Substitution matrix

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.[1]

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 ${\displaystyle (i,j)}$th entry is equal to the probability of the ${\displaystyle i}$th amino acid being transformed into the ${\displaystyle j}$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:

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

${\displaystyle {\begin{bmatrix}1&0&\cdots &0&0\\0&1&&0&0\\\vdots &&\ddots &&\vdots \\0&0&&1&0\\0&0&\cdots &0&1\end{bmatrix}}}$

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.

We express the probabilities of transformation in what are called log-odds scores. The scores matrix S is defined as

${\displaystyle S_{i,j}=\log {\frac {p_{i}\cdot M_{i,j}}{p_{i}\cdot p_{j}}}=\log {\frac {M_{i,j}}{p_{j}}}=\log {\frac {\text{observed frequency}}{\text{expected frequency}}},}$

where ${\displaystyle M_{i,j}}$ is the probability that amino acid ${\displaystyle i}$ transforms into amino acid ${\displaystyle j}$, and ${\displaystyle p_{i}}$, ${\displaystyle p_{j}}$ are the frequencies of amino acids i and j. The base of the logarithm is not important, and the same substitution matrix is often expressed in different bases.

Many specialized substitution matrices have been developed that describe the amino acid substitution rates in specific structural or sequence contexts, such as in transmembrane alpha helices,[2] for combinations of secondary structure states and solvent accessibility states,[3][4][5] or for local sequence-structure contexts.[6] These context-specific substitution matrices lead to generally improved alignment quality at some cost of speed but are not yet widely used. Recently, sequence context-specific amino acid similarities have been derived that do not need substitution matrices but that rely on a library of sequence contexts instead. Using this idea, a context-specific extension of the popular BLAST program has been demonstrated to achieve a twofold sensitivity improvement for remotely related sequences over BLAST at similar speeds (CS-BLAST).

Although "transition matrix" is often used interchangeably with "substitution matrix" in fields other than bioinformatics, the former term is problematic in bioinformatics. With regards to nucleotide substitutions, "transition" is also used to indicate those substitutions that are between the two-ring purines (A → G and G → A) or are between the one-ring pyrimidines (C → T and T → C). Because these substitutions do not require a change in the number of rings, they occur more frequently than the other substitutions. "Transversion" is the term used to indicate the slower-rate substitutions that change a purine to a pyrimidine or vice versa (A ↔ C, A ↔ T, G ↔ C, and G ↔ T).

• Models of DNA evolution
• Substitution model

• Altschul, SF (1991). "Amino acid substitution matrices from an information theoretic perspective". Journal of Molecular Biology. 219 (3): 555–65. doi:10.1016/0022-2836(91)90193-A. PMC 7130686. PMID 2051488.
• Dayhoff, M. O.; Schwartz, R. M.; Orcutt, B. C. (1978). "A model of evolutionary change in proteins". Atlas of Protein Sequence and Structure. 5 (3): 345–352.
• Eddy, SR (2004). "Where did the BLOSUM62 alignment score matrix come from?". Nature Biotechnology. 22 (8): 1035–6. doi:10.1038/nbt0804-1035. PMID 15286655. S2CID 205269887.
• Henikoff, S; Henikoff, JG (1992). "Amino acid substitution matrices from protein blocks". Proceedings of the National Academy of Sciences of the United States of America. 89 (22): 10915–9. Bibcode:1992PNAS...8910915H. doi:10.1073/pnas.89.22.10915. PMC 50453. PMID 1438297.

• PAM Matrix calculator