Autocorrelation | index /home/orient/ProPy/Autocorrelation.py

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This module is used for computing the Autocorrelation descriptors based different
properties of AADs.You can also input your properties of AADs, then it can help you
to compute Autocorrelation descriptors based on the property of AADs. Currently, You
can get 720 descriptors for a given protein sequence based on our provided physicochemical
properties of AADs. You can freely use and distribute it. If you hava any problem,
you could contact with us timely!
References:
[1]: http://www.genome.ad.jp/dbget/aaindex.html
[2]:Feng, Z.P. and Zhang, C.T. (2000) Prediction of membrane protein types based on
the hydrophobic index of amino acids. J Protein Chem, 19, 269-275.
[3]:Horne, D.S. (1988) Prediction of protein helix content from an autocorrelation
analysis of sequence hydrophobicities. Biopolymers, 27, 451-477.
[4]:Sokal, R.R. and Thomson, B.A. (2006) Population structure inferred by local
spatial autocorrelation: an Usage from an Amerindian tribal population. Am J
Phys Anthropol, 129, 121-131.
Authors: Dongsheng Cao and Yizeng Liang.
Date: 2010.11.22
Email: oriental-cds@163.com
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Modules
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Functions
`` |  | CalculateAutoTotal(ProteinSequence) `#################################################################################### A method used for computing all autocorrelation descriptors based on 8 properties of AADs. Usage: result=CalculateGearyAutoTotal(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30*8*3=720 normalized Moreau Broto, Moran, and Geary autocorrelation descriptors based on the given properties(i.e., _AAPropert). ####################################################################################` CalculateEachGearyAuto(ProteinSequence, AAP, AAPName) `#################################################################################### you can use the function to compute GearyAuto descriptors for different properties based on AADs. Usage: result=CalculateEachGearyAuto(protein,AAP,AAPName) Input: protein is a pure protein sequence. AAP is a dict form containing the properties of 20 amino acids (e.g., _AvFlexibility). AAPName is a string used for indicating the property (e.g., '_AvFlexibility'). Output: result is a dict form containing 30 Geary autocorrelation descriptors based on the given property. ####################################################################################` CalculateEachMoranAuto(ProteinSequence, AAP, AAPName) `#################################################################################### you can use the function to compute MoranAuto descriptors for different properties based on AADs. Usage: result=CalculateEachMoranAuto(protein,AAP,AAPName) Input: protein is a pure protein sequence. AAP is a dict form containing the properties of 20 amino acids (e.g., _AvFlexibility). AAPName is a string used for indicating the property (e.g., '_AvFlexibility'). Output: result is a dict form containing 30 Moran autocorrelation descriptors based on the given property. ####################################################################################` CalculateEachNormalizedMoreauBrotoAuto(ProteinSequence, AAP, AAPName) `#################################################################################### you can use the function to compute MoreauBrotoAuto descriptors for different properties based on AADs. Usage: result=CalculateEachNormalizedMoreauBrotoAuto(protein,AAP,AAPName) Input: protein is a pure protein sequence. AAP is a dict form containing the properties of 20 amino acids (e.g., _AvFlexibility). AAPName is a string used for indicating the property (e.g., '_AvFlexibility'). Output: result is a dict form containing 30 Normalized Moreau-Broto autocorrelation descriptors based on the given property. ####################################################################################` CalculateGearyAuto(ProteinSequence, AAProperty, AAPropertyName) `#################################################################################### A method used for computing GearyAuto for all properties Usage: result=CalculateGearyAuto(protein,AAP,AAPName) Input: protein is a pure protein sequence. AAProperty is a list or tuple form containing the properties of 20 amino acids (e.g., _AAProperty). AAPName is a list or tuple form used for indicating the property (e.g., '_AAPropertyName'). Output: result is a dict form containing 30*p Geary autocorrelation descriptors based on the given properties. ####################################################################################` CalculateGearyAutoAvFlexibility(ProteinSequence) `#################################################################################### Calculte the GearyAuto Autocorrelation descriptors based on AvFlexibility. Usage: result=CalculateGearyAutoAvFlexibility(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Geary Autocorrelation descriptors based on AvFlexibility. ####################################################################################` CalculateGearyAutoFreeEnergy(ProteinSequence) `#################################################################################### Calculte the GearyAuto Autocorrelation descriptors based on FreeEnergy. Usage: result=CalculateGearyAutoFreeEnergy(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Geary Autocorrelation descriptors based on FreeEnergy. ####################################################################################` CalculateGearyAutoHydrophobicity(ProteinSequence) `#################################################################################### Calculte the GearyAuto Autocorrelation descriptors based on hydrophobicity. Usage: result=CalculateGearyAutoHydrophobicity(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Geary Autocorrelation descriptors based on hydrophobicity. ####################################################################################` CalculateGearyAutoMutability(ProteinSequence) `#################################################################################### Calculte the GearyAuto Autocorrelation descriptors based on Mutability. Usage: result=CalculateGearyAutoMutability(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Geary Autocorrelation descriptors based on Mutability. ####################################################################################` CalculateGearyAutoPolarizability(ProteinSequence) `#################################################################################### Calculte the GearyAuto Autocorrelation descriptors based on Polarizability. Usage: result=CalculateGearyAutoPolarizability(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Geary Autocorrelation descriptors based on Polarizability. ####################################################################################` CalculateGearyAutoResidueASA(ProteinSequence) `#################################################################################### Calculte the GearyAuto Autocorrelation descriptors based on ResidueASA. Usage: result=CalculateGearyAutoResidueASA(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Geary Autocorrelation descriptors based on ResidueASA. ####################################################################################` CalculateGearyAutoResidueVol(ProteinSequence) `#################################################################################### Calculte the GearyAuto Autocorrelation descriptors based on ResidueVol. Usage: result=CalculateGearyAutoResidueVol(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Geary Autocorrelation descriptors based on ResidueVol. ####################################################################################` CalculateGearyAutoSteric(ProteinSequence) `#################################################################################### Calculte the GearyAuto Autocorrelation descriptors based on Steric. Usage: result=CalculateGearyAutoSteric(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Geary Autocorrelation descriptors based on Steric. ####################################################################################` CalculateGearyAutoTotal(ProteinSequence) `#################################################################################### A method used for computing Geary autocorrelation descriptors based on 8 properties of AADs. Usage: result=CalculateGearyAutoTotal(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30*8=240 Geary autocorrelation descriptors based on the given properties(i.e., _AAPropert). ####################################################################################` CalculateMoranAuto(ProteinSequence, AAProperty, AAPropertyName) `#################################################################################### A method used for computing MoranAuto for all properties Usage: result=CalculateMoranAuto(protein,AAP,AAPName) Input: protein is a pure protein sequence. AAProperty is a list or tuple form containing the properties of 20 amino acids (e.g., _AAProperty). AAPName is a list or tuple form used for indicating the property (e.g., '_AAPropertyName'). Output: result is a dict form containing 30*p Moran autocorrelation descriptors based on the given properties. ####################################################################################` CalculateMoranAutoAvFlexibility(ProteinSequence) `#################################################################################### Calculte the MoranAuto Autocorrelation descriptors based on AvFlexibility. Usage: result=CalculateMoranAutoAvFlexibility(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Moran Autocorrelation descriptors based on AvFlexibility. ####################################################################################` CalculateMoranAutoFreeEnergy(ProteinSequence) `#################################################################################### Calculte the MoranAuto Autocorrelation descriptors based on FreeEnergy. Usage: result=CalculateMoranAutoFreeEnergy(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Moran Autocorrelation descriptors based on FreeEnergy. ####################################################################################` CalculateMoranAutoHydrophobicity(ProteinSequence) `#################################################################################### Calculte the MoranAuto Autocorrelation descriptors based on hydrophobicity. Usage: result=CalculateMoranAutoHydrophobicity(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Moran Autocorrelation descriptors based on hydrophobicity. ####################################################################################` CalculateMoranAutoMutability(ProteinSequence) `#################################################################################### Calculte the MoranAuto Autocorrelation descriptors based on Mutability. Usage: result=CalculateMoranAutoMutability(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Moran Autocorrelation descriptors based on Mutability. ####################################################################################` CalculateMoranAutoPolarizability(ProteinSequence) `#################################################################################### Calculte the MoranAuto Autocorrelation descriptors based on Polarizability. Usage: result=CalculateMoranAutoPolarizability(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Moran Autocorrelation descriptors based on Polarizability. ####################################################################################` CalculateMoranAutoResidueASA(ProteinSequence) `#################################################################################### Calculte the MoranAuto Autocorrelation descriptors based on ResidueASA. Usage: result=CalculateMoranAutoResidueASA(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Moran Autocorrelation descriptors based on ResidueASA. ####################################################################################` CalculateMoranAutoResidueVol(ProteinSequence) `#################################################################################### Calculte the MoranAuto Autocorrelation descriptors based on ResidueVol. Usage: result=CalculateMoranAutoResidueVol(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Moran Autocorrelation descriptors based on ResidueVol. ####################################################################################` CalculateMoranAutoSteric(ProteinSequence) `#################################################################################### Calculte the MoranAuto Autocorrelation descriptors based on AutoSteric. Usage: result=CalculateMoranAutoSteric(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Moran Autocorrelation descriptors based on AutoSteric. ####################################################################################` CalculateMoranAutoTotal(ProteinSequence) `#################################################################################### A method used for computing Moran autocorrelation descriptors based on 8 properties of AADs. Usage: result=CalculateMoranAutoTotal(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30*8=240 Moran autocorrelation descriptors based on the given properties(i.e., _AAPropert). ####################################################################################` CalculateNormalizedMoreauBrotoAuto(ProteinSequence, AAProperty, AAPropertyName) `#################################################################################### A method used for computing MoreauBrotoAuto for all properties. Usage: result=CalculateNormalizedMoreauBrotoAuto(protein,AAP,AAPName) Input: protein is a pure protein sequence. AAProperty is a list or tuple form containing the properties of 20 amino acids (e.g., _AAProperty). AAPName is a list or tuple form used for indicating the property (e.g., '_AAPropertyName'). Output: result is a dict form containing 30*p Normalized Moreau-Broto autocorrelation descriptors based on the given properties. ####################################################################################` CalculateNormalizedMoreauBrotoAutoAvFlexibility(ProteinSequence) `#################################################################################### Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on AvFlexibility. Usage: result=CalculateNormalizedMoreauBrotoAutoAvFlexibility(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation descriptors based on AvFlexibility. ####################################################################################` CalculateNormalizedMoreauBrotoAutoFreeEnergy(ProteinSequence) `#################################################################################### Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on FreeEnergy. Usage: result=CalculateNormalizedMoreauBrotoAutoFreeEnergy(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation descriptors based on FreeEnergy. ####################################################################################` CalculateNormalizedMoreauBrotoAutoHydrophobicity(ProteinSequence) `#################################################################################### Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on hydrophobicity. Usage: result=CalculateNormalizedMoreauBrotoAutoHydrophobicity(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation descriptors based on Hydrophobicity. ####################################################################################` CalculateNormalizedMoreauBrotoAutoMutability(ProteinSequence) `#################################################################################### Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on Mutability. Usage: result=CalculateNormalizedMoreauBrotoAutoMutability(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation descriptors based on Mutability. ####################################################################################` CalculateNormalizedMoreauBrotoAutoPolarizability(ProteinSequence) `#################################################################################### Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on Polarizability. Usage: result=CalculateNormalizedMoreauBrotoAutoPolarizability(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation descriptors based on Polarizability. ####################################################################################` CalculateNormalizedMoreauBrotoAutoResidueASA(ProteinSequence) `#################################################################################### Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on ResidueASA. Usage: result=CalculateNormalizedMoreauBrotoAutoResidueASA(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation descriptors based on ResidueASA. ####################################################################################` CalculateNormalizedMoreauBrotoAutoResidueVol(ProteinSequence) `#################################################################################### Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on ResidueVol. Usage: result=CalculateNormalizedMoreauBrotoAutoResidueVol(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation descriptors based on ResidueVol. ####################################################################################` CalculateNormalizedMoreauBrotoAutoSteric(ProteinSequence) `#################################################################################### Calculte the NormalizedMoreauBorto Autocorrelation descriptors based on Steric. Usage: result=CalculateNormalizedMoreauBrotoAutoSteric(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30 Normalized Moreau-Broto Autocorrelation descriptors based on Steric. ####################################################################################` CalculateNormalizedMoreauBrotoAutoTotal(ProteinSequence) `#################################################################################### A method used for computing normalized Moreau Broto autocorrelation descriptors based on 8 proterties of AADs. Usage: result=CalculateNormalizedMoreauBrotoAutoTotal(protein) Input: protein is a pure protein sequence. Output: result is a dict form containing 30*8=240 normalized Moreau Broto autocorrelation descriptors based on the given properties(i.e., _AAPropert). ####################################################################################` NormalizeEachAAP(AAP) `#################################################################################### All of the amino acid indices are centralized and standardized before the calculation. Usage: result=NormalizeEachAAP(AAP) Input: AAP is a dict form containing the properties of 20 amino acids. Output: result is the a dict form containing the normalized properties of 20 amino acids. ####################################################################################`

Data
`` |  | AALetter = ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V']