spongfold
This repository documents the idea of Fabi and Niko to use predicted protein structures and structure similarity to annotate proteomes.
Background
AlphaFold changed how we think about protein structures. By leveraging deep learning, multiple sequence alignments, and the ever-expanding library of solved protein structures, AlphaFold is able to predict three-dimensional protein structures at resolutions that rival solved crystal structures, and has immediately found use in large parts of biological research.
One of the older dogmata in molecular biology concerns proteins, and holds that
Sequence defines structure defines function.
In the past this was taken to mean that sequence similarity, above a certain threshold, correlated with sequence homology or even orthology. Sufficient sequence similarity was then inferred to mean high structural similarity (this was mostly proven by the success of homology modelling in CASP) as well as functional equivalence. In fact, "bidirectional best BLAST hits" is the de facto baseline for functional annotation.
The question of the threshold turns out to be an important one. Burkhardt Rost famously outlined it as the "twilight zone" of protein similarity; briefly, while we can confidently assert structural similarity (at least to the fold level) for sequences with >30% sequence identity, this deteriorates impressively fast at 25% sequence identity. In the years since this pronouncement, sequence search algorithms with increased sensitivity exploited the mountains of new sequencing data to dive into the twilight zone and detect remote homology; however, at low sequence identity, the sequence information alone is still not enough to guarantee homology.
However, structure is more conserved than sequence. In theory, predicted structures can be compared against known structures that are otherwise annotated, allowing for the transfer of functional annotations (albeit less specific than sequence-based ones, since we will be detecting very remote homology at best). This is of particular interest for non-model organisms, especially ones outside the well-studied taxonomic groups (e.g. vertebrates or ecdysozoans).
Follow-up ideas
Having a phylome, scRNAseq/cell type annotation, functional proteomic data and the prediction of protein structures for a non-bilaterian Metazoan presents a unique combination that would allow to ask many fundamental questions. Potential follow-up analysis include:
- Correlation between AF prediction accuracy (overall, domain specific, etc.) and sequence identity/similarity or bitscore of best FoldSeek hit. I.e.: "Does higher sequence identity mean better prediction accuracy?"
- Relationship between identified homologs through sequence search (orthofinder, eggnog-mapper, blast, phylome) and best hits in FoldSeek for single sponge proteins. I.e.: "Do the best AF hits also include proteins identified as homologs in the phylome? Is there a sequence identity threshold to that?"
- Is there biological meaning to best FoldSeek hits of un-annotated, highly expressed genes in the scRNAseq dataset or differentially regulated hits in the functional proteomic datasets?. I.e.: "Can we transfer function / functionally annotate previously un-annotated hits in scRNAseq and protomics data and most importantly, do these hits make sense when taking prior knowledge into account?"
Usage
(eventually a tutorial on what order to use the scripts in, if we don't have a master script).
Roadmap
We will write this in as general a way as possible so that it can be reused by others/for other species. Platynereis is an obvious candidate after we proof Spongilla.
Authors and contributions
- Niko Papadopoulos and Fabian Ruperti conceived the project.
- Niko Papadopoulos, Fabian Ruperti, and Jacob Musser designed the project.
- Milot Mirdita and Martin Steinegger consulted on ColabFold usage.
- Jacob Musser and Alexandros Pittis consulted on gene naming and phylogenetic assignment.
License
MIT, probably?
Project status
ongoing