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Parameters: 


Returns:  True if all residues (beside gapped ones) are connected, False otherwise. 
AlignToSEQRES
(chain, seqres, try_resnum_first=False, validate=True)¶Aligns the residues of chain to the SEQRES sequence, inserting gaps where needed. The function uses the connectivity of the protein backbone to find consecutive peptide fragments. These fragments are then aligned to the SEQRES sequence.
All the nonligand, peptidelinking residues of the chain must be listed in SEQRES. If there are any additional residues in the chain, the function raises a ValueError.
If ‘try_resnum_first’ is set, building the alignment following residue numbers is tried first.
If ‘validate’ is set (default), the alignment is checked using
ValidateSEQRESAlignment()
.
Parameters: 


Returns:  The alignment of the residues in the chain and the SEQRES entries. 
Return type: 
AlignmentFromChainView
(chain, handle_seq_name='handle', view_seq_name='view')¶Creates and returns the sequence alignment of the given chain view to the chain handle. The alignment contains two sequences, the first containing all nonligand peptidelinking residues, the second containing all nonligand peptidelinking residues that are part of the view.
Parameters: 


Returns:  The alignment 
Return type: 
Conservation
(aln, assign=true, prop_name="cons", ignore_gap=false)¶Calculates conservation scores for each column in the alignment, according to the ConSurf method (Armon et al., J. Mol. Biol. (2001) 307, 447463).
The conservation score is a value between 0 and 1. The bigger the number the more conserved the aligned residues are.
Parameters: 


LocalAlign
(seq1, seq2, subst_weight, gap_open=5, gap_ext=2)¶Performs a Smith/Waterman local alignment of seq1 and seq2 and returns the bestscoring alignments as a list of pairwise alignments.
Example:
seq_a = seq.CreateSequence('A', 'acdefghiklmn')
seq_b = seq.CreateSequence('B', 'acdhiklmn')
alns = seq.alg.LocalAlign(seq_a, seq_b, seq.alg.BLOSUM62)
print alns[0].ToString(80)
# >>> A acdefghiklmn
# >>> B acdhiklmn
Parameters: 


Returns:  A list of bestscoring, nonoverlapping alignments of seq1 and seq2. Since alignments always start with a replacement, the start is stored in the sequence offset of the two sequences. 
GlobalAlign
(seq1, seq2, subst_weight, gap_open=5, gap_ext=2)¶Performs a Needleman/Wunsch global alignment of seq1 and seq2 and returns the bestscoring alignment.
Example:
seq_a = seq.CreateSequence('A', 'acdefghiklmn')
seq_b = seq.CreateSequence('B', 'acdhiklmn')
alns = seq.alg.GlobalAlign(seq_a, seq_b, seq.alg.BLOSUM62)
print alns[0].ToString(80)
# >>> A acdefghiklmn
# >>> B acdhiklmn
Parameters: 


Returns:  Bestscoring alignment of seq1 and seq2. 
ShannonEntropy
(aln, ignore_gaps=True)¶Returns the percolumn Shannon entropies of the alignment. The entropy describes how conserved a certain column in the alignment is. The higher the entropy is, the less conserved the column. For a column with no amino aids, the entropy value is set to NAN.
Parameters: 


Returns:  List of column entropies 
SemiGlobalAlign
(seq1, seq2, subst_weight, gap_open=5, gap_ext=2)¶Performs a semiglobal alignment of seq1 and seq2 and returns the best scoring alignment. The algorithm is Needleman/Wunsch same as GlobalAlign, but without any gap penalty for starting or ending gaps. This is prefereble whenever one of the sequences is significantly shorted than the other. This make it also suitable for fragment assembly.
Example:
seq_a=seq.CreateSequence('A', 'abcdefghijklmnok')
seq_b=seq.CreateSequence('B', 'cdehijk')
alns=seq.alg.GlobalAlign(seq_a, seq_b, seq.alg.BLOSUM62)
print alns[0].ToString(80)
# >>> A abcdefghijklmnok
# >>> B cdehijk
Parameters: 


Returns:  bestscoring alignment of seq1 and seq2. 
This is a set of functions for predicting pairwise contacts from a multiple sequence alignment (MSA). The core method here is mutual information which uses coevolution to predict contacts. Mutual information is complemented by two other methods which score pairs of columns of a MSA from the likelyhood of certain amino acid pairs to form contacts (statistical potential) and the likelyhood of finding certain substitutions of aminioacid pairs in columns of the MSA corresponding to interacting residues.
ContactPredictionScoreResult
¶Object containing the results form a contact prediction.
matrix
¶An NxN FloatMatrix
where N is the length of the alignment.
The element i,j corresponds to the score of the corresponding
columns of the MSA. High scores correspond to high likelyhood of
a contact.
sorted_indices
¶List of all indices pairs i,j, containing (N*N1)/2 elements, as the matrix is symmetrical and elements in the diagonal are ignored. The indices are sorted from the pair most likely to form a contact to the least likely one.
GetScore
(i, j)¶returns matrix(i,j)
Parameters: 


SetScore
(i, j, score)¶Sets matrix(i,j) to score
Parameters: 


PredictContacts
(ali)¶Predicts contacts from a multiple sequence alignment using a combination of Mutual Information (MI) and the Contact Substitution Score (CoEvoSc). MI is calculated with the APC and small number corrections as well as with a transformation into Zscores. The CoEvoSc is calculated using the default PairSubstWeightMatrix (see seq.alg.LoadDefaultPairSubstWeightMatrix). The final score for a pair of columns (i,j) of ali is obtained from:
Sc(i,j)=MI(i,j)exp(CoEvoSc(i,j)) if (i,j) >=0
Sc(i,j)=MI(i,j)exp(1CoEvoSc(i,j)) if (i,j) <0
Parameters:  ali (AlignmentHandle ) – The multiple sequence alignment 

CalculateMutualInformation(aln,weights=LoadConstantContactWeightMatrix(),
apc_correction=true,zpx_transformation=true,small_number_correction=0.05)
Calculates the mutual information (MI) from a multiple sequence alignemnt. Contributions of each pair of aminoacids are weighted using the matrix weights (weighted mutual information). The average product correction (apc_correction) correction and transformation into Zscores (zpx_transofrmation) increase prediciton accuracy by reducing the effect of phylogeny and other noise sources. The small number correction reduces noise for alignments with small number of sequences of low diversity.
Parameters: 


CalculateContactScore
(aln, weights=LoadDefaultContactWeightMatrix())¶Calculates the Contact Score (CoSc) from a multiple sequence alignment. For each pair of residues (i,j) (pair of columns in the MSA), CoSc(i,j) is the average over the values of the weights corresponding to the amino acid pairs in the columns.
Parameters: 


CalculateContactSubstitutionScore(aln,ref_seq_index=0,
weights=LoadDefaultPairSubstWeightMatrix())
Calculates the Contact Substitution Score (CoEvoSc) from a multiple sequence alignment. For each pair of residues (i,j) (pair of columns in the MSA), CoEvoSc(i,j) is the average over the values of the weights corresponding to substituting the amino acid pair in the reference sequence (given by ref_seq_index) with all other pairs in columns (i,j) of the aln.
Parameters: 


LoadDefaultContactWeightMatrix
()¶Returns:  CPE, a ContactWeightMatrix that was calculated from a large (>15000) set of
high quality crystal structures as CPE=log(CF(a,b)/NCF(a,b)) and then normalised so that all its elements are comprised between 0 and 1. CF(a,b) is the frequency of amino acids a and b for pairs of contacting residues and NCF(a,b) is the frequency of amino acids a and b for pairs of noncontacting residues. Apart from weights for the standard amino acids, this matrix gives a weight of 0 to all pairs for which at least one aminoacid is a gap. 

LoadConstantContactWeightMatrix
()¶Returns:  A ContactWeightMatrix . This matrix gives a weight of one to all pairs of
standard aminoacids and a weight of 0 to pairs for which at least one aminoacid is a gap. 

LoadDefaultPairSubstWeightMatrix
()¶Returns:  CRPE, a PairSubstWeightMatrix that was calculated from a large (>15000) set of
high quality crystal structures as CRPE=log(CRF(ab>cd)/NCRF(ab>cd)) and then normalised so that all its elements are comprised between 0 and 1. CRF(ab>cd) is the frequency of replacement of a pair of amino acids a and b by a pair c and d in columns of the MSA corresponding to contacting residues and NCRF(ab>cd) is the frequency of replacement of a pair of amino acids a and b by a pair c and d in columns of the MSA corresponding to noncontacting residues. Apart from weights for the standard amino acids, this matrix gives a weight of 0 to all pair substitutions for which at least one aminoacid is a gap. 

PairSubstWeightMatrix
(weights, aa_list)¶This class is used to associate a weight to any substitution from one aminoacid pair (a,b) to any other pair (c,d).
weights
¶A FloatMatrix4
of size NxNxNxN, where N=len(aa_list)
aa_list
¶A CharList
of one letter codes of the amino acids for which weights are found in the weights matrix.
Enter search terms or a module, class or function name.
seq
– Sequences and Alignments
bindings
– Interfacing external programs
seq.alg
– Algorithms for Sequences