We address the problem of comparing and characterizing the promoter regions of genes with similar expression patterns. This remains a challenging problem in sequence analysis, because often the promoter regions of co-expressed genes do not show discernible sequence conservation. In our approach, thus, we have not directly compared the nucleotide sequence of promoters. Instead, we have obtained predictions of transcription factor binding sites, annotated the predicted sites with the labels of the corresponding binding factors, and aligned the resulting sequences of labels--to which we refer here as transcription factor maps (TF-maps). To obtain the global pairwise alignment of two TF-maps, we have adapted an algorithm initially developed to align restriction enzyme maps. We have optimized the parameters of the algorithm in a small, but well-curated, collection of human-mouse orthologous gene pairs. Results in this dataset, as well as in an independent much larger dataset from the CISRED database, indicate that TF-map alignments are able to uncover conserved regulatory elements, which cannot be detected by the typical sequence alignments.

We present a protein fold recognition method that uses a comprehensive statistical interpretation of structural Hidden Markov Models (HMMs). The structure/fold recognition is done by summing the probabilities of all sequence-to-structure alignments Conventionally, Boltzmann statistics dictate that the optimal alignment can give an estimate of the lowest free energy of the sequence conformation imposed by the structural model. The alignment is optimized for a scoring function that is interpreted as a free energy of an amino acid in a structural environment. Near-optimal alignments are ignored, regardless of how likely they might be compared to the optimal alignment. Here we investigate an alternative view. A structure model can be seen as a statistical representation of an ensemble of similar structures. The optimal alignment is always the most probable, but sub-optimal alignments may have comparable probabilities. These sub-optimal alignments can be interpreted as optimal alignments to the “other” structures from the ensemble or optimal alignments under minor fluctuations in the scoring function. Summing probabilities for all alignments gives an estimate of sequence-model compatibility. We have built a set of structural HMMs for 188 protein structures, and have compared two methods for identifying the structure compatible with a sequence: by the optimal alignment probability and by the total probability. Fold recognition by total probability was 40% more accurate than fold recognition by the optimal alignment probability.

Article Free Access Share on A method for optimal design of a threading scoring function Authors: Jadwiga R. Bieńkowska BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Robert G. Rogers BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Temple F. Smith BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile Authors Info & Claims RECOMB '99: Proceedings of the third annual international conference on Computational molecular biologyApril 1999 Pages 25–32https://doi.org/10.1145/299432.299446Published:01 April 1999Publication History 1citation7,945DownloadsMetricsTotal Citations1Total Downloads7,945Last 12 Months4Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Article The threading approach to the inverse protein folding problem Share on Authors: Temple F. Smith BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Loredana Lo Conte BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA and Department of Computer Science, College of Arts and Sciences, Boston University, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA and Department of Computer Science, College of Arts and Sciences, Boston University, Boston, MAView Profile , Jadwiga Bienkowska BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Bob Rogers BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Chrysanthe Gaitatzes BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MA BioMolecular Engineering Research Center, College of Engineering, Boston University, 36 Cummington Street, Boston, MAView Profile , Richard Lathrop Department of Infomation and Computer Science, University of California, Irvine, CA Department of Infomation and Computer Science, University of California, Irvine, CAView Profile Authors Info & Claims RECOMB '97: Proceedings of the first annual international conference on Computational molecular biologyJanuary 1997 Pages 287–292https://doi.org/10.1145/267521.267887Online:19 January 1997Publication History 3citation95DownloadsMetricsTotal Citations3Total Downloads95Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Given a subset P of a database, the problem of finding the query phi in a given database attribute having the closest extension to P is addressed. In the particular case that is outlined, P is the set of protein sequences in a protein sequence database matching a given protein sequence pattern, whereas phi is a query in the annotation of the database. Ideally, phi is the description of a biological function. If the extension of phi is very similar to P, an association between the pattern and the biological function described by the query may be inferred. An algorithm that efficiently searches the query space when negation is not considered is developed. Since the query language is a first-order language, the query space may be mapped into a set algebra in which a measure of stochastic dependence-an asymptotic approximation of the correlation coefficient-is used as a measure of set similarity. The algorithm uses the algebraic properties of such a measure to reduce the time required to search the query space. A prototype implementation of the algorithm has been tested in different collections of protein sequence patterns.>

A procedure that automatically provides an evaluation of the diagnostic ability of a protein sequence functional pattern is described. The procedure relies on the identification of the closest definable set in terms of a (protein sequence) database functional annotation to the set of database instances containing a given pattern. Assuming annotation correctness and completeness in the protein sequence database, the degree of statistical association between these sets provides an appropriate measure of the diagnostic ability of the pattern. An experimental implementation of the procedure, using the NBRF/PIR protein database, has been applied to a diverse collection of published sequence patterns. Results obtained reveal that frequently it is not possible to define (in NBRF/PIR database terminology) the set of database instances containing a given pattern, suggesting either lack of pattern diagnostic ability or protein database annotation incompleteness and/or inconsistencies.

A new analytical method has been used to examine the set of 40 exon/intron boundaries within the rat embryonic myosin heavy chain (MHCemb) gene. It has also been applied to an additional set of 850 splice sequences selected from GenBank. Strong evidence is obtained for the involvement of 3' ends but not 5' ends of exon sequences in splice site recognition. It can be determined that signal sequences of 5' intron ends concentrate near the splice borders, while the distributions of the 3' intron ends have a diffuse character. The possibility of re-interpreting some known features, in terms of the absence of certain elements rather than the presence of elements forming sequence determinants, is discussed. The analysis undertaken enabled us to work out a more detailed set of recognition sequence requirements for the splicing of nuclear pre-mRNA. In addition to requirements which have already been established we suggest the following: the 'AG-absence' in the immediate 3' terminal intron sequences; and a minimal match between a particular sequence and the known exon/intron consensus sequence of 5' splice junctions.

This issue of CABIOS contains a set of reviewed papers which relate to the recent Macromolecules, Genes and Computers International Workshop and Symposium held August 12-17, 1986 in Waterville Valley, NH. This introductory article reviews that meeting and those ideas which will provide a useful context for the continuing research and discussions on molecular genetic analyses contained within this issue.

An algorithm has been developed for the identification of unknown patterns which are distinctive for a set of short DNA sequences believed to be functionally equivalent. A pattern is defined as being a string, containing fully or partially specified nucleotides at each position of the string. The advantage of this 'vague' definition of the pattern is that it imposes minimum constraints on the characterization of patterns. A new feature of the approach developed here is that it allows a 'fair' simultaneous testing of patterns of all degrees of degeneracy. This analysis is based on an evaluation of inhomogeneity in the empirical occurrence distribution of any such pattern within a set of sequences. The use of the nonparametric kernel density estimation of Parzen allows one to assess small disturbances among the sequence alignments. The method also makes it possible to identify sequence subsets with different characteristic patterns. This algorithm was implemented in the analysis of patterns characteristic of sets of promoters, terminators and splice junction sequences. The results are compared with those obtained by other methods.

A FORTRAN program PRSTRC has been developed for protein secondary structure prediction, which is a modified Chou and Fasman (1978) analysis. This implementation carries out a running average of amino acid structure occurrence frequencies, utilizes a simple set of nucleation conditions, and allows user control over nucleation threshold and cutoff parameters. The algorithm includes prediction of the newly defined secondary structure elements: omega loops (1986). It also generates a charge distribution and hydropathy profile. Output includes a simple graphic display for a printer, or a CRT using color addition. Correct structures are predicted for T. dyscritum hemerythrin and the variable domain of mouse immunoglobin k-chain.