Every virus is a physical structure — a protein assembly with measurable mass, atom count, and geometry. The QAG Virology Lab applies the Quantum Affinity Gravity framework to compute the resonant frequency at which that structure becomes unstable, while leaving host biology untouched. The math is open. The code is open. The question is testable.
Four steps take you from a viral protein's structure to a predicted resonant frequency. Each step is implemented in open Python code and mirrored in the calculator below.
Divide the assembly's molecular weight by its atom count. Heavy, compact proteins score differently than light, diffuse ones — this single number captures the structural "feel" of the virus.
How strongly the structure couples to the surrounding field, scaled by the affinity symmetry bias constant (KASB = 0.013829). Think of it as the structure's "grip" on resonance.
The headline prediction: the frequency at which resonance becomes disruptive. It's anchored to the hydrogen line (fH = 1420.405 MHz) and base-12 symmetry (Φ = 1.194797) — the same constants behind the cosmology work.
A first-order estimate of the zeta-potential shift, checked against a 20 mV safety margin from typical host-cell values. A prediction is only interesting if it's selective.
Constants: fH = 1420.405 MHz · Φ = 1.194797 · KASB = 0.013829 · tpixel = 3.41×10−7 s. Worked example: SARS-CoV-2 spike (PDB 6VXX, 438.26 kDa, 23,694 atoms) → ρ = 0.0185 → Zv = 586.2 → fkill = 3291.59 MHz (S-band).
Enter any protein assembly's mass and atom count — or start from the verified SARS-CoV-2 example — and run the full four-step computation in your browser.
Theoretical prediction only — see research status. This calculator runs entirely in your browser; nothing is uploaded.
Thirty structural targets from the QAG Master Pathogen Index — computed disruptive frequencies for human viruses, bacteria, prions, and fungal targets. All values are theoretical predictions from protein structure data (PDB).
| PDB | Target | fkill (MHz) | MW (kDa) |
|---|
Theoretical predictions only — not validated, not medical advice. See how it works.
A plain-language walkthrough of the theoretical mechanism. Every step below is a hypothesis inside the Quantum Affinity Gravity framework — none of it has been demonstrated in a laboratory.
Proteins are physical objects — folded chains of atoms held together by chemical bonds. Like a bell or a guitar string, any physical object has natural frequencies at which it prefers to vibrate. These are set by its mass, its shape, and the stiffness of its bonds. For a viral capsid protein, those frequencies land in the megahertz to gigahertz range.
The QAG framework computes a disruptive frequency (fkill) from three measurable structural properties: molecular weight (how heavy), atom count (how many pieces), and the resulting structural density. Heavier, denser assemblies resonate lower; lighter ones resonate higher.
This is the oldest idea in the book: an opera singer shattering a wine glass by singing its resonant note. If you drive an object at its natural frequency with enough energy, the oscillations grow until the structure fails. Bridges, buildings, and glassware all obey this.
The theoretical proposal: a viral capsid driven at its computed fkill would experience the same growing oscillation — potentially disrupting the protein assembly. The gap between "wine glass" and "virus" is enormous: viruses sit in warm, wet, noisy biological environments that damp vibration aggressively, and no experiment has shown this working.
Standard biophysics would estimate these frequencies from elasticity theory alone. QAG adds the affinitic impedance (Zv) — a vacuum-coupling term from the framework that shifts where the resonance is predicted to sit. That is why the frequencies on this site differ from what a conventional normal-mode analysis would give.
Whether that vacuum term is real is itself an open theoretical question. The numbers here are what the framework predicts if its axioms hold.
Each target carries a host safety margin: the predicted gap between the viral disruptive frequency and the resonant frequencies of human tissue structures. All targets on this site compute as SECURE, meaning their fkill sits far from tissue resonances.
But: a computed margin is not a measured margin. Tissue is heterogeneous, individuals vary, and the underlying frequency model is unvalidated. A safety calculation inside an unproven theory proves nothing about real-world safety.
This is a theoretical physics exercise: IF the QAG framework's axioms are correct, THEN these are the frequencies at which these protein structures would be most vulnerable to resonant disruption. Both the "if" and the "then" are unproven. Nothing here is a treatment, a device design, or a recommendation. It is a set of predictions awaiting the kind of experimental test that could prove them wrong — which is exactly what a scientific hypothesis is for.
Feline and bovine pathogen registries from the QAG veterinary notebooks — disruptive frequencies computed live in your browser with the same decoherence algorithm (KASB = 0.013829). Canine registry in preparation.
| Pathogen | Target protein | MW (kDa) | fkill (MHz) | Safety |
|---|
Two interactive consoles built on the same math. One pulls live structural data from the RCSB Protein Data Bank; the other renders the predicted pulse waveform.
Fetch any PDB entry live from the RCSB API and run the full viral-decoherence computation — plus the chrono-holographic echo engine and a SPARC galaxy rotation-curve navigator showing the same framework at cosmological scale.
Open the engine →Enter a PDB ID and watch the targeting sequence: live structure fetch, affinitic impedance, disruptive frequency lock, host-safety check, and the athermal pulse waveform rendered over a 10 ns window.
Open the console →We'd rather be funded for what this is than hyped for what it isn't.
Every number on this site can be traced to code you can read and run.
Python engines, the fkill pipeline, RF matcher, PDB sync tools, and the full 14-slide presentation script.
The slide-by-slide walkthrough and the technical blueprint behind the lab's signal-chain design.
Master pathogen index (JSON), research platform table, and the viral decoherence index (CSV) — served live from the shared qag-data API (CORS-open), mirrored in the repo's data/.
Connecting to qag-data API…
Every virus particle is a physical object with a structure — a protein shell (capsid) wrapped around genetic material. Like a wine glass that shatters at one specific musical note, physical structures have resonant frequencies where small inputs create large vibrations. QAG virology hypothesizes that each viral capsid has a characteristic disruptive frequency (fkill) at which resonant energy would mechanically disrupt the shell — like the glass, but at megahertz frequencies for nanometer-scale objects.
The prediction starts from the virus's known 3D protein structure (from the Protein Data Bank). From the atom count and molecular weight, the framework computes a structural density, then a characteristic vibrational frequency zv scaled by the QAG affinity constant (KASB = 0.013829), and finally fkill = zv × 1.5. Larger, denser capsids predict lower frequencies; smaller ones predict higher frequencies. The math is deterministic — the same structure always gives the same number.
Three honest reasons:
Until controlled experiments exist, fkill is a prediction awaiting its first test — interesting, falsifiable, and completely unproven.
Not a treatment. Not medical advice. Not a device you should build. The apparatus schematics are conceptual diagrams behind a safety gate — they explain the reasoning, not the construction.