Is there a function that can calculate a score for aligned sequences given the alignment parameters?

Jessada,

The Blosum62 matrix (note the spelling ;) is in Bio.SubsMat.MatrixInfo and is a dictionary with tuples resolving to scores (so ('A', 'A') is worth 4 pts). It doesn't have the gaps, and it's only one triangle of the matrix (so it might ahve ('T', 'A') but not ('A', 'T'). There are some helper functions in Biopython, including some in Bio.Pairwise, but this is what I came up with as an answer:

from Bio.SubsMat import MatrixInfo

def score_match(pair, matrix):
    if pair not in matrix:
        return matrix[(tuple(reversed(pair)))]
    else:
        return matrix[pair]

def score_pairwise(seq1, seq2, matrix, gap_s, gap_e):
    score = 0
    gap = False
    for i in range(len(seq1)):
        pair = (seq1[i], seq2[i])
        if not gap:
            if '-' in pair:
                gap = True
                score += gap_s
            else:
                score += score_match(pair, matrix)
        else:
            if '-' not in pair:
                gap = False
                score += score_match(pair, matrix)
            else:
                score += gap_e
    return score

seq1 = 'PAVKDLGAEG-ASDKGT--SHVVY----------TI-QLASTFE'
seq2 = 'PAVEDLGATG-ANDKGT--LYNIYARNTEGHPRSTV-QLGSTFE'

blosum = MatrixInfo.blosum62

score_pairwise(seq1, seq2, blosum, -5, -1)

Which returns 82 for your alignment. There's almost certianly prettier ways to do all of this, but that should be a good start.

blosum62 is a dictonary of 276 items.

I prefered to complete with the lacking items, because it represents an iteration of only 276 turns, while the sequences to be analysed are likely to have more than 276 elements. Consequently, if you find the score of each pair with the help of the function score_match() , this function will have to perform the test if pair not in matrix for each of the elements of the sequences, that is to say certainly far more than 276 times.

Another thing that takes a lot of time: each score += something creates a new integer and binds the name score to this new object. Each binding takes an amount of time that doesn't exist with a stream of integers by a generator that are immediatly added to the current amount.

from Bio.SubsMat.MatrixInfo import blosum62 as blosum
from itertools import izip

blosum.update(((b,a),val) for (a,b),val in blosum.items())

def score_pairwise(seq1, seq2, matrix, gap_s, gap_e, gap = True):
    for A,B in izip(seq1, seq2):
        diag = ('-'==A) or ('-'==B)
        yield (gap_e if gap else gap_s) if diag else matrix[(A,B)]
        gap = diag

seq1 = 'PAVKDLGAEG-ASDKGT--SHVVY----------TI-QLASTFE'
seq2 = 'PAVEDLGATG-ANDKGT--LYNIYARNTEGHPRSTV-QLGSTFE'

print sum(score_pairwise(seq1, seq2, blosum, -5, -1))

This score_pairwise() is a generator function because there is yield instead of return.

Edit: Updated code for Python 3:

from Bio.SubsMat.MatrixInfo import blosum62 as blosum

blosum.update(((b,a),val) for (a,b),val in list(blosum.items()))

def score_pairwise(seq1, seq2, matrix, gap_s, gap_e, gap = True):
    for A,B in zip(seq1, seq2):
        diag = ('-'==A) or ('-'==B)
        yield (gap_e if gap else gap_s) if diag else matrix[(A,B)]
        gap = diag

seq1 = 'PAVKDLGAEG-ASDKGT--SHVVY----------TI-QLASTFE'
seq2 = 'PAVEDLGATG-ANDKGT--LYNIYARNTEGHPRSTV-QLGSTFE'

print(sum(score_pairwise(seq1, seq2, blosum, -5, -1)))