Point Accepted Mutations and Dayhoff Matrices

The construction of such matrices is straightforward and natural. By examining a large sample of verified pairwise alignments of amino acids, we can extract frequency information of the form

An *alignment* between two amino acid sequences might look as follows

where

The best (or *maximum likely*) alignment between two sequences is
that alignment which is most probable, ie. that alignment with the
highest score relative to the Dayhoff matrix. Via the classic Needleman and
Wunsch [23] *dynamic programming* algorithm, we are
able to find this maximum quickly and efficiently.
The construction of such alignments is explored in depth in
Chapter - *The Pairwise Alignment of Sequences*.

The following sections provide part of the ground work necessary for performing pairwise alignments. We begin by presenting our mathematical model of evolution and measures for the amount of evolution. We discuss the routines available in Darwin for the construction of the first Dayhoff matrix [9] and explain how their method can be improved upon.

In this chapter we are solely concerned with the mutation events which
are *point accepted mutations* or, as they are sometimes referred
to in the literature, *substitutions*. Chapter
*Insertions and Deletions* describes our model for the two other
forms of mutation: insertions and deletions.

- Modeling Evolution
- The Original Dayhoff Matrices
- Better Dayhoff Matrices
- Estimating Mutation Matrices
- Other Similarity Matrices