Algorithms for the optimal identification of segment neighborhoods
- 1 January 1989
- journal article
- research article
- Published by Springer Nature in Bulletin of Mathematical Biology
- Vol. 51 (1) , 39-54
- https://doi.org/10.1007/bf02458835
Abstract
Two algorithms for the efficient identification of segment neighborhoods are presented. A segment neighborhood is a set of contiguous residues that share common features. Two procedures are developed to efficiently find estimates for the parameters of the model that describe these features and for the residues that define the boundaries of each segment neighborhood. The algorithms can accept nearly any model of segment neighborhood, and can be applied with a broad class of best fit functions including least squares and maximum likelihood. The algorithms successively identify the most important features of the sequence. The application of one of these methods to the haemagglutinin protein of influenza virus reveals a possible mechanism for conformational change through the finding of a break in a strong heptad repeat structure.This publication has 37 references indexed in Scilit:
- Maximum likelihood estimation of subsequence conservationJournal of Theoretical Biology, 1985
- Testing for a Two-Phase Multiple RegressionTechnometrics, 1983
- Prediction of protein antigenic determinants from amino acid sequences.Proceedings of the National Academy of Sciences, 1981
- Structure of the haemagglutinin membrane glycoprotein of influenza virus at 3 Å resolutionNature, 1981
- Hierarchic organization of domains in globular proteinsJournal of Molecular Biology, 1979
- A new look at the statistical model identificationIEEE Transactions on Automatic Control, 1974
- Nucleation, Rapid Folding, and Globular Intrachain Regions in ProteinsProceedings of the National Academy of Sciences, 1973
- A New Approach to Estimating Switching RegressionsJournal of the American Statistical Association, 1972
- Inference in Two-Phase RegressionJournal of the American Statistical Association, 1971
- Mean Square Error of Prediction as a Criterion for Selecting VariablesTechnometrics, 1971