In bioinformatics and evolutionary biology, a substitution matrix describes the frequency at which a character in a nucleotide sequence or a protein sequence changes to other character states over evolutionary time. The information is often in the form of log odds of finding two specific character states aligned and depends on the assumed number of evolutionary changes or sequence dissimilarity between compared sequences. It is an application of a stochastic matrix. Substitution matrices are usually seen in the context of amino acid or DNA sequence alignments, where they are used to calculate similarity scores between the aligned sequences.
Background In the process of evolution, from one generation to the next the amino acid sequences of an organism's proteins are gradually altered through the action of DNA mutations. For example, the sequence
ALEIRYLRD
could mutate into the sequence
ALEINYLRD
in one step, and possibly
AQEINYQRD
over a longer period of evolutionary time. Each amino acid is more or less likely to mutate into various other amino acids. For instance, a hydrophilic residue such as arginine is more likely to be replaced by another hydrophilic residue such as glutamine, than it is to be mutated into a hydrophobic residue such as leucine. (Here, a residue refers to an amino acid stripped of a hydrogen and/or a hydroxyl group and inserted in the polymeric chain of a protein.) This is primarily due to redundancy in the genetic code, which translates similar codons into similar amino acids. Furthermore, mutating an amino acid to a residue with significantly different properties could affect the folding and/or activity of the protein. This type of disruptive substitution is likely to be removed from populations by the action of purifying selection because the substitution has a higher likelihood of rendering a protein nonfunctional. If we have two amino acid sequences in front of us, we should be able to say something about how likely they are to be derived from a common ancestor, or homologous. If we can line up the two sequences using a sequence alignment algorithm such that the mutations required to transform a hypothetical ancestor sequence into both of the current sequences would be evolutionarily plausible, then we'd like to assign a high score to the comparison of the sequences. To this end, we will construct a 20x20 matrix where the ( i , j ) {\displaystyle (i,j)} th entry is equal to the probability of the i {\displaystyle i} th amino acid being transformed into the j {\displaystyle j} th amino acid in a certain amount of evolutionary time. There are many different ways to construct such a matrix, called a substitution matrix. Here are the most commonly used ones:
Identity matrix The simplest possible substitution matrix would be one in which each amino acid is considered maximally similar to itself, but not able to transform into any other amino acid. This matrix would look like
[ 1 0 ⋯ 0 0 0 1 0 0 ⋮ ⋱ ⋮ 0 0 1 0 0 0 ⋯ 0 1 ] {\displaystyle {\begin{bmatrix}1&0&\cdots &0&0\\0&1&&0&0\\\vdots &&\ddots &&\vdots \\0&0&&1&0\\0&0&\cdots &0&1\end{bmatrix}}}
This identity matrix will succeed in the alignment of very similar amino acid sequences but will be miserable at aligning two distantly related sequences. We need to figure out all the probabilities in a more rigorous fashion. It turns out that an empirical examination of previously aligned sequences works best.
Log-odds matrices We express the probabilities of transformation in what are called log-odds scores. The scores matrix S is defined as
S i , j = log p i ⋅ M i , j p i ⋅ p j = log M i , j p j = log observed frequency expected frequency , {\displaystyle S_{i,j}=\log {\frac {p_{i}\cdot M_{i,j}}{p_{i}\cdot p_{j}}}=\log {\frac {M_{i,j}}{p_{j}}}=\log {\frac {\text{observed frequency}}{\text{expected frequency}}},}
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