Inferring the microscopic surface energy of protein–protein interfaces from mutation data

Mutations at protein–protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical values, yielding mean energies of ?48 cal mol^?1 Å^?2 for interactions between hydrophobic surfaces, ?51 to ?80 cal mol^?1 Å^?2 for surfaces of complementary charge, and 66–83 cal mol^?1 Å^?2 for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at ?24 cal mol^?1 Å^?2. Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid‐body interactions (r = 0.66). When used to rerank docking poses, it can place near‐native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50–95% of the cohesive energy. The model is available open‐source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/ . Proteins 2015; 83:640–650. © 2015 Wiley Periodicals, Inc.     

Protein–protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area

Protein–protein interactions play key roles in many cellular processes and their affinities and specificities are finely tuned to the functions they perform. Here, we present a study on the relationship between binding affinity and the size and chemical nature of protein–protein interfaces. Our analysis focuses on heterodimers and includes curated structural and thermodynamic data for 113 complexes. We observe a direct correlation between binding affinity and the amount of surface area buried at the interface. For a given amount of surface area buried, the binding affinity spans four orders of magnitude in terms of the dissociation constant (K_d). Across the entire dataset, we observe no obvious relationship between binding affinity and the chemical composition of the interface. We also calculate the free energy per unit surface area buried, or “surface energy density,” of each heterodimer. For interfacial surface areas between 500 and 2000 Ų, the surface energy density decreases as the buried surface area increases. As the buried surface area increases beyond about 2000 Ų, the surface energy density levels off to a constant value. We believe that these analyses and data will be useful for researchers with an interest in understanding, designing or inhibiting protein–protein interfaces.     

iATTRACT: Simultaneous global and local interface optimization for protein–protein docking refinement

Protein‐protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure‐based force field for intramolecular contributions. The approach was systematically evaluated on a large protein‐protein docking benchmark, starting from an enriched decoy set of rigidly docked protein–protein complexes deviating by up to 15 Å from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 Å were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%. Proteins 2015; 83:248–258. © 2014 Wiley Periodicals, Inc.     

Reassessing buried surface areas in protein–protein complexes

The buried surface area (BSA), which measures the size of the interface in a protein–protein complex may differ from the accessible surface area (ASA) lost upon association (which we call DSA), if conformation changes take place. To evaluate the DSA, we measure the ASA of the interface atoms in the bound and unbound states of the components of 144 protein–protein complexes taken from the Protein–Protein Interaction Affinity Database of Kastritis et al. (2011). We observe differences exceeding 20%, and a systematic bias in the distribution. On average, the ASA calculated in the bound state of the components is 3.3% greater than in their unbound state, and the BSA, 7% greater than the DSA. The bias is observed even in complexes where the conformation changes are small. An examination of the bound and unbound structures points to a possible origin: local movements optimize contacts with the other component at the cost of internal contacts, and presumably also the binding free energy.     

A non‐redundant protein–RNA docking benchmark version 2.0

We present an updated version of the protein–RNA docking benchmark, which we first published four years back. The non‐redundant protein–RNA docking benchmark version 2.0 consists of 126 test cases, a threefold increase in number compared to its previous version. The present version consists of 21 unbound–unbound cases, of which, in 12 cases, the unbound RNAs are taken from another complex. It also consists of 95 unbound–bound cases where only the protein is available in the unbound state. Besides, we introduce 10 new bound–unbound cases where only the RNA is found in the unbound state. Based on the degree of conformational change of the interface residues upon complex formation the benchmark is classified into 72 rigid‐body cases, 25 semiflexible cases and 19 full flexible cases. It also covers a wide range of conformational flexibility including small side chain movement to large domain swapping in protein structures as well as flipping and restacking in RNA bases. This benchmark should provide the docking community with more test cases for evaluating rigid‐body as well as flexible docking algorithms. Besides, it will also facilitate the development of new algorithms that require large number of training set. The protein–RNA docking benchmark version 2.0 can be freely downloaded from http://www.csb.iitkgp.ernet.in/applications/PRDBv2 . Proteins 2017; 85:256–267. © 2016 Wiley Periodicals, Inc.     

Importance of the interaction protein–protein of the CaM–PDE1A and CaM–MLCK complexes in the development of new anti‐CaM drugs

Protein–protein interactions play central roles in physiological and pathological processes. The bases of the mechanisms of drug action are relevant to the discovery of new therapeutic targets. This work focuses on understanding the interactions in protein–protein–ligands complexes, using proteins calmodulin (CaM), human calcium/calmodulin‐dependent 3′,5′‐cyclic nucleotide phosphodiesterase 1A active human (PDE1A), and myosin light chain kinase (MLCK) and ligands αII–spectrin peptide (αII–spec), and two inhibitors of CaM (chlorpromazine (CPZ) and malbrancheamide (MBC)). The interaction was monitored with a fluorescent biosensor of CaM (hCaM M124C–mBBr). The results showed changes in the affinity of CPZ and MBC depending on the CaM–protein complex under analysis. For the Ca²⁺–CaM, Ca²⁺–CaM–PDE1A, and Ca²⁺–CaM–MLCK complexes, CPZ apparent dissociation constants (K_ds) were 1.11, 0.28, and 0.55 μM, respectively; and for MBC K_ds were 1.43, 1.10, and 0.61 μM, respectively. In competition experiments the addition of calmodulin binding peptide 1 (αII–spec) to Ca²⁺–hCaM M124C–mBBr quenched the fluorescence (K_d = 2.55 ± 1.75 pM) and the later addition of MBC (up to 16 μM) did not affect the fluorescent signal. Instead, the additions of αII–spec to a preformed Ca²⁺–hCaM M124C–mBBr–MBC complex modified the fluorescent signal. However, MBC was able to displace the PDE1A and MLCK from its complex with Ca²⁺–CaM. In addition, docking studies were performed for all complexes with both ligands showing an excellent correlation with experimental data. These experiments may help to explain why in vivo many CaM drugs target prefer only a subset of the Ca²⁺–CaM regulated proteins and adds to the understanding of molecular interactions between protein complexes and small ligands. Copyright © 2013 John Wiley & Sons, Ltd.     

A minimal model of protein–protein binding affinities

A minimal model of protein–protein binding affinity that takes into account only two structural features of the complex, the size of its interface, and the amplitude of the conformation change between the free and bound subunits, is tested on the 144 complexes of a structure‐affinity benchmark. It yields K_d values that are within two orders of magnitude of the experiment for 67% of the complexes, within three orders for 88%, and fails on 12%, which display either large conformation changes, or a very high or a low affinity. The minimal model lacks the specificity and accuracy needed to make useful affinity predictions, but it should help in assessing the added value of parameters used by more elaborate models, and set a baseline for evaluating their performances.     

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.     

Intuitive,but not simple: Including explicit water molecules in protein–protein docking simulations improves model quality

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high‐resolution crystallographically characterized “dry” protein–protein complexes and was shown to reliably identify native‐like models. However, most current protein–protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the “truly” bridging waters at the 30 protein–protein interfaces and we utilized them in “solvated” docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ～24% in the average hit‐count within the top‐10 predictions the protein–protein dataset was seen, compared to standard “dry” docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native‐like structure predictions. Proteins 2014; 82:916–932. © 2013 Wiley Periodicals, Inc.     

Disulfide trapping of protein complexes on the yeast surface

Protein complexes are common in nature and play important roles in biology, but studying the quaternary structure formation in vitro is challenging since it involves lengthy and expensive biochemical steps. There are frequent technical difficulties as well with the sensitivity and resolution of the assays. In this regard, a technique that can analyze protein–protein interactions in high throughput would be a useful experimental tool. Here, we introduce a combination of yeast display and disulfide trapping that we refer to as stabilization of transient and unstable complexes by engineered disulfide (STUCKED) that can be used to detect the formation of a broad spectrum of protein complexes on the yeast surface using fluorescence labeling. The technique uses an engineered intersubunit disulfide to covalently crosslink the subunits of a complex, so that the disulfide‐trapped complex can be displayed on the yeast surface for detection and analysis. Transient protein complexes are difficult to display on the yeast surface, since they may dissociate before they can be detected due to a long induction period in yeast. To this end, we show that three different quaternary structures with the subunit dissociation constant K_d ～ 0.5–20 µM, the antibody variable domain (Fv), the IL‐8 dimer, and the p53–MDM2 complex, cannot be displayed on the yeast surface as a noncovalent complex. However, when we introduce an interchain disulfide between the subunits, all three systems are efficiently displayed on the yeast surface, showing that disulfide trapping can help display protein complexes that cannot be displayed otherwise. We also demonstrate that a disulfide forms only between the subunits that interact specifically, the displayed complexes exhibit functional characteristics that are expected of wt proteins, the mutations that decrease the affinity of subunit interaction also reduce the display efficiency, and most of the disulfide stabilized complexes are formed within the secretory pathway during export to the surface. Disulfide crosslinking is therefore a convenient way to study weak protein association in the context of yeast display. Biotechnol. Bioeng. 2010; 106: 27–41. © 2009 Wiley Periodicals, Inc.     

Interaction between bound water molecules and local protein structures: A statistical analysis of the hydrogen bond structures around bound water molecules

Water molecules play an important role in protein folding and protein interactions through their structural association with proteins. Examples of such structural association can be found in protein crystal structures, and can often explain protein functionality in the context of structure. We herein report the systematic analysis of the local structures of proteins interacting with water molecules, and the characterization of their geometric features. We first examined the interaction of water molecules with a large local interaction environment by comparing the preference of water molecules in three regions, namely, the protein–protein interaction (PPI) interfaces, the crystal contact (CC) interfaces, and the non‐interfacial regions. High preference of water molecules to the PPI and CC interfaces was found. In addition, the bound water on the PPI interface was more favorably associated with the complex interaction structure, implying that such water‐mediated structures may participate in the shaping of the PPI interface. The pairwise water‐mediated interaction was then investigated, and the water‐mediated residue–residue interaction potential was derived. Subsequently, the types of polar atoms surrounding the water molecules were analyzed, and the preference of the hydrogen bond acceptor was observed. Furthermore, the geometries of the structures interacting with water were analyzed, and it was found that the major structure on the protein surface exhibited planar geometry rather than tetrahedral geometry. Several previously undiscovered characteristics of water–protein interactions were unfolded in this study, and are expected to lead to a better understanding of protein structure and function. Proteins 2016; 84:43–51. © 2015 Wiley Periodicals, 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.     

Interplay between binding affinity and kinetics in protein–protein interactions

To clarify the interplay between the binding affinity and kinetics of protein–protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound‐state valley is deep with a barrier height of 12 ? 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920–933. © 2016 Wiley Periodicals, Inc.     

Non‐interacting surface solvation and dynamics in protein–protein interactions

Protein–protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure‐based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein–protein binding, even for simple lock‐and‐key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein–protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein–protein surfaces. We compare properties of the interface, rim, and non‐interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non‐interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non‐interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein–protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein–protein complexes from unintended protein–protein interactions. Proteins 2015; 83:445–458. © 2014 Wiley Periodicals, Inc.     

Complex structure of cytochrome c–cytochrome c oxidase reveals a novel protein–protein interaction mode

Mitochondrial cytochrome c oxidase (CcO) transfers electrons from cytochrome c (Cyt.c) to O₂ to generate H₂O, a process coupled to proton pumping. To elucidate the mechanism of electron transfer, we determined the structure of the mammalian Cyt.c–CcO complex at 2.0‐Å resolution and identified an electron transfer pathway from Cyt.c to CcO. The specific interaction between Cyt.c and CcO is stabilized by a few electrostatic interactions between side chains within a small contact surface area. Between the two proteins are three water layers with a long inter‐molecular span, one of which lies between the other two layers without significant direct interaction with either protein. Cyt.c undergoes large structural fluctuations, using the interacting regions with CcO as a fulcrum. These features of the protein–protein interaction at the docking interface represent the first known example of a new class of protein–protein interaction, which we term “soft and specific”. This interaction is likely to contribute to the rapid association/dissociation of the Cyt.c–CcO complex, which facilitates the sequential supply of four electrons for the O₂ reduction reaction.     

An insight into the thermodynamic characteristics of human thrombopoietin complexation with TN1 antibody

Human thrombopoietin (hTPO) primarily stimulates megakaryocytopoiesis and platelet production and is neutralized by the mouse TN1 antibody. The thermodynamic characteristics of TN1 antibody–hTPO complexation were analyzed by isothermal titration calorimetry (ITC) using an antigen‐binding fragment (Fab) derived from the TN1 antibody (TN1‐Fab). To clarify the mechanism by which hTPO is recognized by TN1‐Fab the conformation of free TN1‐Fab was determined to a resolution of 2.0 Å using X‐ray crystallography and compared with the hTPO‐bound form of TN1‐Fab determined by a previous study. This structural comparison revealed that the conformation of TN1‐Fab does not substantially change after hTPO binding and a set of 15 water molecules is released from the antigen‐binding site (paratope) of TN1‐Fab upon hTPO complexation. Interestingly, the heat capacity change (ΔCp) measured by ITC (?1.52 ± 0.05 kJ mol^?1 K^?1) differed significantly from calculations based upon the X‐ray structure data of the hTPO‐bound and unbound forms of TN1‐Fab (?1.02 ～ 0.25 kJ mol^?1 K^?1) suggesting that hTPO undergoes an induced‐fit conformational change combined with significant desolvation upon TN1‐Fab binding. The results shed light on the structural biology associated with neutralizing antibody recognition.     

Crystallization and preliminary X-ray analysis of the Escherichia coli replication terminator protein complexed with DNA

Crystals of the Escherichia coli replication terminator protein (Tus) complexed with its binding site DNA were obtained by a microdialysis method using PEG 4000. They belong to the tetragonal space group P4₁2₁2 or P4₃2₁2 with the unit cell parameter: a = 68.1 Å, c = 230.7 Å and contain one protein-DNA complex in an asymmetric unit. The native data set has been collected to 2.7 Å resolution.     

Protein‐spanning water networks and implications for prediction of protein–protein interactions mediated through hydrophobic effects

Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self‐assembly processes. Protein–protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein–protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy‐based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue‐based water density and single‐linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces. Proteins 2014; 82:3312–3326. © 2014 Wiley Periodicals, Inc.