Specificity and affinity quantification of flexible recognition from underlying energy landscape topography

PLoS Comput Biol. 2014 Aug 21;10(8):e1003782. doi: 10.1371/journal.pcbi.1003782. eCollection 2014 Aug.

Abstract

Flexibility in biomolecular recognition is essential and critical for many cellular activities. Flexible recognition often leads to moderate affinity but high specificity, in contradiction with the conventional wisdom that high affinity and high specificity are coupled. Furthermore, quantitative understanding of the role of flexibility in biomolecular recognition is still challenging. Here, we meet the challenge by quantifying the intrinsic biomolecular recognition energy landscapes with and without flexibility through the underlying density of states. We quantified the thermodynamic intrinsic specificity by the topography of the intrinsic binding energy landscape and the kinetic specificity by association rate. We found that the thermodynamic and kinetic specificity are strongly correlated. Furthermore, we found that flexibility decreases binding affinity on one hand, but increases binding specificity on the other hand, and the decreasing or increasing proportion of affinity and specificity are strongly correlated with the degree of flexibility. This shows more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Our work provides a theoretical foundation and quantitative explanation of the previous qualitative studies on the relationship among flexibility, affinity and specificity. In addition, we found that the folding energy landscapes are more funneled with binding, indicating that binding helps folding during the recognition. Finally, we demonstrated that the whole binding-folding energy landscapes can be integrated by the rigid binding and isolated folding energy landscapes under weak flexibility. Our results provide a novel way to quantify the affinity and specificity in flexible biomolecular recognition.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology
  • Computer Simulation
  • Kinetics
  • Models, Chemical*
  • Pliability
  • Protein Binding
  • Protein Folding
  • Proteins / chemistry*
  • Thermodynamics

Substances

  • Proteins

Grants and funding

This work was supported by the National Science Foundation of China (Grants 21190040, 11174105 and 91227114), 973 Project of China (2010CB933600) and National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.