2026/04/09 by oleg bitsoev · 1 voice
paper · doi:10.5281/zenodo.19485285
openalex publication_date 2026/04/09 · openalex created_date 2026/04/10 · openalex updated_date 2026/07/01
Recent breakthroughs in protein structure prediction, epitomized by AlphaFold and RoseTTAFold, have achieved near-experimental accuracy for individual polypeptide chains. However, the translation of these static models into functional, systems-level biological understanding remains a formidable challenge. We introduce a rigorous mathematical architecture---GRA Meta-Zeroing---that generalizes the prediction problem to a multiverse of nested consistency conditions. The framework organizes structural, interaction, and phenotypic constraints into a hierarchical tower of Hilbert spaces, with each level \(l\) associated with a goal projector \(\cPGl\) and a corresponding ``foam'' functional \(Φ(l)\) that quantifies inter-state disagreement. We prove that under mild commutativity assumptions, a recursive zeroing algorithm converges to a state of absolute cognitive vacuum, wherein all scales of description are mutually coherent. We detail practical implementations that overlay GRA functionals onto existing deep learning pipelines, enabling gradient-based optimization across levels. Three real-world scenarios are examined: (i) de-risking therapeutic candidates by penalizing hidden aggregation propensity, (ii) forecasting viral escape mutations via foam dynamics, and (iii) rational design of synthetic microbial consortia. This work provides both a theoretical foundation and an engineering blueprint for transforming static structure predictors into systemic biological harmonizers.