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

Abstract

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.

Proteome-wide comparison of tertiary protein structures reveal extensive molecular mimicry inPlasmodium-human interactions

“…Indeed, the failure of homology detection has been shown to be one of the important reasons for the presence of lineage-specific genes (Weisman et al, 2020). In fact, multiple studies have demonstrated the effectiveness of using structure data to identify distant homologs that are missed by sequence-based approaches (Andorf et al, 2022;Monzon et al, 2022). Other proteins, however, would represent false-positives of our approach based on our definition of 'molecular mimics', in which we define that a mimic confers benefit to the parasite.…”

Functional Annotation – How to Tackle the Bottleneck in Plant Genomics

“…If the structure of two proteins is similar without orthology, the function might be similar too. FASSO (Andorf et al, 2022) identifies reciprocal best protein structure alignments in the identification of orthologs between two species. It might be feasible to extend such approaches to screen for 3D structural similarity rather than protein sequence similarity.…”

Fusarium Protein Toolkit: AI-powered tools to combat fungal threats to agriculture

“…With FoldSeek, the 15,911 and 17,356 predicted protein structures for two agriculturally important species F. graminearum and F. verticillioides , respectively, were aligned to the predicted protein structures from the other four Fusarium species and four outgroup species. The resulting output was modified to highlight the top 25 hits (based on the highest alignment score) from each species, and to incorporate functional annotations from UniProt, species information, and a visual blue/red color gradient for intuitive interpretation of results [10, 27].…”