Assembling a phosphoproteomic knowledge base using ProtMapper to normalize phosphosite information from databases and text mining
AbstractA major challenge in analyzing large phosphoproteomic datasets is that information on phosphorylating kinases and other upstream regulators is limited to a small fraction of phosphosites. One approach to addressing this problem is to aggregate and normalize information from all available information sources, including both curated databases and large-scale text mining. However, when we attempted to aggregate information on post-translational modifications (PTMs) from six databases and three text mining systems, we found that a substantial proportion of phosphosites were positioned on non-canonical residue positions. These errors were attributable to the use of residue numbers from non-canonical isoforms, mouse or rat proteins, post-translationally processed proteins and also from errors in curation and text mining. Published mass spectrometry datasets from large-scale efforts such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC) also localize many PTMs to non-canonical sequences, precluding their accurate annotation. To address these problems, we developed ProtMapper, an open-source Python tool that automatically normalizes site positions to human protein reference sequences using data from PhosphoSitePlus and Uniprot. ProtMapper identifies valid reference positions with high precision and reasonable recall, making it possible to filter out machine reading errors from text mining and thereby assemble a corpus of 29,400 regulatory annotations for 13,668 sites, a 2.8-fold increase over PhosphoSitePlus, the current gold standard. To our knowledge this corpus represents the most comprehensive source of literature-derived information about phosphosite regulation currently available and its assembly illustrates the importance of sequence normalization. Combining the expanded corpus of annotations with normalization of CPTAC data nearly doubled the number of CPTAC annotated sites and the mean number of annotations per site. ProtMapper is available under an open source BSD 2-clause license at https://github.com/indralab/protmapper, and the corpus of phosphosite annotations is available as Supplementary Data with this paper under a CC-BY-NC-SA license. All results from the paper are reproducible from code available at https://github.com/johnbachman/protmapper_paper.Author SummaryPhosphorylation is a type of chemical modification that can affect the activity, interactions, or cellular location of proteins. Experimentally measured patterns of protein phosphorylation can be used to infer the mechanisms of cell behavior and disease, but this type of analysis depends on the availability of functional information about the regulation and effects of individual phosphorylation sites. In this study we show that inconsistent descriptions of the physical locations of phosphorylation sites on proteins present a barrier to the functional analysis of phosphorylation data. These inconsistencies are found in both pathway databases and text mining results and often come from the underlying scientific publications. We describe a method to normalize phosphosite locations to standard human protein sequences and use this method to robustly aggregate information from many sources. The result is a large body of functional annotations that increases the proportion of phosphosites with known regulators in two large experimental surveys of phosphorylation in cancer.
