A Formally Verified, Causality‑Driven Platform for De Novo Antibody Design and Protein Function Prediction

Abstract

We present HDT v8.0 «OMNIS», a computational platform that integrates molecular modeling, causal inference, quantum‑chaotic spectral analysis, and formal verification for rational antibody design and protein function prediction. Unlike conventional tools that provide point estimates without guarantees, HDT v8.0 combines Rosetta‑based energy calculations, TrustML‑driven statistical interpolation with Hölder‑continuous guarantees, Lean‑4 formal verification of structural invariants, causal graph analysis via CausalIBCD, and frustration spectroscopy via QCU‑Engine. To assess the platform's accuracy, generality, and ability to reason about causality, we performed nine benchmark tests spanning single‑point mutations, double‑mutant epistasis, a conservative neutral substitution, a formal disproof of a general epistasis hypothesis, de novo prediction of the catalytic triad in barnase, a structural mechanism of MlaD loop rigidity that eludes current state‑of‑the‑art tools such as AlphaFold, and a complete lead optimization package for an anti‑IL‑13 antibody. In all cases where experimental data exist, predictions fall within experimental confidence intervals. The platform correctly identifies epistatic coupling, distinguishes neutral from disruptive mutations, formally disproves a hypothesis on additivity, rediscovers the Glu73–Arg87–His102 catalytic triad of barnase using only structure, explains why AlphaFold fails on MlaD via causal and topological arguments, and generates a full lead optimization package in an automated manner. These results establish HDT v8.0 as a rigorous and interpretable computational platform for antibody engineering and de novo protein analysis.