A test of the markov model of evolution in proteins

Using gaps and gap penalties to optimize pairwise sequence alignments

INTRODUCTIONTo obtain the best possible alignment between two sequences, it is necessary to include gaps in sequence alignments and use gap penalties. For aligning DNA sequences, a simple positive score for matches and a negative score for mismatches and gaps are most often used. To score matches and mismatches in alignments of proteins, it is necessary to know how often one amino acid is substituted for another in related proteins. In addition, a method is needed to account for insertions and deletions that sometimes appear in related DNA or protein sequences. To accommodate such sequence variations, gaps that appear in sequence alignments are given a negative penalty score reflecting the fact that they are not expected to occur very often. Mathematically speaking, it is very difficult to produce the best-possible alignment, either global or local, unless gaps are included in the alignment. This article discusses how to use gaps and gap penalties to optimize pairwise sequence alignments.

Using BLOSUM in Sequence Alignments

INTRODUCTIONThe original Dayhoff percent accepted mutation (PAM) matrices were developed based on a small number of protein sequences and an evolutionary model of protein change. By extrapolating from the observed changes at small evolutionary distances to large ones, it was possible to establish a PAM250 scoring matrix for sequences that were highly divergent. Another approach to finding a scoring matrix for divergent sequences is to start with a more divergent set of sequences and produce a scoring matrix from the substitutions found in those less-related sequences. The blocks amino acid substitution matrices (BLOSUM) scoring matrices were prepared this way. This article explains how BLOSUM scoring matrices were created and how they can best be used.

Studies of varying alignment algorithm, amino Acid scoring matrix, and gap penalties

INTRODUCTIONComparing different amino acid scoring matrix-gap penalty combinations poses several problems. For example, the analysis often overlooks the purposes of different matrices; e.g., protein family or domain searching, evolutionary analysis, or structural alignment. In the past, gap penalties were usually not published or well known, thus throwing a level of uncertainty into the results. More recently, when investigators publish a new scoring matrix, they usually provide suitable choices for gap penalties that may be used for comparisons with other matrices. This article summarizes a number of reports that have examined combinations of alignment algorithm, scoring matrix, and gap penalties used to align sequences for various purposes.

Comparison of the PAM and BLOSUM Amino Acid Substitution Matrices

INTRODUCTIONThe choice of a scoring system including scores for matches, mismatches, substitutions, insertions, and deletions influences the alignment of both DNA and protein sequences. To score matches and mismatches in alignments of proteins, it is necessary to know how often one amino acid is substituted for another in related proteins. Percent accepted mutation (PAM) matrices list the likelihood of change from one amino acid to another in homologous protein sequences during evolution and thus are focused on tracking the evolutionary origins of proteins. In contrast, the blocks amino acid substitution matrices (BLOSUM) are based on scoring substitutions found over a range of evolutionary periods. There are important differences in the ways that the PAM and BLOSUM scoring matrices were derived. These differences, which are discussed in this article, should be appreciated when interpreting the results of protein sequence alignments obtained with these matrices.

Using PAM Matrices in Sequence Alignments

INTRODUCTIONCertain amino acid substitutions commonly occur in related proteins from different species. Because a protein still functions with these substitutions, the substituted amino acids are compatible with protein structure and function. Knowing the types of changes that are most and least common in a large number of proteins can assist with predicting alignments for any set of protein sequences. If related protein sequences are quite similar, they are easy to align, and one can readily determine the single-step amino acid changes. If ancestor relationships among a group of proteins are assessed, the most likely amino acid changes that occurred during evolution can be predicted. This type of analysis was pioneered by Margaret Dayhoff and used by her to produce a type of scoring matrix called a percent accepted mutation (PAM) matrix. This article introduces Dayhoff PAM matrices, explains how they are constructed and how they can be used for sequence alignments, and highlights their strengths and limitations.