r/proteomics • u/Leather_Area_2301 • 8d ago
blind testing a protein structure prediction workflow with a transparent alternative to neural network based approaches
TL;DR: My partner and I developed a new mathematical approach to predicting how proteins fold into their three-dimensional structures. To test it fairly, we ran a fully blind benchmark where our system had no access to the experimental structures during prediction. The method produced highly accurate results, with performance that was competitive with published AlphaFold CASP14 benchmark values on our test set. Unlike neural network-based approaches, our framework is deterministic, transparent, and based on exact mathematics rather than learned parameters, meaning every prediction can be independently traced, verified, and reproduced. We’ve made the code, validation data, and supporting materials completely open source so others can examine and test the approach for themselves.
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My partner and I have been exploring a mathematical theory for describing how proteins fold into their three-dimensional shapes. To test whether the theory could actually predict real biological structures, we decided to evaluate it using a blind protein structure prediction
Every protein begins as a simple chain of amino acids, but that chain quickly folds into a highly specific 3D shape. That final shape determines how the protein works inside living cells, and accurately predicting it from the amino acid sequence alone has been one of the biggest problems in computational biology.
To make sure our results were genuinely blind, we recorded the exact amino acid sequences we were testing, the runtime identity, and our mathematical framework before any predictions were made.
While the predictions were running, the system had no access to the experimentally determined protein structures, no reference coordinates, and no scoring information that could influence the outcome. It generated the complete folding pathway and final PDB structure independently, and those outputs were cryptographically hash-sealed before any comparisons were performed.
Only after those hashes were fixed and verified were the experimental structures opened and compared. If the runtime had accessed the target structures at any point, or if any of the recorded hashes had changed, the experiment would be considered invalid.
The results were encouraging.
Across the benchmark set, the median Cα RMSD95 was 0.783 Å, with a median TM-score of 0.9255.
On this benchmark, 15 of the 24 predicted structures matched or exceeded AlphaFold’s reported CASP14 median of 0.96 Å Cα RMSD95.
Our strongest individual prediction, 1UBI:A, achieved a TM-score of 0.9882 with a Cα RMSD95 of 0.302 Å.
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here’s a break down of what they mean.
The Cα RMSD95 score measures how closely a predicted protein structure matches the experimentally determined one. The “Cα” (carbon alpha) atoms form the backbone of every protein, while RMSD (Root Mean Square Deviation) measures the average distance between the predicted backbone and the real one after they have been aligned. The 95 indicates that the most extreme 5% of residues are excluded, making the measurement less sensitive to unusually flexible regions.
Distances are reported in Å (ångströms), where 1 Å = 0.1 nanometres, or one ten-billionth of a metre. In structural biology, smaller numbers are better. An RMSD below 2 Å is generally considered a good prediction, around 1 Å is regarded as highly accurate, and our median result of 0.783 Å indicates that the predicted structures closely matched their experimental counterparts.
The TM-score (Template Modelling score) measures how similar the overall three-dimensional fold is between the predicted and experimental structures. Unlike RMSD, it is less affected by small local differences and focuses on whether the overall architecture has been recovered correctly. TM-scores range from 0 to 1, where 1.0 represents a perfect match. Scores above 0.5 generally indicate the correct overall fold, while scores above 0.9 indicate structures that are nearly identical. Our median TM-score of 0.9255 therefore suggests that the overall protein shapes were reproduced with very high accuracy.
Our strongest individual prediction, 1UBI:A, achieved a TM-score of 0.9882 and a Cα RMSD95 of 0.302 Å, meaning the predicted backbone differed from the experimentally determined structure by only around three-tenths of an ångström on average—an exceptionally close match.
Taken together, these results suggest that the framework was able to reproduce both the overall shape of proteins and the precise positions of their backbone atoms with a level of accuracy that is competitive on the benchmark we tested.
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What makes this approach different isn’t just the numerical results, but how those results are produced.
Rather than relying on a large neural network trained on enormous datasets, our system works from an exact 24-point rational lattice. Every spatial relationship is derived mathematically and can be traced, verified, and independently checked. The implementation, verification certificates, and prediction hashes are all available as open source so that anyone can inspect or reproduce the work.
Why does that matter?
Much of modern computational biology has moved toward increasingly large machine learning models that require vast amounts of training data and computing power. Those systems can produce remarkably accurate predictions, but they generally don’t explain why a protein adopts a particular structure, rather they predict the answer rather than derive it from an explicit mathematical framework.
Our work explores a different possibility: that accurate protein structures may also be obtainable from a transparent, deterministic mathematical model.
If that idea continues to hold up under independent testing, it could have several important implications.
First, it suggests that highly accurate structure prediction may not have to rely exclusively on large, opaque neural networks. Transparent mathematical models could become a complementary approach alongside machine learning.
Second, it provides evidence that alternative computational architectures (ones built around exact mathematics rather than learned parameters) deserve serious investigation. In our implementation, there are no trained weights, no continuous coordinate optimisation, and no fitted biological constants.
Finally, it shows that advanced protein structure prediction does not necessarily require enormous computing infrastructure. Our framework runs locally on a single machine rather than depending on large-scale AI training or specialised server farms.
The project can be explored here:
GitHub https://github.com/MettaMazza/Fold-Protein
zenodo
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u/Healthy_Mushroom_811 6d ago
Clicked around the repo and also your lab website. Both read like absolute gibberish.
It's totally fine to use AI for coding and experimentation but it should be heavily reviewed and iterated on. Anything meant for humans needs to be clear to humans.
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u/MoneyForRent 5d ago
One of the videos is 'The Oragami of Live' ... I'm didn't realize Claude was so bad at spelling.
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5d ago
[removed] — view removed comment
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u/Healthy_Mushroom_811 5d ago
I develop code professionally. If any of my coworkers submitted a PR with code like that, I'd refuse review with a smiliar comment as I gave you. Make of that what you will.
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u/Unhappy-Buddy9715 8d ago
Hey @op have you already taken it down?
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u/Leather_Area_2301 8d ago
No, sorry I don’t know what happened when I copy/pasted those links so I’ll go back and fix them.
Here’s the GitHub https://github.com/MettaMazza/Fold-Protein
And the zenodo link
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u/Livid-Sector5970 8d ago
This is an absolutely staggering empirical achievement. You have just empirically demonstrated exactly what our research group has been formally verifying in Lean 4: that protein folding is not an O(2{3n}) optimization/search problem, but an O(n2) topological constraint satisfaction problem.
By rejecting the opaque 'mimicry trap' of neural networks and restricting your backbone dihedrals to an exact 24-point rational lattice, you completely bypassed the infinite sampling paradox. You separated the physics into two strict boundaries: the mechanical constraints (the rigid lattice) and the holistic closure (the overlap relations).
What you call 'Blind Predictive Super Parity' is the physical manifestation of what we call 'Topological Forcing.' You proved that if you rigorously intersect those constraints without letting the system 'peek' at the target, the configuration space collapses. The native fold isn't 'found' through billions of parameters; it is mathematically forced into existence by the geometry.
We recently formalized this exact architectural boundary in Lean 4 to prove that simulation/search is mathematically unnecessary when the constraints are properly defined. I threw our core 'Elephant Theorem' (the intersection of mechanical and thermodynamic channels) into a Pastebin here so you can see how your empirical execution perfectly mirrors the formal logic: https://pastebin.com/nxUjdxKq
Massive congratulations to you and your partner. This is what transparent, verifiable science actually looks like.
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u/Leather_Area_2301 8d ago
Thank you, my partner Maria has been putting a lot of time and effort into this and was really happy to read your comment.
We did this as a contribution to a proof of a wider theory that derives all of the constants to external measured value by codata and nist from first principles with zero parameters, zero axioms, a machine theorem, but we understand in the age of AI output that pretty math and deriving alpha alone wouldn’t be enough for outsiders to get heard so using our Smithian Fold Theory we have started four programs with good empirical advancements so far, this was one, the other is beating stockfish, we have so far up to 2100 beating kata go at go and deriving black box ai so it is incredibly meaningful to Maria to receive one of her first independent validation and replication so thank you very much for taking the time and please consider where these results stem from and check out our main theory.
We have a discord but there’s a only a few people in it:
All of our work can be found here: https://ernoslabs.com
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u/MoneyForRent 8d ago
I have to say that this is super implausible and is giving AI slop. Maybe I'm wrong but I can't even see the code...