Design guide sequences for C-to-T or A-to-G base editing by comparing target positions, nearby bases that may also change, and sequence context. Includes Python examples for ranking candidates and considering paired edits.
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Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for precise nucleotide changes.
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Reference examples tested with: BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Design a base editor guide for my C-to-T conversion" → Identify guide sequences that position the target nucleotide within the editing window of cytosine (CBE) or adenine (ABE) base editors, predicting editing outcomes and bystander effects.
Bio.Seq, BE-Hive outcome predictionCytosine Base Editors (CBE):
- Convert C to T (or G to A on opposite strand)
- Examples: BE3, BE4, BE4max, AncBE4max
- Editing window: Positions 4-8 (PAM-distal numbering)
Adenine Base Editors (ABE):
- Convert A to G (or T to C on opposite strand)
- Examples: ABE7.10, ABE8e, ABE8.20
- Editing window: Positions 4-7 (narrower than CBE)
Position numbering:
Position 1 = PAM-proximal (next to NGG)
Position 20 = PAM-distal (5' end of spacer)
Editing window is typically positions 4-8 from PAM-distal end
Goal: Identify guide sequences that place a target nucleotide within the base editor's editing window while minimizing bystander edits.
Approach: Scan for PAM sites in both orientations, calculate where the target base falls within the spacer, filter guides where the target lands in the CBE (positions 4-8) or ABE (positions 4-7) editing window, and rank by fewest bystander bases in the window.
from Bio.Seq import Seq
import re
# Editing window positions (1-indexed from PAM-distal end)
# Position 1 is first nt of spacer, position 20 is adjacent to PAM
CBE_WINDOW = (4, 8) # BE4max optimal window
ABE_WINDOW = (4, 7) # ABE8e optimal window
def find_cbe_targets(sequence, target_c_position):
'''Find guides that place a C in the CBE editing window
Args:
sequence: DNA sequence containing the target C
target_c_position: 0-indexed position of C to edit
Returns:
List of guide options with editing predictions
'''
sequence = sequence.upper()
guides = []
# Search for PAMs that would place target C in window
for pam_match in re.finditer(r'(?=(.GG))', sequence):
pam_pos = pam_match.start()
# Calculate where target C falls in the spacer
spacer_start = pam_pos - 20
if spacer_start < 0:
continue
c_position_in_spacer = target_c_position - spacer_start + 1 # 1-indexed
# Check if C is in editing window
if CBE_WINDOW[0] <= c_position_in_spacer <= CBE_WINDOW[1]:
spacer = sequence[spacer_start:pam_pos]
# Find bystander Cs in window (may also be edited)
bystanders = []
for i in range(CBE_WINDOW[0] - 1, CBE_WINDOW[1]):
if i < len(spacer) and spacer[i] == 'C' and (spacer_start + i) != target_c_position:
bystanders.append(i + 1)
guides.append({
'spacer': spacer,
'pam_position': pam_pos,
'target_position_in_spacer': c_position_in_spacer,
'bystander_cs': bystanders,
'bystander_count': len(bystanders),
'strand': '+'
})
# Sort by fewest bystanders
return sorted(guides, key=lambda x: x['bystander_count'])
def find_abe_targets(sequence, target_a_position):
'''Find guides that place an A in the ABE editing window'''
sequence = sequence.upper()
guides = []
for pam_match in re.finditer(r'(?=(.GG))', sequence):
pam_pos = pam_match.start()
spacer_start = pam_pos - 20
if spacer_start < 0:
continue
a_position_in_spacer = target_a_position - spacer_start + 1
if ABE_WINDOW[0] <= a_position_in_spacer <= ABE_WINDOW[1]:
spacer = sequence[spacer_start:pam_pos]
bystanders = []
for i in range(ABE_WINDOW[0] - 1, ABE_WINDOW[1]):
if i < len(spacer) and spacer[i] == 'A' and (spacer_start + i) != target_a_position:
bystanders.append(i + 1)
guides.append({
'spacer': spacer,
'pam_position': pam_pos,
'target_position_in_spacer': a_position_in_spacer,
'bystander_as': bystanders,
'bystander_count': len(bystanders),
'strand': '+'
})
return sorted(guides, key=lambda x: x['bystander_count'])
# Position-dependent editing efficiency
# Based on BE-Hive and published data
# Values represent relative editing efficiency (1.0 = maximum)
CBE_POSITION_EFFICIENCY = {
# Position: efficiency (BE4max)
1: 0.05, 2: 0.10, 3: 0.20,
4: 0.70, 5: 0.90, 6: 1.00, # Peak efficiency
7: 0.85, 8: 0.50,
9: 0.20, 10: 0.10
}
ABE_POSITION_EFFICIENCY = {
# Position: efficiency (ABE8e)
1: 0.02, 2: 0.05, 3: 0.15,
4: 0.60, 5: 0.95, 6: 1.00, # Peak at 5-6
7: 0.70,
8: 0.20, 9: 0.05
}
def predict_editing_efficiency(guide, editor='CBE'):
'''Predict editing efficiency based on position
Interpretation:
- >0.7: High efficiency expected (good candidate)
- 0.4-0.7: Moderate efficiency
- <0.4: Low efficiency (consider alternatives)
'''
pos = guide['target_position_in_spacer']
if editor == 'CBE':
efficiency = CBE_POSITION_EFFICIENCY.get(pos, 0.05)
else: # ABE
efficiency = ABE_POSITION_EFFICIENCY.get(pos, 0.05)
return efficiency
def predict_bystander_edits(spacer, editor='CBE'):
'''Predict which bases in the window will be edited
Bystanders are non-target bases in the editing window
that may also be converted. This is a key consideration
for base editing design.
Returns:
List of predicted edits with efficiency scores
'''
edits = []
if editor == 'CBE':
window = CBE_WINDOW
target_base = 'C'
efficiency_map = CBE_POSITION_EFFICIENCY
else:
window = ABE_WINDOW
target_base = 'A'
efficiency_map = ABE_POSITION_EFFICIENCY
for i in range(window[0] - 1, window[1]):
if i < len(spacer) and spacer[i] == target_base:
pos = i + 1 # 1-indexed
edits.append({
'position': pos,
'original': target_base,
'edited': 'T' if editor == 'CBE' else 'G',
'efficiency': efficiency_map.get(pos, 0.1)
})
return edits
def design_dual_edit(sequence, c_position, a_position, max_distance=50):
'''Design for simultaneous C>T and A>G edits
Some applications require both CBE and ABE edits.
This finds guides where both targets are accessible.
'''
cbe_guides = find_cbe_targets(sequence, c_position)
abe_guides = find_abe_targets(sequence, a_position)
# Find compatible pairs (different PAMs, both in window)
compatible = []
for cbe in cbe_guides:
for abe in abe_guides:
distance = abs(cbe['pam_position'] - abe['pam_position'])
if distance > 0 and distance <= max_distance:
compatible.append({
'cbe_guide': cbe,
'abe_guide': abe,
'distance': distance
})
return compatible
def score_sequence_context(spacer, position, editor='CBE'):
'''Score based on sequence context preferences
CBE context preferences (5' neighbor of target C):
- TC: High efficiency (most preferred)
- CC: Good efficiency
- AC: Moderate efficiency
- GC: Lower efficiency
ABE has less pronounced context preferences.
'''
if position < 2 or position > len(spacer):
return 0.5
idx = position - 1 # 0-indexed
if editor == 'CBE':
if idx > 0:
context = spacer[idx - 1]
context_scores = {'T': 1.0, 'C': 0.8, 'A': 0.6, 'G': 0.4}
return context_scores.get(context, 0.5)
else: # ABE
# ABE is less context-dependent
return 0.8
return 0.5
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