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.AtchleyFactors(typing.NamedTuple)#

The Atchley factors of a peptide.

The Atchley Factors were originally derived by applying multivariate analysis to 494 physical properties of the 20 amino acids and using dimension reduction techniques.

af1#

A factor modeling polarity, accessibility, and hydrophobicity.

Type:

float

af2#

A factor modeling propensity for secondary structure.

Type:

float

af3#

A factor modeling molecular size.

Type:

float

af4#

A factor modeling codon composition.

Type:

float

af5#

A factor modeling electrostatic charge.

Type:

float

References

  • Atchley, W. R., Zhao, J., Fernandes, A. D., Drüke, T. Solving the protein sequence metric problem. Proceedings of the National Academy of Sciences. Apr 2005;102(18):6395-6400. doi:10.1073/pnas.040867710.

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.PRINComponents(typing.NamedTuple)#

The PRIN components of a peptide.

The PRIN components were constructed in Vicator et al (2005) by running PCA on different amino-acid properties.

prin1#

The PRIN1 component, representing hydrophobicity properties.

Type:

float

prin2#

The PRIN2 component, representing the residue size.

Type:

float

prin3#

The PRIN3 component, representing the pkN values of the amino-acids.

Type:

float

References

  • Vicatos, S., Reddy, B.V., and Y. Kaznessis. Prediction of distant residue contacts with the use of evolutionary information. Proteins. 2005 Mar 1;58(4):935-49. doi:10.1002/prot.20370 PMID:15645442.

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.

vhse1#

A descriptor representing hydrophobic properties.

Type:

float

vhse2#

Another descriptor representing hydrophobic properties.

Type:

float

vhse3#

A descriptor representing steric properties.

Type:

float

vhse4#

Another descriptor representing steric properties.

Type:

float

vhse5#

A descriptor representing electronic properties.

Type:

float

vhse6#

A second descriptor representing electronic properties.

Type:

float

vhse7#

A third descriptor representing electronic properties.

Type:

float

vhse8#

A fourth descriptor representing electronic properties.

Type:

float

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.VSTPVDescriptors(typing.NamedTuple)#

The VSTPV descriptors of a peptide.

The VSTPV (vector of structural and topological variables) are derived from principal component analysis (PCA) of 85 structural variables of 166 amino acids.

References

  • Shu, M., Yu, R., Zhang, Y., Wang, J., Wang, L., Lin, Z. Predicting the Activity of Antimicrobial Peptides with Amino Acid Topological Information. Medicinal Chemistry. Feb 2013;9(1):32-44. doi:10.2174/157340613804488350.

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.