Minimotifs are short peptide sequences that are the recognition elements for many protein functions. These short sequences are responsible for protein interaction interfaces involving other proteins (or molecules) in cells, trafficking proteins to specific cellular compartments, or serving as the basis for enzymes to post-translationally modify the minimotif sequence. At present, many minimotif instances and consensus sequences are collected into a wide spanning set of relatively small databases such as MnM, ELM, Domino, PepCyber, and ScanSite [1–5]. Most databases focus on specific subsets of minimotifs. For example, Phospho-ELM has merged with PhosphoBase as a database that focuses on instances of phosphorylation on proteins [6]. Likewise, ScanSite largely concentrates on protein interaction minimotifs for a small subset of domains. In addition to these databases, recent years have seen increased publication rates of high throughput studies that generate minimotif data. Despite this growth in information, many of the reported minimotif attributes have yet to be integrated into any database.
The goal of the MnM project is to integrate well-structured data for a set of defined attributes of minimotifs in a single, non-redundant data repository with high accuracy. The number of reports of minimotifs in the literature has continued to grow since the late 1980’s, recently with more rapid growth due to high throughput functional peptide screens. Previously, we showed that the several thousand minimotifs in MnM can be discretized into a structured syntax which can be directly enforced and modeled in a relational database [1, 7]. Through this process, we recognized the need for a system that manages minimotif annotation, which would help identify papers, reduce the time required for manual annotation, reduce errors, duplications and ambiguities, and aids in maintenance of the database.
Currently, there are no bioinformatics tools designed for annotating minimotifs from the literature. Most reported annotation methodologies concentrate mainly on genomes and proteome scale data [8–10]. A proposed stratification of annotation efforts refers to sequence-based annotation as the first dimension of genome annotation which defines components [11]. The second dimension can be considered those annotations that focus on component interactions. This is exemplified by the human kinome and other types of functional annotations in the SwissProt and Entrez Gene databases [12, 13]. Annotation of minimotifs can be considered a second dimension annotation.
In considering whether to design a novel minimotif annotation system or adapt an existing annotation system used for another purpose, we identified a number of requirements to facilitate accurate, non-redundant, and efficient annotation of minimotif literature. We wanted the system to interface with a relational database that enforces controlled vocabularies from external databases and eliminates duplication. The system should be able to read, write, and edit entries in a database. The system should display papers that have been and are yet to be annotated, as well as support database-driven machine learning that scores papers for minimotif content, paper sorting, and paper filtering. The system should also have the capability to track annotations from multiple annotators. Finally, the system should be capable of accepting the fine-grained information content of minimotifs, in a structured and comprehensive manner.
Despite advances in management and mining of scientific literature, no tool existed that met the requirements we required for accurately annotating minimotif data. For example, each of the existing annotation tools such as MIMAS, Textpresso and Biorat only addresses a subset of the above requirements [14–16].
In this paper, we describe MimoSA, a Mi nimo tif S ystem for A nnotation designed for managing and facilitating minimotif annotation. MimoSA allows for minimotif-centric analysis of PubMed abstracts and annotation of minimotifs. MimoSA’s contents are entirely database driven, thus enabling its adaption as an annotation tool for other information spaces that require extraction of information from the primary literature.
