Infinite Synthesis: Amortized O(1) Protein Folding via 6DoF Manifold Projection
Abstract
Conventional protein folding methods rely on computationally intensive molecular dynamics or iterative optimization heuristics. This work introduces the Chimera framework, which employs a fast 6-degree-of-freedom (6DoF) geometric resolver to map the manifold of physically allowed backbone rotations. A specialized Transformer is trained on high-fidelity “Geometric Truth” extracted from 37 high-resolution PDB structures. The resulting model solves protein structures including unknown segments (“Z-gaps”) in amortized constant time (≈2 ms per protein on consumer Apple Silicon hardware) with sub-ångström backbone precision (motive MSE ≈ 0.0197 on benchmark structures). We demonstrate high throughput (∼43 million proteins/day), effective de-novo bridge sequence design via geometric smoothness and clash scoring, and strong generalization on synthetic chimeric sequences. The approach reframes folding as deterministic manifold projection rather than search, offering a scalable path toward real-time structural biology and therapeutic protein design. Source code and the trained UFT-Global-Brain-V2 weights are provided to ensure full reproducibility of the $O(1)$ benchmark.
Fields of Study & Subjects
Protein Structure and Dynamics
Machine Learning in Bioinformatics
RNA and protein synthesis mechanisms
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 9 February 2026