Biomedical subjects
Publications and source records attributed to D J Lipman.
How can one characterize a set of data collected from different biological species, or indeed any set of data related by an evolutionary tree? The structure imposed by the tree implies that the data are not independent, and for most applications this should be taken into account. We describe strategies for weighting the data that circumvent some of the problems of dependency.
Multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence-structure relationships. Until recently, it has been impractical to apply dynamic programming, the most widely accepted method for producing pairwise alignments, to comparisons of more than three sequences. We describe the design and application of a tool for multiple alignment of amino acid sequences that implements a new algorithm that greatly reduces the computational demands of dynamic programming. This tool is able to align in reasonable time as many as eight sequences the length of an average protein.
We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.
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An algorithm was developed which facilitates the search for similarities between newly determined amino acid sequences and sequences already available in databases. Because of the algorithm's efficiency on many microcomputers, sensitive protein database searches may now become a routine procedure for molecular biologists. The method efficiently identifies regions of similar sequence and then scores the aligned identical and differing residues in those regions by means of an amino acid replacability matrix. This matrix increases sensitivity by giving high scores to those amino acid replacements which occur frequently in evolution. The algorithm has been implemented in a computer program designed to search protein databases very rapidly. For example, comparison of a 200-amino-acid sequence to the 500,000 residues in the National Biomedical Research Foundation library would take less than 2 minutes on a minicomputer, and less than 10 minutes on a microcomputer (IBM PC).
When evaluating sequence similarities among nucleic acids by the usual methods, statistical significance is often found when the biological significance of the similarity is dubious. We demonstrate that the known statistical properties of nucleic acid sequences strongly affect the statistical distribution of similarity values when calculated by standard procedures. We propose a series of models which account for some of these known statistical properties. The utility of the method is demonstrated in evaluating high relative similarity scores in four specific cases in which there is little biological context by which to judge the similarities. In two of the cases we identify the statistical properties which are responsible for the apparent similarity. In the other two cases the statistical significance of the similarity persists even when the known statistical properties of sequences are modelled. For one of these cases biological significance is likely while the other case remains an enigma.
We have studied the statistical constraints on synonymous codon choice to evaluate various proposals regarding the origin of the bias in synonymous codon usage observed by Fiers et al. (1975), Air et al. (1976), Grantham et al. (1980) and others. We have determined the statistical dependence of the degenerate third base on either of its nearest neighbors in mitochondrial, prokaryotic, and eukaryotic coding sequences. We noted an increasing dependence of the third base on its nearest neighbors in moving from mitochondria to prokaryotes to eukaryotes. A statistical model assuming random equiprobable selection of synonymous codons was found grossly adequate for the mitochondria, but totally inadequate for prokaryotes and eukaryotes. A model assuming selection of synonymous codons reflecting a genomic strategy, i.e. the genome hypothesis of Grantham et al. (1980), gave a good approximation of the mitochondrial sequences. A statistical model which exactly maintains codon frequency, but allows the position of corresponding synonymous codons to vary was only grossly adequate for prokaryotes and totally inadequate for eukaryotes. The results of these simulations are consistent with the measures on experimental sequences and suggest that a "frequency constraint" model such as that of Grantham et al. (1980) may be an adequate explanation of the codon usage in mitochondria. However, in addition to this frequency constraint, there may be constraints on synonymous codon choice in prokaryotes due to codon context. Furthermore, any proposal to explain codon usage in eukaryotes must involve a constraint on the context of a codon in the sequence.
With the development of large data banks of protein and nucleic acid sequences, the need for efficient methods of searching such banks for sequences similar to a given sequence has become evident. We present an algorithm for the global comparison of sequences based on matching k-tuples of sequence elements for a fixed k. The method results in substantial reduction in the time required to search a data bank when compared with prior techniques of similarity analysis, with minimal loss in sensitivity. The algorithm has also been adapted, in a separate implementation, to produce rigorous sequence alignments. Currently, using the DEC KL-10 system, we can compare all sequences in the entire Protein Data Bank of the National Biomedical Research Foundation with a 350-residue query sequence in less than 3 min and carry out a similar analysis with a 500-base query sequence against all eukaryotic sequences in the Los Alamos Nucleic Acid Data Base in less than 2 min.
Five recently sequenced hemagglutinin genes from Influenza A virus strains are studied for similarities in a hierarchical fashion. The sequences are compared for similarity, first on the level of sequence homology, and then on several progressively more general levels. Though the HA1 subsequences contain regions where homology drops to that of a Monte Carlo generated reference value, subsequent tests reveal great similarity due to constraints on the level of amino acid sequence. Other tests detect statistically significant differences between subtypes due to constraints acting below the level of amino acid sequence, such as the 2 degrees structure of the viral RNA, or involving translation of the mRNA. The general applicability of the hierarchical approach to sequence analysis is discussed.
We describe two measures of a nucleic acid sequence, derived from Information Theory, which characterize the constraints toward nonuniform base composition, and the constraints on the ordering of the bases. These two measures distinguish extra-chromosomal coding sequences from all other coding sequences examined. The two measures separate eukaryotic coding sequences into two groups: those with introns and those without introns. We have also found a relationship between the general constraints of a subsequence and its degree of conservation in related genes.
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We examined the codon usages in well-conserved and less-well-conserved regions of vertebrate protein genes and found them to be similar. Despite this similarity, there is a statistically significant decrease in codon bias in the less-well-conserved regions. Our analysis suggests that although those codon changes initially fixed under amino acid replacements tend to follow the overall codon usage pattern, they also reduce the bias in codon usage. This decrease in codon bias leads one to predict that the rate of change of synonymous codons should be greater in those regions that are less well conserved at the amino acid level than in the better-conserved regions. Our analysis supports this prediction. Furthermore, we demonstrate a significantly elevated rate of change of synonymous codons among the adjacent codons 5' to amino acid replacement positions. This provides further support for the idea that there are contextual constraints on the choice of synonymous codons in eukaryotes.