On the role of electrostatics in protein-protein interactions

The role of electrostatics in protein-protein interactions and binding is reviewed in this paper. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and the basic electrostatic effects occurring upon the formation of the complex are discussed. The effect of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated which indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartments. The similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity.     

Role of bound water in protein-ligand association processes

The role of water molecules on the protein-ligand interface during macromolecular association has been determined. The free energy of association of insulin has been calculated by the methods of molecular mechanics and continual electrostatics (Poisson-Boltzmann model). The previously developed scheme of the decomposition of association free energy onto contributions from individual interactions has been used to calculate intermolecular interactions, the solvation free energy, and the entropies of the process of macromolecular association. An analysis of the calculated oscillation spectra indicated that the presence of water molecules on the protein-protein interface promotes an increase in the contribution of vibration entropy to the free energy of association due to the enhancement of the flexibility of the complex. It was shown that water molecules involved in the formation of protein-water-ligand hydrogen bond network change the balance of forces in the system.     

Electrostatics-defying interaction between arginine termini as a thermodynamic driving force in protein-protein interaction

Apparent electrostatics-defying clustering of arginines attributed as screening effect of solvent is in this study examined as a possible thermodynamic driving force in protein-protein interaction. A dataset of 266 protein dimers is found to have approximately 22% arginines mutually paired and approximately 17% pairs in interaction across interfaces and thus putative "hotspots" of protein-protein interaction. The pairing, uncorrelated with inter or intramolecular context, could be contributing in protein folding as well, and, uncorrelated with solvent access, could be driven by effects that are generic to solvent and protein structures. Mutually stacked at shorter distances but in diverse geometrical modes otherwise, the cations tend to be in gross deficit of hydrogen-bond partners, and contributing electrostatics across protein-protein interface that, on average, is repulsive for protein-protein interaction. Embedded in local environment enriched in polarizable residues, aromatic, aliphatic, and anionic, the arginines may contribute to protein-protein interaction via environmental polarization response to electrostatics of cation clustering, a possible new principle in molecular recognition.     

Electrostatic control of calcineurin's intrinsically-disordered regulatory domain binding to calmodulin

Calcineurin (CaN) is a serine/threonine phosphatase that regulates a variety of physiological and pathophysiological processes in mammalian tissue. The calcineurin (CaN) regulatory domain (RD) is responsible for regulating the enzyme's phosphatase activity, and is believed to be highly-disordered when inhibiting CaN, but undergoes a disorder-to-order transition upon diffusion-limited binding with the regulatory protein calmodulin (CaM). The prevalence of polar and charged amino acids in the regulatory domain (RD) suggests electrostatic interactions are involved in mediating calmodulin (CaM) binding, yet the lack of atomistic-resolution data for the bound complex has stymied efforts to probe how the RD sequence controls its conformational ensemble and long-range attractions contribute to target protein binding. In the present study, we investigated via computational modeling the extent to which electrostatics and structural disorder facilitate CaM/CaN association kinetics. Specifically, we examined several RD constructs that contain the CaM binding region (CAMBR) to characterize the roles of electrostatics versus conformational diversity in controlling diffusion-limited association rates, via microsecond-scale molecular dynamics (MD) and Brownian dynamic (BD) simulations. Our results indicate that the RD amino acid composition and sequence length influence both the dynamic availability of conformations amenable to CaM binding, as well as long-range electrostatic interactions to steer association. These findings provide intriguing insight into the interplay between conformational diversity and electrostatically-driven protein-protein association involving CaN, which are likely to extend to wide-ranging diffusion-limited processes regulated by intrinsically-disordered proteins.     

Exploring the charge space of protein-protein association: a proteomic study

The rate of association of a protein complex is a function of an intrinsic basal rate and of the magnitude of electrostatic steering. In the present study we analyze the contribution of electrostatics towards the association rate of proteins in a database of 68 transient hetero-protein-protein complexes. Our calculations are based on an upgraded version of the computer algorithm PARE, which was shown to successfully predict the impact of mutations on k(on) by calculating the difference in Columbic energy of interaction of a pair of proteins. HyPare (http://bip.weizmann.ac.il/HyPare), automatically calculates the impact of mutations on a per-residue basis for all residues of a protein-protein interaction, achieving a precision similar to that of PARE. Our calculations show that electrostatics play a marginal role (<10 fold) in determining the rate of association for about half of the complexes in the database. Strong electrostatic steering, which results in an increase of over 100-fold in k(on), was calculated for about 25% of the complexes. Applying HyPare to all 68 complexes in the database shows that a small number of residues are hotspots for association. About 40% of the hotspots are calculated to increase the rate of association upon mutation, and thus increase binding affinity. This is a much higher ratio than found for hotspots for dissociation, where the large majority cause weaker binding. About 40% of the hotspots are located outside the physical boundary of the binding site, making them ideal candidates for protein engineering. Our data shows that a majority of protein-protein complexes are not optimized for fast association. Hotspots are not evenly distributed between all types of amino acids. About 75% of all hotspots are of charged residues. This is understandable, as a charge-reverse mutant changes the total charge by 2. The small number of hydrophobic residues that are hotspots upon mutation probably relates to their location and surrounding. For 18 out of the 68 complexes in the database, experimental values of k(on) are available. For these, a basal rate of association was calculated to be in the range of 10(4)M(-1)s(-1) to 10(7)M(-1)s(-1). Some of these rates were verified independently from experimental mutant data. The basal rates were correlated with the size of the proteins and the shape of the interface.     

Protein-protein docking by simulating the process of association subject to biochemical constraints

We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures.     

Energy landscape and transition state of protein-protein association

Formation of a stereospecific protein complex is favored by specific interactions between two proteins but disfavored by the loss of translational and rotational freedom. Echoing the protein folding process, we have previously proposed a transition state for protein-protein association. Here we clarify the specification of the transition state by working with two types of toy models for protein association. A “hemisphere” model consists of two matching hemispheres as associating proteins, and a “crater” model consists of a spherical protein with a crater to which another spherical protein fits snugly. Short-range pairwise interactions between sites across the interface hold together the bound complex. Small relative translation and rotation between the subunits quickly destroy the interactions, leading to a sharp transition between the bound state with numerous short-range interactions but restricted translation and rotational freedom and the unbound state with, at most, a small number of interactions but expanded configurational freedom. This transition sets the outer boundary of the bound state as well as the transition state for association. The energy landscape is funnel-like, with the deep well of the bound state surrounded by a broad shallow basin. Calculations with the toy models suggest that mutational change in the interaction energy in the x-ray structure of a protein-protein complex, commonly used to approximate the effect on the association constant, overestimates the effect of mutation by 10–20%. With an eye toward specifying the transition states of actual protein complexes, we find that the total number of contacts between the subunits serves as a good surrogate of the interaction energy. This formalism of protein-protein association is applied to the barnase-barstar complex, reproducing the experimental results for the association rate over a wide range of ionic strength.     

Optimal docking area: a new method for predicting protein-protein interaction sites

Understanding energetics and mechanism of protein-protein association remains one of the biggest theoretical problems in structural biology. It is assumed that desolvation must play an essential role during the association process, and indeed protein-protein interfaces in obligate complexes have been found to be highly hydrophobic. However, the identification of protein interaction sites from surface analysis of proteins involved in non-obligate protein-protein complexes is more challenging. Here we present Optimal Docking Area (ODA), a new fast and accurate method of analyzing a protein surface in search of areas with favorable energy change when buried upon protein-protein association. The method identifies continuous surface patches with optimal docking desolvation energy based on atomic solvation parameters adjusted for protein-protein docking. The procedure has been validated on the unbound structures of a total of 66 non-homologous proteins involved in non-obligate protein-protein hetero-complexes of known structure. Optimal docking areas with significant low-docking surface energy were found in around half of the proteins. The 'ODA hot spots' detected in X-ray unbound structures were correctly located in the known protein-protein binding sites in 80% of the cases. The role of these low-surface-energy areas during complex formation is discussed. Burial of these regions during protein-protein association may favor the complexed configurations with near-native interfaces but otherwise arbitrary orientations, thus driving the formation of an encounter complex. The patch prediction procedure is freely accessible at http://www.molsoft.com/oda and can be easily scaled up for predictions in structural proteomics.     

Mapping electrostatic interactions in macromolecular associations.

In the association of electron transfer proteins, electrostatics has been proposed to play a role in maintaining the stability and specificity of the biomolecular complexes formed. An excellent model system is the interaction between mammalian cytochrome b5 and cytochrome c, in which the X-ray structures of the individual components reveal a complementary asymmetry of charges surrounding their respective redox centers. Determining the exact extent of the electrostatic interactions and identifying the specific residues involved in the formation of the electron transfer complex has proved more elusive. We report herein the utilization of high-pressure techniques, together with site-directed mutagenesis, to provide a map of the interaction domains in biomolecular complex formation. The application of high pressure disrupts macromolecular associations since dissociation of the complex results in a decreased volume of the system due to the solvation of charges that had been previously sequestered in the interface region and force solvation of hydrophobic surfaces. Site-directed mutagenesis of a totally synthetic gene for rat liver cytochrome b5, which expresses this mammalian protein in Escherichia coli as a hemecontaining soluble component, was used to selectively alter negatively charged residues of cytochrome b5 to neutral amide side-chains. We have demonstrated that the interaction domain of cytochrome b5 with cytochrome c can be mapped from a comparison of dissociation volumes of these modified cytochrome b5-cytochrome c complexes with the native complex. Using these techniques we can specifically investigate the role of particular residues in the equilibrium association of these two electron transfer proteins. Single-point mutations in the interaction domain give nearly identical effects on the measured dissociation volumes, yet removal of acidic residues outside the recognition surface yield volumes similar to wild-type protein. Multiple mutations in the proposed protein-protein interaction site are found to allow greater solvent-accessibility of the interface as reflected in a diminution in the volume changes on subsequent charge removal. This is indicative that the interprotein salt-bridges in this complex provide a mechanism for a greater exclusion of solvent from the interfacial domain of the complex, resulting in a more stable association.     

Influence of electrostatic forces on the association kinetics and conformational ensemble of an intrinsically disordered protein

Recent work has revealed that the association of a disordered region of a protein with a folded binding partner can occur as rapidly as association between two folded proteins. This is the case for the phosphatase calcineurin (CaN) and its association with its activator calmodulin. Calmodulin binds to the intrinsically disordered regulatory domain of CaN. Previous studies have shown that electrostatic steering can accelerate the binding of folded proteins with disordered ligands. Given that electrostatic forces are strong determinants of disordered protein ensembles, the relationship between electrostatics, conformational ensembles, and quaternary interactions is unclear. Here, we employ experimental approaches to explore the impact of electrostatic interactions on the association of calmodulin with the disordered regulatory region of CaN. We find that estimated association rate constants of calmodulin with our chosen calmodulin-substrates are within the diffusion-limited regime. The association rates are dependent on the ionic strength, indicating that favorable electrostatic forces increase the rate of association. Further, we show that charged amino acids outside the calmodulin-binding site modulate the binding rate. Conformational ensembles obtained from computer simulations suggest that electrostatic interactions within the regulatory domain might bias the conformational ensemble such that the calmodulin binding region is readily accessible. Given the prevalence of charged residues in disordered protein chains, our findings are likely relevant to many protein-protein interactions.     

Interaction energy decomposition in protein-protein association: a quantum mechanical study of barnase-barstar complex

Protein-protein interactions are very important in the function of a cell. Computational studies of these interactions have been of interest, but often they have utilized classical modelling techniques. In recent years, quantum mechanical (QM) treatment of entire proteins has emerged as a powerful approach to study biomolecular systems. Herein, we apply a semi-empirical divide and conquer (DC) methodology coupled with a dielectric continuum model for the solvent, to explore the contribution of electrostatics, polarization and charge transfer to the interaction energy between barnase and barstar in their complex form. Molecular dynamic (MD) simulation was performed to account for the dynamic behavior of the complex. The results show that electrostatics, charge transfer and polarization favor the formation of the complex. Our study shows that electrostatics dominates the interaction between barnase and barstar ( approximately 73%), while charge transfer and polarization are approximately 21% and approximately 6%, respectively. Close inspection of the polarization and charge-transfer effects on the charge distribution of the complex reveals the existence of two, well localized, regions in barstar. The first region includes the residues between P27 and Y47 and the second region is between N65 and D83. Since no such regions could be detected in barnase clearly suggests that barstar is well optimized for efficiently binding barnase. Furthermore, using our interaction energy decomposition scheme, we were able to identify all residues that have been experimentally determined to be important for the complex formation and to suggest other residues never have been investigated. This suggests that our approach will be useful as an aid in further understanding protein-protein contacts for the ultimate goal to produce successful inhibitors for protein complexes.     

Realistic protein-protein association rates from a simple diffusional model neglecting long-range interactions, free energy barriers, and landscape ruggedness

We develop a simple but rigorous model of protein-protein association kinetics based on diffusional association on free energy landscapes obtained by sampling configurations within and surrounding the native complex binding funnels. Guided by results obtained on exactly solvable model problems, we transform the problem of diffusion in a potential into free diffusion in the presence of an absorbing zone spanning the entrance to the binding funnel. The free diffusion problem is solved using a recently derived analytic expression for the rate of association of asymmetrically oriented molecules. Despite the required high steric specificity and the absence of long-range attractive interactions, the computed rates are typically on the order of 10(4)-10(6) M(-1) sec(-1), several orders of magnitude higher than rates obtained using a purely probabilistic model in which the association rate for free diffusion of uniformly reactive molecules is multiplied by the probability of a correct alignment of the two partners in a random collision. As the association rates of many protein-protein complexes are also in the 10(5)-10(6) M(-1) sec(-1) range, our results suggest that free energy barriers arising from desolvation and/or side-chain freezing during complex formation or increased ruggedness within the binding funnel, which are completely neglected in our simple diffusional model, do not contribute significantly to the dynamics of protein-protein association. The transparent physical interpretation of our approach that computes association rates directly from the size and geometry of protein-protein binding funnels makes it a useful complement to Brownian dynamics simulations.     

Automated computational framework for the analysis of electrostatic similarities of proteins

Charge plays an important role in protein-protein interactions. In the case of excessively charged proteins, their electrostatic potentials contribute to the processes of recognition and binding with other proteins or ligands. We present an automated computational framework for determining the contribution of each charged amino acid to the electrostatic properties of proteins, at atomic resolution level. This framework involves computational alanine scans, calculation of Poisson-Boltzmann electrostatic potentials, calculation of electrostatic similarity distances (ESDs), hierarchical clustering analysis of ESDs, calculation of solvation free energies of association, and visualization of the spatial distributions of electrostatic potentials. The framework is useful to classify families of mutants with similar electrostatic properties and to compare them with the parent proteins in the complex. The alanine scan mutants introduce perturbations in the local electrostatic properties of the proteins and aim in delineating the contribution of each mutated amino acid in the spatial distribution of electrostatic potential, and in biological function when electrostatics is a dominant contributing factor in protein-protein interactions. The framework can be used to design new proteins with tailored electrostatic properties, such as immune system regulators, inhibitors, and vaccines, and in guiding experimental studies. We present an example for the interaction of the immune system protein C3d (the d-fragment of complement protein C3) with its receptor CR2, and we discuss our data in view of a binding site controversy.     

New insights into the mechanism of protein-protein association

Association of a protein complex follows a two-step mechanism, with the first step being the formation of an encounter complex that evolves into the final complex. Here, we analyze recent experimental data of the association of TEM1-beta-lactamase with BLIP using theoretical calculations and simulation. We show that the calculated Debye-Hückel energy of interaction for a pair of proteins during association resembles an energy funnel, with the final complex at the minima. All attraction is lost at inter-protein distances of 20 A, or rotation angles of >60 degrees from the orientation of the final complex. For faster-associating protein complexes, the energy funnel deepens and its volume increases. Mutations with the largest impact on association (hotspots for association) have the largest effect on the size and depth of the energy funnel. Analyzing existing evidence, we suggest that the transition state along the association pathway is the formation of the final complex from the encounter complex. Consequently, pairs of proteins forming an encounter complex will tend to dissociate more readily than to evolve into the final complex. Increasing directional diffusion by increasing favorable electrostatic attraction results in a faster forming and slower dissociating encounter complex. The possible applicability of electrostatic calculations for protein-protein docking is discussed.     

Kinetic studies of protein-protein interactions

The structure of a protein-protein interaction, its affinity and thermodynamic characteristics depict a 'frozen' state of a complex. This picture ignores the kinetic nature of complex formation and dissociation, which are of major biological and biophysical interest. This review highlights recent advances in deciphering the kinetic pathway of protein-protein complexation, the nature of the encounter complex, transition state and intermediate along the reaction, and the effects of mutation, viscosity, pH and salt on association.     

Quantitative model for binary measurements of protein-protein interactions.

We develop a stochastic model for quantifying the binary measurements of protein-protein interactions. A key concept in the model is the binary response function (BRF) which represents the conditional probability of successfully detecting a protein-protein interaction with a given number of the protein complexes. A popular form of the BRF is introduced and the effect of the sharpness (Hill's coefficient) of this function is studied. Our model is motivated by the recently developed yeast two-hybrid method for measuring protein-protein interaction networks. We suggest that the same phenomenological BRF can also be applied to the mass spectroscopic measurement of protein-protein interactions. Based on the model, we investigate the contributions to the network topology of protein-protein interactions from (i) the distribution of protein binary association free energy, and from (ii) the cellular protein abundance. It is concluded that the association constants among different protein pairs cannot be totally independent. It is also shown that not only the association constants but also the protein abundance could be a factor in producing the power-law degree distribution of protein-protein interaction networks.     

On the dynamic nature of the transition state for protein-protein association as determined by double-mutant cycle analysis and simulation

The process of protein-protein association starts with their random collision, which may develop into an encounter complex followed by a transition state and final complex formation. Here we aim to experimentally characterize the nature of the transition state of protein-protein association for three different protein-protein interactions; Barnase-Barstar, TEM1-BLIP and IFNalpha2-IFNAR2, and use the data to model the transition state structures. To model the transition state, we determined inter-protein distance-constraints of the activated complex by using double mutant cycles (DMC) assuming that interacting residues are spatially close. Significant DeltaDeltaG(double dagger)(int) values were obtained only between residues on Barnase and Barstar. However, introducing specific mutations that optimize the charge complementarity between BLIP and TEM1 resulted in the introduction of significant DeltaDeltaG(double dagger)(int) values also between residues of these two proteins. While electrostatic interactions make major contributions towards stabilizing the transition state, we show two examples where steric hindrance exerts an effect on the transition state as well. To model the transition-state structures from the experimental DeltaDeltaG(double dagger)(int) values, we introduced a method for structure perturbation, searching for those inter-protein orientations that best support the experimental DeltaDeltaG(double dagger)(int) values. Two types of transition states were found, specific as observed for Barnase-Barstar and the electrostatically optimized TEM1-BLIP mutants, and diffusive as shown for wild-type TEM1-BLIP and IFNalpha2-IFNAR2. The specific transition states are characterized by defined inter-protein orientations, which cannot be modeled for the diffusive transition states. Mutations introduced through rational design can change the transition state from diffusive to specific. Together, these data provide a structural view of the mechanism allowing rates of association to differ by five orders of magnitude between different protein complexes.     

pyDock: electrostatics and desolvation for effective scoring of rigid-body protein-protein docking

The accurate scoring of rigid-body docking orientations represents one of the major difficulties in protein-protein docking prediction. Other challenges are the development of faster and more efficient sampling methods and the introduction of receptor and ligand flexibility during simulations. Overall, good discrimination of near-native docking poses from the very early stages of rigid-body protein docking is essential step before applying more costly interface refinement to the correct docking solutions. Here we explore a simple approach to scoring of rigid-body docking poses, which has been implemented in a program called pyDock. The scheme is based on Coulombic electrostatics with distance dependent dielectric constant, and implicit desolvation energy with atomic solvation parameters previously adjusted for rigid-body protein-protein docking. This scoring function is not highly dependent on specific geometry of the docking poses and therefore can be used in rigid-body docking sets generated by a variety of methods. We have tested the procedure in a large benchmark set of 80 unbound docking cases. The method is able to detect a near-native solution from 12,000 docking poses and place it within the 100 lowest-energy docking solutions in 56% of the cases, in a completely unrestricted manner and without any other additional information. More specifically, a near-native solution will lie within the top 20 solutions in 37% of the cases. The simplicity of the approach allows for a better understanding of the physical principles behind protein-protein association, and provides a fast tool for the evaluation of large sets of rigid-body docking poses in search of the near-native orientation.     

Electrostatics in computational protein design

Catalytic activity and protein-protein recognition have proven to be significant challenges for computational protein design. Electrostatic interactions are crucial for these and other protein functions, and therefore accurate modeling of electrostatics is necessary for successfully advancing protein design into the realm of protein function. This review focuses on recent progress in modeling electrostatic interactions in computational protein design, with particular emphasis on continuum models.