Annotation of protein residues based on a literature analysis: cross-validation against UniProtKB

Background: A protein annotation database, such as the Universal Protein Resource (UniProtKB), is a valuable resource for the validation and interpretation of predicted 3D structure patterns in proteins. Previously, results have been on point mutation extraction methods from biomedical literature which can be used to support the consuming work of manual database curation. However, these methods were limited on point mutation extraction and do not extract features for the annotation of proteins at the residue level. Results: This work introduces a system that identifies protein residue sites in abstract texts and annotate them with features extracted from the context. The performances of all text mining modules were evaluated against a manually annotated corpus. The identified annotation features can be attributed to at least one of six tar-geted categories, e.g. enzymatic reaction. Extracted results were cross-validated against UniProtKB and for 13 annotations of residues that have not been confirmed in the UniProtKB a manual assessment was performed. Conclusions: This work proposes a solution for the automatic extraction of protein residue annotation from biomed-ical articles. The presented approach is an extension to other existing systems in that a wider range of residue entities are considered and that features of residues are extracted as annotations. Backgroun