GOstruct 2.0: Automated Protein Function Prediction for Annotated Proteins

Abstract

Automated Protein Function Prediction is the task of automatically predicting functional annotations for a protein based on gold-standard annotations derived from experimental assays. These experiment-based annotations accumulate over time: proteins without annotations get annotated, and new functions of already annotated proteins are discovered. Therefore, function prediction can be considered a combination of two sub-tasks: making predictions on annotated proteins and making predictions on previously unannotated proteins. In previous work, we analyzed the performance of several protein function prediction methods in these two scenarios. Our results showed that GOstruct, which is based on the structured output framework, had lower accuracy in the task of predicting annotations for proteins with existing annotations, while its performance on un-annotated proteins was similar to the performance in cross-validation. In this work, we present GOstruct 2.0 which includes improvements that allow the model to make use of information of a protein's current annotations to better handle the task of predicting novel annotations for previously annotated proteins. This is highly important for model organisms where most proteins have some level of annotations. Experimental results on human data show that GOstruct 2.0 outperforms the original GOstruct in this task, demonstrating the effectiveness of the proposed improvements. This is the first study that focuses on adapting the structured output framework for applications in which labels are incomplete by nature.