The edit alignment score in "Edit distance alignment1" counted the total number of edit operations implied by an alignment. We could equivalently think of this scoring function as assigning a cost of 1 to each such operation. Another common scoring function awards matched symbols with 1 and penalizes substituted/inserted/deleted symbols equally by assigning each one a score of 0, so that the maximum score of an alignment becomes the length of a longest common subsequence of $$s$$ and $$t$$ (see "Finding a shared spliced motif2"). In general, the alignment score is simply a scoring function that assigns costs to edit operations encoded by the alignment.
One natural way of adding complexity to alignment scoring functions is by changing the alignment score based on which symbols are substituted. Many methods have been proposed for doing this. Another way to do so is to vary the penalty assigned to the insertion or deletion of symbols.
In general, alignment scores can be either maximized or minimized
depending on how scores are established. The general problem of optimizing
a particular alignment score is called global alignment.
To penalize symbol substitutions differently depending on which two symbols are involved in the substitution, we obtain a scoring matrix $$S$$ in which $$S_{i, j}$$ represents the (negative) score assigned to a substitution of the $$i$$-th symbol of our alphabet $$\mathscr{A}$$ with the $$j$$-th symbol of $$\mathscr{A}$$.
A gap penalty is the component deducted from alignment score due to the presence of a gap. A gap penalty may be a function of the length of the gap. For example, a linear gap penalty is a constant $$g$$ such that each inserted or deleted symbol is charged $$g$$. As a result, the cost of a gap of length $$L$$ is equal to $$gL$$.
Write a function globalAlignmentScore that takes two protein strings $$s$$ and $$t$$. The function must return the global alignment score between $$s$$ and $$t$$, using the BLOSUM62 scoring matrix and a linear gap penalty equal to 5 (i.e., a cost of -5 is assessed for each gap symbol).
In the following interactive session, we assume the FASTA file data.faa3 to be located in the current directory.
>>> globalAlignmentScore('PLEASANTLY', 'MEANLY') 8 >>> from Bio import SeqIO >>> globalAlignmentScore(*SeqIO.parse('data.faa', 'fasta')) 2444