--- name: bio-crispr-screens-prime-editing-screens description: "Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function." tool_type: mixed primary_tool: PRIDICT2 --- ## Version Compatibility Reference examples tested with: PRIDICT2 v1.0+ (https://github.com/uzh-dqbm-cmi/PRIDICT2), CRISPResso2 2.2.14+, pandas 2.2+, biopython 1.83+, numpy 1.26+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `python pridict2_pegRNA_design.py single --help`; `python pridict2_pegRNA_design.py batch --help` - Web: PRIDICT2 web interface at https://pridict.it/ If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## Prime-Editing Screen Analysis **"Design or analyze a pooled prime-editor screen"** -> Design pegRNAs (spacer + scaffold + PBS + RTT) for intended edits, predict efficiency with PRIDICT2, filter pre-synthesis to efficient candidates, install variants in the screen, quantify intended-edit vs scaffold-incorporation vs indel via CRISPResso2, and aggregate to per-variant fitness scores. - Python: `PRIDICT2` for pegRNA efficiency prediction - CLI: `CRISPResso --prime_editing_pegRNA_*` for amplicon-level analysis - Workflow: pegRNA library design -> PRIDICT2 filtering -> screen execution -> CRISPResso2 quantification -> per-variant scoring ## Prime Editor Chemistry Comparison | Editor | Year | Mechanism | Indel rate | Use when | |--------|------|-----------|------------|----------| | PE2 (Anzalone 2019) | 2019 | nCas9-RT fusion + pegRNA | 1-3% | Standard PE; lowest indel rate | | PE3 | 2019 | PE2 + nick of opposite strand by additional sgRNA | 2-5% | Higher editing efficiency, slightly more indels | | PE3b | 2019 | PE3 with edit-blocking ssgRNA | 1-3% | When PE3's added nick risks unwanted indels | | PEmax (Chen 2021) | 2021 | Engineered RT + nCas9 | 1-2% | Higher editing rate per pegRNA | | PEAR (Erwood 2023) | 2023 | PE with optimal pegRNA scaffold | 1-2% | Improved PE scaffold | | PE5max (Chen 2021) | 2021 | PEmax with engineered scaffold variants | 1% | Highest efficiency at favorable sites | | Dual-pegRNA / PE6 (2024) | 2024 | Twin pegRNA system | Variable | Specific applications | **Decision rule:** For pooled screens at scale, PE2 or PEmax (less RAM-intensive in cells) is preferred over PE3 (additional sgRNA complicates library architecture). For specific high-efficiency edits, PEmax + PRIDICT2-optimized pegRNA. ## pegRNA Architecture A pegRNA contains four critical elements that determine efficiency: ``` 5' SPACER (20 nt) -- standard sgRNA spacer; defines target locus via NGG PAM + SCAFFOLD (~80 nt) -- canonical or engineered scaffold (Chen 2021 has improved scaffold) + PBS (Primer Binding Site, 8-15 nt) -- complements protospacer downstream of cut site + RTT (Reverse Transcription Template, 10-30 nt) -- encodes intended edit; copied by RT 3' ``` **Key design parameters:** - **PBS length:** 11-13 nt typical; longer for high-GC contexts; PBS GC fraction critical (35-65% target) - **RTT length:** 10-20 nt typical; longer for distant edits (10+ bp away from cut) - **RTT-edit position:** intended edit at position 4-30 from cut site - **Scaffold:** standard sgRNA scaffold OR Chen 2021 engineered scaffold (5-10% higher editing) ## PRIDICT and PRIDICT2 pegRNA Efficiency Prediction **Mathis N et al 2023 *Nat Biotechnol* 41:1151 (PRIDICT v1) / 2025 *Nat Biotechnol* 43(5):712 (PRIDICT2; published online June 2024)** developed deep-learning predictors of per-pegRNA editing efficiency. PRIDICT2 is the current state of the art. ```bash # PRIDICT2 is invoked via CLI: pridict2_pegRNA_design.py # Single sequence input: python pridict2_pegRNA_design.py single \ --sequence-name BRCA1_c5135 \ --sequence "AGCAGCCT(C/T)CTGAATGCCC...60nt_context" \ # parens = intended edit --output-dir predictions/ \ --use_5folds # 5-fold ensemble averaging # Batch input from CSV: python pridict2_pegRNA_design.py batch \ --input-fname variants_to_design.csv \ # CSV: sequence_name, sequence --output-dir predictions/ \ --cores 4 \ --summarize # generate summary table # Output: per-pegRNA predictions in predictions// # Columns: PBS_sequence, PBS_length, RTT_sequence, RTT_length, predicted_editing_efficiency, # predicted_indel_rate, deep_ensemble_score, etc. ``` **Loading PRIDICT2 results in Python:** ```python import pandas as pd from pathlib import Path def load_pridict2_predictions(prediction_dir): '''Load PRIDICT2 batch outputs from prediction_dir/''' summary = pd.read_csv(Path(prediction_dir) / 'pridict2_summary.csv') # summary has columns: sequence_name, PBS, RTT, predicted_efficiency, predicted_indel, etc. return summary ``` **Key determinants of PE efficiency (Mathis 2024 PRIDICT2):** | Feature | Effect on efficiency | |---------|----------------------| | PBS GC content | 40-55% optimal; high GC slows annealing | | PBS length | 11-13 nt optimal; longer for high-GC PBS | | RTT length | 10-20 nt typical; trade-off between coverage and processivity | | Edit position in RTT | Closest to PBS = highest efficiency | | Chromatin context | Open chromatin = 2-5x higher efficiency than closed | | Cell line / Cas9 expression | Variable; piloting required | | Cell cycle phase | S/G2 = higher efficiency | **Critical insight from Mathis 2024:** Chromatin context is the dominant determinant. Sequence-based predictions like PRIDICT under-predict at silenced loci and over-predict at open chromatin. For genome-scale screens, validate predictions empirically at representative loci. ## PRIME Pooled Screen Methodology **Erwood S, Doman JL et al 2023 *Nat Biotechnol* 41:885** established the PRIME pooled-screen methodology (earlier 2022 bioRxiv preprint): - pegRNA library covering thousands of intended variants - Filter pegRNAs to PRIDICT2 efficiency >50% (or pilot top 25%) - Lentiviral delivery at standard MOI 0.3 in PE-expressing cell line - Selection on integration marker - Time-course screen for variant function (e.g., drug sensitivity) - Endpoint amplicon sequencing of each pegRNA target locus - CRISPResso2 quantification of intended-edit % - MAGeCK / drugZ-style hit calling on edit-efficient pegRNAs **Quantified scale:** ~3,699 ClinVar variants installed in a single PRIME screen (Erwood/Doman 2023 *Nat Biotechnol* 41:885), with editing efficiency >5% at >50% of pegRNAs (validation cohort). ## MOSAIC In Situ Saturation Mutagenesis **MOSAIC (Hsu JY, Lam KC, Shih J, Pinello L, Joung JK 2024 bioRxiv 10.1101/2024.04.25.591078)** is a higher-throughput variant of PRIME with multiplexed read-out: - Tile pegRNAs across protein domains for systematic mutagenesis - Saturation: every possible amino acid change in a region - Identify drug-resistance variants in real-time - Smaller per-variant cell numbers (more variants total) **Use case:** Cancer-drug-resistance variant scanning; protein-domain function mapping. ## Run PRIDICT2 on a Custom pegRNA Library **Goal:** Predict editing efficiency for thousands of pegRNAs before library synthesis. **Approach:** Build a CSV with one row per intended edit (sequence + edit notation), run PRIDICT2 in batch mode, parse the per-pegRNA efficiency summary, and filter to candidates above the chosen efficiency threshold. ```bash # Step 1: prepare batch input CSV (sequence_name, sequence with (REF/ALT) edit notation) cat > variants.csv < 50% (Mathis 2024 threshold) filtered = predictions[predictions['predicted_editing_efficiency'] > 50] print(f'pegRNAs passing PRIDICT2 >50%: {len(filtered)} / {len(predictions)}') # Pick top 3 per intended edit top3 = (filtered.sort_values(['sequence_name', 'predicted_editing_efficiency'], ascending=[True, False]) .groupby('sequence_name').head(3)) top3.to_csv('peg_library_filtered.csv', index=False) ``` ## Cross-Validate PE with Base Editor Screens **Goal:** Confirm variant-function calls from PE with orthogonal BE screens. **Approach:** Design parallel BE library for the same variants; run both screens; intersect hits. ```python # BE screen output (target conversion + bystander) be_hits = pd.read_csv('be_screen_hits.tsv', sep='\t') # PE screen output (intended edit + scaffold-incorp + indel) pe_hits = pd.read_csv('pe_screen_hits.tsv', sep='\t') # Intersect on intended variant concordant = be_hits.merge(pe_hits, on='variant_id', suffixes=('_be', '_pe')) # Filter to high-confidence: both methods call variant + same direction concordant['high_confidence'] = (concordant['be_fdr'] < 0.05) & (concordant['pe_fdr'] < 0.05) & \ (np.sign(concordant['be_lfc']) == np.sign(concordant['pe_lfc'])) ``` **Critical:** PE-only hits in BE-coverable variants are suspect (BE should detect them). PE-only hits in non-BE-coverable variants (e.g., transversions) are genuinely PE-unique. ## CRISPResso2 for PE Quantification ```bash CRISPResso \ --fastq_r1 pe_sample.fq.gz \ --amplicon_seq \ --guide_seq <20nt_spacer> \ --prime_editing_pegRNA_spacer_seq \ --prime_editing_pegRNA_extension_seq \ --prime_editing_pegRNA_scaffold_seq \ --quantification_window_size 25 \ # widen to cover edit --output_folder pe_results \ --name sample_id # Output: Prime_editing_outcomes.txt # Columns: intended_edit_pct, scaffold_incorp_pct, indel_pct, unmodified_pct ``` ## Failure Modes ### Low pegRNA efficiency despite high PRIDICT prediction **Trigger:** Sequence-only prediction missed chromatin context. **Mechanism:** Closed chromatin reduces Cas9 binding and RT activity; PRIDICT2 only sees sequence. **Symptom:** PRIDICT2 predicts 60% efficiency; observed is 5%. **Fix:** Cross-reference target with chromatin accessibility data (ATAC-seq) in the cell line; flag pegRNAs at silenced loci; pilot before screen. ### High scaffold incorporation **Trigger:** RTT too short relative to PBS, or RT processivity issue. **Mechanism:** RT reads past edit into scaffold; resulting product is detectable but undesired. **Symptom:** Scaffold incorporation >5%; intended edit efficiency low. **Fix:** Re-design pegRNA with longer RTT; verify with PRIDICT2 score for scaffold_incorp; pilot at representative loci. ### PE2 cell line lacks RT expression **Trigger:** PE2 construct expressed at low level; insufficient RT for productive editing. **Mechanism:** PE2 requires high RT expression; some cell lines down-regulate. **Symptom:** Library-wide editing <10%; not locus-specific. **Fix:** Verify PE2 expression by Western blot; consider PEmax (higher activity); use better-validated cell lines (K562, HEK293T, U2OS). ### Multi-base intended edit but only one base installed **Trigger:** Long RTT designed for multi-base edit; RT prematurely terminates. **Mechanism:** RT processivity drops with longer RTT; multi-base edits often incomplete. **Symptom:** Allele table shows partial-edit alleles (some bases installed, not all). **Fix:** Re-design with shorter RTT covering only the closest edits; or use PE3 to nick opposite strand and force longer RT processivity. ### Library missing intended variant **Trigger:** No suitable PAM/PBS/RTT combination for the intended edit. **Mechanism:** PE requires NGG PAM within 30 nt of edit; rare edits cannot be installed. **Symptom:** Specific variants absent from library. **Fix:** Use SpRY-PE for relaxed PAM; accept that some variants cannot be PE-installed; consider BE if applicable. ## Cas9 vs BE vs PE for Variant Installation | Approach | Bystander | Indels | Coverage | When to use | |----------|-----------|--------|----------|-------------| | Cas9 + HDR | None | High | Variable (depends on template integration) | Precise edits at scale; high indel byproduct | | Base editor | YES | Low (<5%) | Limited by editing window | C->T or A->G at editable position | | Prime editor | NONE | Low (<3%) | NGG-PAM within 30 nt of edit | Precise variants; multi-base; transversions | | Cas9 (no template) | NONE | 70%+ | Anywhere with NGG | LoF only; not variant-specific | **Decision tree:** - C->T or A->G at editing-window position: BE (higher efficiency than PE) - Multi-base / transversion / out-of-window: PE - LoF without specifying variant: Cas9 - Random insertions: HDR (lower throughput than PE) ## Quantitative Thresholds | Threshold | Value | Source / Rationale | |-----------|-------|--------------------| | PRIDICT2 efficiency for library inclusion | >50% | Mathis 2024 | | Intended edit % for screen power | >5% (per Anzalone 2019); >20% at favorable sites | Anzalone 2019 | | Scaffold incorporation | <2% (clean PE); <5% acceptable | Empirical | | Indel byproduct | <3% (PE2); <5% (PE3) | Anzalone 2019; Chen 2021 | | PBS GC content | 40-55% | PRIDICT2 | | PBS length | 11-13 nt | PRIDICT2 | | RTT length | 10-20 nt | PRIDICT2 | | Edit position from cut | 1-30 nt | Anzalone 2019 | | Cell line for PE | K562, HEK293T, U2OS validated | High RT expression | ## Common Errors | Error / symptom | Cause | Solution | |-----------------|-------|----------| | Low editing across library | Cell-line RT inactivity | Verify PE2 expression; switch to validated line | | Scaffold incorporation >10% | RTT too short | Re-design with longer RTT | | Partial multi-base edits | RT processivity limit | Shorter RTT or PE3 | | PRIDICT predicts but observes much lower | Chromatin context | Pilot at chromatin-aware sites | | Library missing variants | No NGG PAM | SpRY-PE; BE alternative | | PE concordant with BE on transitions, disagrees on transversions | PE handles transversions BE doesn't | Expected; trust PE | ## References - Anzalone AV et al. 2019. *Nature* 576:149. Original PE2/PE3 (foundational prime editing paper). - Mathis N et al. 2023. *Nat Biotechnol* 41:1151. PRIDICT v1 deep-learning pegRNA prediction. - Mathis N et al. 2025. *Nat Biotechnol* 43(5):712 (published online June 2024). PRIDICT2 + chromatin context (current state-of-the-art). - Chen PJ et al. 2021. *Cell* 184:5635. PEmax + engineered RT. - Erwood S et al. 2023. *Nature Biotechnology* 41:885. PEAR pegRNA scaffold design. - Hsu JY, Lam KC, Shih J, Pinello L, Joung JK 2024 bioRxiv (doi:10.1101/2024.04.25.591078). MOSAIC in situ saturation mutagenesis via prime editing. - Erwood S, Doman JL et al. 2023. *Nature Biotechnology* 41:885. PRIME pooled-screen methodology (~3,699 ClinVar variants); cross-reference for variant-installation scale. ## Related Skills - crispr-screens/library-design - pegRNA library design - crispr-screens/base-editing-analysis - Orthogonal BE for variant attribution - crispr-screens/crispresso-editing - CRISPResso2 PE mode and quantification - crispr-screens/hit-calling - Per-variant hit calling - crispr-screens/screen-qc - Editing-efficiency QC - variant-calling/variant-annotation - Annotate edited variants - clinical-databases/clinvar-lookup - Variant pathogenicity