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Minimum message length inference of secondary structure from protein coordinate data.

ABSTRACT: MOTIVATION: Secondary structure underpins the folding pattern and architecture of most proteins. Accurate assignment of the secondary structure elements is therefore an important problem. Although many approximate solutions of the secondary structure assignment problem exist, the statement of the problem has resisted a consistent and mathematically rigorous definition. A variety of comparative studies have highlighted major disagreements in the way the available methods define and assign secondary structure to coordinate data. RESULTS: We report a new method to infer secondary structure based on the Bayesian method of minimum message length inference. It treats assignments of secondary structure as hypotheses that explain the given coordinate data. The method seeks to maximize the joint probability of a hypothesis and the data. There is a natural null hypothesis and any assignment that cannot better it is unacceptable. We developed a program SST based on this approach and compared it with popular programs, such as DSSP and STRIDE among others. Our evaluation suggests that SST gives reliable assignments even on low-resolution structures. AVAILABILITY: http://www.csse.monash.edu.au/~karun/sst.

SUBMITTER: Konagurthu AS

PROVIDER: S-EPMC3371855 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Minimum message length inference of secondary structure from protein coordinate data.

Konagurthu Arun S AS Lesk Arthur M AM Allison Lloyd L

Bioinformatics (Oxford, England) 20120601 12

<h4>Motivation</h4>Secondary structure underpins the folding pattern and architecture of most proteins. Accurate assignment of the secondary structure elements is therefore an important problem. Although many approximate solutions of the secondary structure assignment problem exist, the statement of the problem has resisted a consistent and mathematically rigorous definition. A variety of comparative studies have highlighted major disagreements in the way the available methods define and assign ...[more]

PMID: 22689785

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Project description:The advent of genome-wide dense variation data provides an opportunity to investigate ancestry in unprecedented detail, but presents new statistical challenges. We propose a novel inference framework that aims to efficiently capture information on population structure provided by patterns of haplotype similarity. Each individual in a sample is considered in turn as a recipient, whose chromosomes are reconstructed using chunks of DNA donated by the other individuals. Results of this "chromosome painting" can be summarized as a "coancestry matrix," which directly reveals key information about ancestral relationships among individuals. If markers are viewed as independent, we show that this matrix almost completely captures the information used by both standard Principal Components Analysis (PCA) and model-based approaches such as STRUCTURE in a unified manner. Furthermore, when markers are in linkage disequilibrium, the matrix combines information across successive markers to increase the ability to discern fine-scale population structure using PCA. In parallel, we have developed an efficient model-based approach to identify discrete populations using this matrix, which offers advantages over PCA in terms of interpretability and over existing clustering algorithms in terms of speed, number of separable populations, and sensitivity to subtle population structure. We analyse Human Genome Diversity Panel data for 938 individuals and 641,000 markers, and we identify 226 populations reflecting differences on continental, regional, local, and family scales. We present multiple lines of evidence that, while many methods capture similar information among strongly differentiated groups, more subtle population structure in human populations is consistently present at a much finer level than currently available geographic labels and is only captured by the haplotype-based approach. The software used for this article, ChromoPainter and fineSTRUCTURE, is available from http://www.paintmychromosomes.com/.

Dataset Information

Minimum message length inference of secondary structure from protein coordinate data.

Publications

Minimum message length inference of secondary structure from protein coordinate data.

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