Sigma70 Promoter Strength

Evaluate sigma-70 promoter similarity for DNA sequences

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Source

proto-bio/proto-language/proto_language/constraint/sequence_annotation/sigma70_promoter_constraint.py

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Evaluate E. coli sigma-70 promoter similarity using PWM-based scoring.

This constraint function evaluates bacterial promoter similarity by scanning DNA sequences for sigma-70-dependent promoter elements. It identifies putative -35 and -10 boxes, scores them based on similarity to consensus sequences weighted by position-specific conservation probabilities, evaluates the spacer distance between them, and combines these scores into an overall promoter similarity prediction.

The scoring model is based on RegulonDB experimental data for E. coli sigma-70 promoters and uses three components:

1. PWM score: Position weight matrix score based on conservation probabilities
2. Match count: Simple count of consensus matches (out of 12 positions)
3. Spacer length: Deviation from optimal 17 bp spacer

The function scans sequences to find the best-scoring promoter configuration within the allowed spacer range [min_spacer, max_spacer]. For short sequences (≤32 bp), it treats the entire sequence as a fixed promoter. For longer sequences, it exhaustively scans all positions.

API Reference

ConfigSigma70PromoterConfig Source

Configuration for sigma-70 promoter similarity constraint.

This class defines configuration parameters for evaluating bacterial promoter similarity using a position weight matrix (PWM) model of E. coli sigma-70 promoters. The model scores promoter elements based on similarity to consensus sequences for the -35 and -10 boxes, the spacer distance between them, and the total number of matches to consensus. This approach is based on RegulonDB experimental data for E. coli sigma-70-dependent promoters.

The scoring combines three components:

1. PWM score: Similarity to consensus sequences weighted by conservation
2. Match count: Number of exact matches to consensus (out of 12 positions)
3. Spacer length: Distance between -35 and -10 boxes

The constraint scans sequences to find the best-scoring promoter within the allowed spacer range. For sequences ≤32 bp, it treats the entire sequence as a single promoter (first 6 bp = -35, last 6 bp = -10). For longer sequences, it scans all possible positions.

The final penalty combines three components:

1. Box penalty = (1 - match_weight) * PWM_penalty + match_weight * match_penalty
2. Total penalty = (1 - spacer_weight) * box_penalty + spacer_weight * spacer_penalty

consensus_35 · string · default: "TTGACA"

-35 box consensus sequence (6 bp, typically TTGACA for E. coli sigma-70)

consensus_10 · string · default: "TATAAT"

-10 box consensus sequence (6 bp Pribnow box, typically TATAAT for E. coli sigma-70)

probs_35 · List[number] · default: "[0.69, 0.79, 0.61, 0.56, 0.54, 0.54]"

Position-specific conservation probabilities for -35 box (6 values). From RegulonDB.

probs_10 · List[number] · default: "[0.77, 0.76, 0.6, 0.61, 0.56, 0.82]"

Position-specific conservation probabilities for -10 box (6 values). From RegulonDB.

optimal_spacer · integer · default: "17"

Optimal spacer length between -35 and -10 boxes in base pairs (typically 17±1 bp)

spacer_sigma · number · default: "1.5"

Standard deviation for spacer length penalty. Lower values = stricter spacing requirement.

spacer_weight · number · default: "0.3"

Weight (0-1) for spacer penalty in total score. Higher = spacing more important.

gamma · number · default: "0.1"

PWM score exponent for non-linearity. Lower values = more sensitive to mismatches.

k_opt · integer · default: "8"

Optimal number of matches to consensus (out of 12 total positions)

match_sigma · number · default: "2.0"

Standard deviation for match count penalty

match_weight · number · default: "0.3"

Weight (0-1) for match count penalty in total score

min_spacer · integer · default: "14"

Minimum acceptable spacer length in bp

max_spacer · integer · default: "20"

Maximum acceptable spacer length in bp

ReturnsConstraintOutput

One result per sequence. Score ranges from 0.0 (perfect promoter, exact consensus with optimal spacer) to 1.0 (poor/no promoter). metadata carries a single sigma70 dict with the following fields:

For valid promoters found:

• sigma70_score: Float overall penalty score (0.0-1.0)
• pos: Integer start position of the -35 box in the sequence
• box35: String sequence of the -35 box (6 bp)
• box10: String sequence of the -10 box (6 bp)
• spacer_len: Integer spacer length between boxes (bp)
• total_matches: Integer total matches to consensus (out of 12)
• pwm_penalty: Float PWM-based penalty component (0.0-1.0)
• match_penalty: Float match count penalty component (0.0-1.0)
• spacer_penalty: Float spacer length penalty component (0.0-1.0)

For sequences too short (<12 bp):

• sigma70_score: Float 1.0 (maximum penalty)
• reason: String “too_short”

For sequences with invalid spacer (12-32 bp range):

• sigma70_score: Float 1.0 (maximum penalty)
• reason: String “invalid_spacer”

Usage

Evaluating a canonical sigma-70 promoter:

python

>>> from proto_language.core import Sequence, SequenceType
>>> promoter_seq = Sequence(
...     "TTGACAATGATACTTAGATTCACTTATAATACTAGTAG",  # 17 bp spacer
...     "dna",
... )
>>> config = Sigma70PromoterConfig()
>>> results = sigma70_promoter_constraint([(promoter_seq,)], config)
>>> print(results[0].score)  # e.g., 0.08 (strong promoter)
>>> sigma70 = results[0].metadata["sigma70"]
>>> print(f"-35: {sigma70['box35']}, -10: {sigma70['box10']}")  # TTGACA, TATAAT

Metadata

Property	Value
Key	sigma70-promoter
Function	sigma70_promoter_constraint
Category	sequence_annotation
Mode	discrete
Uses GPU	False
Supported Types	dna