sequence-properties

name	sequence-properties
description	Calculate sequence properties like GC content, molecular weight, isoelectric point, and GC skew using Biopython. Use when analyzing sequence composition, computing physical properties, or comparing sequences.
tool_type	python
primary_tool	Bio.SeqUtils

Version Compatibility

Reference examples tested with: BioPython 1.83+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Sequence Properties

Calculate physical and chemical properties of biological sequences using Biopython.

"Calculate GC content" -> Compute the fraction of G+C bases in a nucleotide sequence.

• Python: gc_fraction(seq) (BioPython SeqUtils)

"Analyze protein properties" -> Compute MW, pI, stability, hydrophobicity from an amino acid sequence.

• Python: ProteinAnalysis(str_seq) (BioPython ProtParam)

Required Imports

from Bio.Seq import Seq
from Bio.SeqUtils import gc_fraction, molecular_weight, GC123, GC_skew, nt_search, seq1, seq3
from Bio.SeqUtils.ProtParam import ProteinAnalysis

DNA/RNA Properties

GC Content

from Bio.SeqUtils import gc_fraction

seq = Seq('ATGCGATCGATCGATCGATCG')
gc = gc_fraction(seq)  # Returns 0.476... (fraction)
gc_percent = gc * 100  # Convert to percentage

Handle ambiguous bases:

gc = gc_fraction(seq, ambiguous='ignore')  # Ignore N bases in calculation
gc = gc_fraction(seq, ambiguous='weighted')  # Weight by probability

GC at Codon Positions (GC123)

Analyze GC content at each codon position (useful for codon bias analysis):

from Bio.SeqUtils import GC123

seq = Seq('ATGCGATCGATCGATCGATCG')
gc_total, gc_pos1, gc_pos2, gc_pos3 = GC123(seq)
# gc_total: overall GC%
# gc_pos1: GC% at 1st codon position
# gc_pos2: GC% at 2nd codon position
# gc_pos3: GC% at 3rd codon position (wobble)

GC Skew

Calculate (G-C)/(G+C) in sliding windows to identify replication origins:

from Bio.SeqUtils import GC_skew

seq = Seq('ATGCGATCGATCGATCGATCG' * 10)
skew_values = GC_skew(seq, window=100)  # Returns list of skew values

Molecular Weight

from Bio.SeqUtils import molecular_weight

dna = Seq('ATGCGATCG')
mw = molecular_weight(dna)  # Single-stranded DNA weight

# Double-stranded DNA
mw_ds = molecular_weight(dna, double_stranded=True)

# Circular DNA
mw_circ = molecular_weight(dna, circular=True)

# RNA
rna = Seq('AUGCGAUCG')
mw_rna = molecular_weight(rna, seq_type='RNA')

# Protein
protein = Seq('MRCRS')
mw_prot = molecular_weight(protein, seq_type='protein')

Melting Temperature

from Bio.SeqUtils import MeltingTemp

seq = Seq('ATGCGATCGATCG')

# Basic Tm (Wallace rule - short oligos)
tm_wallace = MeltingTemp.Tm_Wallace(seq)

# GC method (more accurate for longer sequences)
tm_gc = MeltingTemp.Tm_GC(seq)

# Nearest neighbor (most accurate)
tm_nn = MeltingTemp.Tm_NN(seq)

# With salt correction
tm_corrected = MeltingTemp.Tm_NN(seq, Na=50, Mg=1.5)

Sequence Search with IUPAC Codes (nt_search)

Built-in search that handles IUPAC ambiguity codes:

from Bio.SeqUtils import nt_search

seq = 'ATGCGATCGATCGATNGATC'
# Search for pattern with ambiguous bases
result = nt_search(seq, 'GATNGATC')  # N matches any base
# Returns: ['GATNGATC', 8] - pattern and position(s)

Base Composition

def base_composition(seq):
    seq_str = str(seq).upper()
    total = len(seq_str)
    return {base: seq_str.count(base) / total * 100 for base in 'ATGC'}

Amino Acid Code Conversion

Convert between 1-letter and 3-letter amino acid codes:

from Bio.SeqUtils import seq1, seq3

# 3-letter to 1-letter
one_letter = seq1('MetAlaGlyTrp')  # Returns 'MAGW'

# 1-letter to 3-letter
three_letter = seq3('MAGW')  # Returns 'MetAlaGlyTrp'

# With custom separator
three_letter = seq3('MAGW', join='-')  # Returns 'Met-Ala-Gly-Trp'

Protein Properties

Goal: Compute physical and chemical properties of a protein from its amino acid sequence.

Approach: Create a ProteinAnalysis object, then call property methods. Remove stop codons (*) and non-standard residues (X) before analysis.

Using ProteinAnalysis

from Bio.SeqUtils.ProtParam import ProteinAnalysis

protein = ProteinAnalysis('MAEGEITTFTALTEKFNLPPGNYKKPKLLYCSNG')

Basic Properties

mw = protein.molecular_weight()           # Molecular weight in Daltons
pi = protein.isoelectric_point()          # Isoelectric point
aa_comp = protein.amino_acids_percent     # Amino acid composition (property)
aa_count = protein.count_amino_acids()    # Raw amino acid counts

Stability and Hydropathy

instability = protein.instability_index()  # < 40 = stable, > 40 = unstable
gravy = protein.gravy()                    # Negative = hydrophilic, Positive = hydrophobic
arom = protein.aromaticity()               # Fraction of Phe+Trp+Tyr

Charge at Specific pH

charge = protein.charge_at_pH(7.0)  # Net charge at pH 7.0
charge_acidic = protein.charge_at_pH(4.0)
charge_basic = protein.charge_at_pH(10.0)

Flexibility Profile

flexibility = protein.flexibility()  # List of flexibility values per residue
# Based on Vihinen, 1994 scale

Secondary Structure

helix, turn, sheet = protein.secondary_structure_fraction()

Extinction Coefficient

eps_reduced, eps_cystine = protein.molar_extinction_coefficient()
# eps_reduced: all Cys are reduced
# eps_cystine: all Cys form disulfide bonds

Protein Scale Profiles

Calculate profiles using any amino acid scale:

# Kyte-Doolittle hydropathy profile
kd_scale = {'A': 1.8, 'R': -4.5, 'N': -3.5, ...}
profile = protein.protein_scale(kd_scale, window=7)

Code Patterns

Analyze Multiple Sequences

from Bio import SeqIO
from Bio.SeqUtils import gc_fraction

def analyze_fasta(filename):
    results = []
    for record in SeqIO.parse(filename, 'fasta'):
        results.append({
            'id': record.id,
            'length': len(record.seq),
            'gc': gc_fraction(record.seq) * 100
        })
    return results

GC Content Distribution (Sliding Windows)

from Bio.SeqUtils import gc_fraction

def gc_distribution(seq, window_size=100, step=50):
    gc_values = []
    for i in range(0, len(seq) - window_size + 1, step):
        window = seq[i:i + window_size]
        gc_values.append((i, gc_fraction(window) * 100))
    return gc_values

GC Skew Plot Data

from Bio.SeqUtils import GC_skew

def gc_skew_analysis(seq, window=1000):
    skew = GC_skew(seq, window=window)
    positions = list(range(0, len(seq) - window + 1, window))
    cumulative = []
    total = 0
    for s in skew:
        total += s
        cumulative.append(total)
    return positions, skew, cumulative

Full Protein Analysis Report

Goal: Generate a comprehensive summary of protein biophysical properties.

Approach: Compute all key metrics from a single ProteinAnalysis object and return as a dictionary.

from Bio.SeqUtils.ProtParam import ProteinAnalysis

def protein_report(sequence):
    protein = ProteinAnalysis(str(sequence).replace('*', ''))
    helix, turn, sheet = protein.secondary_structure_fraction()
    return {
        'length': len(sequence),
        'molecular_weight': protein.molecular_weight(),
        'isoelectric_point': protein.isoelectric_point(),
        'charge_at_pH7': protein.charge_at_pH(7.0),
        'instability_index': protein.instability_index(),
        'gravy': protein.gravy(),
        'aromaticity': protein.aromaticity(),
        'helix_fraction': helix,
        'turn_fraction': turn,
        'sheet_fraction': sheet,
    }

Dinucleotide Frequencies

from collections import Counter

def dinucleotide_freq(seq):
    seq_str = str(seq)
    dinucs = [seq_str[i:i+2] for i in range(len(seq_str) - 1)]
    counts = Counter(dinucs)
    total = sum(counts.values())
    return {di: count / total for di, count in counts.items()}

CpG Observed/Expected Ratio

def cpg_ratio(seq):
    seq_str = str(seq).upper()
    cpg = seq_str.count('CG')
    c = seq_str.count('C')
    g = seq_str.count('G')
    expected = (c * g) / len(seq_str) if len(seq_str) > 0 else 0
    return cpg / expected if expected > 0 else 0

Property Reference

DNA/RNA Properties

Property	Function	Notes
GC content	gc_fraction()	Returns fraction (0-1)
GC by position	GC123()	Total, pos1, pos2, pos3
GC skew	GC_skew()	(G-C)/(G+C) in windows
Molecular weight	molecular_weight()	In Daltons
Melting temp	MeltingTemp.Tm_*()	Multiple methods
IUPAC search	nt_search()	Handles ambiguity codes

Protein Properties

Property	Method	Typical Range
MW	molecular_weight()	Daltons
pI	isoelectric_point()	0-14
Charge	charge_at_pH(pH)	Varies
Instability	instability_index()	<40 stable
GRAVY	gravy()	-2 to +2
Aromaticity	aromaticity()	0-0.15
Flexibility	flexibility()	Per-residue list

Amino Acid Codes

Function	Input	Output
seq1()	'MetAlaGly'	'MAG'
seq3()	'MAG'	'MetAlaGly'

Common Errors

Error	Cause	Solution
KeyError in ProteinAnalysis	Non-standard amino acid	Remove X, *, etc.
Wrong MW	Wrong seq_type	Specify 'RNA' or 'protein'
Invalid Tm	Sequence too short	Use >10 bp for accurate Tm
Empty GC_skew	Sequence shorter than window	Reduce window size

Decision Tree

Need sequence properties?
├── DNA/RNA sequence?
│   ├── GC content? -> gc_fraction()
│   ├── GC at codon positions? -> GC123()
│   ├── GC skew (replication origin)? -> GC_skew()
│   ├── Molecular weight? -> molecular_weight()
│   ├── Melting temperature? -> MeltingTemp.Tm_NN()
│   └── Search with IUPAC codes? -> nt_search()
├── Protein sequence?
│   └── Create ProteinAnalysis object
│       ├── Size -> molecular_weight()
│       ├── Charge -> isoelectric_point(), charge_at_pH()
│       ├── Stability -> instability_index()
│       ├── Hydrophobicity -> gravy()
│       ├── Flexibility -> flexibility()
│       └── Structure -> secondary_structure_fraction()
└── Convert amino acid codes?
    └── seq1() / seq3()

Related Skills

• seq-objects - Create Seq objects for property calculation
• sequence-io/sequence-statistics - File-level statistics (N50, totals)
• codon-usage - GC123 for codon position analysis
• transcription-translation - Translate before protein analysis
• alignment-files/bam-statistics - samtools stats for alignment-level GC and quality stats