New Research In
Physical Sciences
Social Sciences
Featured Portals
Articles by Topic
Biological Sciences
Featured Portals
Articles by Topic
- Agricultural Sciences
- Anthropology
- Applied Biological Sciences
- Biochemistry
- Biophysics and Computational Biology
- Cell Biology
- Developmental Biology
- Ecology
- Environmental Sciences
- Evolution
- Genetics
- Immunology and Inflammation
- Medical Sciences
- Microbiology
- Neuroscience
- Pharmacology
- Physiology
- Plant Biology
- Population Biology
- Psychological and Cognitive Sciences
- Sustainability Science
- Systems Biology
Hydrophobicity of proteins and nanostructured solutes is governed by topographical and chemical context
Edited by Michael L. Klein, Temple University, Philadelphia, PA, and approved October 26, 2017 (received for review March 23, 2017)

Significance
Numerous biological self-assembly processes, from protein folding to molecular recognition, are driven by hydrophobic interactions, yet characterizing hydrophobicity at the nanoscale has remained a major challenge, because it requires understanding of the strength of protein–water interactions and the ease with which they can be disrupted. Water near a protein responds to its chemistry and topography in a manner that is collective and complex and cannot be captured by commonly used surface area models or hydropathy scales. We demonstrate that water density fluctuations near proteins can characterize protein hydrophobicity and reveal its dependence on curvature and chemical patterns at the nanoscale. Our approach opens new avenues for understanding and efficient characterization of biomolecular interactions.
Abstract
Hydrophobic interactions drive many important biomolecular self-assembly phenomena. However, characterizing hydrophobicity at the nanoscale has remained a challenge due to its nontrivial dependence on the chemistry and topography of biomolecular surfaces. Here we use molecular simulations coupled with enhanced sampling methods to systematically displace water molecules from the hydration shells of nanostructured solutes and calculate the free energetics of interfacial water density fluctuations, which quantify the extent of solute–water adhesion, and therefore solute hydrophobicity. In particular, we characterize the hydrophobicity of curved graphene sheets, self-assembled monolayers (SAMs) with chemical patterns, and mutants of the protein hydrophobin-II. We find that water density fluctuations are enhanced near concave nonpolar surfaces compared with those near flat or convex ones, suggesting that concave surfaces are more hydrophobic. We also find that patterned SAMs and protein mutants, having the same number of nonpolar and polar sites but different geometrical arrangements, can display significantly different strengths of adhesion with water. Specifically, hydroxyl groups reduce the hydrophobicity of methyl-terminated SAMs most effectively not when they are clustered together but when they are separated by one methyl group. Hydrophobin-II mutants show that a charged amino acid reduces the hydrophobicity of a large nonpolar patch when placed at its center, rather than at its edge. Our results highlight the power of water density fluctuations-based measures to characterize the hydrophobicity of nanoscale surfaces and caution against the use of additive approximations, such as the commonly used surface area models or hydropathy scales for characterizing biomolecular hydrophobicity and the associated driving forces of assembly.
Hydrophobic interactions drive many important biological and colloidal self-assembly processes (1⇓⇓⇓⇓–6). During such assembly, the hydration shells of the associating solutes are disrupted, replacing hydrophobic–water contacts with hydrophobic–hydrophobic ones. Characterizing how strongly water adheres to a given solute is, therefore, directly relevant to the strength of hydrophobic interactions between solutes. Macroscopically, surface–water adhesion is quantified by measuring the water droplet contact angle on a surface. However, such characterization does not translate usefully to proteins and other nanoscale solutes. Indeed, characterizing how strongly or weakly a protein surface or a specific patch on it adheres to water (i.e., its hydrophobicity) is incredibly challenging and has necessitated the use of simplifying assumptions.
To this end, simple surface area (SA) models have been used to estimate the driving force for assembly,
Other approximate approaches that use hydropathy scales (or scoring functions) (14⇓⇓⇓–18) assign an index (or a score) to an amino acid residue based on some measure of its aversion to water (e.g., water to oil transfer free energy) and estimate the hydrophobicity of a protein patch as a sum of the hydrophobicities of constituent amino acids (14). Many hydropathy scales exist, and they differ significantly from each other (19, 20). Importantly, while such methods can efficiently extract information from large protein databases and classify them into meaningful groups, they fail to predict the driving forces in specific situations (21⇓–23). Here we trace the failure of such approaches to the assumption that the hydrophobicity of a protein patch can be decomposed into a sum of its constituent parts (i.e., additivity), and that a unique hydrophobicity can be assigned to each residue; our results contribute to the growing consensus that the hydrophobicity of a residue is not unique but depends in a nontrivial manner on its chemical and topographical context in a protein.
To study context-dependent hydrophobicity we use molecular dynamics (MD) simulations coupled with enhanced sampling methods (24, 25) to systematically displace water molecules from the solute hydration shell and quantify the corresponding free energetic cost. The free energetics of interfacial water density fluctuations, and especially the rare ones that result in opening of a cavity adjacent to the solute, then serve to quantify hydrophobicity. Density fluctuations are enhanced and correspondingly it is easier to create a cavity near a hydrophobic surface (26⇓⇓–29), consistent with the weaker surface–water adhesion. Theory of inhomogeneous liquids connects water density fluctuations to other quantities, such as water compressibility, transverse water density correlations, and the free energy of cavity formation, all of which can also serve as molecular measures of context-dependent hydrophobicity (24, 26⇓⇓⇓⇓⇓⇓⇓⇓–35). Such measures have been used previously to study aspects of context-dependent hydrophobicity of nanoscale surfaces (35⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓–48).
Here, we build on this work by characterizing the hydrophobicity of surfaces with systematic variations in curvature and the chemical patterns they display. We show that concave nonpolar surfaces are more hydrophobic than convex ones. We also show that hydrophobic patches with variations in chemical pattern and topography, whether on self-assembled monolayers (SAMs) or on the surface of a protein, hydrophobin-II, can display significantly different hydrophobicity. Our work highlights the power of water density fluctuations-based measures to characterize hydrophobicity of nanoscale surfaces and cautions against the use of additive approximations, such as the commonly used SA models, hydropathy scales, and similar scoring functions, for characterizing biomolecular hydrophobicity and the associated driving forces of assembly.
Results and Discussion
Effect of Nanoscale Curvature on Surface Hydrophobicity.
How curvature influences the hydration of spherical hydrophobic solutes is known. Highly curved solutes smaller than
Effect of nanoscale curvature on hydrophobicity. (A) Simulation snapshot of a (16,16) hCNT in water. Only the water molecules in observation volumes, v, near the concave (blue and white) and the convex (red and white) surfaces are shown for clarity. hCNT–water interactions are scaled by
To sample concave (
Fig. 1B shows the isothermal compressibility of interfacial water near the curved hCNT surfaces, obtained by performing MD simulations over a range of pressures and taking the pressure derivative of the average number of waters,
Our results for the convex surfaces are consistent with those of Sarupria and Garde (34), who showed that the hydration shell compressibility is the lowest near methane-sized hydrophobic solutes and increases monotonically with increasing solute size (decreasing curvature). For convex surfaces, results in Fig. 1B are also in good agreement with those of Jabes et al. (52), who found that hydration shell compressibility reduces by about 22% near a hydrocarbon-coated cylinder with
Fig. 1C shows probability,
These low-N tails in
Surfaces with the Same Chemistry but Different Patterns Can Have Widely Varying Hydrophobicities.
Flat SAMs provide excellent systems to analyze the effects of chemical patterns on hydrophobicity without being encumbered by the effects of surface topography (40, 54, 55). We study SAM surfaces with n hydrophilic sites (–OH head groups) in a background of hydrophobic sites (–CH3 head groups) (see SI Appendix for details). We hold n constant but vary the separation, s, the number of CH3 sites between adjacent OH sites (Fig. 2A). We studied seven patterns with n = 4 (
How chemical patterns influence surface hydrophobicity. (A) Top views of a CH3-SAM containing
Which of the four patches in Fig. 2A is the most hydrophobic and which is the most hydrophilic? Additive models would predict that all patterns are equally hydrophobic; however, the answer is both nonintuitive and nontrivial. Fig. 2B shows the hydrophilicity of the
What leads to such a nonmonotonic response to the chemical context presented by the patch? To answer this question, we visualize dewetting of the patches by following the average water density field in INDUS simulations, where biasing potentials are used to dewet v. For example, consider a biased simulation of the
As v is dewetted for the
Collectively, these results highlight that even for simple flat surfaces, surface–water adhesion strength depends on chemical patterns in a manner that is complex and not anticipated by additive models. Our results are consistent with previous work on the hydration of patterned surfaces. For example, Luzar and Leung (57) showed that in confined geometry a regularly spaced distribution of hydrophilic sites is much more effective in slowing the formation of vapor tubes that trigger the evaporation process. Vanzo et al. (58) also found that uniformly distributing charges (rather than segregating them) in a hydrophobic surface makes the surface significantly more hydrophilic.
Protein Hydrophobicity Is Context-Dependent and Nonadditive.
We use water density fluctuations-based measures to characterize the hydrophobicity of a protein, hydrophobin II (Protein Data Bank ID code 2B97) (59), which is a small globular (
Here we study how altering the local context affects the hydrophobicity of the patch. To this end, we created a swap mutant by switching the positions of residues Asp-59 and Leu-63 in the wild-type protein to Asp-63 and Leu-59 in the mutant (Fig. 3A). Such a swap places a charged residue from the edge to the center of the patch but does not perturb the overall structure of the protein; there are four disulfide bonds in the protein, and the backbone root mean square deviation between equilibrium wild-type and mutant structures are within thermal fluctuations (see SI Appendix for simulation details). To study the collective response of water to the swap we defined an observation volume, v, of width 0.3 nm that envelopes a contiguous region of nine nonpolar residues and Asp-59 (Fig. 3 A and B) and contains roughly 140 waters on average. We calculated the free energetics of water density fluctuations,
Protein hydrophobicity is nonadditive. (A) Wild-type hydrophobin-II shown in space-fill representation (white, nonpolar; green, polar; red, anionic; blue, cationic). Two residues, Leu-63 (gray, at the center) and Asp-59 (red, at the periphery of the patch), were swapped to create the mutant (Right). (B) Top and front views of hydrophobin-II are shown along with the observation volume, v, that covers the hydrophobic patch and Asp-59. (C) The free energetics,
The additional work,
Conclusions and Outlook
Characterizing the hydrophobicity of heterogeneous nanoscale surfaces has remained a major challenge. Additive and context-independent descriptions of hydrophobicity are frequently used by computational drug design approaches and implicit solvent methods to estimate the hydrophobic contribution to association (61). We showed that molecular measures based on water-density fluctuations effectively capture the collective response of water to complex surfaces and serve as robust nanoscale measures of hydrophobicity. We used these measures to show that the hydrophobicity of proteins and nanostructured solutes is strongly influenced by their chemistry and topography in a manner that cannot be captured by additive approaches.
In particular, by studying hemicylindrical nonpolar surfaces we showed that concave nonpolar surfaces are more hydrophobic compared with flat or convex surfaces, as reflected in the higher water compressibility, larger water density fluctuations, and easier cavity formation in the vicinity of concave surfaces. The relative ease of displacing water from a concave region suggests an important role for concave features (e.g., clefts or pockets) in binding to hydrophobic solutes. Molecular details of water density fluctuations in a concave interfacial region and how they are coupled to an approaching ligand are also known to be important in ligand binding kinetics (62).
The asymmetric dependence of hydrophobicity of nonpolar surfaces on curvature implies that introducing nanoscale topographical features on an otherwise flat nonpolar surface would increase its hydrophobicity. Such features may be integral parts of the structure of proteins and other macromolecules, or they may appear fleetingly through conformational changes of a flexible molecule or surface, suggesting that flexible nonpolar surfaces ought to be more hydrophobic than rigid ones. This expectation is consistent with the results of Andreev et al. (63), who showed that flexible nanotubes are more hydrophobic, expel water from their interior, and reduce the flow of water through them. Enhanced hydrophobicity as a result of flexibility should also influence water phase behavior and evaporation rates under nonpolar confinement. Indeed, Altabet and Debenedetti (64) and Altabet et al. (65) have shown that water confined between flexible nonpolar surfaces is less stable and evaporates significantly faster relative to water confined between the corresponding rigid surfaces.
We also showed that patches with the same chemical composition but different geometrical arrangements, either on SAM surfaces or on proteins, can display significantly different hydrophobicities. Specifically, we showed that hydrophilic sites are most effective in increasing the hydrophilicity of a nonpolar surface not when they are clustered together but when they are separated from each other by one hydrophobic site. Favorable direct (electrostatic) interactions between polar or charged sites and water can pin water molecules not only in direct contact with the site but also in subsequent hydration shells. Thus, polar sites separated by
Finally, our work suggests that context-dependent hydration of protein surfaces can be characterized effectively using water-density fluctuations and associated quantities. Such characterization captures many-body effects that are missing in additive models, which presents an important advantage, especially with regard to developing predictive approaches. The success of bioinformatic approaches in predicting protein structure from sequence has relied on the availability of protein sequence–structure information in the Protein Data Bank (67). The sequence–structure relationship is nonadditive and complex, similar to the relationship between chemistry and topography, and hydrophobicity. If extensive data on the hydration of diverse proteins were available, we speculate that data analytics approaches could be applied to estimate the hydrophobicity of protein surfaces. Such information about hydration is not available in the Protein Data Bank, which contains information about only the strongly localized crystal waters. However, our approach using water-density fluctuations-based characterization of hydrophobicity combined with advances in high-performance computing provides a route to developing an extensive “protein hydration data bank,” which could not only support development of nonadditive predictive approaches but also help efficient prediction of biomolecular interactions in complex systems.
Acknowledgments
We thank Cuyler Bates for preparation of nanotube coordinates. S.G. thanks the Center for Computational Innovations at Rensselaer Polytechnic Institute for high-performance computing resources. This work was supported by National Science Foundation Grants UPENN MRSEC DMR-1120901, CBET-1652646, and CBET-1511437 (to A.P.).
Footnotes
↵1E.X. and V.V. contributed equally to this work.
- ↵2To whom correspondence may be addressed. Email: amish.patel{at}seas.upenn.edu or gardes{at}rpi.edu.
Author contributions: A.J.P. and S.G. designed research; E.X., V.V., L.L., N.R., and A.J.P. performed research; E.X., V.V., L.L., N.R., and A.J.P. analyzed data; and E.X., V.V., A.J.P., and S.G. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1700092114/-/DCSupplemental.
Published under the PNAS license.
References
- ↵
- ↵
- Tanford C
- ↵
- ↵
- ↵
- ↵
- Hillyer MB,
- Gibb BC
- ↵
- ↵
- ↵
- ↵
- Genheden S,
- Ryde U
- ↵
- Chaudhari MI,
- Holleran SA,
- Ashbaugh HS,
- Pratt LR
- ↵
- Mobley DL,
- Bayly CI,
- Cooper MD,
- Shirts MR,
- Dill KA
- ↵
- Harris RC,
- Pettitt BM
- ↵
- ↵
- ↵
- Bonella S,
- Raimondo D,
- Milanetti E,
- Tramontano A,
- Ciccotti G
- ↵
- ↵
- ↵
- ↵
- Granick S,
- Bae SC
- ↵
- Kortemme T,
- Baker D
- ↵
- ↵
- Kister AE,
- Phillips JC
- ↵
- ↵
- ↵
- Godawat R,
- Jamadagni SN,
- Garde S
- ↵
- Patel AJ, et al.
- ↵
- ↵
- Patel AJ,
- Garde S
- ↵
- ↵
- ↵
- Giovambattista N,
- Rossky PJ,
- Debenedetti PG
- ↵
- Mittal J,
- Hummer G
- ↵
- ↵
- Jamadagni SN,
- Godawat R,
- Garde S
- ↵
- ↵
- Giovambattista N,
- Debenedetti PG,
- Rossky PJ
- ↵
- ↵
- Giovambattista N,
- Lopez CF,
- Rossky PJ,
- Debenedetti PG
- ↵
- ↵
- ↵
- ↵
- ↵
- Wang J,
- Bratko D,
- Luzar A
- ↵
- ↵
- Factorovich MH,
- Molinero V,
- Scherlis DA
- ↵
- ↵
- Garde S
- ↵
- ↵
- Rajamani S,
- Truskett TM,
- Garde S
- ↵
- ↵
- Jabes BS,
- Bratko D,
- Luzar A
- ↵
- ↵
- Acharya H,
- Mozdzierz NJ,
- Keblinski P,
- Garde S
- ↵
- ↵
- ↵
- ↵
- Vanzo D,
- Bratko D,
- Luzar A
- ↵
- ↵
- Kapcha LH,
- Rossky PJ
- ↵
- Jorgensen WL
- ↵
- Setny P,
- Baron R,
- Kekenes-Huskey PM,
- McCammon JA,
- Dzubiella J
- ↵
- ↵
- Altabet YE,
- Debenedetti PG
- ↵
- Altabet YE,
- Haji-Akbari A,
- Debenedetti PG
- ↵
- ↵
- Baker D,
- Sali A
Citation Manager Formats
Sign up for Article Alerts
Article Classifications
- Physical Sciences
- Chemistry