Protein-protein interactions: hot spots and structurally conserved residues often locate in complemented pockets that pre-organized in the unbound states: implications for docking

Energetic hot spots account for a significant portion of the total binding free energy and correlate with structurally conserved interface residues. Here, we map experimentally determined hot spots and structurally conserved residues to investigate their geometrical organization. Unfilled pockets are pockets that remain unfilled after protein-protein complexation, while complemented pockets are pockets that disappear upon binding, representing tightly fit regions. We find that structurally conserved residues and energetic hot spots are strongly favored to be located in complemented pockets, and are disfavored in unfilled pockets. For the three available protein-protein complexes with complemented pockets where both members of the complex were alanine-scanned, 62% of all hot spots (DeltaDeltaG>2kcal/mol) are within these pockets, and 60% of the residues in the complemented pockets are hot spots. 93% of all red-hot residues (DeltaDeltaG>/=4kcal/mol) either protrude into or are located in complemented pockets. The occurrence of hot spots and conserved residues in complemented pockets highlights the role of local tight packing in protein associations, and rationalizes their energetic contribution and conservation. Complemented pockets and their corresponding protruding residues emerge among the most important geometric features in protein-protein interactions. By screening the solvent, this organization shields backbone hydrogen bonds and charge-charge interactions. Complemented pockets often pre-exist binding. For 18 protein-protein complexes with complemented pockets whose unbound structures are available, in 16 the pockets are identified to pre-exist in the unbound structures. The root-mean-squared deviations of the atoms lining the pockets between the bound and unbound states is as small as 0.9A, suggesting that such pockets constitute features of the populated native state that may be used in docking.     

Hot regions in protein--protein interactions: the organization and contribution of structurally conserved hot spot residues

Structurally conserved residues at protein-protein interfaces correlate with the experimental alanine-scanning hot spots. Here, we investigate the organization of these conserved, computational hot spots and their contribution to the stability of protein associations. We find that computational hot spots are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions. Within the dense clusters, they form a network of interactions and consequently their contributions to the stability of the complex are cooperative; however the contributions of independent clusters are additive. This suggests that the binding free energy is not a simple summation of the single hot spot residue contributions. As expected, around the hot spot residues we observe moderately conserved residues, further highlighting the crucial role of the conserved interactions in the local densely packed environment. The conserved occurrence of these organizations suggests that they are advantageous for protein-protein associations. Interestingly, the total number of hydrogen bonds and salt bridges contributed by hot spots is as expected. Thus, H-bond forming residues may use a "hot spot for water exclusion" mechanism. Since conserved residues are located within highly packed regions, water molecules are easily removed upon binding, strengthening electrostatic contributions of charge-charge interactions. Hence, the picture that emerges is that protein-protein associations are optimized locally, with the clustered, networked, highly packed structurally conserved residues contributing dominantly and cooperatively to the stability of the complex. When addressing the crucial question of "what are the preferred ways of proteins to associate", these findings point toward a critical involvement of hot regions in protein-protein interactions.     

Protein-protein interactions; coupling of structurally conserved residues and of hot spots across interfaces. Implications for docking

Hot spot residues contribute dominantly to protein-protein interactions. Statistically, conserved residues correlate with hot spots, and their occurrence can distinguish between binding sites and the remainder of the protein surface. The hot spot and conservation analyses have been carried out on one side of the interface. Here, we show that both experimental hot spots and conserved residues tend to couple across two-chain interfaces. Intriguingly, the local packing density around both hot spots and conserved residues is higher than expected. We further observe a correlation between local packing density and experimental deltadeltaG. Favorable conserved pairs include Gly coupled with aromatics, charged and polar residues, as well as aromatic residue coupling. Remarkably, charged residue couples are underrepresented. Overall, protein-protein interactions appear to consist of regions of high and low packing density, with the hot spots organized in the former. The high local packing density in binding interfaces is reminiscent of protein cores.     

Protein hot spots: the islands of stability

Understanding the structural basis of protein-protein interactions (PPIs) may shed light on the organization and functioning of signal transduction and metabolic networks and may assist in structure-based design of ligands (drugs) targeting protein-protein interfaces. The residues at the bimolecular interface, designated as the hot spots, contribute most of the free binding energy of PPI. To date, there is no conclusive atomistic explanation for the unique functional properties of the hot spots. We hypothesized that backbone compliance may play a role in protein-protein recognition and in the mechanism of binding of small-molecule compounds to protein surfaces. We used a steered molecular dynamics simulation to explore the compliance properties of the backbone of surface-exposed residues in several model proteins: interleukin-2, mouse double minute protein 2 and proliferating cell nuclear antigen. We demonstrated that protein surfaces exhibit distinct patterns in which highly immobile residues form defined clusters ("stability patches") alternating with areas of moderate to high mobility. These "stability patches" tend to localize in functionally important regions involved in protein-protein recognition. We propose a mechanism by which the distinct structural organization of the hot spots may contribute to their role in mediating PPI and facilitating binding of structurally diverse small-molecule compounds to protein surfaces.     

Small-world network approach to identify key residues in protein-protein interaction

We show that protein complexes can be represented as small-world networks, exhibiting a relatively small number of highly central amino-acid residues occurring frequently at protein-protein interfaces. We further base our analysis on a set of different biological examples of protein-protein interactions with experimentally validated hot spots, and show that 83% of these predicted highly central residues, which are conserved in sequence alignments and nonexposed to the solvent in the protein complex, correspond to or are in direct contact with an experimentally annotated hot spot. The remaining 17% show a general tendency to be close to an annotated hot spot. On the other hand, although there is no available experimental information on their contribution to the binding free energy, detailed analysis of their properties shows that they are good candidates for being hot spots. Thus, highly central residues have a clear tendency to be located in regions that include hot spots. We also show that some of the central residues in the protein complex interfaces are central in the monomeric structures before dimerization and that possible information relating to hot spots of binding free energy could be obtained from the unbound structures.     

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background  

Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.     

Magnitude of the hydrophobic effect at central versus peripheral sites in protein-protein interfaces

Hydrophobic interactions are essential for stabilizing protein-protein complexes, whose interfaces generally consist of a central cluster of hot spot residues surrounded by less important peripheral residues. According to the O-ring hypothesis, a condition for high affinity binding is solvent exclusion from interacting residues. This hypothesis predicts that the hydrophobicity at the center is significantly greater than at the periphery, which we estimated at 21 cal mol(-1) A(-2). To measure the hydrophobicity at the center, structures of an antigen-antibody complex where a buried phenylalanine was replaced by smaller hydrophobic residues were determined. By correlating structural changes with binding free energies, we estimate the hydrophobicity at this central site to be 46 cal mol(-1) A(-2), twice that at the periphery. This context dependence of the hydrophobic effect explains the clustering of hot spots at interface centers and has implications for hot spot prediction and the design of small molecule inhibitors.     

Numerical characteristics of word frequencies and their application to dissimilarity measure for sequence comparison

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids.     

Conservation of polar residues as hot spots at protein interfaces

A number of studies have addressed the question of which are the critical residues at protein-binding sites. These studies examined either a single or a few protein-protein interfaces. The most extensive study to date has been an analysis of alanine-scanning mutagenesis. However, although the total number of mutations was large, the number of protein interfaces was small, with some of the interfaces closely related. Here we show that although overall binding sites are hydrophobic, they are studded with specific, conserved polar residues at specific locations, possibly serving as energy "hot spots." Our results confirm and generalize the alanine-scanning data analysis, despite its limited size. Previously Trp, Arg, and Tyr were shown to constitute energetic hot spots. These were rationalized by their polar interactions and by their surrounding rings of hydrophobic residues. However, there was no compelling reason as to why specifically these residues were conserved. Here we show that other polar residues are similarly conserved. These conserved residues have been detected consistently in all interface families that we have examined. Our results are based on an extensive examination of residues which are in contact across protein interfaces. We utilize all clustered interface families with at least five members and with sequence similarity between the members in the range of 20-90%. There are 11 such clustered interface families, comprising a total of 97 crystal structures. Our three-dimensional superpositioning analysis of the occurrences of matched residues in each of the families identifies conserved residues at spatially similar environments. Additionally, in enzyme inhibitors, we observe that residues are more conserved at the interfaces than at other locations. On the other hand, antibody-protein interfaces have similar surface conservation as compared to their corresponding linear sequence alignment, consistent with the suggestion that evolution has optimized protein interfaces for function.     

An automated decision-tree approach to predicting protein interaction hot spots

Protein-protein interactions can be altered by mutating one or more "hot spots," the subset of residues that account for most of the interface's binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge-based models that improve the ability to predict hot spots: K-FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K-CON uses biochemical contact features. The combined K-FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta-Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta-Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein-2 (BMP-2)/BMP receptor-type I (BMPR-IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface.     

Superadditive and subadditive effects of "hot spot" mutations within the interfaces of placental ribonuclease inhibitor with angiogenin and ribonuclease A.

Previous single-site mutagenesis studies on the complexes of ribonuclease inhibitor (RI) with angiogenin (Ang) and RNase A suggested that in both cases a substantial fraction of the binding energy is concentrated within one small part of the crystallographically observed interface, involving RI residues 434-438. Such energetic "hot spots" are common in protein-protein complexes, but their physical meaning is generally unclear. Here we have investigated this question by examining the detailed interactions within the RI.ligand hot spots and the extent to which they function independently. The effects of Phe versus Ala substitutions show that the key residue Tyr434 interacts with both ligands primarily through its phenyl ring; for Tyr437, the OH group forms the important contacts with RNase A, whereas the phenyl group interacts with Ang. Kinetic characterization of complexes containing multiple substitutions reveals striking, but distinctive, cooperativity in the interactions of RI with the two ligands. The losses in binding energy for the RNase complex associated with replacements of Tyr434 and Asp435, and Tyr434 and Tyr437, are markedly less than additive (i.e., by 2.4 and 1.3 kcal/mol, respectively). In contrast, the energetic effects of the 434 and 435, and 434 and 437, substitution pairs on binding of Ang are fully additive and 2.5 kcal/mol beyond additive, respectively. Superadditivities (0.9-2.4 kcal/mol) are also observed for several multisite replacements involving these inhibitor residues and two Ang residues, Arg5 and Lys40, from this part of the interface. Consequently, the decreases in binding energy for some triple-variant complexes are as large as 8.5-10.1 kcal/mol (compared to a total DeltaG of -21.0 kcal/mol for the wild-type complex). Potential explanations for these functional couplings, many of which occur over distances of >13 A and are not mediated by direct or triangulated contacts, are proposed. These findings show that the basis for the generation of hot spots can be complex, and that these sites can assume significantly more (as with Ang) or less (as with RNase) importance than indicated from the effects of single-site mutations.     

Computational Mapping of Anchoring Spots on Protein Surfaces

Protein-protein and protein-peptide interactions are often controlled by few strong contacts that involve hot spot residues. Computational detection of such contacts, termed here anchoring spots, is important for understanding recognition processes and for predicting interactions; it is an essential step in designing interaction interfaces and therapeutic agents. We describe ANCHORSMAP, an algorithm for computational mapping of amino acid side chains on protein surfaces. The algorithm consists of two stages: A geometry based stage (LSMdet), in which sub-pockets adequate for binding single side chains are detected and amino acid probes are scattered near them, and an energy based stage in which optimal positions of the probes are determined through repeated energy minimization and clustering of nearby poses and their ΔG are calculated. ANCHORSMAP employs a new function for ΔG calculations, which is specifically designed for the context of protein-protein recognition by introducing a correction in the electrostatic energy term that compensates for the dielectric shielding exerted by a hypothetical protein bound to the probe.The algorithm successfully detects known anchoring sites and accurately positions the probes. The calculated ΔG rank high the correct anchoring spots in maps produced for unbound proteins. We find that Arg, Trp, Glu and Tyr, which are favorite hot spot residues, are also more selective of their binding environment. The usefulness of anchoring spots mapping is demonstrated by detecting the binding surfaces in the protein-protein complex barnase/barstar and the protein-peptide complex kinase/PKI, and by identifying phenylalanine anchoring sites on the surface of the nuclear transporter NTF2, C-terminus anchors on PDZ domains and phenol anchors on thermolysin. Finally, we discuss the role of anchoring spots in molecular recognition processes.     

SemiHS: an iterative semi-supervised approach for predicting protein-protein interaction hot spots

Protein-protein interaction hot spots, as revealed by alanine scanning mutagenesis, make dominant contributions to the free energy of binding. Since mutagenesis experiments are expensive and time-consuming, the development of computational methods to identify hot spots is becoming increasingly important. In this study, by using a new combination of sequence, structure and energy features, we propose an iterative semi-supervised algorithm, SemiHS, to incorporate unlabeled data to improve the accuracy of hot spots prediction when sufficient training data is un-available and to overcome the imbalanced data problem. We evaluate the predictive power of SemiHS on a labeled set of 265 alanine-mutated interface residues in 17 complexes and a large unlabeled set of 2465 interface residues with 10-fold cross validation, and get an AUC score of 0.85, with a sensitivity of 0.70 and a specificity of 0.87, which are better than those of the existing methods. Moreover, we validate the proposed method by an independent test and obtain encouraging results.     

Protein subunit interfaces: a statistical analysis of hot spots in Sm proteins

The distinguishing property of Sm protein associations is very high stability. In order to understand this property, we analyzed the interfaces and compared the properties of Sm protein interfaces with those of a test set, the Binding Interface Database (BID). The comparison revealed that the main differences between the interfaces of Sm proteins and those of the BID set are the content of charged residues, the coordination numbers of the residues, knowledge-based pair potentials, and the conservation scores of hot spots. In Sm proteins, the interfaces have more hydrophobic and fewer charged residues than the surfaces, which is also the case for the BID test set and other proteins. However, in the interfaces, the content of charged residues in Sm proteins (26%) is substantially larger than that in the BID set (22%). Hot spots are residues that make up a small fraction of the interfaces, but they contribute most of the binding energy. These residues are critical to protein–protein interactions. Analyses of knowledge-based pair potentials of hot spot and non-hot spot residues in Sm proteins show that they are significantly different; their mean values are 31.5 and 11.3, respectively. In the BID set, this difference is smaller; in this case, the mean values for hot spot and non-hot spot residues are 20.7 and 12.4, respectively. Hence, the pair potentials of hot spots differ significantly for the Sm and BID data sets. In the interfaces of Sm proteins, the amino acids are tightly packed, and the coordination numbers are larger in Sm proteins than in the BID set for both hot spots and non-hot spots. At the same time, the coordination numbers are higher for hot spots; the average coordination number of the hot spot residues in Sm proteins is 7.7, while it is 6.1 for the non-hot spot residues. The difference in the calculated average conservation score for hot spots and non-hot spots in Sm proteins is significantly larger than it is in the BID set. In Sm proteins, the average conservation score for the hot spots is 7.4. Hot spots are surrounded by residues that are moderately conserved (5.9). The average conservation score for the other interface residues is 5.6. The conservation scores in the BID set do not show a significant distinction between hot and non-hot spots: the mean values for hot and non-hot spot residues are 5.5 and 5.2, respectively. These data show that structurally conserved residues and hot spots are significantly correlated in Sm proteins.     

A feature-based approach to modeling protein–protein interaction hot spots

Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to π–related interactions, especially π · · · π interactions.     

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.     

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.     

Structural consequences of mutations in interfacial Tyr residues of a protein antigen-antibody complex. The case of HyHEL-10-HEL

Tyrosine is an important amino acid in protein-protein interaction hot spots. In particular, many Tyr residues are located in the antigen-binding sites of antibodies and endow high affinity and high specificity to these antibodies. To investigate the role of interfacial Tyr residues in protein-protein interactions, we performed crystallographic studies and thermodynamic analyses of the interaction between hen egg lysozyme (HEL) and the anti-HEL antibody HyHEL-10 Fv fragment. HyHEL-10 has six Tyr residues in its antigen-binding site, which were systematically mutated to Phe and Ala using site-directed mutagenesis. The crystal structures revealed several critical roles for these Tyr residues in the interaction between HEL and HyHEL-10 as follows: 1) the aromatic ring of Tyr-50 in the light chain (LTyr-50) was important for the correct ternary structure of variable regions of the immunoglobulin light chain and heavy chain and of HEL; 2) deletion of the hydroxyl group of Tyr-50 in the heavy chain (HTyr-50) resulted in structural changes in the antigen-antibody interface; and 3) the side chains of HTyr-33 and HTyr-53 may help induce fitting of the antibody to the antigen. Hot spot Tyr residues may contribute to the high affinity and high specificity of the antigen-antibody interaction through a diverse set of structural and thermodynamic interactions.     

Complementarity of stability patches at the interfaces of protein complexes: Implication for the structural organization of energetic hot spots

A rational design of protein complexes with defined functionalities and of drugs aimed at disrupting protein–protein interactions requires fundamental understanding of the mechanisms underlying the formation of specific protein complexes. Efforts to develop efficient small‐molecule or protein‐based binders often exploit energetic hot spots on protein surfaces, namely, the interfacial residues that provide most of the binding free energy in the complex. The molecular basis underlying the unusually high energy contribution of the hot spots remains obscure, and its elucidation would facilitate the design of interface‐targeted drugs. To study the nature of the energetic hot spots, we analyzed the backbone dynamic properties of contact surfaces in several protein complexes. We demonstrate that, in most complexes, the backbone dynamic landscapes of interacting surfaces form complementary “stability patches,” in which static areas from the opposing surfaces superimpose, and that these areas are predominantly located near the geometric center of the interface. We propose that a diminished enthalpy–entropy compensation effect augments the degree to which residues positioned within the complementary stability patches contribute to complex affinity, thereby giving rise to the energetic hot spots. These findings offer new insights into the nature of energetic hot spots and the role that backbone dynamics play in facilitating intermolecular recognition. Mapping the interfacial stability patches may provide guidance for protein engineering approaches aimed at improving the stability of protein complexes and could facilitate the design of ligands that target complex interfaces.