Amino acid substitutions at protein–protein interfaces that modulate the oligomeric state

Despite similarities in their sequence and structure, there are a number of homologous proteins that adopt various oligomeric states. Comparisons of these homologous protein pairs, in terms of residue substitutions at the protein–protein interfaces, have provided fundamental characteristics that describe how proteins interact with each other. We have prepared a dataset composed of pairs of related proteins with different homo‐oligomeric states. Using the protein complexes, the interface residues were identified, and using structural alignments, the shadow‐interface residues have been defined as the surface residues that align with the interface residues. Subsequently, we investigated residue substitutions between the interfaces and the shadow interfaces. Based on the degree of the contributions to the interactions, the aligned sites of the interfaces and shadow interfaces were divided into primary and secondary sites; the primary sites are the focus of this work. The primary sites were further classified into two groups (i.e. exposed and buried) based on the degree to which the residue is buried within the shadow interfaces. Using these classifications, two simple mechanisms that mediate the oligomeric states were identified. In the primary‐exposed sites, the residues on the shadow interfaces are replaced by more hydrophobic or aromatic residues, which are physicochemically favored at protein–protein interfaces. In the primary‐buried sites, the residues on the shadow interfaces are replaced by larger residues that protrude into other proteins. These simple rules are satisfied in 23 out of 25 Structural Classification of Proteins (SCOP) families with a different‐oligomeric‐state pair, and thus represent a basic strategy for modulating protein associations and dissociations. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Local and global anatomy of antibody‐protein antigen recognition

Deciphering antibody‐protein antigen recognition is of fundamental and practical significance. We constructed an antibody structural dataset, partitioned it into human and murine subgroups, and compared it with nonantibody protein‐protein complexes. We investigated the physicochemical properties of regions on and away from the antibody‐antigen interfaces, including net charge, overall antibody charge distributions, and their potential role in antigen interaction. We observed that amino acid preference in antibody‐protein antigen recognition is entropy driven, with residues having low side‐chain entropy appearing to compensate for the high backbone entropy in interaction with protein antigens. Antibodies prefer charged and polar antigen residues and bridging water molecules. They also prefer positive net charge, presumably to promote interaction with negatively charged protein antigens, which are common in proteomes. Antibody‐antigen interfaces have large percentages of Tyr, Ser, and Asp, but little Lys. Electrostatic and hydrophobic interactions in the Ag binding sites might be coupled with Fab domains through organized charge and residue distributions away from the binding interfaces. Here we describe some features of antibody‐antigen interfaces and of Fab domains as compared with nonantibody protein‐protein interactions. The distributions of interface residues in human and murine antibodies do not differ significantly. Overall, our results provide not only a local but also a global anatomy of antibody structures.     

Accurate prediction of interfacial residues in two‐domain proteins using evolutionary information: Implications for three‐dimensional modeling

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure–function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two‐domain proteins. We also use information from the three‐dimensional structures of individual domains of two‐domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (～85%) and specific (～95%) to the domain–domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain–domain interaction, rigid‐body docking was able to provide us with accurate full‐length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. Proteins 2014; 82:1219–1234. © 2013 Wiley Periodicals, Inc.     

Binding interface prediction by combining protein–protein docking results

We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein–protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision‐recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta‐PPISP (AUC = 0.43), which is one of the best monomer‐based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta‐PPISP and another monomer‐based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta‐PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues. Proteins 2014; 82:57–66. © 2013 Wiley Periodicals, Inc.     

Extent and nature of contacts between protein molecules in crystal lattices and between subunits of protein oligomers

A survey was compiled of several characteristics of the intersubunit contacts in 58 oligomeric proteins, and of the intermolecular contacts in the lattice for 223 protein crystal structures. The total number of atoms in contact and the secondary structure elements involved are similar in the two types of interfaces. Crystal contact patches are frequently smaller than patches involved in oligomer interfaces. Crystal contacts result from more numerous interactions by polar residues, compared with a tendency toward nonpolar amino acids at oligomer interfaces. Arginine is the only amino acid prominent in both types of interfaces. Potentials of mean force for residue–residue contacts at both crystal and oligomer interfaces were derived from comparison of the number of observed residue–residue interactions with the number expected by mass action. They show that hydrophobic interactions at oligomer interfaces favor aromatic amino acids and methionine over aliphatic amino acids; and that crystal contacts form in such a way as to avoid inclusion of hydrophobic interactions. They also suggest that complex salt bridges with certain amino acid compositions might be important in oligomer formation. For a protein that is recalcitrant to crystallization, substitution of lysine residues with arginine or glutamine is a recommended strategy. Proteins 28:494–514, 1997. © 1997 Wiley-Liss, Inc.     

Local hydrophobicity stabilizes secondary structures in proteins

The probability of occurrence of helix and β-sheet residues in 47 globular proteins was determined as a function of local hydrophobicity, which was defined by the sum of the Nozaki-Tanford transfer free energies at two nearest-neighbors on both sides of the amino acid sequence. In general, hydrophilic amino acids favor neither helix nor β-sheet formations when neighbor residues are also hydrophilic but favor helix formation at higher local hydrophobicity. On the other hand, some hydrophobic amino acids such as Met, Leu, and Ile favor helix formation when neighbor residues are hydrophilic. None of the hydrophobic amino acids favor β-sheet formation with hydrophilic neighbors, but most of them strongly favor β-sheet formation at high local hydrophobicity. When the average of 20 amino acids is taken, both helix and β-sheet residue probabilities are higher at higher local hydrophobicity, although the increase is steeper for β-sheets. Therefore, β-sheet formation is more influenced by local hydrophobicity than helix formation. Generally, helices are nearer the surface and tend to have hydrophilic and hydrophobic faces at opposite sides. The tendency of alternating regions of hydrophilic and hydrophobic residues in a helical sequence was revealed by calculating the correlation of the Nozaki-Tanford values. Such amphipathic helices may be important in protein–protein and protein–lipid interactions and in forming hydrophilic channels in the membrane. The choice of 30 nonhomologous proteins as the data set did not alter the above results.     

Prediction of protein–protein interaction sites from weakly homologous template structures using meta‐threading and machine learning

The identification of protein–protein interactions is vital for understanding protein function, elucidating interaction mechanisms, and for practical applications in drug discovery. With the exponentially growing protein sequence data, fully automated computational methods that predict interactions between proteins are becoming essential components of system‐level function inference. A thorough analysis of protein complex structures demonstrated that binding site locations as well as the interfacial geometry are highly conserved across evolutionarily related proteins. Because the conformational space of protein–protein interactions is highly covered by experimental structures, sensitive protein threading techniques can be used to identify suitable templates for the accurate prediction of interfacial residues. Toward this goal, we developed eFindSite^PPI, an algorithm that uses the three‐dimensional structure of a target protein, evolutionarily remotely related templates and machine learning techniques to predict binding residues. Using crystal structures, the average sensitivity (specificity) of eFindSite^PPI in interfacial residue prediction is 0.46 (0.92). For weakly homologous protein models, these values only slightly decrease to 0.40–0.43 (0.91–0.92) demonstrating that eFindSite^PPI performs well not only using experimental data but also tolerates structural imperfections in computer‐generated structures. In addition, eFindSite^PPI detects specific molecular interactions at the interface; for instance, it correctly predicts approximately one half of hydrogen bonds and aromatic interactions, as well as one third of salt bridges and hydrophobic contacts. Comparative benchmarks against several dimer datasets show that eFindSite^PPI outperforms other methods for protein‐binding residue prediction. It also features a carefully tuned confidence estimation system, which is particularly useful in large‐scale applications using raw genomic data. eFindSite^PPI is freely available to the academic community at http://www.brylinski.org/efindsiteppi . Copyright © 2014 John Wiley & Sons, Ltd.     

Hydrophobic environment is a key factor for the stability of thermophilic proteins

The stability of thermophilic proteins has been viewed from different perspectives and there is yet no unified principle to understand this stability. It would be valuable to reveal the most important interactions for designing thermostable proteins for such applications as industrial protein engineering. In this work, we have systematically analyzed the importance of various interactions by computing different parameters such as surrounding hydrophobicity, inter‐residue interactions, ion‐pairs and hydrogen bonds. The importance of each interaction has been determined by its predicted relative contribution in thermophiles versus the same contribution in mesophilic homologues based on a dataset of 373 protein families. We predict that hydrophobic environment is the major factor for the stability of thermophilic proteins and found that 80% of thermophilic proteins analyzed showed higher hydrophobicity than their mesophilic counterparts. Ion pairs, hydrogen bonds, and interaction energy are also important and favored in 68%, 50%, and 62% of thermophilic proteins, respectively. Interestingly, thermophilic proteins with decreased hydrophobic environments display a greater number of hydrogen bonds and/or ion pairs. The systematic elimination of mesophilic proteins based on surrounding hydrophobicity, interaction energy, and ion pairs/hydrogen bonds, led to correctly identifying 95% of the thermophilic proteins in our analyses. Our analysis was also applied to another, more refined set of 102 thermophilic–mesophilic pairs, which again identified hydrophobicity as a dominant property in 71% of the thermophilic proteins. Further, the notion of surrounding hydrophobicity, which characterizes the hydrophobic behavior of residues in a protein environment, has been applied to the three‐dimensional structures of elongation factor‐Tu proteins and we found that the thermophilic proteins are enriched with a hydrophobic environment. The results obtained in this work highlight the importance of hydrophobicity as the dominating characteristic in the stability of thermophilic proteins, and we anticipate this will be useful in our attempts to engineering thermostable proteins. © Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Protein Structure Classification Based on Conserved Hydrophobic Residues

Protein folding is frequently guided by local residue interactions that form clusters in the protein core. The interactions between residue clusters serve as potential nucleation sites in the folding process. Evidence postulates that the residue interactions are governed by the hydrophobic propensities that the residues possess. An array of hydrophobicity scales has been developed to determine the hydrophobic propensities of residues under different environmental conditions. In this work, we propose a graph-theory-based data mining framework to extract and isolate protein structural features that sustain invariance in evolutionary-related proteins, through the integrated analysis of five well-known hydrophobicity scales over the 3D structure of proteins. We hypothesize that proteins of the same homology contain conserved hydrophobic residues and exhibit analogous residue interaction patterns in the folded state. The results obtained demonstrate that discriminatory residue interaction patterns shared among proteins of the same family can be employed for both the structural and the functional annotation of proteins. We obtained on the average 90 percent accuracy in protein classification with a significantly small feature vector compared to previous results in the area. This work presents an elaborate study, as well as validation evidence, to illustrate the efficacy of the method and the correctness of results reported.     

Protein–protein interactions leave evolutionary footprints: High molecular coevolution at the core of interfaces

Protein–protein interactions are essential to all aspects of life. Specific interactions result from evolutionary pressure at the interacting interfaces of partner proteins. However, evolutionary pressure is not homogeneous within the interface: for instance, each residue does not contribute equally to the binding energy of the complex. To understand functional differences between residues within the interface, we analyzed their properties in the core and rim regions. Here, we characterized protein interfaces with two evolutionary measures, conservation and coevolution, using a comprehensive dataset of 896 protein complexes. These scores can detect different selection pressures at a given position in a multiple sequence alignment. We also analyzed how the number of interactions in which a residue is involved influences those evolutionary signals. We found that the coevolutionary signal is higher in the interface core than in the interface rim region. Additionally, the difference in coevolution between core and rim regions is comparable to the known difference in conservation between those regions. Considering proteins with multiple interactions, we found that conservation and coevolution increase with the number of different interfaces in which a residue is involved, suggesting that more constraints (i.e., a residue that must satisfy a greater number of interactions) allow fewer sequence changes at those positions, resulting in higher conservation and coevolution values. These findings shed light on the evolution of protein interfaces and provide information useful for identifying protein interfaces and predicting protein–protein interactions.     

RBRDetector: Improved prediction of binding residues on RNA‐binding protein structures using complementary feature‐ and template‐based strategies

Computational prediction of RNA‐binding residues is helpful in uncovering the mechanisms underlying protein‐RNA interactions. Traditional algorithms individually applied feature‐ or template‐based prediction strategy to recognize these crucial residues, which could restrict their predictive power. To improve RNA‐binding residue prediction, herein we propose the first integrative algorithm termed RBRDetector (RNA‐Binding Residue Detector) by combining these two strategies. We developed a feature‐based approach that is an ensemble learning predictor comprising multiple structure‐based classifiers, in which well‐defined evolutionary and structural features in conjunction with sequential or structural microenvironment were used as the inputs of support vector machines. Meanwhile, we constructed a template‐based predictor to recognize the putative RNA‐binding regions by structurally aligning the query protein to the RNA‐binding proteins with known structures. The final RBRDetector algorithm is an ingenious fusion of our feature‐ and template‐based approaches based on a piecewise function. By validating our predictors with diverse types of structural data, including bound and unbound structures, native and simulated structures, and protein structures binding to different RNA functional groups, we consistently demonstrated that RBRDetector not only had clear advantages over its component methods, but also significantly outperformed the current state‐of‐the‐art algorithms. Nevertheless, the major limitation of our algorithm is that it performed relatively well on DNA‐binding proteins and thus incorrectly predicted the DNA‐binding regions as RNA‐binding interfaces. Finally, we implemented the RBRDetector algorithm as a user‐friendly web server, which is freely accessible at http://ibi.hzau.edu.cn/rbrdetector . Proteins 2014; 82:2455–2471. © 2014 Wiley Periodicals, Inc.     

Surface shapes and surrounding environment analysis of single‐ and double‐stranded DNA‐binding proteins in protein‐DNA interface

Protein‐DNA bindings are critical to many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the residues shape (peak, flat, or valley) and the surrounding environment of double‐stranded DNA‐binding proteins (DSBs) and single‐stranded DNA‐binding proteins (SSBs) in protein‐DNA interfaces. In the results, we found that the interface shapes, hydrogen bonds, and the surrounding environment present significant differences between the two kinds of proteins. Built on the investigation results, we constructed a random forest (RF) classifier to distinguish DSBs and SSBs with satisfying performance. In conclusion, we present a novel methodology to characterize protein interfaces, which will deepen our understanding of the specificity of proteins binding to ssDNA (single‐stranded DNA) or dsDNA (double‐stranded DNA). Proteins 2016; 84:979–989. © 2016 Wiley Periodicals, Inc.     

Insights from the structural analysis of protein heterodimer interfaces

Protein heterodimer complexes are often involved in catalysis, regulation, assembly, immunity and inhibition. This involves the formation of stable interfaces between the interacting partners. Hence, it is of interest to describe heterodimer interfaces using known structural complexes. We use a non-redundant dataset of 192 heterodimer complex structures from the protein databank (PDB) to identify interface residues and describe their interfaces using amino-acids residue property preference. Analysis of the dataset shows that the heterodimer interfaces are often abundant in polar residues. The analysis also shows the presence of two classes of interfaces in heterodimer complexes. The first class of interfaces (class A) with more polar residues than core but less than surface is known. These interfaces are more hydrophobic than surfaces, where protein-protein binding is largely hydrophobic. The second class of interfaces (class B) with more polar residues than core and surface is shown. These interfaces are more polar than surfaces, where binding is mainly polar. Thus, these findings provide insights to the understanding of protein-protein interactions.     

Using affinity chromatography to engineer and characterize pH‐dependent protein switches

Conformational changes play important roles in the regulation of many enzymatic reactions. Specific motions of side chains, secondary structures, or entire protein domains facilitate the precise control of substrate selection, binding, and catalysis. Likewise, the engineering of allostery into proteins is envisioned to enable unprecedented control of chemical reactions and molecular assembly processes. We here study the structural effects of engineered ionizable residues in the core of the glutathione‐S‐transferase to convert this protein into a pH‐dependent allosteric protein. The underlying rational of these substitutions is that in the neutral state, an uncharged residue is compatible with the hydrophobic environment. In the charged state, however, the residue will invoke unfavorable interactions, which are likely to induce conformational changes that will affect the function of the enzyme. To test this hypothesis, we have engineered a single aspartate, cysteine, or histidine residue at a distance from the active site into the protein. All of the mutations exhibit a dramatic effect on the protein's affinity to bind glutathione. Whereas the aspartate or histidine mutations result in permanently nonbinding or binding versions of the protein, respectively, mutant GST50C exhibits distinct pH‐dependent GSH‐binding affinity. The crystal structures of the mutant protein GST50C under ionizing and nonionizing conditions reveal the recruitment of water molecules into the hydrophobic core to produce conformational changes that influence the protein's active site. The methodology described here to create and characterize engineered allosteric proteins through affinity chromatography may lead to a general approach to engineer effector‐specific allostery into a protein structure.     

Examination of the structure/function relationship in the exchangeable apolipoprotein,apolipophorin‐III

Exchangeable apolipoproteins are proteins that reversibly bind lipoprotein particles to facilitate their transport in vivo. The structure/function relationship of apolipophorin‐III (apo‐III), the only insect exchangeable apolipoprotein, has been investigated by examining the association of this protein with lipid vesicles. The importance of a conserved leucine residue, reported to be essential for apo‐III binding to lipids, has been evaluated through site‐directed mutagenesis. A unique cysteine replaces the conserved leucine at position 30 in recombinant apo‐III (L30C protein). This substitution results in the covalent dimerization of the apo‐III mutant via a disulfide bond. The cysteine mutation causes no difference in surface hydrophobicity of the L30C proteins when compared to the wild type apo‐III. Wild type apo‐III, L30C monomer, and L30C dimer associate with dimyristoylphosphatidylcholine (DMAC) vesicles in a similar manner, resulting in a reduction of turbidity of a phospholipid vesicle suspension. Analysis with transmission electron microscopy (TEM) reveals disk‐like complexes identical to those previously reported with the wild type apo‐III. Because the mutation of the conserved leucine seems to affect the solution behavior and surface hydrophobicity of apo‐III, this residue is likely to be exposed to the aqueous environment. However, the similar behaviors of the wild type protein, the L30C monomer, and L30C dimer with respect to the binding of phospholipid vesicles suggest that this residue is not absolutely required for the protein binding to hydrophobic or amphiphilic interfaces. © 1999 John Wiley & Sons, Inc. Biopoly 50: 486–495, 1999     

Cation–π, amino–π, π–π, and H‐bond interactions stabilize antigen–antibody interfaces

The identification of immunogenic regions on the surface of antigens, which are able to stimulate an immune response, is a major challenge for the design of new vaccines. Computational immunology aims at predicting such regions—in particular B‐cell epitopes—but is far from being reliably applicable on a large scale. To gain understanding into the factors that contribute to the antigen–antibody affinity and specificity, we perform a detailed analysis of the amino acid composition and secondary structure of antigen and antibody surfaces, and of the interactions that stabilize the complexes, in comparison with the composition and interactions observed in other heterodimeric protein interfaces. We make a distinction between linear and conformational B‐cell epitopes, according to whether they consist of successive residues along the polypeptide chain or not. The antigen–antibody interfaces were shown to differ from other protein–protein interfaces by their smaller size, their secondary structure with less helices and more loops, and the interactions that stabilize them: more H‐bond, cation–π, amino–π, and π–π interactions, and less hydrophobic packing; linear and conformational epitopes can clearly be distinguished. Often, chains of successive interactions, called cation/amino–π and π–π chains, are formed. The amino acid composition differs significantly between the interfaces: antigen–antibody interfaces are less aliphatic and more charged, polar and aromatic than other heterodimeric protein interfaces. Moreover, paratopes and epitopes—albeit to a lesser extent—have amino acid compositions that are distinct from general protein surfaces. This specificity holds promise for improving B‐cell epitope prediction. Proteins 2014; 82:1734–1746. © 2014 Wiley Periodicals, Inc.     

i‐Patch: Interprotein contact prediction using local network information

Biological processes are commonly controlled by precise protein‐protein interactions. These connections rely on specific amino acids at the binding interfaces. Here we predict the binding residues of such interprotein complexes. We have developed a suite of methods, i‐Patch, which predict the interprotein contact sites by considering the two proteins as a network, with residues as nodes and contacts as edges. i‐Patch starts with two proteins, A and B, which are assumed to interact, but for which the structure of the complex is not available. However, we assume that for each protein, we have a reference structure and a multiple sequence alignment of homologues. i‐Patch then uses the propensities of patches of residues to interact, to predict interprotein contact sites. i‐Patch outperforms several other tested algorithms for prediction of interprotein contact sites. It gives 59% precision with 20% recall on a blind test set of 31 protein pairs. Combining the i‐Patch scores with an existing correlated mutation algorithm, McBASC, using a logistic model gave little improvement. Results from a case study, on bacterial chemotaxis protein complexes, demonstrate that our predictions can identify contact residues, as well as suggesting unknown interfaces in multiprotein complexes. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Interaction between bound water molecules and local protein structures: A statistical analysis of the hydrogen bond structures around bound water molecules

Water molecules play an important role in protein folding and protein interactions through their structural association with proteins. Examples of such structural association can be found in protein crystal structures, and can often explain protein functionality in the context of structure. We herein report the systematic analysis of the local structures of proteins interacting with water molecules, and the characterization of their geometric features. We first examined the interaction of water molecules with a large local interaction environment by comparing the preference of water molecules in three regions, namely, the protein–protein interaction (PPI) interfaces, the crystal contact (CC) interfaces, and the non‐interfacial regions. High preference of water molecules to the PPI and CC interfaces was found. In addition, the bound water on the PPI interface was more favorably associated with the complex interaction structure, implying that such water‐mediated structures may participate in the shaping of the PPI interface. The pairwise water‐mediated interaction was then investigated, and the water‐mediated residue–residue interaction potential was derived. Subsequently, the types of polar atoms surrounding the water molecules were analyzed, and the preference of the hydrogen bond acceptor was observed. Furthermore, the geometries of the structures interacting with water were analyzed, and it was found that the major structure on the protein surface exhibited planar geometry rather than tetrahedral geometry. Several previously undiscovered characteristics of water–protein interactions were unfolded in this study, and are expected to lead to a better understanding of protein structure and function. Proteins 2016; 84:43–51. © 2015 Wiley Periodicals, Inc.     

Free energetics of rigid body association of ubiquitin binding domains: A biochemical model for binding mediated by hydrophobic interaction

Weak intermolecular interactions, such as hydrophobic associations, underlie numerous biomolecular recognition processes. Ubiquitin is a small protein that represents a biochemical model for exploring thermodynamic signatures of hydrophobic association as it is widely held that a major component of ubiquitin's binding to numerous partners is mediated by hydrophobic regions on both partners. Here, we use atomistic molecular dynamics simulations in conjunction with the Adaptive Biasing Force sampling method to compute potentials of mean force (the reversible work, or free energy, associated with the binding process) to investigate the thermodynamic signature of complexation in this well‐studied biochemical model of hydrophobic association. We observe that much like in the case of a purely hydrophobic solute (i.e., graphene, carbon nanotubes), association is favored by entropic contributions from release of water from the interprotein regions. Moreover, association is disfavored by loss of enthalpic interactions, but unlike in the case of purely hydrophobic solutes, in this case protein‐water interactions are lost and not compensated for by additional water‐water interactions generated upon release of interprotein and moreso, hydration, water. We further find that relative orientations of the proteins that mutually present hydrophobic regions of each protein to its partner are favored over those that do not. In fact, the free energy minimum as predicted by a force field based method recapitulates the experimental NMR solution structure of the complex. Proteins 2014; 82:1453–1468. © 2014 Wiley Periodicals, Inc.