(score) S= Σ of costs (identities, replacements) - Σ of penalties (number of gaps x gap penalties) The expression for calculating the alignment score can be modified accordingly to include gap penalties: By increasing or reducing the value of the gap penalties, the total number of gaps, their length, and their position in the sequence alignment may be controlled. The gap penalty is a parameter that can be changed each time an alignment is run. By this simple rule, we can limit the number of gaps and increase their significance. Gaps are introduced only if they substantially increase the total score of the alignment. Each time the program introduces a gap, it triggers a penalty score, which may decrease or increase the total score of the alignment. That instruction is called the "gap penalty". The number of gaps is always limited when we look at automatically generated sequence alignments (example in the image below).Īpparently, the program has some instructions forcing it to limit the number of gaps and their position in the sequence. When introducing a gap, several questions may arise: How many gaps can we introduce? How to decide where to place them? How long can they be? Apparently, we could try to improve the alignment score by introducing many gaps here and there, but would that be biologically relevant? Intuitively one would think that something must be wrong with this approach. A gap in one of the sequences means that one or more amino acid residues have been deleted from the sequence, or we could also say that there is an insertion in the second sequence. In the example alignment above, we introduced a gap (marked by a dash in the first sequence) to maximize the number of matches. Different sequence alignments may be generated depending on how we handle these insertions and deletions. Sometimes, a whole domain may be inserted into or deleted from a protein. For example, when a group of bacterial sequences is compared to a group of eukaryotic sequences, there will often be some relatively large segments of insertions and deletions. It is expected that when comparing sequences of members of a protein family, we will find that at some positions in some of the sequences, there will be one or more extra residues (insertion) or some missing residues (deletion). However, a question will arise: if we assign a score of 1 to each pair of identical residues, what score should we assign to a substitution like K with R or V with L compared to V with I or V with A? Our software will optimize the score for each possible alignment, and we will need to tell it how to count the contribution for each of the above and many other similar substitutions.Īs an example, let us have a look at a simple alignment of a short segment of two sequences:Īdditional factors to consider when analyzing sequences are insertions and deletions. The above suggests that we must consider both identities and similarities between the amino acids in calculating the alignment score. On the other hand, substituting V with R may have a dramatic negative effect and destabilize or denature a protein. The same applies, e.g., to K and R substitution, since both these residues are usually located on the surface and primarily interact with solvent or with the acidic side chains of E or D. For example, L and V will be equally tolerated within a protein's hydrophobic core, assuming enough space is available for the slightly larger side chain of leucine to be accommodated. For this reason, this type of conservation is called similarity, and it depends on the demand for the conservation of structure and function. Many replaced residues will be chemically equivalent to the "original" ones. The higher this percentage, the closer the compared sequences will be in terms of their evolutionary origin.Įven though many amino acids in a protein sequence can be invariant, depending on the evolutionary distance between the proteins, there will always be a substantial number of residue substitutions caused by mutations. Using this number, we can count the percentage of identical residues – called the percentage of sequence identity. The most straightforward score to assess how closely related two sequences are can be based on the number of identical amino acids that align against each other. This suggests that the alignment score is essential, and its calculation needs careful consideration. The alignment software sorts the generated alignments according to a calculated score, with the output being the one with the highest score. First, however, we must remember that an alignment generated by software will represent only one of many different possible alignments. There are many ways to align two protein sequences against each other.
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