Prediction of Protein-Protein Interaction Sites Using Electrostatic Desolvation Profiles

Protein-protein complex formation involves removal of water from the interface region. Surface regions with a small free energy penalty for water removal or desolvation may correspond to preferred interaction sites. A method to calculate the electrostatic free energy of placing a neutral low-dielectric probe at various protein surface positions has been designed and applied to characterize putative interaction sites. Based on solutions of the finite-difference Poisson equation, this method also includes long-range electrostatic contributions and the protein solvent boundary shape in contrast to accessible-surface-area-based solvation energies. Calculations on a large set of proteins indicate that in many cases (>90%), the known binding site overlaps with one of the six regions of lowest electrostatic desolvation penalty (overlap with the lowest desolvation region for 48% of proteins). Since the onset of electrostatic desolvation occurs even before direct protein-protein contact formation, it may help guide proteins toward the binding region in the final stage of complex formation. It is interesting that the probe desolvation properties associated with residue types were found to depend to some degree on whether the residue was outside of or part of a binding site. The probe desolvation penalty was on average smaller if the residue was part of a binding site compared to other surface locations. Applications to several antigen-antibody complexes demonstrated that the approach might be useful not only to predict protein interaction sites in general but to map potential antigenic epitopes on protein surfaces.     

Fruitful and Futile Encounters along the Association Reaction between Proteins

The association reaction between pairs of proteins proceeds through an encounter complex that develops into the final complex. Here, we combined Brownian dynamics simulations with experimental studies to analyze the structures of the encounter complexes along the association reaction between TEM1-β-lactamase and its inhibitor, β-lactamase-inhibitor protein. The encounter complex can be considered as an ensemble of short-lived low free-energy states that are stabilized primarily by electrostatic forces and desolvation. For the wild-type, the simulation showed two main encounter regions located outside the physical binding site. One of these regions was located near the experimentally determined transition state. To validate whether these encounters are fruitful or futile, we examined three groups of mutations that altered the encounter. The first group consisted of mutations that increased the experimental rate of association through electrostatic optimization. This resulted in an increase in the size of the encounter region located near the experimentally determined transition state, as well as a decrease in the energy of this region and an increase in the number of successful trajectories (i.e., encounters that develop into complex). A second group of mutations was specifically designed to either increase or decrease the size and energy of the second encounter complex, but either way it did not affect k_on. A third group of mutations consisted of residues that increased k_on without significantly affecting the encounter complexes. These results indicate that the size and energy of the encounter regions are only two of several parameters that lead to fruitful association, and that electrostatic optimization is a major driving force in fast association.     

ClusPro: an automated docking and discrimination method for the prediction of protein complexes

MOTIVATION: Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. RESULTS: We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. AVAILABILITY: The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu     

Probing molecular docking in a charged model binding site

A model binding site was used to investigate charge-charge interactions in molecular docking. This simple site, a small (180A(3)) engineered cavity in cyctochrome c peroxidase (CCP), is negatively charged and completely buried from solvent, allowing us to explore the balance between electrostatic energy and ligand desolvation energy in a system where many of the common approximations in docking do not apply. A database with about 5300 molecules was docked into this cavity. Retrospective testing with known ligands and decoys showed that overall the balance between electrostatic interaction and desolvation energy was captured. More interesting were prospective docking scre"ens that looked for novel ligands, especially those that might reveal problems with the docking and energy methods. Based on screens of the 5300 compound database, both high-scoring and low-scoring molecules were acquired and tested for binding. Out of 16 new, high-scoring compounds tested, 15 were observed to bind. All of these were small heterocyclic cations. Binding constants were measured for a few of these, they ranged between 20microM and 60microM. Crystal structures were determined for ten of these ligands in complex with the protein. The observed ligand geometry corresponded closely to that predicted by docking. Several low-scoring alkyl amino cations were also tested and found to bind. The low docking score of these molecules owed to the relatively high charge density of the charged amino group and the corresponding high desolvation penalty. When the complex structures of those ligands were determined, a bound water molecule was observed interacting with the amino group and a backbone carbonyl group of the cavity. This water molecule mitigates the desolvation penalty and improves the interaction energy relative to that of the "naked" site used in the docking screen. Finally, six low-scoring neutral molecules were also tested, with a view to looking for false negative predictions. Whereas most of these did not bind, two did (phenol and 3-fluorocatechol). Crystal structures for these two ligands in complex with the cavity site suggest reasons for their binding. That these neutral molecules do, in fact bind, contradicts previous results in this site and, along with the alkyl amines, provides instructive false negatives that help identify weaknesses in our scoring functions. Several improvements of these are considered.     

Computation of electrostatic complements to proteins: a case of charge stabilized binding.

Recent evidence suggests that the net effect of electrostatics is generally to destabilize protein binding due to large desolvation penalties. A novel method for computing ligand-charge distributions that optimize the tradeoff between ligand desolvation penalty and favorable interactions with a binding site has been applied to a model for barnase. The result is a ligand-charge distribution with a favorable electrostatic contribution to binding due, in part, to ligand point charges whose direct interaction with the binding site is unfavorable, but which make strong intra-molecular interactions that are uncloaked on binding and thus act to lessen the ligand desolvation penalty.     

Optimization of binding electrostatics: charge complementarity in the barnase-barstar protein complex

Theoretical and experimental studies have shown that the large desolvation penalty required for polar and charged groups frequently precludes their involvement in electrostatic interactions that contribute strongly to net stability in the folding or binding of proteins in aqueous solution near room temperature. We have previously developed a theoretical framework for computing optimized electrostatic interactions and illustrated use of the algorithm with simplified geometries. Given a receptor and model assumptions, the method computes the ligand-charge distribution that provides the most favorable balance of desolvation and interaction effects on binding. In this paper the method has been extended to treat complexes using actual molecular shapes. The barnase-barstar protein complex was investigated with barnase treated as a target receptor. The atomic point charges of barstar were varied to optimize the electrostatic binding free energy. Barnase and natural barstar form a tight complex (K(d) approximately 10(-14) M) with many charged and polar groups near the interface that make this a particularly relevant system for investigating the role of electrostatic effects on binding. The results show that sets of barstar charges (resulting from optimization with different constraints) can be found that give rise to relatively large predicted improvements in electrostatic binding free energy. Principles for enhancing the effect of electrostatic interactions in molecular binding in aqueous environments are discussed in light of the optima. Our findings suggest that, in general, the enhancements in electrostatic binding free energy resulting from modification of polar and charged groups can be substantial. Moreover, a recently proposed definition of electrostatic complementarity is shown to be a useful tool for examining binding interfaces. Finally, calculational results suggest that wild-type barstar is closer to being affinity optimized than is barnase for their mutual binding, consistent with the known roles of these proteins.     

Role of charged residues in the catalytic mechanism of hepatitis C virus NS3 protease: electrostatic precollision guidance and transition-state stabilization

Maturational cleavage of the hepatitis C virus polyprotein involves the viral chymotrypsin-like serine protease NS3. The substrate binding site of this enzyme is unusually flat and featureless. We here show that NS3 has a highly asymmetric charge distribution that is characterized by strong positive potentials in the vicinity of its active site and in the S5/S6 region. Using electrostatic potential calculations, we identified determinants of this positive potential, and the role of six different residues was explored by site-directed mutagenesis. Mutation of residues in the vicinity of the active site led to changes in k(cat) values of a peptide substrate indicating that basic amino acids play a role in the stabilization of the transition state. Charge neutralization in the S5/S6 region increased the K(m) values of peptide substrates in a manner that depended on the presence of negatively charged residues in the P5 and P6 positions. K(i) values of hexapeptide acids spanning P6-P1 (product inhibitors) were affected by charge neutralization in both the active site region and the S5/S6 region. Pre-steady-state kinetic data showed that the electrostatic surface potential is used by this enzyme to enhance collision rates between peptidic ligands and the active site. Calculations of the interaction energies of protease-substrate or protease-inhibitor complexes showed that electrostatic interaction energies oppose the formation of a tightly bound complex due to an unfavorable change in the desolvation energy. We propose that desolvation costs are minimized by avoiding the formation of individual ion pair interactions through the use of clusters of positively charged residues in the generation of local electrostatic potentials.     

Structural determinants of trypsin affinity and specificity for cationic inhibitors

The binding free energies of four inhibitors to bovine beta-trypsin are calculated. The inhibitors use either ornithine, lysine, or arginine to bind to the S1 specificity site. The electrostatic contribution to binding free energy is calculated by solving the finite difference Poisson-Boltzmann equation, the contribution of nonpolar interactions is calculated using a free energy-surface area relationship and the loss of conformational entropy is estimated both for trypsin and ligand side chains. Binding free energy values are of a reasonable magnitude and the relative affinity of the four inhibitors for trypsin is correctly predicted. Electrostatic interactions are found to oppose binding in all cases. However, in the case of ornithine- and lysine-based inhibitors, the salt bridge formed between their charged group and the partially buried carboxylate of Asp189 is found to stabilize the complex. Our analysis reveals how the molecular architecture of the trypsin binding site results in highly specific recognition of substrates and inhibitors. Specifically, partially burying Asp189 in the inhibitor-free enzyme decreases the penalty for desolvation of this group upon complexation. Water molecules trapped in the binding interface further stabilize the buried ion pair, resulting in a favorable electrostatic contribution of the ion pair formed with ornithine and lysine side chains. Moreover, all side chains that form the trypsin specificity site are partially buried, and hence, relatively immobile in the inhibitor-free state, thus reducing the entropic cost of complexation. The implications of the results for the general problem of recognition and binding are considered. A novel finding in this regard is that like charged molecules can have electrostatic contributions to binding that are more favorable than oppositely charged molecules due to enhanced interactions with the solvent in the highly charged complex that is formed.     

A hierarchical method for generating low-energy conformers of a protein-ligand complex

A novel dynamical protocol for finding the low-energy conformations of a protein-ligand complex is described. The energy functions examined consist of an empirical force field with four different dielectric screening models; the generalized Born/surface area model also is examined. Application of the method to three complexes of known crystal structure provides insights into the energy functions used for selecting low-energy docked conformations and into the structure of the binding-energy surface. Evidence is presented that the local energy minima of a ligand in a binding site are arranged in a hierarchical fashion. This observation motivates the construction of a hierarchical docking algorithm that substantially enriches the population of ligand conformations close to the crystal conformation. The algorithm is also adapted to permit docking into a flexible binding site and preliminary tests of this method are presented. Proteins 33:475–495, 1998. © 1998 Wiley-Liss, Inc.     

Scoring a diverse set of high-quality docked conformations: a metascore based on electrostatic and desolvation interactions

Predicting protein-protein interactions involves sampling and scoring docked conformations. Barring some large structural rearrangement, rapidly sampling the space of docked conformations is now a real possibility, and the limiting step for the successful prediction of protein interactions is the scoring function used to reduce the space of conformations from billions to a few, and eventually one high affinity complex. An atomic level free-energy scoring function that estimates in units of kcal/mol both electrostatic and desolvation interactions (plus van der Waals if appropriate) of protein-protein docked conformations is used to rerank the blind predictions (860 in total) submitted for six targets to the community-wide Critical Assessment of PRediction of Interactions (CAPRI; http://capri.ebi.ac.uk). We found that native-like models often have varying intermolecular contacts and atom clashes, making unlikely that one can construct a universal function that would rank all these models as native-like. Nevertheless, our scoring function is able to consistently identify the native-like complexes as those with the lowest free energy for the individual models of 16 (out of 17) human predictors for five of the targets, while at the same time the modelers failed to do so in more than half of the cases. The scoring of high-quality models developed by a wide variety of methods and force fields confirms that electrostatic and desolvation forces are the dominant interactions determining the bound structure. The CAPRI experiment has shown that modelers can predict valuable models of protein-protein complexes, and improvements in scoring functions should soon solve the docking problem for complexes whose backbones do not change much upon binding. A scoring server and programs are available at http://structure.pitt.edu.     

Methodology for protein-ligand binding studies: application to a model for drug resistance, the HIV/FIV protease system.

A protocol for the rapid energetic analysis of protein-ligand complexes has been developed. This protocol involves the generation of protein-ligand complex ensembles followed by an analysis of the binding free energy components. We apply this methodology toward understanding the origin of binding specificity within the human immunodeficiency virus/feline immunodeficiency virus (HIV/FIV) protease system, a model system for drug resistance studies. A distinct difference in the internal strain of an inhibitor within each protein environment clearly favors the HIV protease complex, as observed experimentally. Our analysis also predicts that residues within the S2-S3 pockets of the FIV protease active site are responsible for this strain. Close examination of the active site residue contributions to interaction energy and desolvation energy identifies specific amino acids that may also play a role in determining the binding preferences of these two enzymes. Proteins 1999;36:318-331.     

Optimal clustering for detecting near-native conformations in protein docking

Clustering is one of the most powerful tools in computational biology. The conventional wisdom is that events that occur in clusters are probably not random. In protein docking, the underlying principle is that clustering occurs because long-range electrostatic and/or desolvation forces steer the proteins to a low free-energy attractor at the binding region. Something similar occurs in the docking of small molecules, although in this case shorter-range van der Waals forces play a more critical role. Based on the above, we have developed two different clustering strategies to predict docked conformations based on the clustering properties of a uniform sampling of low free-energy protein-protein and protein-small molecule complexes. We report on significant improvements in the automated prediction and discrimination of docked conformations by using the cluster size and consensus as a ranking criterion. We show that the success of clustering depends on identifying the appropriate clustering radius of the system. The clustering radius for protein-protein complexes is consistent with the range of the electrostatics and desolvation free energies (i.e., between 4 and 9 Angstroms); for protein-small molecule docking, the radius is set by van der Waals interactions (i.e., at approximately 2 Angstroms). Without any a priori information, a simple analysis of the histogram of distance separations between the set of docked conformations can evaluate the clustering properties of the data set. Clustering is observed when the histogram is bimodal. Data clustering is optimal if one chooses the clustering radius to be the minimum after the first peak of the bimodal distribution. We show that using this optimal radius further improves the discrimination of near-native complex structures.     

On the role of electrostatic interactions in the design of protein-protein interfaces

Here, the methods of continuum electrostatics are used to investigate the contribution of electrostatic interactions to the binding of four protein-protein complexes; barnase-barstar, human growth hormone and its receptor, subtype N9 influenza virus neuraminidase and the NC41 antibody, the Ras binding domain (RBD) of kinase cRaf and a Ras homologue Rap1A. In two of the four complexes electrostatics are found to strongly oppose binding (hormone-receptor and neuraminidase-antibody complexes), in one case the net effect is close to zero (barnase-barstar) and in one case electrostatics provides a significant driving force favoring binding (RBD-Rap1A). In order to help understand the wide range of electrostatic contributions that were calculated, the electrostatic free energy was partitioned into contributions of individual charged and polar residues, salt bridges and networks involving salt bridges and hydrogen bonds. Although there is no one structural feature that accounts for the differences between the four interfaces, the extent to which the desolvation of buried charges is compensated by the formation of hydrogen bonds and ion pairs appears to be an important factor. Structural features that are correlated with contribution of an individual residue to stability are also discussed. These include partial burial of a charged group in the free monomer, the formation of networks involving charged and polar amino acids, and the formation of partially exposed ion-pairs. The total electrostatic contribution to binding is found to be inversely correlated with buried total and non-polar surface area. This suggests that different interfaces can be designed to exploit electrostatic and hydrophobic forces in very different ways.     

Modeling side-chains using molecular dynamics improve recognition of binding region in CAPRI targets

The CAPRI-II experiment added an extra level of complexity to the problem of predicting protein-protein interactions by including 5 targets for which participants had to build or complete the 3-dimensional (3D) structure of either the receptor or ligand based on the structure of a close homolog. In this article, we describe how modeling key side-chains using molecular dynamics (MD) in explicit solvent improved the recognition of the binding region of a free energy- based computational docking method. In particular, we show that MD is able to predict with relatively high accuracy the rotamer conformation of the anchor side-chains important for molecular recognition as suggested by Rajamani et al. (Proc Natl Acad Sci USA 2004;101:11287-11292). As expected, the conformations are some of the most common rotamers for the given residue, while latch side-chains that undergo induced fit upon binding are forced into less common conformations. Using these models as starting conformations in conjunction with the rigid-body docking server ClusPro and the flexible docking algorithm SmoothDock, we produced valuable predictions for 6 of the 9 targets in CAPRI-II, missing only the 3 targets that underwent significant structural rearrangements upon binding. We also show that our free energy- based scoring function, consisting of the sum of van der Waals, Coulombic electrostatic with a distance-dependent dielectric, and desolvation free energy successfully discriminates the nativelike conformation of our submitted predictions. The latter emphasizes the critical role that thermodynamics plays on our methodology, and validates the generality of the algorithm to predict protein interactions.     

Allosteric discrimination at the NADH/ADP regulatory site of glutamate dehydrogenase

Glutamate dehydrogenase (GDH) is a target for treating insulin‐related disorders, such as hyperinsulinism hyperammonemia syndrome. Modeling native ligand binding has shown promise in designing GDH inhibitors and activators. Our computational investigation of the nicotinamide adenine diphosphate hydride (NADH)/adenosine diphosphate (ADP) site presented in this paper provides insight into the opposite allosteric effects induced at a single site of binding inhibitor NADH versus activator ADP to GDH. The computed binding free‐energy difference between NADH and ADP using thermodynamic integration is ?0.3 kcal/mol, which is within the ?0.275 and ?1.7 kcal/mol experimental binding free‐energy difference range. Our simulations show an interesting model of ADP with dissimilar binding conformations at each NADH/ADP site in the GDH trimer, which explains the poorly understood strong binding but weak activation shown in experimental studies. In contrast, NADH showed similar inhibitory binding conformations at each NADH/ADP site. The structural analysis of the important residues in the NADH/ADP binding site presented in this paper may provide potential targets for mutation studies for allosteric drug design.     

Scoring docked conformations generated by rigid-body protein-protein docking

Rigid-body methods, particularly Fourier correlation techniques, are very efficient for docking bound (co-crystallized) protein conformations using measures of surface complementarity as the target function. However, when docking unbound (separately crystallized) conformations, the method generally yields hundreds of false positive structures with good scores but high root mean square deviations (RMSDs). This paper describes a two-step scoring algorithm that can discriminate near-native conformations (with less than 5 A RMSD) from other structures. The first step includes two rigid-body filters that use the desolvation free energy and the electrostatic energy to select a manageable number of conformations for further processing, but are unable to eliminate all false positives. Complete discrimination is achieved in the second step that minimizes the molecular mechanics energy of the retained structures, and re-ranks them with a combined free-energy function which includes electrostatic, solvation, and van der Waals energy terms. After minimization, the improved fit in near-native complex conformations provides the free-energy gap required for discrimination. The algorithm has been developed and tested using docking decoys, i.e., docked conformations generated by Fourier correlation techniques. The decoy sets are available on the web for testing other discrimination procedures. Proteins 2000;40:525-537.     

Calculating proton uptake/release and binding free energy taking into account ionization and conformation changes induced by protein-inhibitor association: application to plasmepsin, cathepsin D and endothiapepsin-pepstatin complexes

The protein-inhibitor binding energies of enzymes are often pH dependent, and binding induces either proton uptake or proton release. The proton uptake/release and the binding energy for three complexes with available experimental data were numerically studied: pepstatin-cathepsin D, pepstatin-plasmepsin II and pepstatin-endothiapepsin. Very good agreement with the experimental data was achieved when conformational changes were taken into account. The role of the desolvation energy and the conformational changes was revealed by modeling the complex, the separated molecules in the complex conformation and the free molecules. It was shown that the conformational changes induced by the complex formation are as important for the proton transfer as the loss of solvation energy caused by the burial of interface residues. The residues responsible for the proton transfer were identified and their contribution to the proton uptake/release calculated. These residues were found to be scattered along the whole protein rather than being localized only at the active site. In the case of cathepsin D, these residues were found to be highly conserved among the cathepsin D sequences of other species. It was shown that conformation and ionization changes induced by the complex formation are critical for the correct calculation of the binding energy. Taking into account the electrostatics and the van der Waals (vdW) energies within the Boltzmann distribution of energies and allowing ionization and conformation changes to occur makes the calculated binding energy more realistic and closer to the experimental value. The interplay between electrostatic and vdW forces makes the pH dependence of the binding energy smoother, because the vdW force acts in reaction to the changes of the electrostatic energy. It was found that a small fraction of the ionizable groups remain uncharged in both the free and complexed molecules. The sequence and structural position of these groups aligns well within the three proteases, suggesting that these may have specific role.     

Ligand entry into the calyx of β‐lactoglobulin

Although the thermodynamic principles that control the binding of drug molecules to their protein targets are well understood, the detailed process of how a ligand reaches a protein binding site has been an intriguing question over decades. The short time interval between the encounter between a ligand and its receptor to the formation of the stable complex has prevented experimental observations. Bovine β‐lactoglobulin (βlg) is a lipocalin member that carries fatty acids (FAs) and other lipids in the cellular environment. Βlg accommodates a FA molecule in its highly hydrophobic cavity and exhibits the capability of recognizing a wide variety of hydrophobic ligands. To elucidate the ligand entry process on βlg, we report molecular dynamics simulations of the encounter between palmitate (PA) or laurate (LA) and βlg. Our results show that residues localized in loops at the cavity entrance play an important role in the ligand penetration process. Analysis of the short‐term interaction energies show that the forces operating on the systems lead to average conformations very close to the crystallographic holo‐forms. Whereas the binding free energy analysis using the molecular mechanics Generalized Born surface area method shows that these conformations were thermodynamically favorable. © 2013 Wiley Periodicals, Inc. Biopolymers 101: 744–757, 2014.