High-accuracy mapping of human and viral direct physical protein-protein interactions using the novel computational system AlphaFold-pairs
Bibliographic record
Abstract
Abstract Protein-protein interactions are central, highly flexible components of regulatory mechanisms in all living cells. Over the years, diverse methods have been developed to map protein-protein interactions. These methods have revealed the organization of protein complexes and networks in numerous cells and conditions. However, these methods are also time consuming, costly and sensitive to various experimental artifacts. To avoid these caveats, we have taken advantage of the AlphaFold-Multimer software, which succeeded in predicting the structure of many protein complexes. We designed a relatively simple algorithm based on assessing the physical proximity of a test protein with other AlphaFold structures. Using this method, named AlphaFold-pairs, we have successfully defined the probability of a protein-protein interaction forming. AlphaFold-pairs was validated using well-defined protein-protein interactions found in the literature and specialized databases. All pairwise interactions forming within the 12-subunit transcription machinery RNA Polymerase II, according to available structures, have been identified. Out of 66 possible interactions (excluding homodimers), 19 specific interactions have been found, and an additional previously unknown interaction has been unveiled. The SARS-CoV-2 surface glycoprotein Spike (or S) was confirmed to interact with high preference with the human ACE2 receptor when compared to other human receptors. Notably, two additional receptors, INSR and FLT4, were found to interact with S. For the first time, we have successfully identified protein-protein interactions that are likely to form within the reassortant Eurasian avian-like (EA) H1N1 swine G4 genotype Influenza A virus, which poses a potential zoonotic threat. Testing G4 proteins against human transcription factors and molecular chaperones (a total of 100 proteins) revealed strong specific interactions between the G4 HA and HSP90B1, the G4 NS and the PAQosome subunit RPAP3, as well as the G4 PA and the POLR2A subunit. We predict that AlphaFold-pairs will revolutionize the study of protein-protein interactions in a large number of healthy and diseased systems in the years to come.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".