Functional profiling of the sequence stockpile: a review and assessment of in silico prediction tools

In silico functional annotation of proteins is crucial to narrowing the sequencing-accelerated gap in our understanding of protein activities. Numerous function annotation methods exist, and their ranks have been growing, particularly so with the recent deep learning-based developments. However, it is unclear if these tools are truly predictive. As we are not aware of any methods that can identify new terms in functional ontologies, we ask if they can, at least, identify molecular functions of new protein sequences that are non-homologous to or far-removed from known protein families. Here, we explore the potential and limitations of the existing methods in predicting molecular functions of thousands of such orphan proteins. Lacking the ground truth functional annotations, we transformed the assessment of function prediction into evaluation of functional similarity of orphan siblings, i.e. pairs of proteins that likely share function, but that are unlike any of the currently functionally annotated sequences. Notably, our approach transcends the limitations of functional annotation vocabularies and provides a platform to compare different methods without the need for mapping terms across ontologies. We find that most existing methods are limited to identifying functional similarity of homologous sequences and are thus descriptive, rather than predictive of function. Curiously, despite their seemingly unlimited by-homology scope, novel deep learning methods also remain far from capturing functional signal encoded in protein sequence. We believe that our work will inspire the development of a new generation of methods that push our knowledge boundaries and promote exploration and discovery in the molecular function domain.