Annotating Metagenomically Assembled Bacteriophage from a Unique Ecological System using Protein Structure Prediction and Structure Homology Search
Bibliographic record
Abstract
ABSTRACT Emergent long read sequencing technologies such as Oxford’s Nanopore platform are invaluable in constructing high quality and complete genomes from a metagenome, and are needed investigate unique ecosystems on a genetic level. However, generating informative functional annotations from sequences which are highly divergent to existing nucleotide and protein sequence databases is a major challenge. In this study, we present wet and dry lab techniques which allowed us to generate 5432 high quality sub-genomic sized metagenomic circular contigs from 10 samples of microbial communities. This unique ecological system exists in an environment enriched with naphthenic acid (NA), which is a major toxic byproduct in crude oil refining and the major carbon source to this community. Annotation by sequence homology alone was insufficient to characterize the community, so as proof of principle we took a subset of 227 putative bacteriophage and greatly improved our existing annotations by predicting the structures of hypothetical proteins with ColabFold and using structural homology searching with Foldseek. The proportion of proteins for each bacteriophage that were highly similar to known proteins increased from approximately 10% to about 50%, while the number of annotations with KEGG or GO terms increased from essentially 0% to 15%. Therefore, protein structure prediction and homology searches can produce more informative annotations for microbes in unique ecological systems. The characterization of novel microbial ecosystems involved in the bioremediation of crude oil-process-affected wastewater can be greatly improved and this method opens the door to the discovery of novel NA degrading pathways. IMPORTANCE Functional annotation of metagenomic assembled sequences from novel or unique microbial communities is challenging when the sequences are highly dissimilar to organisms or proteins in the known databases. This is a major obstacle for researchers attempting to characterize the functional capabilities of unique ecosystems. In this study, we demonstrate that including protein structure prediction and homology search based methods vastly improves the annotation of predicted genes identified in novel putative bacteriophage in a bacterial community that degrades naphthenic acids the major toxic component of oil refinery wastewater. This method can be extended to similar genomics studies of unique, uncharacterized ecosystems, to improve their annotations. P lease read the Instructions to Authors carefully, or browse the FAQs for further details.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".