# PyProtein module¶

A class used for computing different types of protein descriptors!

You can freely use and distribute it. If you have any problem,

you could contact with us timely.

Authors: Zhijiang Yao and Dongsheng Cao.

Date: 2016.06.04

class PyProtein.PyProtein(ProteinSequence='')[source]

This GetProDes class aims at collecting all descriptor calcualtion modules into a simple class.

AALetter = ['A', 'R', 'N', 'D', 'C', 'E', 'Q', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V']
GetAAComp()[source]

amino acid compositon descriptors (20)

Usage:

result = GetAAComp()

GetAAindex1(name, path='.')[source]

Get the amino acid property values from aaindex1

Usage:

result=GetAAIndex1(name)

Input: name is the name of amino acid property (e.g., KRIW790103)

Output: result is a dict form containing the properties of 20 amino acids

GetAAindex23(name, path='.')[source]

Get the amino acid property values from aaindex2 and aaindex3

Usage:

result=GetAAIndex23(name)

Input: name is the name of amino acid property (e.g.,TANS760101,GRAR740104)

Output: result is a dict form containing the properties of 400 amino acid pairs

GetALL()[source]

Calcualte all descriptors except tri-peptide descriptors

GetAPAAC(lamda=10, weight=0.5)[source]

Amphiphilic (Type II) Pseudo amino acid composition descriptors

default is 30

Usage:

result = GetAPAAC(lamda=10,weight=0.5)

lamda factor reflects the rank of correlation and is a non-Negative integer, such as 15.

Note that (1)lamda should NOT be larger than the length of input protein sequence;

1. lamda must be non-Negative integer, such as 0, 1, 2, ...; (3) when lamda =0, the

output of PseAA server is the 20-D amino acid composition.

weight factor is designed for the users to put weight on the additional PseAA components

with respect to the conventional AA components. The user can select any value within the

region from 0.05 to 0.7 for the weight factor.

GetCTD()[source]

Composition Transition Distribution descriptors (147)

Usage:

result = GetCTD()

GetDPComp()[source]

dipeptide composition descriptors (400)

Usage:

result = GetDPComp()

GetGearyAuto()[source]

Geary autocorrelation descriptors (240)

Usage:

result = GetGearyAuto()

GetGearyAutop(AAP={}, AAPName='p')[source]

Geary autocorrelation descriptors for the given property (30)

Usage:

result = GetGearyAutop(AAP={},AAPName=’p’)

AAP is a dict containing physicochemical properities of 20 amino acids

GetMoranAuto()[source]

Moran autocorrelation descriptors (240)

Usage:

result = GetMoranAuto()

GetMoranAutop(AAP={}, AAPName='p')[source]

Moran autocorrelation descriptors for the given property (30)

Usage:

result = GetMoranAutop(AAP={},AAPName=’p’)

AAP is a dict containing physicochemical properities of 20 amino acids

GetMoreauBrotoAuto()[source]

Normalized Moreau-Broto autocorrelation descriptors (240)

Usage:

result = GetMoreauBrotoAuto()

GetMoreauBrotoAutop(AAP={}, AAPName='p')[source]

Normalized Moreau-Broto autocorrelation descriptors for the given property (30)

Usage:

result = GetMoreauBrotoAutop(AAP={},AAPName=’p’)

AAP is a dict containing physicochemical properities of 20 amino acids

GetPAAC(lamda=10, weight=0.05)[source]

Type I Pseudo amino acid composition descriptors (default is 30)

Usage:

result = GetPAAC(lamda=10,weight=0.05)

lamda factor reflects the rank of correlation and is a non-Negative integer, such as 15.

Note that (1)lamda should NOT be larger than the length of input protein sequence;

1. lamda must be non-Negative integer, such as 0, 1, 2, ...; (3) when lamda =0, the

output of PseAA server is the 20-D amino acid composition.

weight factor is designed for the users to put weight on the additional PseAA components

with respect to the conventional AA components. The user can select any value within the

region from 0.05 to 0.7 for the weight factor.

GetPAACp(lamda=10, weight=0.05, AAP=[])[source]

Type I Pseudo amino acid composition descriptors for the given properties (default is 30)

Usage:

result = GetPAACp(lamda=10,weight=0.05,AAP=[])

lamda factor reflects the rank of correlation and is a non-Negative integer, such as 15.

Note that (1)lamda should NOT be larger than the length of input protein sequence;

1. lamda must be non-Negative integer, such as 0, 1, 2, ...; (3) when lamda =0, the

output of PseAA server is the 20-D amino acid composition.

weight factor is designed for the users to put weight on the additional PseAA components

with respect to the conventional AA components. The user can select any value within the

region from 0.05 to 0.7 for the weight factor.

AAP is a list form containing the properties, each of which is a dict form.

GetQSO(maxlag=30, weight=0.1)[source]

Quasi sequence order descriptors default is 50

result = GetQSO(maxlag=30, weight=0.1)

maxlag is the maximum lag and the length of the protein should be larger

than maxlag. default is 45.

GetQSOp(maxlag=30, weight=0.1, distancematrix={})[source]

Quasi sequence order descriptors default is 50

result = GetQSO(maxlag=30, weight=0.1)

maxlag is the maximum lag and the length of the protein should be larger

than maxlag. default is 45.

distancematrix is a dict form containing 400 distance values

GetSOCN(maxlag=45)[source]

Sequence order coupling numbers default is 45

Usage:

result = GetSOCN(maxlag=45)

maxlag is the maximum lag and the length of the protein should be larger

than maxlag. default is 45.

GetSOCNp(maxlag=45, distancematrix={})[source]

Sequence order coupling numbers default is 45

Usage:

result = GetSOCN(maxlag=45)

maxlag is the maximum lag and the length of the protein should be larger

than maxlag. default is 45.

distancematrix is a dict form containing 400 distance values

GetSubSeq(ToAA='S', window=3)[source]

obtain the sub sequences wit length 2*window+1, whose central point is ToAA

Usage:

result = GetSubSeq(ToAA=’S’,window=3)

ToAA is the central (query point) amino acid in the sub-sequence.

window is the span.

GetTPComp()[source]

tri-peptide composition descriptors (8000)

Usage:

result = GetTPComp()

GetTriad()[source]

Calculate the conjoint triad features from protein sequence.

Useage:

Version = 1.0