GPT-5-Pro Output - no directive

You’ve engaged GPT‑5 Pro as a principal investigator and method engineer. Below I invent and critically evaluate novel in‑silico biochemistry experiments around a concrete question and deliver a fully self‑contained design your wet‑lab partners could later prototype after safety/ethics review. I do not include step‑by‑step wet‑lab instructions.

Assumptions (filling your CONTEXT placeholders)

System/organism or matrix: human CD8⁺ T cells (activated, ex vivo–like state).
Biological question / target pathway: How do PD‑1 ITIM/ITSM phosphorylation states, SHP2/SHP1 recruitment, and PD‑1 microcluster spatial organization jointly suppress early TCR/CD28 signaling? PD‑1 = PDCD1; ligands PD‑L1/PD‑L2.
Constraints: low cell input per condition; require isoform awareness (membrane vs soluble PD‑1 splice isoforms); avoid mass spectrometry; keep all models lightweight.
Available readouts: NGS (targeted isoform RNA-seq), dPCR, flow cytometry (phospho‑flow), and imaging (confocal/TIRF‑like data); PLA and native separations are allowed as methods (no procedural steps here).
Prior methods to leverage: in situ PLA for proximity, native (non‑denaturing) separations, aptamer/sensor concepts, DSI‑Seq‑like summarization ideas.
In‑silico budget: ~1 CPU‑day; models must be ODE/Markov/agent‑based light; no HPC.

Key literature facts grounding this plan: PD‑1 contains ITIM/ITSM motifs that, when phosphorylated, recruit SHP2; PD‑1 forms microclusters with TCR at the immunological synapse; multiple groups report preferential dephosphorylation of CD28 by PD‑1; SHP1 vs SHP2 engagement is context‑dependent; soluble PD‑1 (sPD‑1) splice isoforms exist and may modulate signaling. RUPress PMC+3PMC+3PMC+3 eLife Nature

1) Problem framing

Hypotheses (5–10) with brief mechanistic rationale and expected effect sizes

H1. ITSM phosphorylation drives SHP2 occupancy on PD‑1 more strongly than ITIM, yielding larger inhibitory effect on proximal signaling. Rationale: SHP2 SH2 domains bind PD‑1 ITSM with higher apparent affinity/competence. Expected effect: 25–45% drop in pZAP70 median; 95% plausible range 15–60%. eLife

H2. PD‑1 microclusters co‑localize with TCR microclusters and transiently enrich SHP2 at the synapse, sharpening inhibition within ~1 µm of the contact zone. Effect: ~30–50% reduction in synaptic pCD3ζ within central 0.5 µm; 95% range 15–60%. PMC

H3. CD28 is the primary phosphatase target of PD‑1/SHP2 under typical co‑stimulation, and its dephosphorylation explains a substantial fraction of early inhibition. Effect: 30–50% drop in pCD28; 95% range 20–60%. PMC

H4. SHP1 partially compensates when SHP2 is reduced, leaving a residual (smaller) inhibitory effect. Effect: residual 5–20% drop in pZAP70; 95% range 0–30%. ScienceDirect

H5. Phosphorylation increases negative charge and lowers the apparent pI of the PD‑1 tail, modestly stabilizing SH2‑mediated interactions. Effect: ΔpI ≈ −0.3 to −0.8 per additional phosphotyrosine; 95% range −0.1 to −1.2. PubMed

H6. Conformational compaction of the PD‑1 tail upon SHP2 binding is detectable as a FRET E increase of ~0.10–0.20 (95% range 0.05–0.25). Rationale: PD‑1 tail–SHP2 complex alters effective donor‑acceptor distance; E≈1/[1+(r/R0)^6]. PMC

H7. Soluble PD‑1 (sPD‑1) splice isoforms act as ligand sinks, reducing PD‑1 engagement on the T cell and paradoxically increasing pZAP70/pAKT at fixed ligand. Effect: pZAP70 +5–15% over baseline for top tertile sPD‑1; 95% range 0–20%. Nature Frontiers

H8. PD‑1 density and synaptic dwell time drive heterogeneity: cells with high PD‑1 surface density show steeper radial gradients and larger inhibition; 95% of single‑cell effects vary ±40% around the condition mean. Nature

H9. ITSM Y→F abrogates SHP2 recruitment, collapsing PLA proximity signals to near‑background. Effect: ≥70% reduction in PLA puncta per cell; 95% range 50–90%. RUPress

Causal DAG (ASCII)

PD-L1/PD-L2 density  ->  PD-1 engagement  ->  ITIM/ITSM phosphorylation -> SHP2 recruitment
                               |                                         \-> SHP1 recruitment (weaker)
                               v
                         PD-1 microclusters (spatial colocalization with TCR)

SHP2 recruitment -> dephosphorylation of CD28 (primary) -> ↓pAKT/PI3K
                  -> dephosphorylation of TCR/CD3ζ (context) -> ↓pZAP70

Modifiers/confounders: PD-1 density, CD28/TCR density, sPD-1 isoforms (ligand sink),
                       imaging/assay noise, cell cycle state, batch effects.
Outputs: pCD28, pCD3ζ, pZAP70 (flow); PLA puncta; radial intensity profiles; FRET E; isoform ratios.

2) Candidate experiment designs (≥6)

Legend: Axis coverage tags → [MW] size, [pI] charge, [CONF] conformation/stability, [ASSY] native assembly, [ENZ] enzyme activity, [SPAT] spatial context.

D1. Motif‑occupancy PLA (state) — [pI][CONF][ENZ]

Schematic: PD‑1 tail with Y223(ITIM)/Y248(ITSM); phospho‑dependent SHP2 proximity detected by PLA dots (state proxy of binding).
What measured: State (PD‑1↔SHP2 proximity).
Knobs: pY fraction (0–1), Kd_ITSM, [SHP2]/[competitors], motif mutants (Y→F).
Falsifiable readout: PLA puncta/cell shift ≥2× vs Y→F or PD‑L1‑off controls; distribution not explainable by null/bridging model. PMC RUPress

D2. Conformational FRET on PD‑1 tail (state) — [CONF]

Schematic: Intramolecular donor/acceptor flanking PD‑1 tail segment; E increases on compaction with SHP2 binding.
Measured: State (distance r).
Knobs: R0, donor–acceptor orientation factor κ², tail persistence length, SHP2 presence.
Falsifiable readout: ΔE≥0.10 (95% CI excludes 0) under PD‑1 engagement; orientation/photophysics controls separate. PMC

D3. Native‑assembly & clustering (function‑adjacent) — [ASSY][SPAT]

D4. Enzyme‑activity ODE (function) — [ENZ]

Schematic: Minimal ODE linking PD‑1→SHP2 to pCD28 and pCD3ζ dephosphorylation and pZAP70 output; read out by phospho‑flow.
Measured: Function (phosphorylation state distributions).
Knobs: occupancy f, catalytic weight α for CD28 vs CD3ζ, residual SHP1.
Falsifiable readout: ≥0.06–0.10 absolute drop in pZAP70 median with engagement; reversible with SHP2 disruption. PMC

D5. Charge/pI shift vs binding propensity (state) — [pI]

Schematic: Phosphorylation adds negative charges; simulate ΔpI and a generic “apparent mobility index” (AMI) in a native separation surrogate.
Measured: State (bulk electrostatic signature).
Knobs: number of phosphotyrosines (0–2), baseline pI₀, ionic strength proxy.
Falsifiable readout: Monotonic AMI shift with added phosphates; Y→F collapses to baseline. PubMed

D6. Spatial gradient mapping (state→function) — [SPAT][ASSY]

Schematic: Reaction–diffusion radial profile of PD‑1 intensity at the synapse; fit decay length λ.
Measured: State (radial intensity; AUC).
Knobs: λ, initial synaptic enrichment I₀, dwell time.
Falsifiable readout: Significant λ decrease and AUC reduction with ligand. Nature

D7. Isoform‑aware dPCR (state→function link) — [MW]

Schematic: Model counts for full‑length PDCD1 (membrane) vs sPD‑1 (soluble) isoforms; regress against predicted inhibition.
Measured: State (isoform fraction) correlated with functional proxy (pZAP70).
Knobs: isoform ratio, ligand, PD‑1 density.
Falsifiable readout: Positive correlation (ρ≈0.3–0.6) between sPD‑1 fraction and pZAP70 at constant ligand. Nature Frontiers

D8. SHP2 vs SHP1 competition (function attribution) — [ENZ][CONF]

3) In‑silico analyses

Below are minimal models, parameter choices (with justification), and synthetic datasets (CSV‑style) for representative scenarios per design. All numbers are unit‑less unless noted; parameters chosen to produce realistic effect sizes and do not imply procedural conditions.

D1. Motif‑occupancy PLA (mass action + Poisson counts)

Model: Fraction bound f=pY⋅[SHP2]Kd+[SHP2]f = pY \cdot \frac{[\text{SHP2}]}{K_d + [\text{SHP2}]}f=pY⋅Kd+[SHP2][SHP2]. Competitors inflate KdK_dKd to Kd′=Kd(1+[C]/Ki)K_d' = K_d(1+[C]/K_i)Kd′=Kd(1+[C]/Ki). Expected PLA counts per cell λ=λ0+c⋅f\lambda = \lambda_0 + c \cdot fλ=λ0+c⋅f.
Chosen values: Kd=0.2K_d=0.2Kd=0.2 (ITSM‑like), [SHP2]=1[\text{SHP2}]=1[SHP2]=1, λ0=2\lambda_0=2λ0=2, c=20c=20c=20. (Toy magnitudes; ITSM affinity > ITIM consistent with literature.) eLife

Synthetic data (12 cells per scenario)

scenario,counts
effect_present (pY=0.8, Kd=0.2),10,11,18,10,17,18,14,19,11,17,12,16
absent_control (pY=0.05, Kd=0.2),3,3,4,7,1,4,5,5,3,2,3,4
edge_competition (pY=0.8, Kd'=1.0),11,13,8,11,6,8,9,17,10,12,11,7

Summary & power (two‑sample t; α=0.05, 1‑β=0.8):
Means ~14.4 vs 3.7 (Δ≈10.7; pooled SD≈2.5–3.2) ⇒ n≈4/group; to absorb batch/overdispersion, plan ≥8/group.

D2. Conformational FRET (Förster theory + Gaussian noise)

Model: E=11+(r/R0)6E = \frac{1}{1+(r/R_0)^6}E=1+(r/R0)61; R0=5.4R_0=5.4R0=5.4 nm. Scenario means: baseline r≈5.6r\approx5.6r≈5.6 ⇒ E≈0.45E\approx0.45E≈0.45; engaged/compacted r≈5.0r\approx5.0r≈5.0 ⇒ E≈0.61E\approx0.61E≈0.61. Add σ≈0.06. PMC

Synthetic data (12 cells)

scenario,E
baseline,0.412,0.449,0.467,0.459,0.426,0.440,0.469,0.418,0.442,0.462,0.431,0.463
engaged,0.610,0.638,0.657,0.588,0.585,0.592,0.633,0.576,0.657,0.628,0.596,0.646
edge_bleach (E-0.05),0.53,0.62,0.61,0.52,0.48,0.54,0.62,0.58,0.67,0.60,0.47,0.57

Power: Detecting ΔE≈0.12 with σ≈0.08 ⇒ n≈7/group.

D3. Native‑assembly & clustering (agent‑based surrogate)

Model: Per‑cell cluster count ~ Poisson(λ); cluster sizes ~ geometric(p).
Scenarios: Null λ=3, p=0.5; Engaged λ=10, p=0.3.

Synthetic per‑cell summary (20 cells)

scenario,clusters,avg_size,total_members
null,3,1.0,3
null,1,1.0,1
...
null,1,2.0,2
engaged,9,3.22,29
engaged,13,3.69,48
...
engaged,14,2.29,32

Power (Poisson rate test): λ 2.7→9.8 ⇒ n≈2/group; use ≥10/group to buffer imaging/batch noise.

D4. Enzyme‑activity ODE (steady state; phospho‑flow surrogate)

Model: pCD28=p0,281+α28fp_{CD28} = \frac{p_{0,28}}{1+\alpha_{28} f}pCD28=1+α28fp0,28, pCD3ζ=p0,3ζ1+α3ζfp_{CD3\zeta}=\frac{p_{0,3\zeta}}{1+\alpha_{3\zeta} f}pCD3ζ=1+α3ζfp0,3ζ; pZAP70=β⋅pCD3ζK+pCD3ζp_{ZAP70} = \beta \cdot \frac{p_{CD3\zeta}}{K+p_{CD3\zeta}}pZAP70=β⋅K+pCD3ζpCD3ζ.
Values: p0,28=0.6p_{0,28}=0.6p0,28=0.6, p0,3ζ=0.5p_{0,3\zeta}=0.5p0,3ζ=0.5, α28=1.2\alpha_{28}=1.2α28=1.2, α3ζ=0.8\alpha_{3\zeta}=0.8α3ζ=0.8, β=0.9 \beta=0.9β=0.9, K=0.3K=0.3K=0.3. Compare f=0f=0f=0 vs f=0.7f=0.7f=0.7.

Synthetic distributions (30 cells; mean±SD)

marker,baseline_mean,baseline_sd,engaged_mean,engaged_sd
pCD28,0.586,0.095,0.309,0.089
pCD3z,0.514,0.068,0.338,0.059
pZAP70,0.549,0.064,0.469,0.057

Power: ΔpZAP70≈0.08 with σ≈0.06 ⇒ n≈9/group; plan ≥12/group. CD28 primacy aligns with prior work. PMC

D5. Charge/pI shift (toy electrostatics + mobility proxy)

Model: Each phosphotyrosine contributes ≈ −2 effective charges around neutral pH; approximate ΔpI per phosphate −0.4 (sequence‑agnostic toy). AMI (apparent mobility index) === a + b·(net charge).
Values: baseline pI₀=6.2; AMI a=15, b=1.2; baseline charge Q₀=+2.

Synthetic table

n_phosphates,pI_pred,AMI_pred
0,6.20,17.40
1,5.80,15.00
2,5.40,12.60

Interpretation matches general observations that phosphorylation lowers pI and alters mobility. PubMed

D6. Spatial gradient mapping (radial decay)

Model: I(r)=I0 e−r/λ+ϵI(r)=I_0\,e^{-r/\lambda} + \epsilonI(r)=I0e−r/λ+ϵ.
Scenarios: Null λ=0.6, I₀=0.8; Engaged λ=0.3, I₀=0.8. Radii r=0…1.0 in 0.2 steps.

Synthetic profiles

r, I_null, I_engaged
0.0,0.816,0.816
0.2,0.543,0.439
0.4,0.465,0.179
0.6,0.274,0.125
0.8,0.155,0.031
1.0,0.193,0.000
AUC,0.388,0.236

Power: AUC drop ≈0.152; with per‑cell AUC SD~0.07 ⇒ n≈6/group.

D7. Isoform‑aware dPCR (state→function link)

Model: Simulate per‑donor FL and sPD‑1 counts; occupancy factor f=max⁡(0,min⁡(1,a FL−b sPD1)) f=\max(0, \min(1, a\,FL - b\,sPD1))f=max(0,min(1,aFL−bsPD1)); map to pZAP70 via D4 equations.
Representative donors (n=12)

donor,FL_counts,sPD1_counts,sPD1_frac,occupancy_f,pZAP70_pred
1,4740,1003,0.175,0.468,0.493
2,5228,1439,0.216,0.483,0.491
3,4652,952,0.170,0.463,0.494
4,6473,562,0.080,0.721,0.463
5,6880,2914,0.298,0.534,0.485
...
12,8206,2053,0.200,0.709,0.465

Result: correlation(sPD‑1 fraction, pZAP70) ρ≈+0.60 in this toy draw (direction consistent with sPD‑1 ligand sink). Power: with |ρ|≈0.5, n≈23 to reach 80% power two‑sided (Fisher z). Nature Frontiers

D8. SHP2 vs SHP1 competition (necessity/sufficiency)

Model: Effective dephosphorylation coefficient keff=k2 [SHP2] f+k1 [SHP1] f′k_{\text{eff}} = k_2\,[\text{SHP2}]\,f + k_1\,[\text{SHP1}]\,f'keff=k2[SHP2]f+k1[SHP1]f′. Feed into D4.
Synthetic summary

condition,pCD3z_ss,pZAP70_pred
WT,0.374,0.500
SHP2_KO (SHP1 compensates),0.463,0.546
SHP2_partial_i,0.446,0.538

Decision note: A ≥0.05–0.10 pZAP70 increase upon SHP2 reduction would support SHP2 dominance; weaker shifts suggest compensation/alternative mechanisms. ScienceDirect

Noise/batch sensitivity overview

PLA: overdispersion (NB θ≈5) shifts needed n from ~4 to 8–10/group.
FRET: photobleaching/orientation inflate σ to 0.10 → n≈13/group. Nature
Flow phospho: day‑to‑day SD +0.02 absolute → n≈14/group advisable.
Spatial profiles: segmentation bias inflates AUC SD to ~0.1 → n≈11/group.

4) Controls, confounders, and failure signatures

Controls

Negatives: PD‑1 ITSM Y→F (recruitment‑dead), isotype/PLA probe mismatches, ligand‑absent surface, PD‑1‑low cells. PMC
Positives: PD‑1 engagement with canonical ligand; phosphatase trap mutant to stabilize PD‑1:SHP2 proximity (conceptual); CD28‑high activation to expose CD28‑targeting. PMC
Orthogonal comparators: phospho‑flow (pCD28/pCD3ζ/pZAP70), spatial microcluster imaging, isoform‑ratio dPCR cross‑section. PMC

Confounders

Variable PD‑1 density; ligand heterogeneity; cell cycle; cytoskeletal perturbations; antibody cross‑linking artifacts (especially PLA).

≥8 failure modes & diagnostic patterns in synthetic data

PLA bridging artifact: uniformly high PLA counts including Y→F control; variance low across scenarios.
Photobleaching in FRET: time‑dependent E decrease; edge_bleach table (D2) pattern where E shifts down ~0.05 across all conditions. Nature
Orientation factor bias (κ² drift): E spread widens (σ↑) without matching ΔE in means.
Segmentation errors (SPAT): AUC distributions broaden; λ estimates unstable with radius‑bin outliers at r→1.0.
Batch phospho‑flow drift: global pZAP70 baseline shift across all conditions; Δ between paired conditions shrinks on certain days.
SHP1 hyper‑compensation: D8 shows minimal pZAP70 change in SHP2_KO; instead pCD28 shifts modestly—pattern suggests alternate phosphatase usage. ScienceDirect
Isoform primer mis‑assignment (dPCR): sPD‑1 fraction bimodal/inflated with no pZAP70 correlation.
Ligand presentation failure: D3/D6 show no cluster/AUC change despite strong PLA; spatial assembly missing while proximity is present.
Non‑specific aptamer/sensor binding: charge/pI proxy (D5) changes without corresponding PLA or phospho‑flow effects.

5) Prior art + novelty risk check (brief)

Known methods/insights: PD‑1 microclusters recruiting SHP2 at the synapse; preferential CD28 targeting; SHP2 SH2 domains bridging PD‑1 motifs; PLA for in situ proximities; general FRET/WLC distance modeling; soluble PD‑1 isoforms in blood. PMC+4PMC+4PMC+4 Frontiers
Novelty in this design: the multi‑axis, in‑silico‑first integration (motif occupancy ↔ conformation ↔ native assembly ↔ enzyme activity ↔ spatial gradients ↔ isoform‑genetic context) without mass spectrometry, paired with synthetic data contracts that define go/no‑go gates across modalities.
Overlap/mitigation: If PLA+microcluster imaging are deemed crowded IP spaces, pivot to (a) isoform‑aware causal modeling as the primary novelty, (b) charge‑sensitive native separation surrogates as orthogonal state readouts, and (c) SHP1/SHP2 attribution maps to adjudicate mechanistic debates. ScienceDirect

6) Prioritized plan

Ranked by expected value × feasibility

D4 + D1 joint readout (function + state): phospho‑flow pZAP70/pCD28 anchored to PLA‑quantified PD‑1:SHP2 occupancy.
D3 + D6 spatial package: microcluster statistics with radial AUC decay to connect proximity → spatial assembly.
D7 isoform modeling: adds donor‑level covariate explaining heterogeneity; useful for translational stratification.
D2 FRET: strong mechanistic clarity; added instrumentation complexity.
D8 SHP2/SHP1 attribution: decisive but requires perturbation contexts.
D5 pI/charge proxy: supportive mechanistic triangulation.

Top‑2 decision gates (numeric, in silico thresholds)

Gate A (D4+D1):
- Go if ΔpZAP70 ≥ 0.06 (95% CI excludes 0) and PLA shows ≥2.0× shift vs Y→F in matched pairs; Bayes factor >10 for model including CD28 term over TCR‑only term.
- No‑go/pivot if ΔpZAP70 < 0.03 or PLA/Y→F ratio <1.3 consistently.
Gate B (D3+D6):
- Go if mean clusters/cell increases ≥3× and AUC drops ≥0.10 with λ shrinkage ≥0.15 µm equivalent in model fits.
- No‑go if cluster distributions overlap (KS p>0.2) and AUC change <0.05.

Materials/skills dependencies (non‑operational)

Validated antibodies for PD‑1 tail phospho‑motifs and SHP2 epitopes (for PLA).
Standard phospho‑flow panels (CD28, CD3ζ, ZAP70).
High‑NA TIRF/confocal access for microcluster imaging; image analysis pipeline.
Targeted isoform dPCR primer/probe designs.

IRB/biosafety note
Primary human T‑cell work requires IRB/consent and BSL‑2 laboratory practices; no pathogen exposure implied. Imaging/flow pose minimal risk to staff and donors under standard procedures.

7) Communication‑ready artifacts

Figures (ASCII sketches)

F1 (D1+D4 concept): occupancy → function

PD-L1 ----> PD-1(ITIM/ITSM-pY) --(binds)--> SHP2
   |                                    |
   v                                    v
 microclusters                    dephosphorylation
     |                         CD28    |    CD3ζ
     v                                  v
   PLA dots  ------------------>  pZAP70 (flow)
               calibration curve: PLA f  -> ΔpZAP70

F2 (D3+D6 concept): spatial gradient

Synapse center (r=0) --------------------> Periphery (r=1.0 μm)
I(r)=I0 e^{-r/λ}
Engaged: steeper decay (λ small), higher center density
Null: shallow decay (λ large)

150‑word abstracts (top 2 designs)

Abstract A — Motif occupancy to function (D1+D4)
Checkpoint inhibition by PD‑1 emerges from phosphotyrosine‑dependent recruitment of SHP2 and subsequent dephosphorylation of CD28 and CD3ζ. We propose an integrated in‑silico framework coupling a mass‑action model of PD‑1:SHP2 occupancy (quantified by in situ proximity ligation surrogate) to a minimal ODE model predicting phospho‑flow outputs (pCD28, pCD3ζ, pZAP70). Synthetic datasets define decision thresholds: ≥2× PLA shift versus Y→F mutants and ≥0.06 absolute pZAP70 reduction at matched ligand density indicate meaningful inhibition. Power analyses account for overdispersion and batch drift, prescribing 8–12 observations per group for robust detection. This design triangulates the debated roles of ITSM vs ITIM and of CD28 preferential dephosphorylation while remaining fully non‑MS and low‑input compatible. Results will directly prioritize conditions for mechanistic validation and quantify how motif‑level binding states map to functional signaling decrements. eLife PMC+1

Abstract B — Spatial assembly package (D3+D6)
We model the formation of PD‑1 microclusters within the immunological synapse and their impact on local signaling intensity profiles. An agent‑based clustering surrogate and a radial reaction–diffusion model quantify two falsifiable metrics: per‑cell cluster count/size distributions and synaptic intensity AUC/decay length (λ). Synthetic data show engaged conditions shifting from λ≈0.6 to 0.3 and cluster counts from ~3 to ~10 per cell. Decision gates require ≥3× cluster increase and ≥0.10 AUC drop. These spatial metrics link molecular proximity (from D1) to mesoscale assembly, providing a mechanistic bridge between receptor engagement and localized phosphatase action. The package is agnostic to mass spectrometry and compatible with low‑input imaging. By codifying expected spatial signatures and their variance, the design supports rigorous go/no‑go choices for microcluster‑targeted interventions. PMC Nature

One‑slide executive summary (bullets)

Goal: Decompose PD‑1 inhibition into motif occupancy, enzyme activity, and spatial assembly (in‑silico first; no MS).
Top signals: PLA occupancy ↑ (≥2×) ↔ pZAP70 ↓ (≥0.06); microclusters ↑ (≥3×), radial AUC ↓ (≥0.10).
Mechanistic crux: ITSM‑biased SHP2 recruitment; CD28 is primary dephosphorylation target; SHP1 may partially compensate. eLife PMC ScienceDirect
Heterogeneity driver: sPD‑1 isoforms reduce engagement; expect ρ≈0.3–0.6 with pZAP70 at fixed ligand. Nature
Power & feasibility: 8–12 obs/group covers overdispersion; spatial AUC needs ~6–11/group depending on segmentation noise.
Risk mitigations: explicit Y→F controls (PLA), time‑resolved photophysics (FRET), batch‑paired phospho‑flow, blinded image analysis.
Next step: execute D1+D4 modeling‑plus‑data contract; if passes Gate A, expand to D3+D6 spatial package and D7 isoform stratification.

References (selected, supporting major claims)

PD‑1 microclusters recruit SHP2 and suppress phosphorylation near the synapse. PMC PubMed
CD28 is a primary PD‑1 target in intact cells. PMC PubMed
ITSM/ITIM contributions and SHP2 SH2 domain engagement. eLife PMC
SHP2 requirement vs compensation debate. ScienceDirect
PLA method enables in situ proximity at <~40 nm. PMC
FRET distance equation E=1/[1+(r/R0)6]E=1/[1+(r/R_0)^6]E=1/[1+(r/R0)6]. PMC
Phosphorylation lowers pI and shifts mobility distributions. PubMed
sPD‑1 isoforms and clinical presence. Nature Frontiers

If you want this reframed to a different target/pathway (e.g., cGAS–STING in myeloid cells, JAK/STAT in NK cells, or a secreted‑protease axis in plasma) while keeping the in‑silico playbook, I can produce a parallel package immediately.

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GPT-5-Pro Output - no directive

GPT-5-Pro Output - no directive