Fast optimization of statistical potentials for structurally constrained phylogenetic models

Background Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained ( SC ) models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-struct...

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Published in:	BMC evolutionary biology Vol. 9; no. 1; p. 227
Main Authors:	Bonnard, Cécile, Kleinman, Claudia L, Rodrigue, Nicolas, Lartillot, Nicolas
Format:	Journal Article
Language:	English
Published:	London BioMed Central 09.09.2009 BioMed Central Ltd BMC
Subjects:	Algorithms Analysis Animal Systematics/Taxonomy/Biogeography Applications Bayes Theorem Biochemistry, Molecular Biology Biodiversity Biomedical and Life Sciences Computational Biology - methods Computer science Entomology Evolution Evolutionary Biology Gene mutations Genetics and Population Dynamics Genomics Life Sciences Likelihood Functions Markov Chains Markov processes Methodology Methodology Article Methods Models, Genetic Models, Statistical Monte Carlo Method Mutation Nucleotide sequence Phylogenetics Phylogeny Physiological aspects Populations and Evolution Protein folding Statistics Studies Systematics, Phylogenetics and taxonomy Gradient Descent Markov Chain Monte Carlo Joint Method Maximum Likelihood Principle Fixation Probability
ISSN:	1471-2148, 1471-2148
Online Access:	Get full text
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Summary:	Background Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained ( SC ) models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-structure compatibility. In a previous work, we defined a statistical framework for optimizing knowledge-based potentials especially suited to SC models. Our method used the maximum likelihood principle and provided what we call the joint potentials. However, the method required numerical estimations by the use of computationally heavy Markov Chain Monte Carlo sampling algorithms. Results Here, we develop an alternative optimization procedure, based on a leave-one-out argument coupled to fast gradient descent algorithms. We assess that the leave-one-out potential yields very similar results to the joint approach developed previously, both in terms of the resulting potential parameters, and by Bayes factor evaluation in a phylogenetic context. On the other hand, the leave-one-out approach results in a considerable computational benefit (up to a 1,000 fold decrease in computational time for the optimization procedure). Conclusion Due to its computational speed, the optimization method we propose offers an attractive alternative for the design and empirical evaluation of alternative forms of potentials, using large data sets and high-dimensional parameterizations.
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ISSN:	1471-2148 1471-2148
DOI:	10.1186/1471-2148-9-227