API Reference

Peptide

class peptides.Peptide(typing.Sequence[str])

A sequence of amino acids.

sequence

The peptide primary sequence, encoded in the IUPAC one-letter code.

Type:

str

__init__(sequence: str) → None

Create a new peptide object with the given sequence.

Parameters:

sequence (str) – A sequence of amino acids encoded with the IUPAC one-letter code. Non-standard (O, U), ambiguous (B, Z, J) and unknown (X) residues are supported in some methods, but not all of them.

aliphatic_index() → float

Compute the aliphatic index of the peptide.

The aliphatic index of a protein was proposed in Ikai (1980). It is defined as the relative volume occupied by aliphatic side chains (Alanine, Valine, Isoleucine, and Leucine). It may be regarded as a positive factor for the increase of thermostability of globular proteins.

Returns:

float – The computed aliphatic index for the peptide sequence, between 0 and 100.

Example

>>> peptide = Peptide("SDKEVDEVDAALSDLEITLE")
>>> peptide.aliphatic_index()
117.0

References

  • Ikai, A. Thermostability and Aliphatic Index of Globular Proteins. Journal of Biochemistry. Dec 1980;88(6):1895–98. PMID:7462208.

auto_correlation(table: Dict[str, float], lag: int = 1, center: bool = True) → float

Compute the auto-correlation index of a peptide sequence.

Example

>>> peptide = Peptide("SDKEVDEVDAALSDLEITLE")
>>> table = peptides.tables.HYDROPHOBICITY["KyteDoolittle"]
>>> peptide.auto_correlation(table=table)
-0.3519908...
>>> peptide.auto_correlation(table=table, lag=5)
0.00113355...
auto_covariance(table: Dict[str, float], lag: int = 1, center: bool = True) → float

Compute the auto-covariance index of a peptide sequence.

Example

>>> peptide = Peptide("SDKEVDEVDAALSDLEITLE")
>>> table = peptides.tables.HYDROPHOBICITY["KyteDoolittle"]
>>> peptide.auto_covariance(table)
-0.414005...
>>> peptide.auto_covariance(table, lag=5)
0.0010003...
blosum_indices() → BLOSUMIndices

Compute the BLOSUM62-derived indices of the peptide.

See BLOSUMIndices for more information.

Returns:

peptides.BLOSUMIndices – The computed average BLOSUM indices for all the amino acids in the peptide.

Example

>>> peptide = Peptide("KLKLLLLLKLK")
>>> for i, b in enumerate(peptide.blosum_indices()):
...     print(f"BLOSUM{i+1:<3} {b: .4f}")
BLOSUM1   -0.4827
BLOSUM2   -0.5618
BLOSUM3   -0.8509
BLOSUM4   -0.4173
BLOSUM5    0.3173
BLOSUM6    0.2527
BLOSUM7    0.1464
BLOSUM8    0.1427
BLOSUM9   -0.2145
BLOSUM10  -0.3218
boman() → float

Compute the Boman (potential peptide interaction) index.

The potential interaction index proposed by Boman (2003) is an index computed by averaging the solubility values for all residues in a sequence. It can be used to give an overall estimate of the potential of a peptide to bind to membranes or other proteins.

Returns:

float – The Boman index for the peptide. A value greater than 2.48 indicates that a protein has high binding potential.

Example

>>> peptide = Peptide("FLPVLAGLTPSIVPKLVCLLTKKC")
>>> peptide.boman()
-1.2358...

Note

The potential protein interaction index was originally proposed as an easy way to differentiate between the action mechanism of hormones (protein/protein) and antimicrobial peptides (protein/membrane).

References

charge(pH: float = 7, pKscale: str = 'Lehninger') → float

Compute the theoretical net charge of a peptide sequence.

This function computes the theoretical net charge of a peptide sequence, based on the Henderson-Hasselbach equation described by Dexter S. Moore (1985). The net charge can be computed at a given pH using one of the 9 pKa scales available.

Parameters:

  • pH (float) – The pH value for which to compute the charge.

  • pKscale (str) – The name of the pKa scale to be used. A list of all the allowed values can be retrieved from the keys of the peptides.tables.PK dictionary.

Returns:

float – The net charge of the peptide.

Example

>>> peptide = Peptide("FLPVLAGLTPSIVPKLVCLLTKKC")
>>> peptide.charge(pKscale="Bjellqvist")
2.7373...
>>> peptide.charge(pKscale="EMBOSS")
2.9141...
>>> peptide.charge(pKscale="Murray")
2.9075...
>>> peptide.charge(pKscale="Sillero")
2.9198...
>>> peptide.charge(pKscale="Solomon")
2.8444...
>>> peptide.charge(pKscale="Stryer")
2.8765...
>>> peptide.charge(pKscale="Lehninger")
2.8731...
>>> peptide.charge(pKscale="Dawson")
2.8444...
>>> peptide.charge(pKscale="Rodwell")
2.8197...

References

  • Bjellqvist, B., G. J. Hughes, C. Pasquali, N. Paquet, F. Ravier, J. C. Sanchez, S. Frutiger, and D. Hochstrasser. The Focusing Positions of Polypeptides in Immobilized pH Gradients Can Be Predicted from Their Amino Acid Sequences. Electrophoresis. 1993 Oct;14(10):1023–31. doi:10.1002/elps.11501401163. PMID:8125050.

  • Dawson, R. M. C. and D. C. Elliott. Data for Biochemical Research. Oxford: Clarendon Press. 2002;3:592. ISBN:978-0-19-855299-4.

  • Kiraga, J. Analysis and computer simulations of variability of isoelectric point of proteins in the proteomes. PhD thesis, University of Wroclaw, Poland. 2008.

  • Lehninger, A. L., D. L. Nelson, and M. M. Cox. Lehninger Principles of Biochemistry. 4th ed. New York: W.H. Freeman. 2005;4:1100. ISBN:978-0-7167-4339-2.

  • Murray, R. K. Harper’s Illustrated Biochemistry. New York: Lange Medical Books/McGraw-Hill. 2006;27. ISBN:978-0-07-146197-9.

  • Rodwell, J.D. Heterogeneity of Component Bands in Isoelectric Focusing Patterns. Analytical Biochemistry. 1982 Jan;119(2):440-49. doi:10.1016/0003-2697(82)90611-x. PMID:7072964.

  • Sillero, A., and A. Maldonado. Isoelectric Point Determination of Proteins and Other Macromolecules: Oscillating Method. Computers in Biology and Medicine. 2006 Feb;36(2): 157–66. doi:10.1016/j.compbiomed.2004.09.006. PMID:16389075.

  • Solomons, T. W. G. Fundamentals of Organic Chemistry. New York: Wiley. 1997;5. ISBN:978-0-471-28298-3.

  • Stryer, L., J. Augustyniak, and J. Michejda. Biochemia. Warszawa: Wydawnictwo Naukowe PWN. 2000. ISBN:978-83-01-12044-3.

counts() → Dict[str, int]

Return a table of amino-acid counts in the peptide.

Returns:

dict – A dictionary mapping each amino-acid code to the number of times it occurs in the peptide sequence.

Example

>>> p = Peptide("SDKEVDEVDAALS")
>>> {k:v for k,v in p.counts().items() if v != 0}
{'A': 2, 'D': 3, 'E': 2, 'L': 1, 'K': 1, 'S': 2, 'V': 2}
cross_covariance(table1: Dict[str, float], table2: Dict[str, float], lag: int = 1, center: bool = True) → float

Compute the cross-covariance index of a peptide sequence.

Example

>>> peptide = Peptide("SDKEVDEVDAALSDLEITLE")
>>> table1 = peptides.tables.HYDROPHOBICITY["KyteDoolittle"]
>>> table2 = peptides.tables.HYDROPHOBICITY["Eisenberg"]
>>> peptide.cross_covariance(table1, table2)
-0.3026609...
>>> peptide.cross_covariance(table1, table2, lag=5)
0.0259803...
cruciani_properties() → CrucianiProperties

Compute the Cruciani properties of the peptide.

See CrucianiProperties for more information.

Returns:

peptides.CrucianiProperties – The computed average Cruciani properties of all the amino acids in the corresponding peptide sequence.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, b in enumerate(peptide.cruciani_properties()):
...     print(f"PP{i+1:<3} {b: .4f}")
PP1   -0.1130
PP2   -0.0220
PP3    0.2735
descriptors() → Dict[str, float]

Create a dictionary containing every protein descriptor available.

Example

>>> peptide = Peptide("SDKEVDEVDAALSDLEITLE")
>>> sorted(peptide.descriptors().keys())
['BLOSUM1', ..., 'F1', ..., 'KF1', ..., 'MSWHIM1', ..., 'PP1', ...]

Hint

Use this method to create a DataFrame containing the descriptors for several sequences.

fasgai_vectors() → FasgaiVectors

Compute the FASGAI vectors of the peptide.

See FasgaiVectors for more information.

Returns:

peptides.FasgaiVectors – The computed average FASGAI vectors for all the amino acids in the peptide.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, b in enumerate(peptide.fasgai_vectors()):
...     print(f"F{i+1:<3} {b: .5f}")
F1   -0.13675
F2   -0.45485
F3   -0.11695
F4   -0.45800
F5   -0.38015
F6    0.52740
frequencies() → Dict[str, float]

Return a table of amino-acid frequencies in the peptide.

Returns:

dict – A dictionary mapping each amino-acid code to its frequency in the peptide sequence.

Example

>>> p = Peptide("AALS")
>>> {k:v for k,v in p.frequencies().items() if v != 0}
{'A': 0.5, 'L': 0.25, 'S': 0.25}
hydrophobic_moment(window: int = 11, angle: int = 100) → float

Compute the maximal hydrophobic moment of a protein sequence.

This function computes the hydrophobic moment based on Eisenberg et al (1984). Hydrophobic moment is a quantitative measure of the amphiphilicity perpendicular to the axis of any periodic peptide structure, such as the α-helix or β-sheet.

Parameters:

  • angle (int) – A protein rotational angle, in degrees. Usual values are 100 for α-helix, and 160 for β-sheet.

  • window (int) – The size of the sliding window for which to compute the local hydrophobic moment.

Returns:

float – The maximal hydrophobic moment of the peptide.

Example

>>> peptide = Peptide("FLPVLAGLTPSIVPKLVCLLTKKC")
>>> peptide.hydrophobic_moment(angle=100)
0.519922...
>>> peptide.hydrophobic_moment(angle=160)
0.270590...

See also

The hydrophobic_moment_profile method, which builds a profile for each amino acid position instead of simply extracting the global maximum.

References

  • Eisenberg, D., R. M. Weiss, and T. C. Terwilliger. The Hydrophobic Moment Detects Periodicity in Protein Hydrophobicity. Proceedings of the National Academy of Sciences of the United States of America. 1984 Jan;81(1):140–44. doi:10.1073/pnas.81.1.140. PMID:6582470.

hydrophobic_moment_profile(window: int = 11, angle: int = 100) → Sequence[float]

Build a hydrophobic moment profile of a sliding window.

This function builds a profile computing the hydrophobic moment of a section of the peptide based on the primary sequecne.

Parameters:

  • window (int) – The size of the sliding window for which to compute the local hydrophobic moment.

  • angle (int) – A protein rotational angle, in degrees. Usual values are 100 for α-helix, and 160 for β-sheet.

Example

>>> peptide = Peptide("ARQQNLFINFCLILIFLLLI")
>>> uH = peptide.hydrophobic_moment_profile(window=12, angle=100)
>>> [round(x, 3) for x in uH]
[0.353, 0.317, 0.274, 0.274, 0.253, 0.113, 0.113, 0.108, 0.132]

See also

The hydrophobic_moment method, which computes the maximal hydrophobic moment instead of building a profile.

hydrophobicity(scale: str = 'KyteDoolittle') → float

Compute the hydrophobicity index of a protein sequence.

This function calculates the hydrophobicity index of an amino acid sequence by averaging the hydrophobicity values of each residue using one of the 39 scales from different sources.

Parameters:

scale (str) – The name of the hydrophobicity scale to be used. A list of all the allowed values can be retrieved from the keys of the peptides.tables.HYDROPHOBICITY dictionary.

Returns:

float – The hydrophobicity index of the peptide.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> peptide.hydrophobicity(scale="Aboderin")
3.84...
>>> peptide.hydrophobicity(scale="AbrahamLeo")
0.092...

Note

The hydrophobicity is an important stabilization force in protein folding; this force changes depending on the solvent in which the protein is found.

References

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hydrophobicity_profile(window: int = 11, scale: str = 'KyteDoolittle') → Sequence[float]

Build a hydrophobicity profile of a sliding window.

Example

>>> peptide = Peptide("ARQQNLFINFCLILIFLLLI")
>>> h = peptide.hydrophobicity_profile(window=12, scale="Eisenberg")
>>> [round(x, 3) for x in h]
[0.083, 0.147, 0.446, 0.632, 0.802, 0.955, 0.955, 0.944, 0.944]
instability_index() → float

Compute the instability index of a protein sequence.

This function calculates the instability index proposed by Guruprasad et al (1990). This index predicts the stability of a protein based on its dipeptide composition.

Returns:

float – The instability index of the peptide. A protein whose instability index is smaller than 40 is predicted as stable, a value above 40 predicts that the protein may be unstable.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> round(peptide.instability_index(), 2)
83.68

References

  • Guruprasad, K., B.V. Bhasker Reddy, and M. W. Pandit. Correlation between Stability of a Protein and Its Dipeptide Composition: A Novel Approach for Predicting in Vivo Stability of a Protein from Its Primary Sequence. Protein Engineering, Design and Selection. 1990 Dec;4(2):155–61. doi:10.1093/protein/4.2.155. PMID:2075190.

isoelectric_point(pKscale: str = 'EMBOSS') → float

Compute the isoelectric point of a protein sequence.

The isoelectric point (pI), is the pH at which a particular molecule or surface carries no net electrical charge.

Parameters:

pKscale (str) – The name of the pKa scale to be used. A list of all the allowed values can be retrieved from the keys of the peptides.tables.PK dictionary.

Returns:

float – The pH at which the peptide has a neutral net charge.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> peptide.isoelectric_point(pKscale="EMBOSS")
9.71...
>>> peptide.isoelectric_point(pKscale="Murray")
9.81...
>>> peptide.isoelectric_point(pKscale="Sillero")
9.89...
>>> peptide.isoelectric_point(pKscale="Solomon")
9.58...
>>> peptide.isoelectric_point(pKscale="Stryer")
9.62...
>>> peptide.isoelectric_point(pKscale="Lehninger")
9.93...
>>> peptide.isoelectric_point(pKscale="Dawson")
9.56...
>>> peptide.isoelectric_point(pKscale="Rodwell")
9.71...

Note

The pI is a variable that affects the solubility of the peptides under certain conditions of pH. When the pH of the solvent is equal to the pI of the protein, it tends to precipitate and lose its biological function.

References

kidera_factors() → KideraFactors

Compute the Kidera factors of the peptide.

See KideraFactors for more information.

Returns:

peptides.KideraFactors – The computed average Kidera factors for all the amino acids in the peptide.

Example

>>> peptide = Peptide("KLKLLLLLKLK")
>>> for i, kf in enumerate(peptide.kidera_factors()):
...     print(f"KF{i+1:<3} {kf: .4f}")
KF1   -0.7855
KF2    0.2982
KF3   -0.2364
KF4   -0.0818
KF5    0.2100
KF6   -1.8936
KF7    1.0291
KF8   -0.5127
KF9    0.1118
KF10   0.8100
linker_preference_profile(window: int = 15) → Sequence[float]

Compute the linker preference profile of a protein sequence.

The linker preference profile is a measure used as a basis for the DomCut method in Suyama & Ohara (2002). The resulting profile can then be used to identify putative domain boundaries in the input protein, either:

  • Using prior knowledge of the estimated domain count \(D\), in which case the \(D-1\) global minimums in the sequence can be used as cutting points

  • Without prior knowledge of the domain count, using a fixed threshold to estimate the number of domains and linkers. A cutoff value of \(-0.09\) was selected by the authors optimizing on the specificity / selectivity tradeoff.

References

New in version 0.3.0.

mass_shift(aa_shift: Optional[Union[str, Dict[str, float]]] = 'silac_13c', monoisotopic: bool = True) → float

Compute the mass difference of modified peptides.

This function calculates the mass difference of peptides introduced by chemical modifications or heavy isotope labelling.

Parameters:

  • aa_shift (str or dict) – Either the key to a pre-defined isotope label (see peptides.tables.MASS_SHIFT), or a dictionary mapping each amino acid to it mass difference in Dalton (use nTer and cTer keys for N-terminal and C-terminal modifications).

  • monoisotopic (bool) – Flag whether monoisotopic weights of amino-acids should be used.

Returns:

float – The mass difference of the modified peptide.

Example

>>> peptide = Peptide("EGVNDNECEGFFSAR")
>>> peptide.mass_shift(aa_shift="silac_13c")
6.020129...
>>> peptide.mass_shift(aa_shift=dict(R=10.00827))
10.00827...

References

  • Ong, S-E., I. Kratchmarova, and M. Mann. Properties of 13C-Substituted Arginine in Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC). Journal of Proteome Research. Apr 2003;2(2):173–81. doi:10.1021/pr0255708. PMID:12716131.

  • Picotti, P., B. Bodenmiller, L. N. Mueller, B. Domon, and R. Aebersold. Full Dynamic Range Proteome Analysis of S. Cerevisiae by Targeted Proteomics. Cell. Aug 2009;138(4):795–806. doi:10.1016/j.cell.2009.05.051. PMID:19664813.

membrane_position_profile(window: int = 11, angle: int = 100) → List[str]

Compute the theoretical class of a protein sequence.

This function builds a profile predicting the theoretical class of a section of the peptide based on the relationship between the hydrophobic moment and hydrophobicity scale as proposed by Eisenberg (1984).

Parameters:

  • window (int) – The window size to consider when building the profile.

  • angle (int) – The protein rotational angle, in degrees, for which to compute the hydrophobic moment profile. Usual values are 100 for α-helix, and 160 for β-sheet.

Returns:

list of str – A list containing a one-character code for each window starting position: either 'G' for globular, 'T' for transmembrane, or 'S' for surface.

Example

>>> peptide = Peptide("ARQQNLFINFCLILIFLLLI")
>>> peptide.membrane_position_profile(window=12, angle=100)
['G', 'G', 'G', 'T', 'S', 'T', 'T', 'T', 'T']
>>> peptide.membrane_position_profile(window=12, angle=160)
['G', 'G', 'G', 'S', 'S', 'S', 'S', 'S', 'S']

References

molecular_weight(average: str = 'expasy', aa_shift: Optional[Union[str, Dict[str, float]]] = None) → float

Compute the molecular weight of a protein sequence.

This function calculates the molecular weight of a protein sequence. It is calculated as the sum of the mass of each amino acid using one of the 3 available scales. It also supports mass calculation of proteins with predefined or custom stable isotope mass labels.

Parameters:

  • average (str) – The name of the average amino acid average weight scale. See peptides.tables.MOLECULAR_WEIGHT for a list of appropriate values.

  • aa_shift (str, dict or None) – Either an appropriate shift value to pass to Peptide.mass_shift, or None to get the unmodified weight.

Returns:

float – The molecular weight of the peptide, in Dalton.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> peptide.molecular_weight()
2485.91...
>>> peptide.molecular_weight(average="mascot")
2485.89...
>>> peptide.molecular_weight(average="monoisotopic")
2484.11...

References

  • Wilkins, M. R., E. Gasteiger, A. Bairoch, J. C. Sanchez, K. L. Williams, R. D. Appel, and D. F. Hochstrasser. Protein Identification and Analysis Tools in the ExPASy Server. Methods in Molecular Biology. 1992;112: 531–52. doi:10.1385/1-59259-584-7:531. PMID:10027275

ms_whim_scores() → MSWHIMScores

Compute the MS-WHIM scores of the peptide.

See MSWHIMScores for more information.

Returns:

peptides.MSWHIMScores – The compute average of MS-WHIM scores of all the amino acids in the peptide.

Example

>>> peptide = Peptide("KLKLLLLLKLK")
>>> for i, mw in enumerate(peptide.ms_whim_scores()):
...     print(f"MSWHIM{i+1:<3} {mw: .4f}")
MSWHIM1   -0.6564
MSWHIM2    0.4873
MSWHIM3    0.1164
mz(charge: int = 2, aa_shift: Optional[Union[str, Dict[str, float]]] = None, cysteins: float = 57.021464) → float

Compute the m/z (mass over charge) ratio for a peptide.

This function calculates the (monoisotopic) mass over charge ratio (m/z) for peptides, as measured in mass spectrometry.

Parameters:

  • charge (int) – The net charge for which the m/z should be computed.

  • aa_shift (str, dict or None) – Either an appropriate shift value to pass to Peptide.mass_shift, or None to get the unmodified weight.

  • cysteins (float) – The mass shift (in Dalton) of blocked cysteins. Default corresponds to cysteins blocked by iodoacetamide.

Returns:

float – The m/z ratio of the peptide.

Example

>>> peptide = Peptide("EGVNDNECEGFFSAR")
>>> peptide.mz()
865.857...
>>> peptide.mz(aa_shift=dict(K=6.020129, R=6.020129))
868.867...
>>> peptide.mz(aa_shift="silac_13c", cysteins=58.005479)
869.359...
pcp_descriptors() → PCPDescriptors

Compute the Physical-Chemical Properties descriptors of the peptide.

See PCPDescriptors for more information.

Returns:

peptides.PCPDescriptors – The computed average of PCP descriptors of all the amino acids in the peptide.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, pcp in enumerate(peptide.pcp_descriptors()):
...     print(f"E{i+1:<3} {pcp: .5f}")
E1    0.01090
E2    0.03810
E3    0.12505
E4    0.04095
E5   -0.10595
physical_descriptors() → PhysicalDescriptors

Compute the Physical Descriptors of the peptide.

See PhysicalDescriptors for more information.

Returns:

peptides.PhyiscalDescriptors – The computed average of Physical Descriptors of all the amino acids in the peptide. PD1 is related to volume while PD2 is related to hydrophilicity.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, pd in enumerate(peptide.physical_descriptors()):
...     print(f"PD{i+1:<3} {pd: .4f}")
PD1    0.1190
PD2    0.2825
profile(table: Dict[str, float], window: int = 1, default: float = 0.0) → Sequence[float]

Compute a generic per-residue profile from per-residue indices.

Parameters:

  • table (dict) – The values per residue to apply to the whole protein sequence.

  • window (int) – The window size for computing the profile. Leave as 1 to return per-residue values.

  • default (float) – The default value to use for amino-acids that are not present in the given table.

Returns:

collections.abc.Sequence of float – The per-residue profile values, averaged in the given window size. When window is larger than the available number of resiudes, an empty sequence is returned.

Example

>>> peptide = Peptide("PKLVCLKKC")
>>> peptide.profile(peptides.tables.CHARGE['sign'])
[0.0, 1.0, 0.0, 0.0, -1.0, 0.0, 1.0, 1.0, -1.0]
>>> peptide.profile(peptides.tables.MOLECULAR_WEIGHT['expasy'], 5)
[108..., 111..., 111..., 114..., 115...]

New in version 0.3.0.

protfp_descriptors() → ProtFPDescriptors

Compute the ProtFP descriptors of the peptide.

See ProtFPDescriptors for more information.

Returns:

peptides.ProtFPDescriptors – The computed average of ProtFP descriptors of all the amino acids in the peptide.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, fp in enumerate(peptide.protfp_descriptors()):
...     print(f"ProtFP{i+1:<3} {fp: .4f}")
ProtFP1    0.2065
ProtFP2   -0.0565
ProtFP3    1.9930
ProtFP4   -0.2845
ProtFP5    0.7315
ProtFP6    0.7000
ProtFP7    0.1715
ProtFP8    0.1135
classmethod sample(length: int, frequencies: str = 'SwissProt2021') → Peptide

Generate a peptide with the given amino-acid frequencies.

This method is useful for testing, but using amino-acid frequencies to generate a peptide is not a biologically accurate method, instead consider sampling based on dipeptide frequencies in a particular organism, or using k-mer shuffling.

Parameters:

  • length (int) – The desired length for the generated peptide.

  • frequencies (str) – The name of the amino-acid frequency table to use: either KingJukes to use the amino-acid frequencies for vertebrate organisms reported in King & Jukes (1969), or SwissProt2021 to use the amino-acid frequencies in all the proteins from the January 2021 release of SwissProt.

Returns:

Peptide – A new peptide. The first amino-acid will always be a Methionine for biological accuracy.

References

sneath_vectors() → SneathVectors

Compute the Sneath vectors for the peptide.

See SneathVectors for more information.

Returns:

peptides.SneathVectors – The computed average of Sneath vectors of all the amino acids in the peptide.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, fp in enumerate(peptide.sneath_vectors()):
...     print(f"SV{i+1:<3} {fp: .5f}")
SV1    0.19620
SV2    0.04655
SV3    0.04050
SV4    0.02775
st_scales() → STScales

Compute the ST-scales of the peptide.

See STScales for more information.

Returns:

peptides.STScales – The computed average of ST-scales of all the amino acids in the peptide.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, st in enumerate(peptide.st_scales()):
...     print(f"ST{i+1:<3} {st: .5f}")
ST1   -0.63760
ST2    0.07965
ST3    0.05150
ST4    0.07135
ST5   -0.27905
ST6   -0.80995
ST7    0.58020
ST8    0.54400
structural_class(frequencies: str = 'Nakashima', distance: str = 'mahalanobis') → str

Predict the structural class of the peptide from its sequence.

The structural class of a protein, as defined in Levitt and Chothia (1976), can be either α, β, α+β, or α/β, with ζ being later defined for irregular proteins. It depends on the secondary structure of the protein. Several methods have been proposed to elucidate the structural class from the amino acid sequence, all based on similarity with proteins which structures have been elucidated.

Parameters:

  • frequencies (str) – The frequencies of the amino acids in proteins of different structural classes to use as reference centroids. Use "Chou" to load the frequencies of the 64 proteins analyzed in Chou (1989), "Nakashima" to use the normalized frequencies of the 135 proteins analyzed in Nakashima et al. (1986) and Zhang & Chou (1995), or "ChouZhang" to load the frequencies of 120 proteins used in Chou & Zhang (1995).

  • distance (str) – The distance metric to use in the 20-D space formed by the 20 usual amino acid to find the nearest structural class for the peptide. Use "cityblock" to use the Manhattan distance like in Chou (1989), "euclidean" to use the Euclidean distance like in Nakashima et al (1986), "correlation" to use the correlation distance like in Chou & Zhang (1992), "mahalanobis" to use the Mahalanobis distance like in Chou & Zhang (1995), or "discriminant" to use the Bayes discriminant like in Chou et al. (1998).

Returns:

str – The structural class the protein most likely belongs to. Note that some classes may not be predictable, depending on the reference frequencies being used (at the moment, the ζ class can only be predicted from the Nakashima frequencies with euclidean or manhattan distances).

Example

Predict the structural class of the skipjack tuna Cytochrome C, (P0025), an α protein

>>> p = Peptide(
...     "MGDVAKGKKTFVQKCAQCHTVENGGKHKVGPNLWGLFGRKTGQAEGYSYT"
...     "DANKSKGIVWNENTLMEYLENPKKYIPGTKMIFAGIKKKGERQDLVAYLK"
...     "SATS"
... )
>>> p.structural_class("Nakashima", distance="mahalanobis")
'alpha'
>>> p.structural_class("ChouZhang", distance="mahalanobis")
'beta'
>>> p.structural_class("Chou", distance="correlation")
'alpha'
>>> p.structural_class("Nakashima", distance="euclidean")
'alpha'
>>> p.structural_class("Chou", distance="cityblock")
'alpha+beta'

Predict the structural class of the sea krait Erabutoxin B (Q90VW1), a β protein:

>>> p = Peptide(
...     "MKTLLLTLVVVTIVCLDLGYTRICFNHQSSQPQTTKTCSPGESSCYHKQW"
...     "SDFRGTIIERGCGCPTVKPGIKLSCCESEVCNN"
... )
>>> p.structural_class("Nakashima", distance="mahalanobis")
'beta'
>>> p.structural_class("ChouZhang", distance="mahalanobis")
'alpha+beta'
>>> p.structural_class("Chou", distance="correlation")
'beta'
>>> p.structural_class("Nakashima", distance="euclidean")
'zeta'
>>> p.structural_class("Chou", distance="cityblock")
'beta'

Predict the structural class of the Arthrospira platensis Ferredoxin (P00246), a ζ protein:

>>> p = Peptide(
...     "MATYKVTLINEAEGINETIDCDDDTYILDAAEEAGLDLPYSCRAGACSTC"
...     "AGTITSGTIDQSDQSFLDDDQIEAGYVLTCVAYPTSDCTIKTHQEEGLY"
... )
>>> p.structural_class("Nakashima", distance="euclidean")
'zeta'

References

  • Chou, K-C., W-M. Liu, G. M. Maggiora, and C-T. Zhang. Prediction and Classification of Domain Structural Classes. Proteins: Structure, Function, and Genetics. Apr 1998;31(1):97–103. PMID:9552161.

  • Chou, K-C., and C-T. Zhang. Prediction of Protein Structural Classes. Critical Reviews in Biochemistry and Molecular Biology. Feb 1995;30:275–349. doi:10.3109/10409239509083488. PMID:7587280.

  • Chou, K-C., and C-T. Zhang. A Correlation-Coefficient Method to Predicting Protein-Structural Classes from Amino Acid Compositions. European Journal of Biochemistry. 1992;207(2):429–33. doi:10.1111/j.1432-1033.1992.tb17067.x. PMID:1633801.

  • Chou, P. Y. Prediction of Protein Structural Classes from Amino Acid Compositions. In Prediction of Protein Structure and the Principles of Protein Conformation, edited by G. D. Fasman. Springer US. 1989:549–86. doi:10.1007/978-1-4613-1571-1. ISBN:978-0-306-43131-9.

  • Nakashima, H., K. Nishikawa, and T. Ooi. The Folding Type of a Protein Is Relevant to the Amino Acid Composition. Journal of Biochemistry. Jan 1986;99(1):153–62. doi:10.1093/oxfordjournals.jbchem.a135454. PMID:3957893.

  • Zhang, Chun-Ting, and Kuo-Chen Chou. An Eigenvalue-Eigenvector Approach to Predicting Protein Folding Types. Journal of Protein Chemistry. Jul 1995;14(5):309–26. doi:10.1007/BF01886788. PMID:8590599.

  • Zhou, G.P., and N. Assa-Munt. Some Insights into Protein Structural Class Prediction. Proteins: Structure, Function, and Bioinformatics. 2001;44(1):57–59. doi:10.1002/prot.1071. PMID:11354006.

svger_descriptors() → SVGERDescriptors

Compute the SVGER descriptors of the peptide.

See SVGERDescriptors for more information.

Returns:

peptides.SVGERDescriptors – The computed average of SVGER descriptors of all the amino acids in the peptide.

New in version 0.3.2.

t_scales() → TScales

Compute the T-scales of the peptide.

See TScales for more information.

Returns:

peptides.TScales – The computed average of T-scales of all the amino acids in the peptide.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, t in enumerate(peptide.t_scales()):
...     print(f"T{i+1:<3} {t: .4f}")
T1   -3.2700
T2   -0.0035
T3   -0.3855
T4   -0.1475
T5    0.7585
vhse_scales() → VHSEScales

Compute the VHSE-scales of the peptide.

See VHSEScales for more information.

Returns:

peptides.VHSEScales – The computed average of VHSE-scales of the amino acids in the peptide. VHSE1 and VHSE2 represent hydrophobic properties, VHSE3 and VHSE4 represent steric properties, while VHSE5, VHSE6, VHSE7 and VHSE8 represent electronic properties.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, vhse in enumerate(peptide.vhse_scales()):
...     print(f"VHSE{i+1:<3} {vhse: .4f}")
VHSE1   -0.1150
VHSE2    0.0630
VHSE3   -0.0055
VHSE4    0.7955
VHSE5    0.4355
VHSE6    0.2485
VHSE7    0.1740
VHSE8   -0.0960
z_scales() → ZScales

Compute the Z-scales of the peptide.

See ZScales for more information.

Returns:

peptides.ZScales – The computed average of Z-scales of all the amino acid in the peptide.

Example

>>> peptide = Peptide("QWGRRCCGWGPGRRYCVRWC")
>>> for i, z in enumerate(peptide.z_scales()):
...     print(f"Z{i+1:<3} {0.0+round(z,5): .4f}")
Z1    0.5520
Z2    0.0985
Z3    0.0000
Z4    0.8130
Z5   -0.8285

Descriptors

class peptides.BLOSUMIndices(typing.NamedTuple)

The BLOSUM62-derived indices of a peptide.

BLOSUM indices were derived of physicochemical properties that have been subjected to a VARIMAX analysis and an alignment matrix of the 20 natural AAs using the BLOSUM62 matrix.

References

class peptides.CrucianiProperties(typing.NamedTuple)

The Cruciani properties of a peptide.

The Cruciani properties are a collection of scaled principal component scores that summarize a broad set of descriptors calculated based on the interaction of each amino acid residue with several chemical groups (or “probes”), such as charged ions, methyl, hydroxyl groups, and so forth.

References

  • Cruciani, G., M. Baroni, E. Carosati, M. Clementi, R. Valigi, and S. Clementi. Peptide Studies by Means of Principal Properties of Amino Acids Derived from MIF Descriptors. Journal of Chemometrics. 2004;18(3-4):146–55. doi:10.1002/cem.856.

class peptides.FasgaiVectors(typing.NamedTuple)

FASGAI vectors of a peptide.

The FASGAI vectors (Factor Analysis Scales of Generalized Amino Acid Information) are a set of amino acid descriptors, that reflect hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility, and electronic properties, that can be utilized to represent the sequence structural features of peptides or protein motifs.

References

  • Liang, G., G. Chen, W. Niu, and Z. Li. Factor Analysis Scales of Generalized Amino Acid Information as Applied in Predicting Interactions between the Human Amphiphysin-1 SH3 Domains and Their Peptide Ligands. Chemical Biology & Drug Design. Apr 2008;71(4):345–51. doi:10.1111/j.1747-0285.2008.00641.x. PMID:18318694.

class peptides.KideraFactors(typing.NamedTuple)

The Kidera factors of a peptide.

The Kidera Factors were originally derived by applying multivariate analysis to 188 physical properties of the 20 amino acids and using dimension reduction techniques.

kf1

A factor modeling the helix / bend preference.

Type:

float

kf2

A factor modeling the side-chain size of each residue (AAindex:KIDA850101).

Type:

float

kf3

A factor modeling the extended structured preference.

Type:

float

kf4

A factor representing the hydrophobicity.

Type:

float

kf5

A factor modeling the double-bend preference.

Type:

float

kf6

A factor modeling the partial specific volume.

Type:

float

kf7

A factor modeling the flat extended preference.

Type:

float

kf8

A factor modeling the occurence in alpha regions.

Type:

float

kf9

A factor encoding the pK-C.

Type:

float

kf10

A factor representing the surrounding hydrophobicity.

Type:

float

References

  • Kidera, A., Y. Konishi, M. Oka, T. Ooi, and H. A. Scheraga. Statistical Analysis of the Physical Properties of the 20 Naturally Occurring Amino Acids. Journal of Protein Chemistry. Feb 1985;4(1):23–55. doi:10.1007/BF01025492.

class peptides.MSWHIMScores(typing.NamedTuple)

The MS-WHIM scores of a peptide.

MS-WHIM scores were derived from 36 electrostatic potential properties derived from the three-dimensional structure of the 20 natural amino acids.

References

  • Bravi, G., E. Gancia, P. Mascagni, M. Pegna, R. Todeschini, and A. Zaliani. MS-WHIM, New 3D Theoretical Descriptors Derived from Molecular Surface Properties: A Comparative 3D QSAR Study in a Series of Steroids. Journal of Computer-Aided Molecular Design. Jan 1997;11(1):79-92. doi:10.1023/a:1008079512289. PMID:9139115

  • Gancia, E., G. Bravi, P. Mascagni, and A. Zaliani. Global 3D-QSAR Methods: MS-WHIM and Autocorrelation. Journal of Computer-Aided Molecular Design. Mar 2000;14(3):293–306. doi:10.1023/a:1008142124682. PMID:10756483.

  • Zaliani, A., and E. Gancia. MS-WHIM Scores for Amino Acids: A New 3D-Description for Peptide QSAR and QSPR Studies. Journal of Chemical Information and Computer Sciences. May 1999;39(3):525–33. doi:10.1021/ci980211b.

class peptides.PhysicalDescriptors(typing.NamedTuple)

The Physical Descriptors of a peptide.

The PP descriptors were constructed by improving on existing PCA-derived descriptors (Z-scales, MS-WHIM and T-scales) after correcting for the hydrophilicity of Methionine, Asparagine and Tryptophan based on Feng et al.

pd1

A descriptor related to residue volume.

Type:

float

pd2

A descriptor related to hydrophilicity.

Type:

float

Note

Barley et al insisted on maintaining a minimal number of descriptors as a way to reduce the chances of finding spurious QSAM models that would be affected by mutation between interaction sites.

References

  • Barley, M. H., N. J. Turner, and R. Goodacre. Improved Descriptors for the Quantitative Structure–Activity Relationship Modeling of Peptides and Proteins. Journal of Chemical Information and Modeling. Feb 2018;58(2):234–43. doi:10.1021/acs.jcim.7b00488. PMID:29338232.

  • Feng, X., J. Sanchis, M. T. Reetz, and H. Rabitz. Enhancing the Efficiency of Directed Evolution in Focused Enzyme Libraries by the Adaptive Substituent Reordering Algorithm. Chemistry. Apr 2012;18(18):5646–54. doi:10.1002/chem.201103811. PMID:22434591.

class peptides.PCPDescriptors(typing.NamedTuple)

The Physical-Chemical Properties descriptors of a peptide.

The PCP descriptors were constructed by performing multidimensional scaling of 237 physical-chemical properties.

References

  • Mathura, V. S., and W. Braun. New Quantitative Descriptors of Amino Acids Based on Multidimensional Scaling of a Large Number of Physical–Chemical Properties. Molecular Modeling Annual. Dec 2001;7(12):445–53. doi:10.1007/s00894-001-0058-5.

  • Mathura, V. S., D. Paris, and M. J. Mullan. A Novel Physico-Chemical Property Based Model for Studying the Effects of Mutation on the Aggregation of Peptides. Protein and Peptide Letters. 2009;16(8):991–98. doi:10.2174/092986609788923220. PMID:19689427.

class peptides.ProtFPDescriptors(typing.NamedTuple)

The ProtFP descriptors of a peptide.

The ProtFP set was constructed from a large initial selection of indices obtained from the AAindex database for all 20 naturally occurring amino acids.

References

  • van Westen, G. J., R. F. Swier, J. K. Wegner, A. P. Ijzerman, H. W. van Vlijmen, and A. Bender. Benchmarking of Protein Descriptor Sets in Proteochemometric Modeling (Part 1): Comparative Study of 13 Amino Acid Descriptor Sets. Journal of Cheminformatics. Sep 2013;5(1):41. doi:10.1186/1758-2946-5-41. PMID:24059694.

  • van Westen, G. J., R. F. Swier, I. Cortes-Ciriano, J. K. Wegner, J. P. Overington, A. P. Ijzerman, H. W. van Vlijmen, and A. Bender. Benchmarking of Protein Descriptor Sets in Proteochemometric Modeling (Part 2): Modeling Performance of 13 Amino Acid Descriptor Sets. Journal of Cheminformatics. Sep 2013;5(1):42. doi:10.1186/1758-2946-5-42. PMID:24059743.

class peptides.SneathVectors(typing.NamedTuple)

The Sneath vectors of a peptide.

These vectors were obtained in Sneath (1996) by running PCA on the ϕ coefficient to explain the dissimilarity between the 20 natural amino acids based on binary state encoding of 134 physical and chemical properties (such as presence/absence of a —CH₃ group, step-wise optical rotation, etc.).

sv1

A descriptor representing mainly aliphatic properties of each residue (AAindex:SNEP660101).

Type:

float

sv2

A descriptor putatively modeling the number of reactive groups (AAindex:SNEP660102).

Type:

float

sv3

A descriptor representing the aromatic properties of each residue (AAindex:SNEP660103).

Type:

float

sv4

A descriptor with uncertain interpretation (AAindex:SNEP660104).

Type:

float

References

class peptides.STScales(typing.NamedTuple)

The ST-scales of a peptide.

The ST-scales were proposed in Yang et al (2010), taking 827 properties into account which are mainly constitutional, topological, geometrical, hydrophobic, electronic, and steric properties of a total set of 167 amino acids.

References

  • Yang, L., M. Shu, K. Ma, H. Mei, Y. Jiang, and Z. Li. ST-Scale as a Novel Amino Acid Descriptor and Its Application in QSAM of Peptides and Analogues. Amino Acids. Mar 2010;38(3):805–16. doi:10.1007/s00726-009-0287-y. PMID:19373543.

class peptides.SVGERDescriptors(typing.NamedTuple)

The SVGER descriptors of a peptide.

SVGER descriptors were constructed by Principal Component Analysis of 74 geometrical descriptors (svger1 to svger6), 44 eigenvalue descriptors (svger7, svger8 and svger9), and 41 Randić descriptors (svger10 and svger11) computed for the 20 proteinogenic amino acids.

References

  • Tong, J., L. Li, M. Bai, and K. Li. A New Descriptor of Amino Acids-SVGER and Its Applications in Peptide QSAR. Molecular Informatics 36, no. 5–6 (2017): 1501023. doi:10.1002/minf.201501023.

  • Randic, M. Molecular Shape Profiles. Journal of Chemical Information and Computer Sciences 35, no. 3 (1 May 1995): 373–82. doi:10.1021/ci00025a005.

class peptides.TScales(typing.NamedTuple)

The T-scales of a peptide.

The T-scales are based on 67 common topological descriptors of 135 amino acids. These topological descriptors are based on the connectivity table of amino acids alone, and to not explicitly consider 3D properties of each structure.

References

  • Tian, F., P. Zhou, and Z. Li. T-Scale as a Novel Vector of Topological Descriptors for Amino Acids and Its Application in QSARs of Peptides. Journal of Molecular Structure. Mar 2007;830(1):106–15. doi:10.1016/j.molstruc.2006.07.004.

class peptides.VHSEScales(typing.NamedTuple)

The VHSE-scales of a peptide.

The VHSE-scales (principal components score Vectors of Hydrophobic, Steric, and Electronic properties), are derived from principal components analysis (PCA) on independent families of 18 hydrophobic properties, 17 steric properties, and 15 electronic properties, respectively, which are included in total 50 physicochemical variables of 20 coded amino acids.

Attribute:

vhse1 (float): A descriptor representing hydrophobic properties. vhse2 (float): Another descriptor representing hydrophobic

properties.

vhse3 (float): A descriptor representing steric properties. vhse4 (float): Another descriptor representing steric properties. vhse5 (float): A descriptor representing electronic properties. vhse6 (float): A second descriptor representing electronic

properties.

vhse7 (float): A third descriptor representing electronic

properties.

vhse8 (float): A fourth descriptor representing electronic

properties.

References

  • Mei, H., Z. H. Liao, Y. Zhou, and S. Z. Li. A New Set of Amino Acid Descriptors and Its Application in Peptide QSARs. Biopolymers. 2005;80(6):775-86. doi:10.1002/bip.20296. PMID:15895431.

class peptides.ZScales(typing.NamedTuple)

The Z-scales of a peptide.

The Z-scales were proposed in Sandberg et al (1998) based on physicochemical properties of proteogenic and non-proteogenic amino acids, including NMR data and thin-layer chromatography (TLC) data.

z1

A descriptor quantifying lipophilicity.

Type:

float

z2

A descriptor modeling steric properties like steric bulk and polarizability.

Type:

float

z3

A descriptor quantifying electronic properties like polarity and charge.

Type:

float

z4

A descriptor relating to electronegativity, heat of formation, electrophilicity and hardness.

Type:

float

z5

Another descriptor relating to electronegativity, heat of formation, electrophilicity and hardness.

Type:

float

References

  • Sandberg, M., L. Eriksson, J. Jonsson, M. Sjöström, and S. Wold. New Chemical Descriptors Relevant for the Design of Biologically Active Peptides. A Multivariate Characterization of 87 Amino Acids. Journal of Medicinal Chemistry. Jul 1998;41(14):2481–91. doi:10.1021/jm9700575. PMID:9651153.