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.     

Geometrically centered region: A “wet” model of protein binding hot spots not excluding water molecules

A protein interface can be as “wet” as a protein surface in terms of the number of immobilized water molecules. This important water information has not been explicitly taken by computational methods to model and identify protein binding hot spots, overlooking the water role in forming interface hydrogen bonds and in filing cavities. Hot spot residues are usually clustered at the core of the protein binding interfaces. However, traditional machine learning methods often identify the hot spot residues individually, breaking the cooperativity of the energetic contribution. Our idea in this work is to explore the role of immobilized water and meanwhile to capture two essential properties of hot spots: the compactness in contact and the far distance from bulk solvent. Our model is named geometrically centered region (GCR). The detection of GCRs is based on novel tripartite graphs, and atom burial levels which are a concept more intuitive than SASA. Applying to a data set containing 355 mutations, we achieved an F measure of 0.6414 when ΔΔG ≥ 1.0 kcal/mol was used to define hot spots. This performance is better than Robetta, a benchmark method in the field. We found that all but only one of the GCRs contain water to a certain degree, and most of the outstanding hot spot residues have water‐mediated contacts. If the water is excluded, the burial level values are poorly related to the ΔΔG, and the model loses its performance remarkably. We also presented a definition for the O‐ring of a GCR as the set of immediate neighbors of the residues in the GCR. Comparative analysis between the O‐rings and GCRs reveals that the newly defined O‐ring is indeed energetically less important than the GCR hot spot, confirming a long‐standing hypothesis. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Engineered oligomerization state of OmpF protein through computational design decouples oligomer dissociation from unfolding

Biogenesis of β-barrel membrane proteins is a complex, multistep, and as yet incompletely characterized process. The bacterial porin family is perhaps the best-studied protein family among β-barrel membrane proteins that allows diffusion of small solutes across the bacterial outer membrane. In this study, we have identified residues that contribute significantly to the protein-protein interaction (PPI) interface between the chains of outer membrane protein F (OmpF), a trimeric porin, using an empirical energy function in conjunction with an evolutionary analysis. By replacing these residues through site-directed mutagenesis either with energetically favorable residues or substitutions that do not occur in natural bacterial outer membrane proteins, we succeeded in engineering OmpF mutants with dimeric and monomeric oligomerization states instead of a trimeric oligomerization state. Moreover, our results suggest that the oligomerization of OmpF proceeds through a series of interactions involving two distinct regions of the extensive PPI interface: two monomers interact to form a dimer through the PPI interface near G19. This dimer then interacts with another monomer through the PPI interface near G135 to form a trimer. We have found that perturbing the PPI interface near G19 results in the formation of the monomeric OmpF only. Thermal denaturation of the designed dimeric OmpF mutant suggests that oligomer dissociation can be separated from the process of protein unfolding. Furthermore, the conserved site near G57 and G59 is important for the PPI interface and might provide the essential scaffold for PPIs.     

How oligomerization contributes to the thermostability of an archaeon protein. Protein L-isoaspartyl-O-methyltransferase from Sulfolobus tokodaii

To study how oligomerization may contribute to the thermostability of archaeon proteins, we focused on a hexameric protein, protein L-isoaspartyl-O-methyltransferase from Sulfolobus tokodaii (StoPIMT). The crystal structure shows that StoPIMT has a distinctive hexameric structure composed of monomers consisting of two domains: an S-adenosylmethionine-dependent methyltransferase fold domain and a C-terminal alpha-helical domain. The hexameric structure includes three interfacial contact regions: major, minor, and coiled-coil. Several C-terminal deletion mutants were constructed and characterized. The hexameric structure and thermostability were retained when the C-terminal alpha-helical domain (Tyr(206)-Thr(231)) was deleted, suggesting that oligomerization via coiled-coil association using the C-terminal alpha-helical domains did not contribute critically to hexamerization or to the increased thermostability of the protein. Deletion of three additional residues located in the major contact region, Tyr(203)-Asp(204)-Asp(205), led to a significant decrease in hexamer stability and chemico/thermostability. Although replacement of Thr(146) and Asp(204), which form two hydrogen bonds in the interface in the major contact region, with Ala did not affect hexamer formation, these mutations led to a significant decrease in thermostability, suggesting that two residues in the major contact region make significant contributions to the increase in stability of the protein via hexamerization. These results suggest that cooperative hexamerization occurs via interactions of "hot spot" residues and that a couple of interfacial hot spot residues are responsible for enhancing thermostability via oligomerization.     

Allosteric control of the oligomerization of carbamoyl phosphate synthetase from Escherichia coli

Carbamoyl phosphate synthetase (CPS) from Escherichia coli is allosterically regulated by the metabolites ornithine, IMP, and UMP. Ornithine and IMP function as activators, whereas UMP is an inhibitor. CPS undergoes changes in the state of oligomerization that are dependent on the protein concentration and the binding of allosteric effectors. Ornithine and IMP promote the formation of an (alphabeta)4 tetramer while UMP favors the formation of an (alphabeta)2 dimer. The three-dimensional structure of the (alphabeta)4 tetramer has unveiled two regions of molecular contact between symmetry-related monomeric units. Identical residues within two pairs of allosteric domains interact with one another as do twin pairs of oligomerization domains. There are thus two possible structures for an (alphabeta)2 dimer: an elongated dimer formed at the interface of two allosteric domains and a more compact dimer formed at the interface between two oligomerization domains. Mutations at the two interfacial sites of oligomerization were constructed in an attempt to elucidate the mechanism for assembly of the (alphabeta)4 tetramer through disruption of the molecular binding interactions between monomeric units. When Leu-421 (located in the oligomerization domain) was mutated to a glutamate residue, CPS formed an (alphabeta)2 dimer in the presence of ornithine, UMP, or IMP. In contrast, when Asn-987 (located in the allosteric binding domain) was mutated to an aspartate, an (alphabeta) monomer was formed regardless of the presence of any allosteric effectors. These results are consistent with a model for the structure of the (alphabeta)2 dimer that is formed through molecular contact between two pairs of allosteric domains. Apparently, the second interaction, between pairs of oligomerization domains, does not form until after the interaction between pairs of allosteric domains is formed. The binding of UMP to the allosteric domain inhibits the dimerization of the (alphabeta)2 dimer, whereas the binding of either IMP or ornithine to this same domain promotes the dimerization of the (alphabeta)2 dimer. In the oligomerization process, ornithine and IMP must exert a conformational alteration on the oligomerization domain, which is approximately 45 A away from their site of binding within the allosteric domain. No significant dependence of the specific catalytic activity on the protein concentration could be detected, and thus the effects induced by the allosteric ligands on the catalytic activity and the state of oligomerization are unlinked from one another.     

Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences

Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time‐consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K‐nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state‐of‐the‐art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx . Proteins 2013; 81:1351–1362 © 2013 Wiley Periodicals, Inc.     

Analysis and network representation of hotspots in protein interfaces using minimum cut trees

We propose a novel approach to analyze and visualize residue contact networks of protein interfaces by graph‐based algorithms using a minimum cut tree (mincut tree). Edges in the network are weighted according to an energy function derived from knowledge‐based potentials. The mincut tree, which is constructed from the weighted residue network, simplifies and summarizes the complex structure of the contact network by an efficient and informative representation. This representation offers a comprehensible view of critical residues and facilitates the inspection of their organization. We observed, on a nonredundant data set of 38 protein complexes with experimental hotspots that the highest degree node in the mincut tree usually corresponds to an experimental hotspot. Further, hotspots are found in a few paths in the mincut tree. In addition, we examine the organization of hotspots (hot regions) using an iterative clustering algorithm on two different case studies. We find that distinct hot regions are located on specific sites of the mincut tree and some critical residues hold these clusters together. Clustering of the interface residues provides information about the relation of hot regions with each other. Our new approach is useful at the molecular level for both identification of critical paths in the protein interfaces and extraction of hot regions by clustering of the interface residues. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Hot‐spot analysis to dissect the functional protein–protein interface of a tRNA‐modifying enzyme

Interference with protein–protein interactions of interfaces larger than 1500 Ų by small drug‐like molecules is notoriously difficult, particularly if targeting homodimers. The tRNA modifying enzyme Tgt is only functionally active as a homodimer. Thus, blocking Tgt dimerization is a promising strategy for drug therapy as this protein is key to the development of Shigellosis. Our goal was to identify hot‐spot residues which, upon mutation, result in a predominantly monomeric state of Tgt. The detailed understanding of the spatial location and stability contribution of the individual interaction hot‐spot residues and the plasticity of motifs involved in the interface formation is a crucial prerequisite for the rational identification of drug‐like inhibitors addressing the respective dimerization interface. Using computational analyses, we identified hot‐spot residues that contribute particularly to dimer stability: a cluster of hydrophobic and aromatic residues as well as several salt bridges. This in silico prediction led to the identification of a promising double mutant, which was validated experimentally. Native nano‐ESI mass spectrometry showed that the dimerization of the suggested mutant is largely prevented resulting in a predominantly monomeric state. Crystal structure analysis and enzyme kinetics of the mutant variant further support the evidence for enhanced monomerization and provide first insights into the structural consequences of the dimer destabilization. Proteins 2014; 82:2713–2732. © 2014 Wiley Periodicals, Inc.     

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.     

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.     

Prediction of quaternary structure by analysis of hot spot residues in protein-protein interfaces: the case of anthranilate phosphoribosyltransferases

It is an important goal of computational biology to correctly predict the association state of a protein based on its amino acid sequence and the structures of known homologues. We have pursued this goal on the example of anthranilate phosphoribosyltransferase (AnPRT), an enzyme that is involved in the biosynthesis of the amino acid tryptophan. Firstly, known crystal structures of naturally occurring homodimeric AnPRTs were analyzed using the Protein Interfaces, Surfaces, and Assemblies (PISA) service of the European Bioinformatics Institute (EBI). This led to the identification of two hydrophobic “hot spot” amino acids in the protein-protein interface that were predicted to be essential for self-association. Next, in a comprehensive multiple sequence alignment (MSA), naturally occurring AnPRT variants with hydrophilic or charged amino acids in place of hydrophobic residues in the two hot spot positions were identified. Representative variants were characterized in terms of thermal stability, enzymatic activity, and quaternary structure. We found that AnPRT variants with charged residues in both hot spot positions exist exclusively as monomers in solution. Variants with hydrophilic amino acids in one hot spot position occur in both forms, monomer and dimer. The results of the present study provide a detailed characterization of the determinants of the AnPRT monomer-dimer equilibrium and show that analysis of hot spots in combination with MSAs can be a valuable tool in prediction of protein quaternary structures.     

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.     

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.     

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.     

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.     

Interolog interfaces in protein–protein docking

Proteins are essential elements of biological systems, and their function typically relies on their ability to successfully bind to specific partners. Recently, an emphasis of study into protein interactions has been on hot spots, or residues in the binding interface that make a significant contribution to the binding energetics. In this study, we investigate how conservation of hot spots can be used to guide docking prediction. We show that the use of evolutionary data combined with hot spot prediction highlights near‐native structures across a range of benchmark examples. Our approach explores various strategies for using hot spots and evolutionary data to score protein complexes, using both absolute and chemical definitions of conservation along with refinements to these strategies that look at windowed conservation and filtering to ensure a minimum number of hot spots in each binding partner. Finally, structure‐based models of orthologs were generated for comparison with sequence‐based scoring. Using two data sets of 22 and 85 examples, a high rate of top 10 and top 1 predictions are observed, with up to 82% of examples returning a top 10 hit and 35% returning top 1 hit depending on the data set and strategy applied; upon inclusion of the native structure among the decoys, up to 55% of examples yielded a top 1 hit. The 20 common examples between data sets show that more carefully curated interolog data yields better predictions, particularly in achieving top 1 hits. Proteins 2015; 83:1940–1946. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Identification of hot spot residues at protein-protein interface

It is known that binding free energy of protein-protein interaction is mainly contributed by hot spot (high energy) interface residues. Here, we investigate the characteristics of hot spots by examining inter-atomic sidechain-sidechain interactions using a dataset of 296 alanine-mutated interface residues. Results show that hot spots participate in strong and energetically favorable sidechain-sidechain interactions. Subsequently, we describe a novel, yet simple 'hot spot' prediction model with an accuracy that is similar to many available approaches. The model is also shown to efficiently distinguish specific protein-protein interactions from non-specific interactions.