Identification of protein-protein interaction sites from docking energy landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.     

Grafting of protein-protein interaction epitope

Transferring the biological function of one protein to another is a key issue in understanding the structure and function relationship of proteins. We have developed a strategy for grafting protein-protein interaction epitopes. As a first step, residues at the interface of the ligand protein which strongly interact with the receptor protein were identified. Then protein scaffolds were docked onto receptor protein based on geometric complementarity. Only high docking score matches were saved. For each saved match, the scaffold protein was accepted if it had suitable positions for grafting key interaction residues of the ligand protein. These candidate residues were mutated to corresponding residues in the ligand protein at each relevant position and the mutated scaffold protein was co-minimized with receptor protein. Finally, the minimized complexes were evaluated by a scoring function deduced from statistical analysis of rigid binding data sets. As a test case, the binding epitope of barstar, the inhibitor of barnase, was grafted onto smaller proteins. Pheromone Er-1 (PDB entry 1erc) has been found to be a good scaffold. The calculated binding free energy for mutated Pheromone Er-1 is equivalent to that of barstar.     

Incorporating replacement free energy of binding‐site waters in molecular docking

Binding‐site water molecules play a crucial role in protein‐ligand recognition, either being displaced upon ligand binding or forming water bridges to stabilize the complex. However, rigorously treating explicit binding‐site waters is challenging in molecular docking, which requires to fully sample ensembles of waters and to consider the free energy cost of replacing waters. Here, we describe a method to incorporate structural and energetic properties of binding‐site waters into molecular docking. We first developed a solvent property analysis (SPA) program to compute the replacement free energies of binding‐site water molecules by post‐processing molecular dynamics trajectories obtained from ligand‐free protein structure simulation in explicit water. Next, we implemented a distance‐dependent scoring term into DOCK scoring function to take account of the water replacement free energy cost upon ligand binding. We assessed this approach in protein targets containing important binding‐site waters, and we demonstrated that our approach is reliable in reproducing the crystal binding geometries of protein‐ligand‐water complexes, as well as moderately improving the ligand docking enrichment performance. In addition, SPA program (free available to academic users upon request) may be applied in identifying hot‐spot binding‐site residues and structure‐based lead optimization. Proteins 2014; 82:1765–1776. © 2014 Wiley Periodicals, Inc.     

Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.     

Potential of mean force for protein-protein interaction studies.

Calculating protein-protein interaction energies is crucial for understanding protein-protein associations. On the basis of the methodology of mean-field potential, we have developed an empirical approach to estimate binding free energy for protein-protein interactions. This knowledge-based approach has been used to derive distance-dependent free energies of protein complexes from a nonredundant training set in the Protein Data Bank (PDB), with a careful treatment of homology. We calculate atom pair potentials for 16 pair interactions, which can reflect the importance of hydrophobic interactions and specific hydrogen-bonding interactions. The derived potentials for hydrogen-bonding interactions show a valley of favorable interactions at a distance of approximately 3 A, corresponding to that of an established hydrogen bond. For the test set of 28 protein complexes, the calculated energies have a correlation coefficient of 0.75 compared with experimental binding free energies. The performance of the method in ranking the binding energies of different protein-protein complexes shows that the energy estimation can be applied to value binding free energies for protein-protein associations.     

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.     

Constructing ensembles of flexible fragments in native proteins by iterative stochastic elimination is relevant to protein-protein interfaces

We investigate the extent to which ensembles of flexible fragments (FF), generated by our loop conformational search method, include conformations that are near experimental and reflect conformational changes that these FFs undergo when binary protein-protein complexes are formed. Twenty-eight FFs, which are located in protein-protein interfaces and have different conformations in the bound structure (BS) and unbound structure (UbS) were extracted. The conformational space of these fragments in the BS and UbS was explored with our method which is based on the iterative stochastic elimination (ISE) algorithm. Conformational search of BSs generated bound ensembles and conformational search of UbSs produced unbound ensembles. ISE samples conformations near experimental (less than 1.05 A root mean square deviation, RMSD) for 51 out of the 56 examined fragments in the bound and unbound ensembles. In 14 out of the 28 unbound fragments, it also samples conformations within 1.05 A from the BS in the unbound ensemble. Sampling the bound conformation in the unbound ensemble demonstrates the potential biological relevance of the predicted ensemble. The 10 lowest energy conformations are the best choice for docking experiments, compared with any other 10 conformations of the ensembles. We conclude that generating conformational ensembles for FFs with ISE is relevant to FF conformations in the UbS and BS. Forming ensembles of the isolated proteins with our method prior to docking represents more comprehensively their inherent flexibility and is expected to improve docking experiments compared with results obtained by docking only UbSs.     

Probing the effect of point mutations at protein-protein interfaces with free energy calculations

We have studied the effect of point mutations of the primary binding residue (P1) at the protein-protein interface in complexes of chymotrypsin and elastase with the third domain of the turkey ovomucoid inhibitor and in trypsin with the bovine pancreatic trypsin inhibitor, using molecular dynamics simulations combined with the linear interaction energy (LIE) approach. A total of 56 mutants have been constructed and docked into their host proteins. The free energy of binding could be reliably calculated for 52 of these mutants that could unambiguously be fitted into the binding sites. We find that the predicted binding free energies are in very good agreement with experimental data with mean unsigned errors between 0.50 and 1.03 kcal/mol. It is also evident that the standard LIE model used to study small drug-like ligand binding to proteins is not suitable for protein-protein interactions. Three different LIE models were therefore tested for each of the series of protein-protein complexes included, and the best models for each system turn out to be very similar. The difference in parameterization between small drug-like compounds and protein point mutations is attributed to the preorganization of the binding surface. Our results clearly demonstrate the potential of free energy calculations for probing the effect of point mutations at protein-protein interfaces and for exploring the principles of specificity of hot spots at the interface.     

Molecular dynamics-solvated interaction energy studies of protein-protein interactions: the MP1-p14 scaffolding complex

Using the MP1-p14 scaffolding complex from the mitogen-activated protein kinase signaling pathway as model system, we explored a structure-based computational protocol to probe and characterize binding affinity hot spots at protein-protein interfaces. Hot spots are located by virtual alanine-scanning consensus predictions over three different energy functions and two different single-structure representations of the complex. Refined binding affinity predictions for select hot-spot mutations are carried out by applying first-principle methods such as the molecular mechanics generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) to the molecular dynamics (MD) trajectories for mutated and wild-type complexes. Here, predicted hot-spot residues were actually mutated to alanine, and crystal structures of the mutated complexes were determined. Two mutated MP1-p14 complexes were investigated, the p14(Y56A)-mutated complex and the MP1(L63A,L65A)-mutated complex. Alternative ways to generate MD ensembles for mutant complexes, not relying on crystal structures for mutated complexes, were also investigated. The SIE function, fitted on protein-ligand binding affinities, gave absolute binding affinity predictions in excellent agreement with experiment and outperformed standard MM-GBSA predictions when tested on the MD ensembles of Ras-Raf and Ras-RalGDS protein-protein complexes. For wild-type and mutant MP1-p14 complexes, SIE predictions of relative binding affinities were supported by a yeast two-hybrid assay that provided semiquantitative relative interaction strengths. Results on the MP1-mutated complex suggested that SIE predictions deteriorate if mutant MD ensembles are approximated by just mutating the wild-type MD trajectory. The SIE data on the p14-mutated complex indicated feasibility for generating mutant MD ensembles from mutated wild-type crystal structure, despite local structural differences observed upon mutation. For energetic considerations, this would circumvent costly needs to produce and crystallize mutated complexes. The sensitized protein-protein interface afforded by the p14(Y56A) mutation identified here has practical applications in screening-based discovery of first-generation small-molecule hits for further development into specific modulators of the mitogen-activated protein kinase signaling pathway.     

Structural adaptation of extreme halophilic proteins through decrease of conserved hydrophobic contact surface

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.     

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.     

Statistics and Physical Origins of pK and Ionization State Changes upon Protein-Ligand Binding

This work investigates statistical prevalence and overall physical origins of changes in charge states of receptor proteins upon ligand binding. These changes are explored as a function of the ligand type (small molecule, protein, and nucleic acid), and distance from the binding region. Standard continuum solvent methodology is used to compute, on an equal footing, pK changes upon ligand binding for a total of 5899 ionizable residues in 20 protein-protein, 20 protein-small molecule, and 20 protein-nucleic acid high-resolution complexes. The size of the data set combined with an extensive error and sensitivity analysis allows us to make statistically justified and conservative conclusions: in 60% of all protein-small molecule, 90% of all protein-protein, and 85% of all protein-nucleic acid complexes there exists at least one ionizable residue that changes its charge state upon ligand binding at physiological conditions (pH = 6.5). Considering the most biologically relevant pH range of 4-8, the number of ionizable residues that experience substantial pK changes (ΔpK > 1.0) due to ligand binding is appreciable: on average, 6% of all ionizable residues in protein-small molecule complexes, 9% in protein-protein, and 12% in protein-nucleic acid complexes experience a substantial pK change upon ligand binding. These changes are safely above the statistical false-positive noise level. Most of the changes occur in the immediate binding interface region, where approximately one out of five ionizable residues experiences substantial pK change regardless of the ligand type. However, the physical origins of the change differ between the types: in protein-nucleic acid complexes, the pK values of interface residues are predominantly affected by electrostatic effects, whereas in protein-protein and protein-small molecule complexes, structural changes due to the induced-fit effect play an equally important role. In protein-protein and protein-nucleic acid complexes, there is a statistically significant number of substantial pK perturbations, mostly due to the induced-fit structural changes, in regions far from the binding interface.     

Geometric and chemical patterns of interaction in protein--ligand complexes and their application in docking

We present a new method for representing the binding site of a protein receptor that allows the use of the DOCK approach to screen large ensembles of receptor conformations for ligand binding. The site points are constructed from templates of what we called "attached points" (ATPTS). Each template (one for each type of amino acid) is composed of a set of representative points that are attached to side-chain and backbone atoms through internal coordinates, carry chemical information about their parent atoms and are intended to cover positions that might be occupied by ligand atoms when complexed to the protein. This method is completely automatic and proved to be extremely fast. With the aim of obtaining an experimental basis for this approach, the Protein Data Bank was searched for proteins in complex with small molecules, to study the geometry of the interactions between the different types of protein residues and the different types of ligand atoms. As a result, well-defined patterns of interaction were obtained for most amino acids. These patterns were then used for constructing a set of templates of attached points, which constitute the core of the ATPTS approach. The quality of the ATPTS representation was demonstrated by using this method, in combination with the DOCK matching and orientation algorithms, to generate correct ligand orientations for >1000 protein--ligand complexes.     

Assessment of blind predictions of protein-protein interactions: current status of docking methods

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional structure is assessed from a first major evaluation of blind predictions. This evaluation was performed as part of a communitywide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Seven newly determined structures of protein-protein complexes were available as targets for this experiment. These were the complexes between a kinase and its protein substrate, between a T-cell receptor beta-chain and a superantigen, and five antigen-antibody complexes. For each target, the predictors were given the experimental structures of the free components, or of one free and one bound component in a random orientation. The structure of the complex was revealed only at the time of the evaluation. A total of 465 predictions submitted by 19 groups were evaluated. These groups used a wide range of algorithms and scoring functions, some of which were completely novel. The quality of the predicted interactions was evaluated by comparing residue-residue contacts and interface residues to those in the X-ray structures and by analyzing the fit of the ligand molecules (the smaller of the two proteins in the complex) or of interface residues only, in the predicted versus target complexes. A total of 14 groups produced predictions, ranking from acceptable to highly accurate for five of the seven targets. The use of available biochemical and biological information, and in one instance structural information, played a key role in achieving this result. It was essential for identifying the native binding modes for the five correctly predicted targets, including the kinase-substrate complex where the enzyme changes conformation on association. But it was also the cause for missing the correct solution for the two remaining unpredicted targets, which involve unexpected antigen-antibody binding modes. Overall, this analysis reveals genuine progress in docking procedures but also illustrates the remaining serious limitations and points out the need for better scoring functions and more effective ways for handling conformational flexibility.     

Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations

Small-molecules that inhibit interactions between specific pairs of proteins have long represented a promising avenue for therapeutic intervention in a variety of settings. Structural studies have shown that in many cases, the inhibitor-bound protein adopts a conformation that is distinct from its unbound and its protein-bound conformations. This plasticity of the protein surface presents a major challenge in predicting which members of a protein family will be inhibited by a given ligand. Here, we use biased simulations of Bcl-2-family proteins to generate ensembles of low-energy conformations that contain surface pockets suitable for small molecule binding. We find that the resulting conformational ensembles include surface pockets that mimic those observed in inhibitor-bound crystal structures. Next, we find that the ensembles generated using different members of this protein family are overlapping but distinct, and that the activity of a given compound against a particular family member (ligand selectivity) can be predicted from whether the corresponding ensemble samples a complementary surface pocket. Finally, we find that each ensemble includes certain surface pockets that are not shared by any other family member: while no inhibitors have yet been identified to take advantage of these pockets, we expect that chemical scaffolds complementing these “distinct” pockets will prove highly selective for their targets. The opportunity to achieve target selectivity within a protein family by exploiting differences in surface fluctuations represents a new paradigm that may facilitate design of family-selective small-molecule inhibitors of protein-protein interactions.     

Identification of an iron-regulated outer membrane protein of Neisseria meningitidis involved in the utilization of hemoglobin complexed to haptoglobin.

Hemoglobin complexed to the plasma protein haptoglobin can be used by Neisseria meningitidis as a source of iron to support growth in vitro. An N meningitidis mutant, DNM2E4, was generated by insertion of the mini-Tn3erm transposon into the gene coding for an 85-kDa iron-regulated outer membrane protein. Membrane proteins prepared from DNM2E4 were identical to those of the wild-type strain except that the 85-kDa protein was not produced. This mutant was unable to use hemoglobin-haptoglobin complexes as an iron source to support growth and was also impaired in the utilization of free hemoglobin. The mutant failed to bind free hemoglobin, hemoglobin-haptoglobin complexes, or apo-haptoglobin in a solid-phase dot blot assay. The 85-kDa protein was affinity purified when hemoglobin-haptoglobin complexes were used as a ligand but was not purified when free hemoglobin was used. We hypothesize that the 85-kDa iron-regulated protein is the hemoglobin-haptoglobin receptor and designate this protein Hpu (for hemoglobin-haptoglobin utilization).     

Construction of protein binding sites in scaffold structures

We have developed a strategy for grafting a protein-protein interface based on the known crystal structure of a native ligand and receptor proteins in a complex. The key interaction residues at the ligand protein binding interface are transferred onto a scaffold protein so that the mutated scaffold protein will bind the receptor protein in the same manner as the ligand protein. First, our method identifies key residues and atoms in the ligand protein, which strongly interact with the receptor protein. Second, this method searches the scaffold protein for combinations of candidate residues, among which the distance between any two candidate residues is similar to that between relevant key interaction residues in the ligand protein. These candidate residues are mutated to key interaction residues in the ligand protein respectively. The scaffold protein is superposed onto the ligand protein based upon the coordinates of corresponding atoms, which are assumed to strongly interact with the receptor protein. Complementarity between scaffold and receptor proteins is evaluated. Scaffold proteins with a low superposing rms difference and high complementary score are accepted for further analysis. Then, the relative position of the scaffold protein is adjusted so that the interfaces between the scaffold and receptor proteins have a reasonable packing density. Other mutations are also considered to reduce the desolvation energy or bad steric contacts. Finally, the scaffold protein is cominimized with the receptor protein and evaluated. To test the method, the binding interface of barstar, the inhibitor of barnase, was grafted onto small proteins. Four scaffold proteins with high complementary scores are accepted.     

Efficient docking of peptides to proteins without prior knowledge of the binding site

Reliability in docking of ligand molecules to proteins or other targets is an important challenge for molecular modeling. Applications of the docking technique include not only prediction of the binding mode of novel drugs, but also other problems like the study of protein-protein interactions. Here we present a study on the reliability of the results obtained with the popular AutoDock program. We have performed systematical studies to test the ability of AutoDock to reproduce eight different protein/ligand complexes for which the structure was known, without prior knowledge of the binding site. More specifically, we look at factors influencing the accuracy of the final structure, such as the number of torsional degrees of freedom in the ligand. We conclude that the Autodock program package is able to select the correct complexes based on the energy without prior knowledge of the binding site. We named this application blind docking, as the docking algorithm is not able to "see" the binding site but can still find it. The success of blind docking represents an important finding in the era of structural genomics.     

Pre-existing soft modes of motion uniquely defined by native contact topology facilitate ligand binding to proteins

Modeling protein flexibility constitutes a major challenge in accurate prediction of protein-ligand and protein-protein interactions in docking simulations. The lack of a reliable method for predicting the conformational changes relevant to substrate binding prevents the productive application of computational docking to proteins that undergo large structural rearrangements. Here, we examine how coarse-grained normal mode analysis has been advantageously applied to modeling protein flexibility associated with ligand binding. First, we highlight recent studies that have shown that there is a close agreement between the large-scale collective motions of proteins predicted by elastic network models and the structural changes experimentally observed upon ligand binding. Then, we discuss studies that have exploited the predicted soft modes in docking simulations. Two general strategies are noted: pregeneration of conformational ensembles that are then utilized as input for standard fixed-backbone docking and protein structure deformation along normal modes concurrent to docking. These studies show that the structural changes apparently "induced" upon ligand binding occur selectively along the soft modes accessible to the protein prior to ligand binding. They further suggest that proteins offer suitable means of accommodating/facilitating the recognition and binding of their ligand, presumably acquired by evolutionary selection of the suitable three-dimensional structure.