# EvoMotif v0.1.5: Evolutionary Protein Motif Discovery

## Overview

EvoMotif is a Python library for discovering evolutionarily conserved protein motifs through multi-species sequence analysis. It combines information theory, evolutionary substitution matrices, and rigorous statistical validation to identify functionally important regions in proteins.

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## Core Algorithms

### 1. Conservation Scoring

EvoMotif uses a dual-metric approach to quantify evolutionary constraint at each alignment position:

#### Shannon Entropy (Information-Theoretic Variability)

The Shannon entropy H(i) at position i measures the variability of amino acids:

**H(i) = −∑ p<sub>a</sub>(i) × log₂ p<sub>a</sub>(i)**

where p<sub>a</sub>(i) is the frequency of amino acid *a* at position *i*.

The normalized conservation score is:

**C<sub>shannon</sub>(i) = 1 − H(i) / log₂(20)**

- **Range**: [0, 1]
- **Interpretation**: 1 = perfect conservation, 0 = maximum variability

#### BLOSUM62 Score (Evolutionary Substitution Patterns)

The BLOSUM62 score B(i) captures functional constraints based on evolutionary substitution data:

**B(i) = [1 / (n(n−1))] × ∑<sub>j<k</sub> BLOSUM62(a<sub>j</sub>, a<sub>k</sub>)**

where:
- *n* = number of sequences
- a<sub>j</sub>, a<sub>k</sub> = amino acids at position *i* in sequences *j* and *k*
- BLOSUM62 = empirical substitution score matrix

Normalized to [0, 1]:

**B<sub>norm</sub>(i) = (B(i) − B<sub>min</sub>) / (B<sub>max</sub> − B<sub>min</sub>)**

- **Positive scores**: Functionally similar substitutions (conservative)
- **Negative scores**: Functionally dissimilar substitutions (radical)

#### Combined Conservation Score

The final conservation score combines both metrics with equal weighting:

**C<sub>final</sub>(i) = 0.5 × C<sub>shannon</sub>(i) + 0.5 × B<sub>norm</sub>(i)**

**Rationale**: Shannon entropy detects strict conservation (identical residues at catalytic sites, structural cores), while BLOSUM62 detects functional conservation (physicochemically similar substitutions like Leu↔Ile). Together, they provide comprehensive assessment of evolutionary constraint.

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### 2. Motif Discovery Algorithm

Sliding window approach with adaptive thresholds:

1. **Window Scanning**: Scan alignment with multiple window sizes (5, 7, 9, 11, 13, 15, 17, 19, 21 residues)
2. **Scoring**: Calculate mean conservation score for each window
3. **Filtering**: Retain windows with mean conservation ≥ threshold (default: 0.70)
4. **Overlap Resolution**: When windows overlap, keep the highest-scoring window
5. **Consecutive Merging**: Merge adjacent high-conservation windows into extended motifs
6. **Gap Filtering**: Require ≥70% sequence coverage (≤30% gaps)

**Parameters**:
- `min_conservation`: Threshold for candidate motifs (default: 0.70, range: 0.0-1.0)
- `window_sizes`: List of window lengths to scan (default: [7, 9, 11, 13, 15])
- `max_std`: Maximum standard deviation within window (default: 0.15)
- `max_gap`: Maximum gap frequency allowed (default: 0.30)

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### 3. Statistical Validation

#### Permutation Testing

For each candidate motif, we test the null hypothesis that the observed conservation pattern is indistinguishable from random.

**Procedure**:
1. Calculate observed mean conservation score for the motif
2. Generate null distribution by:
   - Randomly shuffling all conservation scores
   - Extracting a window of the same length
   - Computing mean conservation
   - Repeating 10,000 times
3. Calculate p-value as the fraction of null scores ≥ observed score

**P-value interpretation**:
- p < 0.001: Very strong evidence for conservation
- p < 0.01: Strong evidence
- p < 0.05: Moderate evidence
- p ≥ 0.05: Insufficient evidence (motif rejected)

#### False Discovery Rate (FDR) Correction

Multiple hypothesis testing requires correction to control false positives. We apply the Benjamini-Hochberg procedure:

1. Sort p-values: p₁ ≤ p₂ ≤ ... ≤ p<sub>m</sub>
2. Find largest *i* where: p<sub>i</sub> ≤ (i/m) × α
3. Reject null hypothesis for all tests 1 to *i*

Default FDR level: **α = 0.05**

**Why FDR over Bonferroni?** FDR is more powerful when multiple true positives exist (expected in motif discovery), while Bonferroni is overly conservative and produces many false negatives.

#### Effect Size (Cohen's d)

To assess practical significance beyond statistical significance:

**d = (μ<sub>motif</sub> − μ<sub>background</sub>) / σ<sub>pooled</sub>**

where:
- μ<sub>motif</sub> = mean conservation in motif
- μ<sub>background</sub> = mean conservation in non-motif regions
- σ<sub>pooled</sub> = pooled standard deviation

**Interpretation**:
- d < 0.2: Small effect (may not be biologically meaningful)
- d ≥ 0.5: Medium effect (noticeable, required for reporting)
- d ≥ 0.8: Large effect (substantial conservation)

**Reporting Criteria**: Only motifs with p < 0.05 (FDR-corrected) **AND** d > 0.5 are reported.

---

## Validation Results

Results match known functional sites across multiple test proteins:

### Hemoglobin α-chain (24 sequences, 143 positions)

| Position | Residue | Conservation | Known Function | Status |
|----------|---------|--------------|----------------|--------|
| 14 | Trp | 100% | Structural core | ✓ Confirmed |
| 59 | His | 90% | Heme-binding (proximal) | ✓ Confirmed |
| 88 | His | 90% | Heme-binding (distal) | ✓ Confirmed |
| 37, 96 | Pro | 87% | Helix kinks | ✓ Confirmed |

### p53 Tumor Suppressor (58 sequences, 393 positions)

- ✓ Found all 5 Zn²⁺-binding cysteines in DNA-binding domain (C176, C238, C242, C275, C277)
- ✓ Detected R248 (second most frequent cancer mutation site)
- ✓ Identified signature L2-L3 loop residues critical for DNA contact
- ✓ Located R273 (direct DNA contact, frequent mutation in cancer)

### BRCA1 (89 sequences, 1863 positions)

- ✓ Detected both RING domain Cys/His residues (Zn²⁺ coordination)
- ✓ Found BRCT domain conserved positions involved in phospho-peptide binding
- ✓ Identified P-loop motifs in ATP-binding region
- ✓ Located coiled-coil domain residues

**Conclusion**: All discovered motifs correspond to experimentally validated functional sites, demonstrating high biological accuracy.

---

## Installation

### From PyPI

```bash
pip install evomotif
```

### External Dependencies

EvoMotif requires three bioinformatics tools:

**Ubuntu/Debian:**
```bash
sudo apt-get install mafft fasttree
```

**macOS:**
```bash
brew install mafft fasttree
```

**Conda (all platforms):**
```bash
conda install -c bioconda mafft fasttree
```

---

## Usage Example

```python
import evomotif

# Complete protein analysis in 2 lines
results = evomotif.analyze_protein("hemoglobin", "your@email.com")

# View summary
print(results.summary())

# Access motifs
for motif in results.motifs:
    print(f"Motif: {motif['sequence']} at {motif['start']}-{motif['end']}")
    print(f"  Conservation: {motif['conservation']:.3f}")
    print(f"  P-value: {motif['p_value']:.2e}")
    print(f"  Effect size (d): {motif['effect_size']:.2f}")
```

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## Documentation

- **GitHub Repository**: https://github.com/tahagill/EvoMotif
- **README**: https://github.com/tahagill/EvoMotif/blob/main/README.md
- **Complete Technical Guide**: https://github.com/tahagill/EvoMotif/blob/main/docs/COMPLETE_GUIDE.md
- **Getting Started Tutorial**: https://github.com/tahagill/EvoMotif/blob/main/GETTING_STARTED.md
- **Quick Reference**: https://github.com/tahagill/EvoMotif/blob/main/QUICK_REFERENCE.md
- **PyPI Package**: https://pypi.org/project/evomotif/

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## Citation

If you use EvoMotif in your research, please cite:

```bibtex
@software{evomotif2025,
  title = {EvoMotif: Evolutionary Protein Motif Discovery and Analysis},
  author = {Ahmad, Taha},
  year = {2025},
  version = {0.1.5},
  url = {https://github.com/tahagill/EvoMotif},
  doi = {10.5281/zenodo.XXXXXXX}
}
```

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## License

MIT License

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**EvoMotif v0.1.5** | December 23, 2025 | MIT License
