PASSScoutNOVEL -- No prior work combining ITC displacement DeltaDeltaG with receptor mutant scanning for phage resistance prediction (0 papers found). Huss 2021 eLife mapped phage-side fitness landscape via DMS; E1-H5 Session 2026-04-15...Discovered by Alberto TriveroBiophysical Measurement MethodsStructural & Imaging MethodsPhage Biology & Therapy

DeltaDeltaG Mutant Scanning of FhuA Loops L3/L10 with T5 pb5 Distinguishes Fitness-Constrained vs Free Resistance Mutations, with Phase-Variation Rate Included as a Competing Pathway

Measuring binding energy could predict which bacterial mutations will actually resist a virus — and which ones cost too much to survive.

Isothermal titration calorimetry (biophysics)
Phage therapy optimization (clinical microbiology)

ITC displacement DeltaDeltaG scanning of bacterial receptor mutants predicts phage resistance trajectories by mapping the thermodynamic fitness landscape of escape mutations.

StrategyTool Repurposing
Session Funnel7 generated
Field Distance
0.60
Session DateApr 15, 2026
5 bridge concepts
ITC Kd for tail fiber-receptor bindingDeltaH/DeltaS temperature predictionstoichiometry n for aviditycompetition ITC with serumreceptor mutant screening
Composite
7.3/ 10
Confidence
5
Groundedness
5
How this score is calculated ›

6-Dimension Weighted Scoring

Each hypothesis is scored across 6 dimensions by the Ranker agent, then verified by a 10-point Quality Gate rubric. A +0.5 bonus applies for hypotheses crossing 2+ disciplinary boundaries.

Novelty20%

Is the connection unexplored in existing literature?

Mechanistic Specificity20%

How concrete and detailed is the proposed mechanism?

Cross-field Distance10%

How far apart are the connected disciplines?

Testability20%

Can this be verified with existing methods and data?

Impact10%

If true, how much would this change our understanding?

Groundedness20%

Are claims supported by retrievable published evidence?

Composite = weighted average of all 6 dimensions. Confidence and Groundedness are assessed independently by the Quality Gate agent (35 reasoning turns of Opus-level analysis).

R

Quality Gate Rubric

0/10 PASS
R1 Abc StructureR5 Test ProtocolR4 Counter EvidenceR7 Novelty VerifiedR9 Language PreciseR2 Mechanism SpecificR10 Per Claim GroundingR6 Confidence CalibratedR8 Groundedness AccurateR3 Falsifiable Prediction
CriterionResult
R1 Abc Structure[object Object]
R5 Test Protocol[object Object]
R4 Counter Evidence[object Object]
R7 Novelty Verified[object Object]
R9 Language Precise[object Object]
R2 Mechanism Specific[object Object]
R10 Per Claim Grounding[object Object]
R6 Confidence Calibrated[object Object]
R8 Groundedness Accurate[object Object]
R3 Falsifiable Prediction[object Object]
V

Claim Verification

8 verified3 parametric1 unverifiable
Strength: Receptor-side thermodynamic fitness landscape with dual-pathway (point mutation vs phase variation) race model is genuinely novel and well-specified. Displacement ITC protocol resolves the key technical barrier of sub-100 pM Kd. Biologically-anchored DeltaDeltaG threshold replaces arbitrary estimate.
Risk: DAPT competitor identity unverifiable; fhuA regulatory mechanism misattributed (Fur, not OmpR/EnvZ); phase variation rate is estimated, not measured for fhuA specifically. If T5 pb5 binds FhuA cork domain rather than extracellular loops, the entire loop-mutant panel targets the wrong region.
E

Empirical Evidence

Evidence Score (EES)
4.2/ 10
Convergence
None found
Clinical trials, grants, patents
Dataset Evidence
15/ 25 claims confirmed
HPA, GWAS, ChEMBL, UniProt, PDB
How EES is calculated ›

The Empirical Evidence Score measures independent real-world signals that converge with a hypothesis — not cited by the pipeline, but discovered through separate search.

Convergence (45% weight): Clinical trials, grants, and patents found by independent search that align with the hypothesis mechanism. Strong = direct mechanism match.

Dataset Evidence (55% weight): Molecular claims verified against public databases (Human Protein Atlas, GWAS Catalog, ChEMBL, UniProt, PDB). Confirmed = data matches the claim.

S
View Session Deep DiveFull pipeline journey, narratives, all hypotheses from this run
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Bacteria and the viruses that infect them (called bacteriophages, or 'phages') are locked in an evolutionary arms race. Bacteria can mutate a surface protein to dodge a phage, but those same proteins often serve critical functions — like importing iron, a nutrient bacteria desperately need. So not every possible escape mutation is a viable one; some would save the bacterium from the virus but starve it to death. This hypothesis proposes a clever way to map that trade-off using a lab technique called isothermal titration calorimetry, or ITC — essentially a device that measures the heat released when two molecules bind together. The idea is to take a bacterial surface protein called FhuA — which the T5 phage uses as its docking port — and systematically mutate the bits the phage grabs onto. For each mutant, you'd measure two things: how badly the mutation disrupts the phage's grip (using ITC to quantify binding energy), and how well the bacterium can still import iron through that same protein. Plotting these two values creates a map with four regions. Mutations that block the phage AND preserve iron import are the dangerous ones — the bacteria can 'escape for free.' Mutations that block the phage but cripple iron import are evolutionary dead ends. The hypothesis adds another wrinkle: bacteria can also dodge phages through a completely different trick called phase variation, where they essentially switch certain genes on or off at random. Whether bacteria use point mutations or phase variation to escape might depend entirely on whether iron is scarce or abundant in the environment. This matters because phage therapy — using viruses to kill drug-resistant bacteria — is being explored as a weapon against superbugs. One of its biggest problems is that bacteria evolve resistance, sometimes within days. If you could predict in advance which escape routes are genuinely available to a bacterium and which are thermodynamic dead ends, you could design phage therapies that corner the bacteria evolutionarily, leaving them with no good options.

This is an AI-generated summary. Read the full mechanism below for technical detail.

Why This Matters

If confirmed, this approach could give clinicians and researchers a thermodynamic 'resistance map' before deploying a phage therapy, identifying which bacterial escape mutations are biologically feasible versus which would be self-defeating — essentially predicting the evolutionary future of an infection. It could also explain why iron availability in different body compartments (bloodstream versus gut versus lungs) might change how quickly and through what mechanism bacteria develop phage resistance, informing smarter treatment protocols. The framework could extend beyond this specific phage-receptor pair to other phage-bacteria systems, offering a generalizable tool for rational phage cocktail design. Given the global urgency of antibiotic resistance, even a partial ability to anticipate and pre-empt bacterial escape makes this hypothesis well worth testing experimentally.

M

Mechanism

ITC displacement protocol (weak competitor pre-loaded, T5 pb5 titrant) measures DeltaDeltaG for ~20 FhuA surface loop mutants relative to wild-type. Each mutant simultaneously characterized for ferrichrome transport competence. The two-dimensional landscape (DeltaDeltaG_pb5 vs DeltaG_ferrichrome) defines four quadrants distinguishing free vs constrained resistance mutations. Phase variation at ~10^-4/generation competes as a parallel resistance pathway. Under iron limitation, point mutations at Quadrant II (high DeltaDeltaG, low fitness cost) dominate; under iron repletion, phase variation dominates. DeltaDeltaG threshold of 22.8 kJ/mol derived from published k_ads minimum for bacterial clearance.

+

Supporting Evidence

T5 pb5-FhuA: Kd <100 pM (Plancon 2002 JMB, PMID 12051859). PDB 8A8C confirms complex structure. FhuA loop functional data from Endriss and Braun 2004 J Bacteriol. Displacement ITC validated (Sigurskjold 2000 Anal Biochem). Adjacent: Huss 2021 eLife mapped phage-side fitness landscape via DMS of T7 RBP (1660 variants). Strong convergence: April 2026 bioRxiv (1050 genome-wide screens, 255 phages) and mBio 2025 (PP01 gp38-OmpC crystal structure at 2.1A) independently map the same receptor interface E1-H5 proposes to scan thermodynamically.

?

How to Test

Express 20 FhuA loop mutants (L3/L4/L8/L10/L11 alanine substitutions). Displacement ITC with weak competitor pre-loaded (20 uM, HEPES pH 7.4, 150 mM NaCl, 0.05% DDM), T5 pb5 syringe (0.5 uM). Ferrichrome transport assay per mutant. Plot 2D landscape; identify Quadrant II. Run 20 parallel T5 serial-passage experiments (10 iron-replete, 10 iron-limited); sequence after 10 passages. TRUE if resistance mutations in iron-limited medium cluster at Quadrant II positions. FALSE if mutations spread uniformly or phase variation dominates even under iron limitation. Timeline: 6-9 months.

What Would Disprove This

See the counter-evidence and test protocol sections above for conditions that would falsify this hypothesis. Every surviving hypothesis must pass a falsifiability check in the Quality Gate — ideas that cannot be proven wrong are automatically rejected.

X

Cross-Model Validation

Independently assessed by Gemini 3.1 Pro for triangulation.

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