David W. Mount

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.

Choosing a Method for Phylogenetic Prediction

INTRODUCTIONThree methods--maximum parsimony, distance, and maximum likelihood--are generally used to find the evolutionary tree or trees that best account for the observed variation in a group of sequences. Each of these methods uses a different type of analysis. Programs based on distance methods are commonly used in the molecular biology laboratory because they are straightforward and can be used with a large number of sequences. Maximum likelihood methods are more challenging and require a greater understanding of the evolutionary models on which they are based. Because they involve so many computational steps and because the number of steps increases dramatically with the number of sequences, maximum likelihood programs are limited to a smaller number of sequences. They can be implemented on a supercomputer in order to analyze a greater number of sequences. This article presents an overview for the researcher who has a set of related sequences and wants to analyze them to predict the best trees that depict the phylogenetic relationships among the sequences.

Maximum Parsimony Method for Phylogenetic Prediction

INTRODUCTIONMaximum parsimony predicts the evolutionary tree or trees that minimize the number of steps required to generate the observed variation in the sequences from common ancestral sequences. For this reason, the method is also sometimes referred to as the minimum evolution method. A multiple sequence alignment (msa) is required to predict which sequence positions are likely to correspond. These positions will appear in vertical columns in the msa. For each aligned position, phylogenetic trees that require the smallest number of evolutionary changes to produce the observed sequence changes from ancestral sequences are identified. This analysis is continued for every position in the sequence alignment. Finally, those trees that produce the smallest number of changes overall for all sequence positions are identified. This method is best suited for sequences that are quite similar and is limited to small numbers of sequences.

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.

Steps Used by the BLAST Algorithm

INTRODUCTIONThe BLAST algorithm performs DNA and protein sequence similarity searches by an algorithm that is faster than FASTA but considered to be equally as sensitive. BLAST is very popular due to availability of the program on the World Wide Web through a large server at the National Center for Biotechnology Information (NCBI) and at many other sites. The BLAST algorithm has evolved to provide a set of very powerful search tools for the molecular biologist that are freely available to run on many computer platforms. This article provides a list of steps that describe how the BLAST algorithm searches a sequence database.

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.

Using a FASTA Sequence Database Similarity Search.

INTRODUCTIONFASTA is a program for rapid alignment of pairs of protein and DNA sequences. Rather than comparing individual residues in the two sequences, FASTA searches for matching sequence patterns or words, called k-tuples. These patterns comprise k consecutive matches of letters in both sequences. The program then attempts to build a local alignment based on these word matches. Due to the ability of the algorithm to find matching sequences in a sequence database with high speed, FASTA is useful for routine database searches of this type. Comparable methods are the BLAST program, which is faster than FASTA, is of comparable sensitivity for protein queries, and also does DNA searches, and programs that use the Smith-Waterman dynamic programming algorithm for protein and DNA searches, which are slower but more sensitive when full-length protein sequences are used as queries.

Using Progressive Methods for Global Multiple Sequence Alignment

Finding a global optimal alignment of more than two sequences that includes matches, mismatches, and gaps and that takes into account the degree of variation in all of the sequences at the same time is especially difficult. The dynamic programming algorithm used for optimal alignment of pairs of sequences can be extended to global alignment of three sequences, but for more than three sequences, only a small number of relatively short sequences may be analyzed. Thus, approximate methods are used for global sequence alignment. One class of these methods is progressive global alignment, which starts with an alignment of the most alike sequences and then builds an alignment by adding more sequences. This article introduces three programs that use progressive alignment methodology.

Using Iterative Methods for Global Multiple Sequence Alignment

Finding a global optimal alignment of more than two sequences that includes matches, mismatches, and gaps and that takes into account the degree of variation in all of the sequences at the same time is especially difficult. The dynamic programming algorithm used for optimal alignment of pairs of sequences can be extended to global alignment of three sequences, but for more than three sequences, only a small number of relatively short sequences may be analyzed. Thus, approximate methods are used for global alignment. One class of these is iterative global alignment, which makes an initial global alignment of groups of sequences and then revises the alignment to achieve a more reasonable result. This article discusses several iterative alignment methods. In particular, steps are provided for using the Sequence Alignment by Genetic Algorithm (SAGA).

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.