Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Non-adaptive origins of interactome complexity

Nature volume 474, pages 502–505 (2011)Cite this article

Subjects

An Addendum to this article was published on 26 November 2014

Abstract

The boundaries between prokaryotes, unicellular eukaryotes and multicellular eukaryotes are accompanied by orders-of-magnitude reductions in effective population size, with concurrent amplifications of the effects of random genetic drift and mutation1. The resultant decline in the efficiency of selection seems to be sufficient to influence a wide range of attributes at the genomic level in a non-adaptive manner2. A key remaining question concerns the extent to which variation in the power of random genetic drift is capable of influencing phylogenetic diversity at the subcellular and cellular levels2,3,4. Should this be the case, population size would have to be considered as a potential determinant of the mechanistic pathways underlying long-term phenotypic evolution. Here we demonstrate a phylogenetically broad inverse relation between the power of drift and the structural integrity of protein subunits. This leads to the hypothesis that the accumulation of mildly deleterious mutations in populations of small size induces secondary selection for protein–protein interactions that stabilize key gene functions. By this means, the complex protein architectures and interactions essential to the genesis of phenotypic diversity may initially emerge by non-adaptive mechanisms.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Structural deficiencies in soluble proteins promote protein associations.
Figure 2: Structural degradation enhances PWIT and promotes protein interactivity in species with small population sizes.

References

  1. Lynch, M. & Conery, J. S. The origins of genome complexity. Science 302, 1401–1404 (2003)

    Article  ADS  CAS  Google Scholar 

  2. Lynch, M. The Origins of Genome Architecture (Sinauer, 2007)

    Google Scholar 

  3. Stoltzfus, A. On the possibility of constructive neutral evolution. J. Mol. Evol. 49, 169–181 (1999)

    Article  ADS  CAS  Google Scholar 

  4. Gray, M. W., Lukes, J., Archibald, J. M., Keeling, P. J. & Doolittle, W. F. Cell biology. Irremediable complexity? Science 330, 920–921 (2001)

    Article  ADS  Google Scholar 

  5. Rowlinson, J. S. & Widom, B. Molecular Theory of Capillarity (Oxford Univ. Press, 1982)

    Google Scholar 

  6. Lynch, M. The frailty of adaptive hypotheses for the origins of organismal complexity. Proc. Natl Acad. Sci. USA 104 (Suppl.). 8597–8604 (2007)

    Article  ADS  CAS  Google Scholar 

  7. Levy, E. D., Pereira-Leal, J. B., Chothia, C. & Teichmann, S. A. 3D Complex: a structural classification of protein complexes. PLOS Comput. Biol. 2, e155 (2006)

    Article  ADS  Google Scholar 

  8. Clackson, T., Ultsch, M. H., Wells, J. A. & de Vos, A. M. Structural and functional analysis of the 1:1 growth hormone:receptor complex reveals the molecular basis for receptor affinity. J. Mol. Biol. 277, 1111–1128 (1998)

    Article  CAS  Google Scholar 

  9. Levy, E. D., Boeri Erba, E., Robinson, C. V. & Teichmann, S. A. Assembly reflects evolution of protein complexes. Nature 453, 1262–1265 (2008)

    Article  ADS  CAS  Google Scholar 

  10. Fenimore, P. W., Frauenfelder, H., McCammon, B. H. & Young, R. D. Bulk solvent and hydration-shell fluctuations, similar to α- and β-fluctuations in glasses, control protein motions and functions. Proc. Natl Acad. Sci. USA 101, 14408–14413 (2004)

    Article  ADS  CAS  Google Scholar 

  11. Ostlund, G. et al. InParanoid 7: new algorithms and tools for eukaryotic orthology analysis. Nucleic Acids Res. 38, D196–D203 (2010)

    Article  Google Scholar 

  12. Gabaldon, T. et al. Joining forces in the quest for orthologs. Genome Biol. 10, 403 (2009)

    Article  Google Scholar 

  13. Sali, A. & Blundell, T. L. Comparative protein modeling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993)

    Article  CAS  Google Scholar 

  14. Zhou, H. & Skolnick, J. Improving threading algorithms for remote homology modeling by combining fragment and template comparisons. Proteins 78, 2041–2048 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Wiederstein, M. & Sippl, M. J. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35, W407–W410 (2007)

    Article  Google Scholar 

  16. Moran, N. A. Accelerated evolution and Muller’s ratchet in endosymbiotic bacteria. Proc. Natl Acad. Sci. USA 93, 2873–2878 (1996)

    Article  ADS  CAS  Google Scholar 

  17. Kuriyan, J. & Eisenberg, D. The origin of protein interactions and allostery in colocalization. Nature 450, 983–990 (2007)

    Article  ADS  CAS  Google Scholar 

  18. Rizzo, R. C. & Jorgensen, W. L. OPLS All-atom model for amines: resolution of the amine hydration problem. J. Am. Chem. Soc. 121, 4827–4836 (1999)

    Article  CAS  Google Scholar 

  19. Jorgensen, W. L., Chandrasekhar, J., Madura, J., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983)

    Article  ADS  CAS  Google Scholar 

  20. Fernández, A. & Berry, R. S. Golden rule for buttressing vulnerable soluble proteins. J. Proteome Res. 9, 2643–2648 (2010)

    Article  Google Scholar 

  21. Canutescu, A. A., Shelenkov, A. & Dunbrack, R. L. A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci. 12, 2001–2014 (2003)

    Article  CAS  Google Scholar 

  22. Pietrosemoli, N., Crespo, A. & Fernández, A. Dehydration propensity of order-disorder intermediate regions in soluble proteins. J. Proteome Res. 6, 3519–3526 (2007)

    Article  CAS  Google Scholar 

  23. Schutz, C. N. & Warshel, A. What are the dielectric constants of proteins and how to validate electrostatic models? Proteins Struct. Funct. Genet. 44, 400–417 (2001)

    Article  CAS  Google Scholar 

  24. Scott, R., Boland, M., Rogale, K. & Fernández, A. Continuum equations for dielectric response to macromolecular assemblies at the nanoscale. J. Phys. A 37, 9791–9803 (2004)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  25. Fernández, A., Sosnick, T. R. & Colubri, A. Dynamics of hydrogen-bond desolvation in folding proteins. J. Mol. Biol. 321, 659–675 (2002)

    Article  Google Scholar 

  26. Lindahl, E., Hess, B. & Van der Spoel, D. GROMACS 3.0: a package for molecular simulation and trajectory analysis. J. Mol. Model. 7, 302–317 (2001)

    Article  Google Scholar 

  27. Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: an N log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993)

    Article  CAS  Google Scholar 

  28. Hoover, W. G. Canonical dynamics: equilibrium phase-space distributions. Phys. Rev. A 31, 1695–1697 (1985)

    Article  ADS  CAS  Google Scholar 

  29. Berendsen, H. J., Postma, J. P., van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984)

    Article  ADS  CAS  Google Scholar 

  30. Li, X., Romero, P., Rani, M., Dunker, A. K. & Obradovic, Z. Predicting protein disorder for N-, C-, and internal regions. Genome Informat. 10, 30–40 (1999)

    CAS  Google Scholar 

Download references

Acknowledgements

A.F. was supported by National Institutes of Health grant R01GM072614, and by the Institute of Biophysical Dynamics and the Department of Computer Science at The University of Chicago. M.L. was supported by National Institutes of Health grant R01GM036827 and National Science Foundation grant EF-0827411.

Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Chicago, Chicago, 60637, Illinois, USA

    Ariel Fernández

  2. Department of Bioengineering, Rice University, Houston, 77005, Texas, USA

    Ariel Fernández

  3. Department of Biology, Indiana University, Bloomington, 47405, Indiana, USA

    Michael Lynch

Authors
  1. Ariel Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Lynch
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.F. and M.L. conceived the project and wrote the paper. A.F. collected the orthologue groups across 36 species with sufficient structural representation, performed the structural analysis and determined the interaction propensities across orthologues.

Corresponding authors

Correspondence to Ariel Fernández or Michael Lynch.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

The file contains Supplementary Information Parts 1 and 2, which include Supplementary Text and Data, Supplementary Tables 1-7, Supplementary Figures 1-7 with legends and additional references. (PDF 1858 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Fernández, A., Lynch, M. Non-adaptive origins of interactome complexity. Nature 474, 502–505 (2011). https://doi.org/10.1038/nature09992

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature09992

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing