In-Silico Library of Hydro-Optimized Peptide Constructs via Ontological Metrics (TCDS Genesis Dataset)

Current computational biology paradigms rely heavily on evolutionary homology to predict protein stability, limiting design space to structures with natural antecedents. This work introduces the Synchronous Chromodynamic Theory (TCDS), a deterministic framework that models protein stability as a vector equilibrium between Internal Pressure ($Q$) and Substrate Density ($\Sigma$).We present a unified dataset of 100 de novo peptide sequences generated by the OmniKernel Genesis engine. These constructs are optimized for hydrodynamic locking in aqueous environments ($\Sigma=1.0$) but possess internal density metrics ($Q > 25.0$) that suggest hyper-stability in high-energy environments where biological matter typically fails. Crucially, these sequences have zero Multiple Sequence Alignment (MSA) matches, yet are predicted by AlphaFold to fold with high confidence (pLDDT > 90), validating the TCDS hypothesis that physical geometry, not just evolutionary history, dictates structural viability.This dataset serves as the foundational proof-of-concept for "Solid-State Biology," a new class of programmable matter designed for extreme substrates including vacuum and stellar plasma.