Identification of sequence pattern with profile analysis.
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Amino Acid Sequence↗
Biomedical subjects
M Gribskov
Publications and source records attributed to M Gribskov.
Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching.
In this paper, we borrow the idea of the receiver operating characteristic (ROC) from clinical medicine and demonstrate its application to sequence comparison. The ROC includes elements of both sensitivity and specificity, and is a quantitative measure of the usefulness of a diagnostic. The ROC is used in this work to investigate the effects of scoring table and gap penalties on database searches. Studies on three families of proteins, 4Fe-4S ferredoxins, lysR bacterial regulatory proteins, and bacterial RNA polymerase sigma-factors lead to the following conclusions: sequence families are quite idiosyncratic, but the best PAM distance for database searches using the Smith-Waterman method is somewhat larger than predicted by theoretical methods, about 200 PAM. The length independent gap penalty (gap initiation penalty) is quite important, but shows a broad peak at values of about 20-24. The length dependent gap penalty (gap extension penalty) is almost irrelevant suggesting that successful database searches rely only to a limited degree on gapped alignments. Taken together, these observations lead to the conclusion that the optimal conditions for alignments and database searches are not, and should not be expected to be, the same.
Bacterial Proteins↗
The megaprior heuristic for discovering protein sequence patterns.
Several computer algorithms for discovering patterns in groups of protein sequences are in use that are based on fitting the parameters of a statistical model to a group of related sequences. These include hidden Markov model (HMM) algorithms for multiple sequence alignment, and the MEME and Gibbs sampler algorithms for discovering motifs. These algorithms are sometimes prone to producing models that are incorrect because two or more patients have been combined. The statistical model produced in this situation is a convex combination (weighted average) of two or more different models. This paper presents a solution to the problem of convex combinations in the form of a heuristic based on using extremely low variance Dirichlet mixture priors as part of the statistical model. This heuristic, which we call the megaprior heuristic, increase the strength (i.e., decreases the variance) of the prior in proportion to the size of the sequence dataset. This causes each column in the final model to strongly resemble the mean of a single component of the prior, regardless of the size of the dataset. We describe the cause of the convex combination problem, analyze it mathematically, motivate and describe the implementation of the megaprior heuristic, and show how it can effectively eliminate the problem of convex combinations in protein sequence pattern discovery.
Algorithms↗
Translational initiation factors IF-1 and eIF-2 alpha share an RNA-binding motif with prokaryotic ribosomal protein S1 and polynucleotide phosphorylase.
Initiation of translation is a complicated process involving numerous accessory factors whose functions remain incompletely understood. Bacterial ribosomal protein S1 is known to contain a repeated sequence motif (S1-RM), also found in polynucleotide phosphorylase, that is thought to be involved in binding to RNA. Using the technique of profile analysis, the S1-RM can also be found in bacterial and chloroplast translation initiation factor IF-1 sequences, and in the sequences of eukaryotic translation initiation factor eIF-2 alpha chains. The significance of the similarity of the sequences is very high suggesting that the occurrence of the S1-RM in these diverse proteins represents homology. The similarity of S1 to IF-1 further suggests that S1 has evolved from an IF-1 like ancestor, and therefore that the two proteins have a similar or competitive function. The most obvious common function of the proteins containing the S1-RM seems to be RNA binding, suggesting that IF-1 and eIF-2 alpha may bind to RNA.
Amino Acid Sequence↗
The sigma 70 family: sequence conservation and evolutionary relationships.
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Amino Acid Sequence↗
Identification and characterization of genes and mutants for an N-terminal acetyltransferase from yeast.
A gene from Saccharomyces cerevisiae has been mapped, cloned, sequenced and shown to encode a catalytic subunit of an N-terminal acetyltransferase. Regions of this gene, NAT1, and the chloramphenicol acetyltransferase genes of bacteria have limited but significant homology. A nat1 null mutant is viable but exhibits a variety of phenotypes, including reduced acetyltransferase activity, derepression of a silent mating type locus (HML) and failure to enter G0. All these phenotypes are identical to those of a previously characterized mutant, ard1. NAT1 and ARD1 are distinct genes that encode proteins with no obvious similarity. Concomitant overexpression of both NAT1 and ARD1 in yeast causes a 20-fold increase in acetyltransferase activity in vitro, whereas overexpression of either NAT1 or ARD1 alone does not raise activity over basal levels. A functional iso-1-cytochrome c protein, which is N-terminally acetylated in a NAT1 strain, is not acetylated in an isogenic nat1 mutant. At least 20 other yeast proteins, including histone H2B, are not N-terminally acetylated in either nat1 or ard1 mutants. These results suggest that NAT1 and ARD1 proteins function together to catalyze the N-terminal acetylation of a subset of yeast proteins.
Acetyltransferases↗
Profile scanning for three-dimensional structural patterns in protein sequences.
Profile analysis measures the similarity between a target sequence and a group of aligned sequences (the probe). The probe sequences are used to produce a position-specific scoring table (the profile) that can be aligned with any sequence (the target) using standard dynamic programming methods. We are developing a library of profiles, each describing a different structural motif. This allows any target sequence to be rapidly scanned for the presence of structural motifs. Levels of significance for the comparison of target sequences with the profile are determined in advance, permitting an objective decision to be made as to whether a protein is likely to possess a structural motif.
Amino Acid Sequence↗
Profile analysis: detection of distantly related proteins.
Profile analysis is a method for detecting distantly related proteins by sequence comparison. The basis for comparison is not only the customary Dayhoff mutational-distance matrix but also the results of structural studies and information implicit in the alignments of the sequences of families of similar proteins. This information is expressed in a position-specific scoring table (profile), which is created from a group of sequences previously aligned by structural or sequence similarity. The similarity of any other sequence (target) to the group of aligned sequences (probe) can be tested by comparing the target to the profile using dynamic programming algorithms. The profile method differs in two major respects from methods of sequence comparison in common use: (i) Any number of known sequences can be used to construct the profile, allowing more information to be used in the testing of the target than is possible with pairwise alignment methods. (ii) The profile includes the penalties for insertion or deletion at each position, which allow one to include the probe secondary structure in the testing scheme. Tests with globin and immunoglobulin sequences show that profile analysis can distinguish all members of these families from all other sequences in a database containing 3800 protein sequences.
Amino Acid Sequence↗
Sigma factors from E. coli, B. subtilis, phage SP01, and phage T4 are homologous proteins.
We show, using dot matrix comparisons and statistical analysis of sequence alignments, that seven sequenced sigma factors, E. coli sigma-70 and sigma-32, B. subtilis sigma-43 and sigma-29, phage SP01 gene products 28 and 34, and phage T4 gene product 55, comprise a homologous family of proteins. Sigma-70, sigma-32, and sigma-43 each have two copies of a sequence similar to the helix-turn-helix DNA binding motif seen in CRP, and lambda repressor and cro proteins. B. subtilis sigma-29, SP01 gp28, and SP01 gp34 have at least one copy similar to this sequence. We propose that a second sequence, conserved in all seven proteins is the core RNA polymerase binding site. A third region, present only in sigma-70 and sigma-43, may also be involved in interaction with core. Available mutational evidence supports our model for sigma factor structure.
Amino Acid Sequence↗
PEPPLOT, a protein secondary structure analysis program for the UWGCG sequence analysis software package.
We describe a program for the analysis of protein secondary structure that operates with the Sequence Analysis Software Package of the University of Wisconsin Genetics Computer Group (UWGCG). The program produces both graphic and printed output. Structure prediction using the Chou and Fasman and Robson et al methods, and hydropathy analysis by the method of Kyte and Doolittle are included along with a simplified method of hydrophobic moment analysis. The power of the program is the coordinated presentation of many different kinds of structural information on the same plot.
Amino Acid Sequence↗
The codon preference plot: graphic analysis of protein coding sequences and prediction of gene expression.
The codon preference plot is useful for locating genes in sequenced DNA, predicting the relative level of their expression and for detecting DNA sequencing errors resulting in the insertion or deletion of bases within a coding sequence. The three possible reading frames are displayed in parallel along with the open reading frames and plots of the location of rare codons in each reading frame.
Amino Acid Sequence↗
Overexpression and purification of the sigma subunit of Escherichia coli RNA polymerase.
We have constructed a plasmid that overexpresses 100-fold the sigma subunit of Escherichia coli RNA polymerase. The plasmid was constructed by placing the pLoL promoter-operator of bacteriophage lambda upstream from rpoD, the gene encoding the sigma subunit. A simple procedure for purification of the overexpressed protein has been developed based on guanidine hydrochloride denaturation/renaturation, DEAE cellulose chromatography, and Sephacryl S-200 chromatography. The purified product has been characterized and found to be indistinguishable from normally expressed sigma protein purified by previous protocols as judged by enzymatic activity, heat inactivation, and partial proteolysis.
Bacteriophage lambda↗