gcell-protein

Get Protein Sequences

from gcell.protein.data import (
    get_seq_from_gene_name,
    get_uniprot_from_gene_name
)

# Get protein sequence from gene name
seq = get_seq_from_gene_name('TP53')
seq = get_seq_from_gene_name('EGFR')
seq = get_seq_from_gene_name('BRCA1')

# Get UniProt accession
uniprot_id = get_uniprot_from_gene_name('TP53')

AlphaFold2 Confidence Scores

from gcell.protein.data import get_lddt_from_gene_name

# Get pLDDT (predicted local distance difference test) scores
# Higher scores = higher confidence in structure prediction
plddt = get_lddt_from_gene_name('TP53')
plddt = get_lddt_from_gene_name('EGFR')

# pLDDT interpretation:
# > 90: Very high confidence
# 70-90: Confident
# 50-70: Low confidence
# < 50: Very low confidence (likely disordered)

Full Protein Analysis

from gcell.protein.protein import Protein

# Load protein from gene name
protein = Protein.from_gene_name('EGFR')
protein = Protein.from_gene_name('TP53')

# Access protein data
print(protein.sequence)
print(protein.length)
print(protein.plddt)  # AlphaFold confidence

# 3D structure visualization
protein.plot_structure()  # Interactive 3D view

Protein-Protein Interactions

from gcell.protein.string import get_string_interactions

# Get interactions from STRING database
interactions = get_string_interactions('TP53')

# Filter by confidence score
high_conf = interactions[interactions['score'] > 0.7]

Key Classes and Functions

| Name | Purpose | |------|---------| | Protein | Full protein analysis class | | get_seq_from_gene_name() | Get amino acid sequence | | get_uniprot_from_gene_name() | Get UniProt ID | | get_lddt_from_gene_name() | Get AlphaFold pLDDT scores | | get_string_interactions() | Get protein interactions |

Data Sources

• Protein sequences: UniProt
• Structures: AlphaFold Database
• Interactions: STRING Database
• Data cached in: ~/.gcell_data/cache/