FASSO: An AlphaFold based method to assign functional annotations by combining sequence and structure orthology

Methods to predict orthology play an important role in bioinformatics for phylogenetic analysis by identifying orthologs within or across any level of biological classification. Sequence-based reciprocal best hit approaches are commonly used in functional annotation since orthologous genes are expected to share functions. The process is limited as it relies solely on sequence data and does not consider structural information and its role in function. Previously, determining protein structure was highly time-consuming, inaccurate, and limited to the size of the protein, all of which resulted in a structural biology bottleneck. With the release of AlphaFold, there are now over 200 million predicted protein structures, including full proteomes for dozens of key organisms. The reciprocal best structural hit approach uses protein structure alignments to identify structural orthologs. We propose combining both sequence- and structure-based reciprocal best hit approaches to obtain a more accurate and complete set of orthologs across diverse species, called Functional Annotations using Sequence and Structure Orthology (FASSO). Using FASSO, we annotated orthologs between five plant species (maize, sorghum, rice, soybean, Arabidopsis) and three distance outgroups (human, budding yeast, and fission yeast). We inferred over 270,000 functional annotations across the eight proteomes including annotations for over 5,600 uncharacterized proteins. FASSO provides confidence labels on ortholog predictions and flags potential misannotations in existing proteomes. We further demonstrate the utility of the approach by exploring the annotation of the maize proteome.