Semiempirical Solution to the Protein Folding Problem Through a Combination of Structural and Epistructural Approaches

  • Ariel Fernández Stigliano


This chapter unravels a provisional solution to the protein folding problem. The solution requires a combination of structural and epistructural approaches to the problem. The structural approach focuses on the molecular basis of cooperativity. We explore the concept of protein wrapping, its intimate relation to cooperativity, and its bearing on the expediency of the folding process for single-domain natural proteins. As previously described, wrapping refers to the environmental modulation or protection of intramolecular electrostatic interactions through an exclusion of surrounding water that takes place as the chain folds onto itself. Thus, a special many-body picture of the folding process emerges where the folding chain interacts with itself and also shapes the microenvironments that stabilize or destabilize the intramolecular interactions. This picture reflects a competition between chain folding and backbone hydration leading to the prevalence of backbone hydrogen bonds through cooperative interactions. On the other hand, the epistructural analysis provides a crucial component to the free energy of structural assemblage: the reversible work required to span the protein–water interface. Failures of cooperativity, i.e., wrapping deficiencies known as dehydrons, generate interfacial tension which, in turn, promotes cooperativity, so that an underlying principle of interfacial energy minimization becomes operative. The interfacial contribution to the free energy complements and steers the many-body wrapping dynamics arising from the structure-centric analysis, leading to a semiempirical solution to the protein folding problem.


Intramolecular Hydrogen Bond Folding Process Effective Permittivity Interfacial Free Energy Folding Pathway 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.National Research Council–CONICETBuenos AiresArgentina
  2. 2.Former Karl F. Hasselmann Endowed Chair Professor of BioengineeringRice UniversityHoustonUSA

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