Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution
Date
2011-12-22
Authors
Murrell, Ben
Weighill, Thomas
Buys, Jan
Ketteringham, Robert
Moola, Sasha
Benade, Gerdus
du Buisson, Lise
Kaliski, Daniel
Hands, Tristan
Scheffler, Konrad
Journal Title
Journal ISSN
Volume Title
Publisher
PLOS
Abstract
Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG)
adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments.
Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single
organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform
generalist models, but in most instances there simply are not enough data available to estimate them. We propose a
method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to
suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from
a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important
variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller
dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been
constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be
required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing
methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid
properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies
across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies
are different from, and have improved likelihood over, those inferred under standard models.
Description
The orginal publication is at www.plosone.org
Keywords
Proteins -- Separation, Biomedical research, Generalist models, Specialist models, Non-negative matrix factorization (NNMF), Amino acid synthesis
Citation
Murrell B, Weighill T, Buys J, Ketteringham R, Moola S, et al. (2011) Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution. PLoS ONE 6(12): e28898.