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Detecting Amino Acid Coevolution with Bayesian Graphical Models

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Computational Methods in Protein Evolution

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1851))

Abstract

The comparative study of homologous proteins can provide abundant information about the functional and structural constraints on protein evolution. For example, an amino acid substitution that is deleterious may become permissive in the presence of another substitution at a second site of the protein. A popular approach for detecting coevolving residues is by looking for correlated substitution events on branches of the molecular phylogeny relating the protein-coding sequences. Here we describe a machine learning method (Bayesian graphical models) implemented in the open-source phylogenetic software package HyPhy, http://hyphy.org, for extracting a network of coevolving residues from a sequence alignment.

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Notes

  1. 1.

    The scripts in this chapter were tested with HyPhy version 2.220170201beta and release 2.2.7. HyPhy is a large and complex software package that is constantly undergoing development by a small team of researchers and programmers, and some of the more specialized features such as BGMs may temporarily break as newer versions are released. If you compiled HyPhy from source, make sure that you are using a single-threaded (HYPHYSP) or multiprocessing-enabled (HYPHYMP) build and not a message passing interface (MPI)-enabled (HYPHYMPI) build; at the time of writing, there were residual issues in the source code related to MPI processing. If you encounter any other problems, please submit an issue at https://github.com/veg/hyphy/issues and we will attend to it as soon as possible.

  2. 2.

    For this type of analysis, we prefer using maximum likelihood (ML) methods to reconstruct trees. If it is not feasible to use ML methods due to excessive numbers of sequence and/or sequence lengths, we suggest using the approximate ML program FastTree 2 [37], which can be orders of magnitude faster than the standard ML programs. Neighbor-joining (NJ) methods also scale favorably with larger alignments, but tend to be less accurate for reconstructing branch lengths. While there are NJ and ML tree reconstruction methods implemented in HyPhy, they are not as efficient as these specialized programs and we do not recommend using them for larger data sets.

  3. 3.

    A bootstrap support value is an empirical measure of confidence in a specific clade given the data. Most phylogeny reconstruction programs should have an option to omit these values. If you already have a Newick tree file and you just need to remove the support values, you can use the following UNIX command: sed -E ’s/)[0-9.]+:/):/g’ [input] > [output].

  4. 4.

    From this point onward, we assume that you are using the command-line interface. Unfortunately, this script may not work properly with the GUI because of how HyPhy handles file paths. Even on the command line, this is not straight-forward. For example, we used the following invocation in the macOS Terminal: HYPHYMP BASEPATH=/usr/local/lib/hyphy/ ‘pwd‘/fit_codon_model.bf If you want to take advantage of a multi-core CPU, you can add the argument CPU=[number of cores] immediately after HYPHYMP. Note that not all steps in this analysis are able to utilize multiple threads.

  5. 5.

    If you want to examine this scaling factor, you can find it in the serialized likelihood function generated by this script by searching for the parameter name scalingB.

  6. 6.

    If you’re using an operating system with a desktop environment, it’s often easier to drag the icon representing your file into the terminal window instead of typing out the corresponding path. This works when running HyPhy on the command line, but you need to use backspace to remove the space that is automatically appended to end of the path. HyPhy won’t be able to locate the file otherwise.

  7. 7.

    Prior to version 2.3.4, the text in HyPhy implies that these options allow rates to vary among branches, not sites: “…branch lengths come from a user-chosen distribution.” We have revised this help text as of version 2.3.4 to indicate that the distributions are used to model rate variation across sites, not branches.

  8. 8.

    A standard codon model is described by a 61-by-61 transition rate matrix and a single parameter R that corresponds to the ratio of non-synonymous and synonymous substitution rates. The model assumes that the system moves from one codon to another by single nucleotide substitutions; codon substitutions that require more than one nucleotide change are not allowed.

  9. 9.

    Some phylogeny reconstruction programs truncate sequence labels and cause an error at this stage—for example, neither RAxML or FastTree2 will read sequence labels beyond a whitespace character. A quick fix in this situation is to replace all whitespace characters with underscores in a text editor or with sed.

  10. 10.

    By convention, we use the file extension .lf and keep the same basename as the codon data file. This makes it easier to track files that belong to the same workflow.

  11. 11.

    NEXUS is a widespread format with known issues with standardization and usability, and has been implemented in diverse and often incompatible ways by multiple programs.

  12. 12.

    We have previously found this list output to be a more convenient format for debugging the script. It’s usually a good idea to manually compare entries in this list against your sequence alignment to make sure that things make sense.

  13. 13.

    Most phylogenetic tree reconstruction methods, such as maximum likelihood or neighbor-joining, will output an unrooted tree. For an unrooted tree, the labels will be generated for the deepest internal node.

  14. 14.

    For example, you can customize on a node-by-node basis the number of “parental” nodes on which a given node can be conditionally dependent. You can also load a serialized BGM from a XML Bayesian Interchange format file and use this model to simulate additional data sets. For more details, please refer to the file bayesgraph.ibf and the batch file tests/hbltests/BayesianGraphicalModels/TestBGM.bf in the HyPhy source code distribution.

  15. 15.

    As a general rule of thumb, we try to not build a BGM model that has many more nodes than observations. The number of substitutions provides a meaningful criterion for reducing the dimensionality of our data.

  16. 16.

    This is where the ability to customize the analysis implemented in the bayesgraph.bf script can be very useful. If you have prior information that a subset of codon sites are involved in a large number of interactions, the computational complexity of increasing the number of parents can be greatly reduced by modifying this parameter for only these sites.

  17. 17.

    (In an MCMC run, we observe autocorrelation when we sample parameter values that are very close in the parameter space and unrepresentative of the true underlying posterior distribution. Therefore, we try to decrease autocorrelation so that the MCMC sample provides a more precise estimate of the posterior sample. One way to accomplish this is by down-sampling to every n-th step).

  18. 18.

    We have provided most of the data files in this example on our GitHub repository at https://github.com/PoonLab/comet-prot/tree/master/data.

  19. 19.

    To generate an amino acid sequence from the column labels, we used the regular expression “[0-9]+,*” to replace all instances with an empty string. In Python, this can be achieved with the re module: seq = re.sub(’[0-9]+,*’, ’’, header.strip()), where header is a string variable containing the first line of the CSV file.

  20. 20.

    This can be accomplished with the following R commands:

    require(coda)

    chain1 <- read.csv("chain1.trace.csv", header=F)

    chain2 <- read.csv("chain2.trace.csv", header=F)

    chains <- mcmc.list(mcmc(chain1$V1), mcmc(chain2$V1))

    gelman.diag(chains, autoburnin=F)

    where the file names may be different for your run.

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Acknowledgements

This study was supported in part by the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-131), and by grants from the Canadian Institutes of Health Research (PJT-153391 and BOP-149562). AFYP was supported by a CIHR New Investigator Award (FRN-130609).

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Authors and Affiliations

  1. Department of Pathology and Laboratory Medicine, Western University, London, Canada

    Mariano Avino & Art F. Y. Poon

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  1. Mariano Avino
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Correspondence to Mariano Avino .

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  1. GlaxoSmithKline, Cellzome – a GSK company Meyerhofstrasse 1, Heidelberg, Baden-Württemberg, Germany

    Tobias Sikosek

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Avino, M., Poon, A.F.Y. (2019). Detecting Amino Acid Coevolution with Bayesian Graphical Models. In: Sikosek, T. (eds) Computational Methods in Protein Evolution. Methods in Molecular Biology, vol 1851. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8736-8_6

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  • DOI: https://doi.org/10.1007/978-1-4939-8736-8_6

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