We demonstrate in this work that the surface tension, water-organic solvent, transfer-free energies and the thermodynamics of melting of linear alkanes provide fundamental insights into the nonpolar driving forces for protein folding and protein binding reactions. We first develop a model for the curvature dependence of the hydrophobic effect and find that the macroscopic concept of interfacial free energy is applicable at the molecular level. Application of a well-known relationship involving surface tension and adhesion energies reveals that dispersion forces play little or no net role in hydrophobic interactions; rather, the standard model of disruption of water structure (entropically driven at 25 degrees C) is correct. The hydrophobic interaction is found, in agreement with the classical picture, to provide a major driving force for protein folding. Analysis of the melting behavior of hydrocarbons reveals that close packing of the protein interior makes only a small free energy contribution to folding because the enthalpic gain resulting from increased dispersion interactions (relative to the liquid) is countered by the freezing of side chain motion. The identical effect should occur in association reactions, which may provide an enormous simplification in the evaluation of binding energies. Protein binding reactions, even between nearly planar or concave/convex interfaces, are found to have effective hydrophobicities considerably smaller than the prediction based on macroscopic surface tension. This is due to the formation of a concave collar region that usually accompanies complex formation. This effect may preclude the formation of complexes between convex surfaces.

Protein folding and association: insights from the interfacial and thermodynamic properties of hydrocarbons.

A Gaussian solvent-exclusion model for the solvation free energy is developed. It is based on theoretical considerations and parametrized with experimental data. When combined with the CHARMM 19 polar hydrogen energy function, it provides an effective energy function (EEF1) for proteins in solution. The solvation model assumes that the solvation free energy of a protein molecule is a sum of group contributions, which are determined from values for small model compounds. For charged groups, the self-energy contribution is accounted for primarily by the exclusion model. Ionic side-chains are neutralized, and a distance-dependent dielectric constant is used to approximate the charge–charge interactions in solution. The resulting EEF1 is subjected to a number of tests. Molecular dynamics simulations at room temperature of several proteins in their native conformation are performed, and stable trajectories are obtained. The deviations from the experimental structures are similar to those observed in explicit water simulations. The calculated enthalpy of unfolding of a polyalanine helix is found to be in good agreement with experimental data. Results reported elsewhere show that EEF1 clearly distinguishes correctly from incorrectly folded proteins, both in static energy evaluations and in molecular dynamics simulations and that unfolding pathways obtained by high-temperature molecular dynamics simulations agree with those obtained by explicit water simulations. Thus, this energy function appears to provide a realistic first approximation to the effective energy hypersurface of proteins. Proteins 1999;35:133–152. © 1999 Wiley-Liss, Inc.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291097-0134%2819990501%2935%3A2%3C133%3A%3AAID-PROT1%3E3.0.CO%3B2-N

Effective energy function for proteins in solution.

Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structure-based ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the volume and area of pockets and cavities; and the area and circumference of mouth openings. CAST analysis of over 100 proteins has been carried out; proteins examined include a set of 51 monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the FK506 binding protein, 30 HIV-1 protease-inhibitor complexes, and a number of small and large protein inhibitors. Medium-sized globular proteins typically have 10-20 pockets/cavities. Most often, binding sites are pockets with 1-2 mouth openings; much less frequently they are cavities. Ligand binding pockets vary widely in size, most within the range 10(2)-10(3)A3. Statistical analysis reveals that the number of pockets and cavities is correlated with protein size, but there is no correlation between the size of the protein and the size of binding sites. Most frequently, the largest pocket/cavity is the active site, but there are a number of instructive exceptions. Ligand volume and binding site volume are somewhat correlated when binding site volume is < or =700 A3, but the ligand seldom occupies the entire site. Auxiliary pockets near the active site have been suggested as additional binding surface for designed ligands (Mattos C et al., 1994, Nat Struct Biol 1:55-58). Analysis of elastase-inhibitor complexes suggests that CAST can identify ancillary pockets suitable for recruitment in ligand design strategies. Analysis of the FK506 binding protein, and of compounds developed in SAR by NMR (Shuker SB et al., 1996, Science 274:1531-1534), indicates that CAST pocket computation may provide a priori identification of target proteins for linked-fragment design. CAST analysis of 30 HIV-1 protease-inhibitor complexes shows that the flexible active site pocket can vary over a range of 853-1,566 A3, and that there are two pockets near or adjoining the active site that may be recruited for ligand design.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/pro.5560070905

Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design

SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions.

The estradiol pharmacophore: Ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site

A fast algorithm that detects internal cavities in proteins and predicts the positions of buried water molecules is described. The cavities are characterized in terms of volume, surface area, polarity, and the presence of bound waters. The algorithm is applied to 12 proteins whose structures are known to high resolution and successfully predicts the locations of over 80% of internal water molecules. Most proteins are found to have a number of internal cavities ranging in volume from 10 to 180 A3. Some of these cavities contain water and some do not, with the probability of containing a buried water increasing with cavity size. However, many large cavities are found to be empty (i.e., they do not contain a crystallographically determined water). For multidomain proteins over half of the total cavity volume is at the interdomain interface. Possible implications for the energetics of cavity formation and for the functional role of internal cavities are discussed.

Internal cavities and buried waters in globular proteins.

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Internal cavities and buried waters in globular proteins.