Analyze What Papers Actually Reported

 Quick Analysis Plan Copied

 Long Analysis Plan

Method Performance & Core Limitations

What works: On simulated PYP intermediates (pR0, pR1, pR2, pB0), noiseless reconstruction achieves PCC=1.0 for all four states with R=0. Adding Gaussian noise at SNR 1.0 preserves real-space PCC>0.85 for all recovered states; at extreme SNR 0.1, maps remain interpretable though weak density is obscured Kinetic decomposition simulation benchmark. In experimental bacterial phytochrome TR-SX (17 timepoints, femto–microsecond), decomposition into four states raises mean ΔF/σ ~1.5× and reflections >3σ from ~37% to >55% Phytochrome SNR improvement evidence.

Key limitations (reported): (1) Concentration profiles are fixed inputs, not fitted — decomposition validates implementation, not kinetic model correctness; (2) coherent atomic motions at sub-picosecond delays violate the population-state approximation; (3) no independent ground truth for experimental phytochrome states; (4) requiring reflections present at all 17 timepoints discards ~40% of data Method limitations from authors.

Reflection Coverage: Filtering Sensitivity

A practical mitigation: relaxing the all-timepoint requirement to ≥12/17 retains 131,257 of 141,174 reflections with map PCC=0.98 vs raw, compared to 84,478 reflections at PCC=0.92 with the strict all-17 filter Reflection coverage filtering sensitivity.

Analysis Plan (Python)

• Ingest & validate: Load MTZ/structure factors via gemmi or CCP4 tools; verify dark-state phase/sign conventions and Ursby-Bourgeois weighting before decomposition.
• Simulate ground truth: Generate PYP-like mixed data with phenix.fmodel; inject known Gaussian noise to test recovery across SNR grid (0.1, 0.5, 1.0, 2.0).
• Run decomposition: Apply least-squares matrix inversion (Eq. 5) with known profiles; cross-check with real-space PCC and Fourier residual diagnostics.
• Model-correctness test: Repeat with deliberately wrong concentration profiles — if state DED maps converge, the profile choice is underdetermined; if they diverge, profiles are identifiable.
• Fit-vs-fixed comparison: Implement joint least-squares fitting of concentration profiles (nonlinear least squares over the kinetic matrix) as an alternative; compare map PCC and ΔF/σ against fixed-profile results.
• Coverage sweep: Scan reflection-coverage thresholds (n≥k/17) to quantify reflections-vs-PCC tradeoff, plotting Pareto frontiers.
• Sample-delivery coupling: For experimental design, use fixed-target or segmented-droplet approaches to reduce protein consumption (97% savings reported for segmented droplets) Sample delivery methods review.