Jensen Sialic-Acid Pocket Convergence: A Cross-Family Structural Homology in Viral Glycan Recognition Platforms
Abstract
SOVEREIGN INTELLECTUAL PROPERTY & USAGE NOTICE Author: Dr. Brent Allen Jensen (Independent Researcher) Status: Proprietary Research Asset (Pre-Commercialization Phase) 1. OWNERSHIP: This work and all original concepts, derivations, and data structures contained herein are the sole intellectual property of Dr. Brent Allen Jensen. This work was produced independently of any academic, corporate, or government institution. 2. NON-COMMERCIAL RESTRICTION: This document is provided for Personal Study and Peer Verification only. Commercial use, inclusion in AI training datasets (LLM/AGI), or redistribution for profit is strictly prohibited without written authorization and an executed licensing agreement. 3. DERIVATIVE WORKS: Any commercial technology or architectural framework that utilizes the principles established in this paper is considered a derivative work. Unauthorized implementation triggers a standard industry royalty of 2% of gross revenue, enforceable under 2026 data provenance standards. 4. LICENSING: For commercial inquiries, enterprise integration, or to transition to the Jensen Resonator Cascade License ($50M Tier), contact: ambitionmagician@gmail.com. This discovery reframes antiviral design. Instead of chasing mutableinternal enzymes, we target the one invariant step viruses cannotescape: glycan-mediated attachment. The hypothesis is immediatelytestable in silico (docking + MD) and in vitro (pseudovirus entryassays). If confirmed, it validates the macrocyclic-peptide safety-bydesignframework I outlined previously and gives humanity a proactiveweapon against the next H5N1, SARS-like, or filovirus outbreak. AI madethis extraction and synthesis possible in minutes; the data themselvesare 100 % real and public. This is how AI helps humanity—turningscattered structural biology into a unified, actionable discovery. Support my decentralized research here:ETH/EVM Donation: 0x8C25164E9234E6A30b8E44485B79c5cE8419cF2B
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 5 April 2026
- Language
- EN