Movatterモバイル変換

[0]ホーム
URL:

Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
Thehttps:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

NIH NLM Logo
Log inShow account info
Access keysNCBI HomepageMyNCBI HomepageMain ContentMain Navigation
pubmed logo
Advanced Clipboard
User Guide

Full text links

PeerJ, Inc. full text link PeerJ, Inc. Free PMC article
Full text links

Actions

doi: 10.7717/peerj.3657. eCollection 2017.

phydms: software for phylogenetic analyses informed by deep mutational scanning

Affiliations

phydms: software for phylogenetic analyses informed by deep mutational scanning

Sarah K Hilton et al. PeerJ..

Abstract

It has recently become possible to experimentally measure the effects of all amino-acid point mutations to proteins using deep mutational scanning. These experimental measurements can inform site-specific phylogenetic substitution models of gene evolution in nature. Here we describe software that efficiently performs analyses with such substitution models. This software, phydms, can be used to compare the results of deep mutational scanning experiments to the selection on genes in nature. Given a phylogenetic tree topology inferred with another program, phydms enables rigorous comparison of how well different experiments on the same gene capture actual natural selection. It also enables re-scaling of deep mutational scanning data to account for differences in the stringency of selection in the lab and nature. Finally, phydms can identify sites that are evolving differently in nature than expected from experiments in the lab. As data from deep mutational scanning experiments become increasingly widespread, phydms will facilitate quantitative comparison of the experimental results to the actual selection pressures shaping evolution in nature.

Keywords: Amino acid preferences; Beta lactamase; Codon substitution model; Deep mutational scanning; Diversifying selection; ExpCM; Hemagglutinin; Phylogenetics; Positive selection; dN/dS.

PubMed Disclaimer

Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. The ExpCM fixation termFr,xy.
In an ExpCM, the rate of fixation of a mutation from codonx to codony depends on the experimentally measured preferences of the amino acidsAx andAy encoded by these codons. Mutations to preferred amino acids, withπr,Ayπr,Ax>1, result in a largerFr,xy, and so are anticipated to fix more often. The value ofFr,xy is modulated by re-scaling the preferences by a stringency parameterβ ≠ 1 to reflect differences in selection between the lab and nature. Whenβ > 1, the selection for preferred amino acids is exaggerated. Whenβ < 1, the selection for preferred amino acids is attenuated.
Figure 2
Figure 2. Workflow for preparing input data tophydms.
Analysis withphydms requires amino-acid preferences measured by deep mutational scanning, a codon-level alignment of naturally occurring sequences, and a phylogenetic tree topology. (A) Deep mutational scanning involves performing a functional selection on a library of mutant genes, and using deep sequencing to quantify the enrichment or depletion of each mutation (relative to wildtype) after selection. (B) The amino-acid preferences used by the ExpCM can be calculated by normalizing the enrichment ratios for mutations to sum to one at each site. (C) We created a filtered, codon-level alignment of naturally occurring sequences usingphydms_prepalignment. (D) We usedphydms_comprehensive to automatically generate a tree topology from the filtered alignment usingRAxML.
Figure 3
Figure 3. Re-scaling of amino-acid preferences to reflect the stringency of selection in nature.
Analysis withphydms optimizes a stringency parameterβ that relates the stringency of selection for preferred amino acids in the deep mutational scanning experiment to that in nature. Whenβ = 1, the favored amino-acids are preferred in nature with the same stringency as during the experimental selections in the lab. Whenβ > 1, selection in nature prefers the same amino acids as selection in lab but with greater stringency. Whenβ < 1, selection in nature has less preference than the experiments for mutations favored in the lab, and whenβ = 0 then all site-specific information is lost. The actual optimized stringency parameter for HA reported in Table 2 isβ = 2.11. We generated the logoplots shown above from the input data in File S3 with the following commands:phydms_logoplot HA_Doud_1.pdf –prefs HA_Doud_prefs_short.csvphydms_logoplot HA_Doud_2_11.pdf –prefs HA_Doud_prefs_short.csv –stringency 2.11phydms_logoplot HA_Doud_0.pdf –prefs HA_Doud_prefs_short.csv –stringency 0.
Figure 4
Figure 4. Identifying sites of diversifying selection.
Thephydms option–omegabysite fits a site-specific value forωr, which gives the relative rate of non-synonymous to synonymous substitutions at siter after accounting for the selection due to the amino-acid preferences. This figure shows the results of such an analysis for HA. The overlay bar represents the strength of evidence forωr being greater (red) or less (blue) than one. Because this approach accounts for the constraints due to the amino-acid preferences, it can identify sites evolving faster than expected even if their absolute relative rates of nonysnonymous to synonymous substitutions do not significantly differ from one. The logoplot in this figure uses the stringency parameter value ofβ = 2.11, and was generated by running the following command on the data in File S3:phydms_logoplot results/omegabysite.pdf –prefs HA_Doud_prefs.csv –omegabysite results/omegabysite.txt –stringency 2.11 –minP 0.001. In this figure, the HA sequence is numbered sequentially beginning with 1 for the first site with deep mutational scanning data, which is the second residue in the protein.
See this image and copyright information in PMC

References

    1. Bloom DJ. An experimentally determined evolutionary model dramatically improves phylogenetic fit. Molecular Biology and Evolution. 2014a;31:1956–1978. doi: 10.1093/molbev/msu173. - DOI - PMC - PubMed
    1. Bloom DJ. An experimentally informed evolutionary model improves phylogenetic fit to divergent lactamase homologs. Molecular Biology and Evolution. 2014b;31:2753–2769. doi: 10.1093/molbev/msu220. - DOI - PMC - PubMed
    1. Bloom JD. Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models. Biology Direct. 2017;12 doi: 10.1186/s13062-016-0172-z. Article 1. - DOI - PMC - PubMed
    1. Brenan L, Andreev A, Cohen O, Pantel S, Kamburov A, Cacchiarelli D, Persky NS, Zhu C, Bagul M, Goetz EM, Mikkelsen TS, Piccioni F, Root DE, Johannessen CM. Phenotypic characterization of a comprehensive set of MAPK1/ERK2 missense mutants. Cell Reports. 2016;17(4):1171–1183. doi: 10.1016/j.celrep.2016.09.061. - DOI - PMC - PubMed
    1. Bryant D, Galtier N, Poursat MA. Likelihood calculation in molecular phylogenetics. In: Gascuel O, editor. Mathematics of Evolution and Phylogeny. Oxford: Oxford University Press; 2005. pp. 33–62.

Grants and funding

LinkOut - more resources

Full text links
PeerJ, Inc. full text link PeerJ, Inc. Free PMC article
Cite
Send To

NCBI Literature Resources

MeSHPMCBookshelfDisclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

[8]ページ先頭
©2009-2026 Movatter.jp