study explores the relationship between kinases and potential phosphorylation sites within proteins, proposing a methodology for predicting and comparing kinase-substrate relationships. By creating a unified prediction resource called KinPred, researchers can utilize algorithms to predict kinase relationships with specific phosphorylation sites. This resource aims to establish best practices for long-term usability and sustainability of predictive algorithms in studying cellular processes.