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Cited by 51Evolution and Genetic DynamicsProtein Structure and DynamicsRNA and protein synthesis mechanisms

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Evolutionary Innovations and the Organization of Protein Functions in Genotype Space

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Ferrada, Evandro, Wagner, Andreas·2010·PLoS ONE
DOI 10.1371/journal.pone.0014172PMC PMC2994758
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Abstract

The organization of protein structures in protein genotype space is well studied. The same does not hold for protein functions, whose organization is important to understand how novel protein functions can arise through blind evolutionary searches of sequence space. In systems other than proteins, two organizational features of genotype space facilitate phenotypic innovation. The first is that genotypes with the same phenotype form vast and connected genotype networks. The second is that different neighborhoods in this space contain different novel phenotypes. We here characterize the organization of enzymatic functions in protein genotype space, using a data set of more than 30,000 proteins with known structure and function. We show that different neighborhoods of genotype space contain proteins with very different functions. This property both facilitates evolutionary innovation through exploration of a genotype network, and it constrains the evolution of novel phenotypes. The phenotypic diversity of different neighborhoods is caused by the fact that some functions can be carried out by multiple structures. We show that the space of protein functions is not homogeneous, and different genotype neighborhoods tend to contain a different spectrum of functions, whose diversity increases with increasing distance of these neighborhoods in sequence space. Whether a protein with a given function can evolve specific new functions is thus determined by the protein's location in sequence space.

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Figures

Figure 1 Distribution of structures over functions. (a) Distribution of the number of structures associated with a particular function. The total number of different structures (457) in our dataset composed of 39.529 sequences are classified according to the enzyme function that they perform and counted (min = 1 ; max = 14 ; mean = 1.2). The inset shows the same distribution, but with a log10-transformed vertical axis. (b) Distribution of structural promiscuity. Structural promiscuity (RF) is an entropy-like measure (see main text) calculated from the distribution of enzyme functions over different protein domains. The data shown is based on the finest-grained, fourth level of the EC hierarchy. (min = 0.0; max = 0.35; mean = 0.01).
Figure 1 Distribution of structures over functions. (a) Distribution of the number of structures associated with a particular function. The total number of different structures (457) in our dataset composed of 39.529 sequences are classified according to the enzyme function that they perform and counted (min = 1 ; max = 14 ; mean = 1.2). The inset shows the same distribution, but with a log10-transformed vertical axis. (b) Distribution of structural promiscuity. Structural promiscuity (RF) is an entropy-like measure (see main text) calculated from the distribution of enzyme functions over different protein domains. The data shown is based on the finest-grained, fourth level of the EC hierarchy. (min = 0.0; max = 0.35; mean = 0.01).CC BY · PubMed Central
Figure 2 Genotype neighborhoods. Illustration of genotype neighborhoods by a schematic two-dimensional projection of protein sequence space. The neighborhood of a genotype (NG1(r) ) is defined as the set of all the genotypes found at a sequence distance equal or shorter than a radius (r) from the genotype of interest. Two such neighborhoods may contain different sets of functions, S1 and S2, respectively. We define the fraction of functions unique to a neighborhood as Fu : = (|S1|+|S2|−2|S1∩ S2|)/ |S1 ∪ S2|.
Figure 2 Genotype neighborhoods. Illustration of genotype neighborhoods by a schematic two-dimensional projection of protein sequence space. The neighborhood of a genotype (NG1(r) ) is defined as the set of all the genotypes found at a sequence distance equal or shorter than a radius (r) from the genotype of interest. Two such neighborhoods may contain different sets of functions, S1 and S2, respectively. We define the fraction of functions unique to a neighborhood as Fu : = (|S1|+|S2|−2|S1∩ S2|)/ |S1 ∪ S2|.CC BY · PubMed Central
Figure 3 Different genotypic neighborhoods contain highly diverse functions. (a) The figure shows a heatmap of the fraction of unique functions (Fu) at different combinations of neighborhood radii (r) and sequences distances (d). The dataset analyzed here is based on 10 random subsets of 28,862 sequences from our original data, where we required that each sequence in each subset is longer than 100 amino acids. (The sequences in each subset adopted, on average 337 structures and perform 1,036 different enzyme functions.) From each of these 10 subsets, we then chose 105 pairs of sequences at random, and computed their values of r, d, and Fu. We repeated this random selection of 105 sequence pairs n times, until the results no longer changed. For the dataset of the figure, this convergence occurred around n = 10, but data are shown for n = 100. The heatmap shows the average values across the 10 samples observed for each combination of distance and radius. (b) Fraction of unique functions Fu versus sequence distance (expressed in percent) at constant neighborhood radii, as shown in the legend. Due to the sparsity of data, we grouped values into 20 different distance bins, each spanning d = 5. Error bars represent standard errors calculated for each of these 20 bins.
Figure 3 Different genotypic neighborhoods contain highly diverse functions. (a) The figure shows a heatmap of the fraction of unique functions (Fu) at different combinations of neighborhood radii (r) and sequences distances (d). The dataset analyzed here is based on 10 random subsets of 28,862 sequences from our original data, where we required that each sequence in each subset is longer than 100 amino acids. (The sequences in each subset adopted, on average 337 structures and perform 1,036 different enzyme functions.) From each of these 10 subsets, we then chose 105 pairs of sequences at random, and computed their values of r, d, and Fu. We repeated this random selection of 105 sequence pairs n times, until the results no longer changed. For the dataset of the figure, this convergence occurred around n = 10, but data are shown for n = 100. The heatmap shows the average values across the 10 samples observed for each combination of distance and radius. (b) Fraction of unique functions Fu versus sequence distance (expressed in percent) at constant neighborhood radii, as shown in the legend. Due to the sparsity of data, we grouped values into 20 different distance bins, each spanning d = 5. Error bars represent standard errors calculated for each of these 20 bins.CC BY · PubMed Central
Figure 4 Genotypic neighborhoods of the TIM barrel domain. The figure shows the dependency between the radius and distance of genotype neighborhoods, and the fraction Fu of functions unique to one neighborhood, for sequences adopting the TIM barrel domain (see Methods). (a) Heatmap of the fraction of unique functions (Fu) at different combinations of neighborhood radii (r) and sequences distances (d). We analysed these 4,132 sequences exhaustively. That is, for all possible pairwise sequence comparisons we computed their values of r, d and Fu. The heatmap shows values of Fu at each combination of d and r. (b) Fraction of unique functions versus sequence distance (expressed in percent) at constant neighborhood radii, as shown in the legend. Due to the sparsity of data, we grouped values into 20 different distance bins, each spanning d = 5. Error bars represent standard errors calculated for each of these 20 bins.
Figure 4 Genotypic neighborhoods of the TIM barrel domain. The figure shows the dependency between the radius and distance of genotype neighborhoods, and the fraction Fu of functions unique to one neighborhood, for sequences adopting the TIM barrel domain (see Methods). (a) Heatmap of the fraction of unique functions (Fu) at different combinations of neighborhood radii (r) and sequences distances (d). We analysed these 4,132 sequences exhaustively. That is, for all possible pairwise sequence comparisons we computed their values of r, d and Fu. The heatmap shows values of Fu at each combination of d and r. (b) Fraction of unique functions versus sequence distance (expressed in percent) at constant neighborhood radii, as shown in the legend. Due to the sparsity of data, we grouped values into 20 different distance bins, each spanning d = 5. Error bars represent standard errors calculated for each of these 20 bins.CC BY · PubMed Central

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Cites

Research works printed but not linked: 2

  1. [20]Buchler NEG Goldstein RA 1999 Effect of alphabet size and foldability requirements on protein structure designability. Proteins Struct Funct Bioinf 34 113 124 10336377
  2. [30]Todd AE Orengo CA Thornton JM 2001 Evolution of function in protein superfamilies, from a structural perspective. J Mol Biol 307 1113 1143 11286560

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Metadata

Authors
Ferrada, E., & Wagner, A.
Affiliations
SIB Swiss Institute of Bioinformatics (CH)
Corresponding author
Ferrada, Evandro
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2010-11-30
Volume
5
Issue
11
Pages
e14172
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0014172
PMC
PMC2994758
OpenAlex
W2082738116
Cited by
51
References
73
Citations by year
2026: 2 · 2025: 3 · 2024: 2 · 2023: 4 · 2021: 6 · 2020: 4 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 7 · 2015: 2 · 2014: 6 · 2013: 2 · 2012: 4
FWCI
1.98
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
  • Zurich Open Repository and Archive (University of Zurich)
Funders
  • National Science Foundation
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (315200, 315230, 119697, 129708, 116814)
  • Universität Zürich
  • SystemsX.ch
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords
protein function evolution, genotype space, sequence spaces, enzyme function, protein structure-function relationship, genotype networks, phenotypic innovation, functional diversity
MeSH terms
Algorithms, Binding Sites, Enzymes, Genotype, Proteins, Substrate Specificity, Protein Structure, Tertiary, Evolution, Molecular, Databases, Protein, Biocatalysis

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