Exploring the charge space of protein-protein association: a proteomic study

Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations

We develop an approach to characterize the effects of gating by a multiconformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterize them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multistate gating theory from which we derive a gating factor γ that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open, and wide-open) and two inaccessible (closed and a newly identified, "parted") macrostate conformations. The parted conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because γ = 0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease─having a wide range of experimental association constants (∼10⁴-10¹⁰ M^-1 s^-1)─yields gated rate constants in the range of ∼0.5-5.7 × 10⁸ M^-1 s^-1. This indicates that, whereas the association rate of some inhibitors could be described by the model, for many inhibitors either subsequent conformational transitions or alternate binding mechanisms may be rate-limiting. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands.

Protein Diffusion on Charged Biopolymers: DNA versus Microtubule

Protein diffusion in lower-dimensional spaces is used for various cellular functions. For example, sliding on DNA is essential for proteins searching for their target sites, and protein diffusion on microtubules is important for proper cell division and neuronal development. On the one hand, these linear diffusion processes are mediated by long-range electrostatic interactions between positively charged proteins and negatively charged biopolymers and have similar characteristic diffusion coefficients. On the other hand, DNA and microtubules have different structural properties. Here, using computational approaches, we studied the mechanism of protein diffusion along DNA and microtubules by exploring the diffusion of both protein types on both biopolymers. We found that DNA-binding and microtubule-binding proteins can diffuse on each other's substrates; however, the adopted diffusion mechanism depends on the molecular properties of the diffusing proteins and the biopolymers. On the protein side, only DNA-binding proteins can perform rotation-coupled diffusion along DNA, with this being due to their higher net charge and its spatial organization at the DNA recognition helix. By contrast, the lower net charge on microtubule-binding proteins enables them to diffuse more quickly than DNA-binding proteins on both biopolymers. On the biopolymer side, microtubules possess intrinsically disordered, negatively charged C-terminal tails that interact with microtubule-binding proteins, thus supporting their diffusion. Thus, although both DNA-binding and microtubule-binding proteins can diffuse on the negatively charged biopolymers, the unique molecular features of the biopolymers and of their natural substrates are essential for function.

Selecting for Fast Protein-Protein Association As Demonstrated on a Random TEM1 Yeast Library Binding BLIP

Protein-protein interactions mediate the vast majority of cellular processes. Though protein interactions obey basic chemical principles also within the cell, the in vivo physiological environment may not allow for equilibrium to be reached. Thus, in vitro measured thermodynamic affinity may not provide a complete picture of protein interactions in the biological context. Binding kinetics composed of the association and dissociation rate constants are relevant and important in the cell. Therefore, changes in protein-protein interaction kinetics have a significant impact on the in vivo activity of the proteins. The common protocol for the selection of tighter binders from a mutant library selects for protein complexes with slower dissociation rate constants. Here we describe a method to specifically select for variants with faster association rate constants by using pre-equilibrium selection, starting from a large random library. Toward this end, we refine the selection conditions of a TEM1-β-lactamase library against its natural nanomolar affinity binder β-lactamase inhibitor protein (BLIP). The optimal selection conditions depend on the ligand concentration and on the incubation time. In addition, we show that a second sort of the library helps to separate signal from noise, resulting in a higher percent of faster binders in the selected library. Fast associating protein variants are of particular interest for drug development and other biotechnological applications.

Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation

Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning

Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Low-stringency selection of TEM1 for BLIP shows interface plasticity and selection for faster binders

Protein-protein interactions occur via well-defined interfaces on the protein surface. Whereas the location of homologous interfaces is conserved, their composition varies, suggesting that multiple solutions may support high-affinity binding. In this study, we examined the plasticity of the interface of TEM1 β-lactamase with its protein inhibitor BLIP by low-stringency selection of a random TEM1 library using yeast surface display. Our results show that most interfacial residues could be mutated without a loss in binding affinity, protein stability, or enzymatic activity, suggesting plasticity in the interface composition supporting high-affinity binding. Interestingly, many of the selected mutations promoted faster association. Further selection for faster binders was achieved by drastically decreasing the library-ligand incubation time to 30 s. Preequilibrium selection as suggested here is a novel methodology for specifically selecting faster-associating protein complexes.

Accelerating the Association of the Most Stable Protein-Ligand Complex by More than Two Orders of Magnitude

The complex between the bacterial type 1 pilus subunit FimG and the peptide corresponding to the N-terminal extension (termed donor strand, Ds) of the partner subunit FimF (DsF) shows the strongest reported noncovalent molecular interaction, with a dissociation constant (KD ) of 1.5×10(-20)  m. However, the complex only exhibits a slow association rate of 330 m(-1)  s(-1) that limits technical applications, such as its use in affinity purification. Herein, a structure-based approach was used to design pairs of FimGt (a FimG variant lacking its own N-terminal extension) and DsF variants with enhanced electrostatic surface complementarity. Association of the best mutant FimGt/DsF pairs was accelerated by more than two orders of magnitude, while the dissociation rates and 3D structures of the improved complexes remained essentially unperturbed. A KD  value of 8.8×10(-22)  m was obtained for the best mutant complex, which is the lowest value reported to date for a protein/ligand complex.

Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/

Protein-protein interactions (PPI) are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design. Given their importance, it is vital to determine which protein interactions have functional relevance and to characterize the protein competition inherent to crowded environments, as the cytoplasm or the cellular organelles. We show that combining coarse-grain molecular cross-docking simulations and binding site predictions based on evolutionary sequence analysis is a viable route to identify true interacting partners for hundreds of proteins with a variate set of protein structures and interfaces. Also, we realize a large-scale analysis of protein binding promiscuity and provide a numerical characterization of partner competition and level of interaction strength for about 28000 false-partner interactions. Finally, we demonstrate that binding site prediction is useful to discriminate native partners, but also to scale up the approach to thousands of protein interactions. This study is based on the large computational effort made by thousands of internautes helping World Community Grid over a period of 7 months. The complete dataset issued by the computation and the analysis is released to the scientific community.

Weak frustration regulates sliding and binding kinetics on rugged protein-DNA landscapes

A fundamental step in gene-regulatory activities, such as repression, transcription, and recombination, is the binding of regulatory DNA-binding proteins (DBPs) to specific targets in the genome. To rapidly localize their regulatory genomic sites, DBPs reduce the dimensionality of the search space by combining three-dimensional (3D) diffusion in solution with one-dimensional (1D) sliding along DNA. However, the requirement to form a thermodynamically stable protein-DNA complex at the cognate genomic target sequence imposes a challenge on the protein because, as it navigates one-dimensionally along the genome, it may come in close contact with sites that share partial or even complete sequence similarity with the functional DNA sequence. This puzzling issue creates a conflict between two basic requirements: finding the cognate site quickly and stably binding it. Here, we structurally assessed the interface adopted by a variety of DBPs to bind DNA specifically and nonspecifically, and found that many DBPs utilize one interface to specifically recognize a DNA sequence and another to assist in propagating along the DNA through nonspecific associations. While these two interfaces overlap each other in some proteins, they present partial overlap in others and frustrate the protein-DNA interface. Using coarse-grained molecular dynamics simulations, we demonstrate that the existence of frustration in DBPs is a compromise between rapid 1D diffusion along other regions in the genome (high frustration smoothens the landscape for sliding) and rapid formation of a stable and essentially active protein-DNA complex (low frustration reduces the free energy barrier for switching between the two binding modes).

On the binding affinity of macromolecular interactions: daring to ask why proteins interact

Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (K_d), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition.

Evolving the [myoglobin, cytochrome b(5)] complex from dynamic toward simple docking: charging the electron transfer reactive patch

We describe photoinitiated electron transfer (ET) from a suite of Zn-substituted myoglobin (Mb) variants to cytochrome b(5) (b(5)). An electrostatic interface redesign strategy has led to the introduction of positive charges into the vicinity of the heme edge through D/E → K charge-reversal mutation combinations at "hot spot" residues (D44, D60, and E85), augmented by the elimination of negative charges from Mb or b(5) by neutralization of heme propionates. These variations create an unprecedentedly large range in the product of the ET partners' total charges (-5 < -q(Mb)q(b(5)) < 40). The binding affinity (K(a)) increases 1000-fold as -q(Mb)q(b(5)) increases through this range and exhibits a surprisingly simple, exponential dependence on -q(Mb)q(b(5)). This is explained in terms of electrostatic interactions between a "charged reactive patch" (crp) on each partner's surface, defined as a compact region around the heme edge that (i) contains the total protein charge of each variant and (ii) encompasses a major fraction of the "reactive region" (Rr) comprising surface atoms with large matrix elements for electron tunneling to the heme. As -q(Mb)q(b(5)) increases, the complex undergoes a transition from fast to slow-exchange dynamics on the triplet ET time scale, with a correlated progression in the rate constants for intracomplex (k(et)) and bimolecular (k(2)) ET. This progression is analyzed by integrating the crp and Rr descriptions of ET into the textbook steady-state treatment of reversible binding between partners that undergo intracomplex ET and found to encompass the full range of behaviors predicted by the model. The generality of this approach is demonstrated by its application to the extensive body of data for the ET complex between the photosynthetic reaction center and cytochrome c(2). Deviations from this model also are discussed.

Automated prediction of protein association rate constants

The association rate constants (k(a)) of proteins with other proteins or other macromolecular targets are a fundamental biophysical property. Observed rate constants span over ten orders of magnitude, from 1 to 10(10) M(-1)s(-1). Protein association can be rate limited either by the diffusional approach of the subunits to form a transient complex, with near-native separation and orientation but without short-range native interactions, or by the subsequent conformational rearrangement to form the native complex. Our transient-complex theory showed promise in predicting k(a) in the diffusion-limited regime. Here, we develop it into a web server called TransComp (http://pipe.sc.fsu.edu/transcomp/) and report on the server's accuracy and robustness based on applications to over 100 protein complexes. We expect this server to be a valuable tool for systems biology applications and for kinetic characterization of protein-protein and protein-nucleic acid association in general.

Analysis of the binding forces driving the tight interactions between beta-lactamase inhibitory protein-II (BLIP-II) and class A beta-lactamases

β-Lactamases hydrolyze β-lactam antibiotics to provide drug resistance to bacteria. β-Lactamase inhibitory protein-II (BLIP-II) is a potent proteinaceous inhibitor that exhibits low picomolar affinity for class A β-lactamases. This study examines the driving forces for binding between BLIP-II and β-lactamases using a combination of presteady state kinetics, isothermal titration calorimetry, and x-ray crystallography. The measured dissociation rate constants for BLIP-II and various β-lactamases ranged from 10(-4) to 10(-7) s(-1) and are comparable with those found in some of the tightest known protein-protein interactions. The crystal structures of BLIP-II alone and in complex with Bacillus anthracis Bla1 β-lactamase revealed no significant side-chain movement in BLIP-II in the complex versus the monomer. The structural rigidity of BLIP-II minimizes the loss of the entropy upon complex formation and, as indicated by thermodynamics experiments, may be a key determinant of the observed potent inhibition of β-lactamases.

Electrostatics in proteins and protein-ligand complexes

Accurate computational methods for predicting electrostatic energies are of major importance for our understanding of protein energetics in general for computer-aided drug design as well as for the design of novel biocatalysts and protein therapeutics. Electrostatic energies are of particular importance in such applications as virtual screening, drug design and protein-protein docking due to the high charge density of protein ligands and small-molecule drugs, and the frequent protonation state changes observed when drugs bind to their protein targets. Therefore, the development of a reliable and fast algorithm for the evaluation of electrostatic free energies, as an important contributor to the overall protein energy function, has been the focus for many scientists over the past three decades. In this review we describe the current state-of-the-art in modeling electrostatic effects in proteins and protein-ligand complexes. We focus mainly on the merits and drawbacks of the continuum methodology, and speculate on future directions in refining algorithms for calculating electrostatic energies in proteins using experimental data.

Predicting kinetic constants of protein-protein interactions based on structural properties

Elucidating kinetic processes of protein-protein interactions (PPI) helps to understand how basic building blocks affect overall behavior of living systems. In this study, we used structure-based properties to build predictive models for kinetic constants of PPI. A highly diverse PPI dataset, protein-protein kinetic interaction data and structures (PPKIDS), was built. PPKIDS contains 62 PPI with complex structures and kinetic constants measured experimentally. The influence of structural properties on kinetics of PPI was studied using 35 structure-based features, describing different aspects of complex structures. Linear models for the prediction of kinetic constants were built by fitting with selected subsets of structure-based features. The models gave correlation coefficients of 0.801, 0.732, and 0.770 for k(off), k(on), and K(d), respectively, in leave-one-out cross validations. The predictive models reported here use only protein complex structures as input and can be generally applied in PPI studies as well as systems biology modeling. Our study confirmed that different properties play different roles in the kinetic process of PPI. For example, k(on) was affected by overall structural features of complexes, such as the composition of secondary structures, the change of translational and rotational entropy, and the electrostatic interaction; while k(off) was determined by interfacial properties, such as number of contacted atom pairs per 100 Ų. This information provides useful hints for PPI design.

Effect of the ordered interfacial water layer in protein complex formation: A nonlocal electrostatic approach

Using a nonlocal electrostatic approach that incorporates the short-range structure of the contacting media, we evaluated the electrostatic contribution to the energy of the complex formation of two model proteins. In this study, we have demonstrated that the existence of an ordered interfacial water layer at the protein-solvent interface reduces the charging energy of the proteins in the aqueous solvent, and consequently increases the electrostatic contribution to the protein binding (change in free energy upon the complex formation of two proteins). This is in contrast with the finding of the continuum electrostatic model, which suggests that electrostatic interactions are not strong enough to compensate for the unfavorable desolvation effects.

Photoinitiated singlet and triplet electron transfer across a redesigned [myoglobin, cytochrome b5] interface

We describe a strategy by which reactive binding of a weakly bound, 'dynamically docked (DD)' complex without a known structure can be strengthened electrostatically through optimized placement of surface charges, and discuss its use in modulating complex formation between myoglobin (Mb) and cytochrome b(5) (b(5)). The strategy employs paired Brownian dynamics (BD) simulations, one which monitors overall binding, the other reactive binding, to examine [X --> K] mutations on the surface of the partners, with a focus on single and multiple [D/E --> K] charge reversal mutations. This procedure has been applied to the [Mb, b(5)] complex, indicating mutations of Mb residues D44, D60, and E85 to be the most promising, with combinations of these showing a nonlinear enhancement of reactive binding. A novel method of displaying BD profiles shows that the 'hits' of b(5) on the surfaces of Mb(WT), Mb(D44K/D60K), and Mb(D44K/D60K/E85K) progressively coalesce into two 'clusters': a 'diffuse' cluster of hits that are distributed over the Mb surface and have negligible electrostatic binding energy and a 'reactive' cluster of hits with considerable stability that are localized near its heme edge, with short Fe-Fe distances favorable to electron transfer (ET). Thus, binding and reactivity progressively become correlated by the mutations. This finding relates to recent proposals that complex formation is a two-step process, proceeding through the formation of a weakly bound encounter complex to a well-defined bound complex. The design procedure has been tested through measurements of photoinitiated ET between the Zn-substituted forms of Mb(WT), Mb(D44K/D60K), and Mb(D44K/D60K/E85K) and Fe(3+)b(5). Both mutants convert the complex from the DD regime exhibited by Mb(WT), in which the transient complex is in fast kinetic exchange with its partners, k(off) >> k(et), to the slow-exchange regime, k(et) >> k(off), and both mutants exhibit rapid intracomplex ET from the triplet excited state to Fe(3+)b(5) (rate constant, k(et) approximately 10(6) s(-1)). The affinity constants of the mutant Mbs cannot be derived through conventional analysis procedures because intracomplex singlet ET quenching causes the triplet-ground absorbance difference to progressively decrease during a titration, but this effect has been incorporated into a new procedure for computing binding constants. Most importantly, these measurements reveal the presence of fast photoinduced singlet ET across the protein-protein interface, (1)k(et) approximately 2 x 10(8) s(-1).

Improved binding of raf to Ras.GDP is correlated with biological activity

The GTP-binding protein Ras plays a central role in the regulation of various cellular processes, acting as a molecular switch that triggers signaling cascades. Only Ras bound to GTP is able to interact strongly with effector proteins like Raf kinase, phosphatidylinositol 3-kinase, and RalGDS, whereas in the GDP-bound state, the stability of the complex is strongly decreased, and signaling is interrupted. To determine whether this process is only controlled by the stability of the complex, we used computer-aided protein design to improve the interaction between Ras and effector. We challenged the Ras.Raf complex in this study because Raf among all effectors shows the highest Ras affinity and the fastest association kinetics. The proposed mutations were characterized as to their changes in dynamics and binding strength. We demonstrate that Ras-Raf interaction can only be improved at the cost of a loss in specificity of Ras.GTP versus Ras.GDP. As shown by NMR spectroscopy, the Raf mutation A85K leads to a shift of Ras switch I in the GTP-bound as well as in the GDP-bound state, thereby increasing the complex stability. In a luciferase-based reporter gene assay, Raf A85K is associated with higher signaling activity, which appears to be a mere matter of Ras-Raf affinity.

On the electrostatic component of protein-protein binding free energy

Calculations of electrostatic properties of protein-protein complexes are usually done within framework of a model with a certain set of parameters. In this paper we present a comprehensive statistical analysis of the sensitivity of the electrostatic component of binding free energy (DeltaDeltaGel) with respect with different force fields (Charmm, Amber, and OPLS), different values of the internal dielectric constant, and different presentations of molecular surface (different values of the probe radius). The study was done using the largest so far set of entries comprising 260 hetero and 2148 homo protein-protein complexes extracted from a previously developed database of protein complexes (ProtCom). To test the sensitivity of the energy calculations with respect to the structural details, all structures were energy minimized with corresponding force field, and the energies were recalculated. The results indicate that the absolute value of the electrostatic component of the binding free energy (DeltaDeltaGel) is very sensitive to the force field parameters, the minimization procedure, the values of the internal dielectric constant, and the probe radius. Nevertheless our results indicate that certain trends in DeltaDeltaGel behavior are much less sensitive to the calculation parameters. For instance, the fraction of the homo-complexes, for which the electrostatics was found to oppose binding, is 80% regardless of the force fields and parameters used. For the hetero-complexes, however, the percentage of the cases for which electrostatics opposed binding varied from 43% to 85%, depending on the protocol and parameters employed. A significant correlation was found between the effects caused by raising the internal dielectric constant and decreasing the probe radius. Correlations were also found among the results obtained with different force fields. However, despite of the correlations found, the absolute DeltaDeltaGel calculated with different force field parameters could differ more than tens of kcal/mol in some cases. Set of rules of obtaining confident predictions of absolute DeltaDeltaGel and DeltaDeltaGel sign are provided in the conclusion section.PACS codes: 87.15.A-, 87.15. km.

The solution structure of BMPR-IA reveals a local disorder-to-order transition upon BMP-2 binding

The structure of the extracellular domain of BMP receptor IA was determined in solution by NMR spectroscopy and compared to its structure when bound to its ligand BMP-2. While most parts of the secondary structure are highly conserved between the bound and unbound forms, large conformational rearrangements can be observed in the beta4beta5 loop of BMPR-IA, which is in contact with BMP-2 and harbors the main binding determinants for the BMPR-IA-BMP-2 interaction. In its unbound form, helix alpha1 in BMPR-IA, which is in the center of the binding epitope for BMP-2, is missing. Since BMP-2 also shows conformational changes in the type I receptor epitope upon binding to BMPR-IA, both binding partners pass through an induced fit mechanism to adapt their binding interfaces to a given interaction surface. The inherent flexibility of both partners possibly explains the promiscuous ligand-receptor interaction observed in the BMP protein superfamily.

Interaction of onconase with the human ribonuclease inhibitor protein

One of the tightest known protein-protein interactions in biology is that between members of the ribonuclease A superfamily and the ribonuclease inhibitor protein (RI). Some members of this superfamily are able to kill cancer cells, and the ability to evade RI is a major determinant of whether a ribonuclease will be cytotoxic. The archetypal cytotoxic ribonuclease, onconase (ONC), is in late-stage clinical trials for the treatment of malignant mesothelioma. We present here the first measurement of the inhibition of the ribonucleolytic activity of ONC by RI. This inhibition occurs with K(i)=0.15muM in a solution of low salt concentration.

Coevolution at protein complex interfaces can be detected by the complementarity trace with important impact for predictive docking

Protein surfaces are under significant selection pressure to maintain interactions with their partners throughout evolution. Capturing how selection pressure acts at the interfaces of protein-protein complexes is a fundamental issue with high interest for the structural prediction of macromolecular assemblies. We tackled this issue under the assumption that, throughout evolution, mutations should minimally disrupt the physicochemical compatibility between specific clusters of interacting residues. This constraint drove the development of the so-called Surface COmplementarity Trace in Complex History score (SCOTCH), which was found to discriminate with high efficiency the structure of biological complexes. SCOTCH performances were assessed not only with respect to other evolution-based approaches, such as conservation and coevolution analyses, but also with respect to statistically based scoring methods. Validated on a set of 129 complexes of known structure exhibiting both permanent and transient intermolecular interactions, SCOTCH appears as a robust strategy to guide the prediction of protein-protein complex structures. Of particular interest, it also provides a basic framework to efficiently track how protein surfaces could evolve while keeping their partners in contact.

Translational mini-review series on complement factor H: structural and functional correlations for factor H

The 155-kDa glycoprotein, complement factor H (CFH), is a regulator of complement activation that is abundant in human plasma. Three-dimensional structures of over half the 20 complement control protein (CCP) modules in CFH have been solved in the context of single-, double- and triple-module segments. Proven binding sites for C3b occupy the N and C termini of this elongated molecule and may be brought together by a bend in CFH mediated by its central CCP modules. The C-terminal CCP 20 is key to the ability of the molecule to adhere to polyanionic markers on self-surfaces where CFH acts to regulate amplification of the alternative pathway of complement. The surface patch on CCP 20 that binds to model glycosaminoglycans has been mapped using nuclear magnetic resonance (NMR), as has a second glycosaminoglycan-binding patch on CCP 7. These patches include many of the residue positions at which sequence variations have been linked to three complement-mediated disorders: dense deposit disease, age-related macular degeneration and atypical haemolytic uraemic syndrome. In one plausible model, CCP 20 anchors CFH to self-surfaces via a C3b/polyanion composite binding site, CCP 7 acts as a 'proof-reader' to help discriminate self- from non-self patterns of sulphation, and CCPs 1-4 disrupt C3/C5 convertase formation and stability.

Evaluation of the influence of the internal aqueous solvent structure on electrostatic interactions at the protein-solvent interface by nonlocal continuum electrostatic approach

The dielectric properties of the polar solvent on the protein-solvent interface at small intercharge distances are still poorly explored. To deconvolute this problem and to evaluate the pair-wise electrostatic interaction (PEI) energies of the point charges located at the protein-solvent interface we used a nonlocal (NL) electrostatic approach along with a static NL dielectric response function of water. The influence of the aqueous solvent microstructure (determined by a strong nonelectrostatic correlation effect between water dipoles within the orientational Debye polarization mode) on electrostatic interactions at the interface was studied in our work. It was shown that the PEI energies can be significantly higher than the energies evaluated by the classical (local) consideration, treating water molecules as belonging to the bulk solvent with a high dielectric constant. Our analysis points to the existence of a rather extended, effective low-dielectric interfacial water shell on the protein surface. The main dielectric properties of this shell (effective thickness together with distance- and orientation-dependent dielectric permittivity function) were evaluated. The dramatic role of this shell was demonstrated when estimating the protein association rate constants.

Intraspecies regulation of ribonucleolytic activity

The evolutionary rate of proteins involved in obligate protein-protein interactions is slower and the degree of coevolution higher than that for nonobligate protein-protein interactions. The coevolution of the proteins involved in certain nonobligate interactions is, however, essential to cell survival. To gain insight into the coevolution of one such nonobligate protein pair, the cytosolic ribonuclease inhibitor (RI) proteins and secretory pancreatic-type ribonucleases from cow (Bos taurus) and human (Homo sapiens) were produced in Escherichia coli and purified, and their physicochemical properties were analyzed. The two intraspecies complexes were found to be extremely tight (bovine Kd = 0.69 fM; human Kd = 0.34 fM). Human RI binds to its cognate ribonuclease (RNase 1) with 100-fold greater affinity than to the bovine homologue (RNase A). In contrast, bovine RI binds to RNase 1 and RNase A with nearly equal affinity. This broader specificity is consistent with there being more pancreatic-type ribonucleases in cows (20) than humans (13). Human RI (32 cysteine residues) also has 4-fold less resistance to oxidation by hydrogen peroxide than does bovine RI (29 cysteine residues). This decreased oxidative stability of human RI, which is caused largely by Cys74, implies a larger role for human RI as an antioxidant. The conformational and oxidative stabilities of both RIs increase upon complex formation with ribonucleases. Thus, RI has evolved to maintain its inhibition of invading ribonucleases, even when confronted with extreme environmental stress. That role appears to take precedence over its role in mediating oxidative damage.

Bis-methionyl coordination in the crystal structure of the heme-binding domain of the streptococcal cell surface protein Shp

Surface proteins Shr, Shp, and the ATP-binding cassette (ABC) transporter HtsABC are believed to make up the machinery for heme uptake in Streptococcus pyogenes. Shp transfers its heme to HtsA, the lipoprotein component of HtsABC, providing the only experimentally demonstrated example of direct heme transfer from a surface protein to an ABC transporter in Gram-positive bacteria. To understand the structural basis of heme transfer in this system, the heme-binding domain of Shp (Shp(180)) was crystallized, and its structure determined to a resolution of 2.1 A. Shp(180) exhibits an immunoglobulin-like beta-sandwich fold that has been recently found in other pathogenic bacterial cell surface heme-binding proteins, suggesting that the mechanisms of heme acquisition are conserved. Shp shows minimal amino acid sequence identity to these heme-binding proteins and the structure of Shp(180) reveals a unique heme-iron coordination with the axial ligands being two methionine residues from the same Shp molecule. A negative electrostatic surface of protein structure surrounding the heme pocket may serve as a docking interface for heme transfer from the more basic outer cell wall heme receptor protein Shr. The crystal structure of Shp(180) reveals two exogenous, weakly bound hemins, which form a large interface between the two Shp(180) molecules in the asymmetric unit. These "extra" hemins form a stacked pair with a structure similar to that observed previously for free hemin dimers in aqueous solution. The propionates of the protein-bound heme coordinate to the iron atoms of the exogenous hemin dimer, contributing to the stability of the protein interface. Gel filtration and analytical ultracentrifugation studies indicate that both full-length Shp and Shp(180) are monomeric in dilute aqueous solution.

Progress in computational protein design

Current progress in computational structure-based protein design is reviewed in the areas of methodology and applications. Foundational advances include new potential functions, more efficient ways of computing energetics, flexible treatments of solvent, and useful energy function approximations, as well as ensemble-based approaches to scoring designs for inclusion of entropic effects, improvements to guaranteed and to stochastic search techniques, and methods to design combinatorial libraries for screening and selection. Applications include new approaches and successes in the design of specificity for protein folding, binding, and catalysis, in the redesign of proteins for enhanced binding affinity, and in the application of design technology to study and alter enzyme catalysis. Computational protein design continues to mature and advance.

Prediction of interacting proteins from homology-modeled complex structures using sequence and structure scores

Protein-protein interactions support most biological processes, and it is important to find specifically interacting partner proteins among homologous proteins in order to elucidate cellular functions such as signal transduction systems. Various high-throughput experimental methods for identifying these interactions have been invented, and used to generate a huge amount of data. Because these experiments have been applied to only a few organisms, and their accuracy is believed to be limited, it would be valuable to develop computational methods for predicting protein-protein interactions from their amino acid sequences or tertiary structural information. In this study, we describe a prediction method of interacting proteins based on homology-modeled complex structures. We employed the statistical residue-residue contact energy used in a previous study, and two types of new scores, simple electrostatic energy and sequence similarity between target sequences and template structures. The validity of each protein-protein complex model was measured using their single and combined scores. We applied our method to all the protein heterodimers of Saccharomyces cerevisiae. To evaluate the prediction performance of our method, we prepared two types of protein-protein interaction dataset: a complete dataset and high confidence dataset. The complete dataset (10,325 protein dimer models) contains all the yeast protein heterodimers whose complex structures can be modeled. Among them, pairs registered in the DIP database are defined as interacting pairs, and those not registered are defined as non-interacting protein pairs. The high confidence dataset (3,219 protein dimer models) is a more reliable subset of the complete dataset extracted using the criteria of the common subcellular localization. Both datasets show that sequence similarity has a much higher discrimination power than the other structure-based scores, but that the inclusion of contact energy results in significant improvement over predictions using sequence similarity alone. These results suggest that the sequence similarity is indispensable for the prediction, whereas structure scores can play supporting roles.

On the dynamic nature of the transition state for protein-protein association as determined by double-mutant cycle analysis and simulation

The process of protein-protein association starts with their random collision, which may develop into an encounter complex followed by a transition state and final complex formation. Here we aim to experimentally characterize the nature of the transition state of protein-protein association for three different protein-protein interactions; Barnase-Barstar, TEM1-BLIP and IFNalpha2-IFNAR2, and use the data to model the transition state structures. To model the transition state, we determined inter-protein distance-constraints of the activated complex by using double mutant cycles (DMC) assuming that interacting residues are spatially close. Significant DeltaDeltaG(double dagger)(int) values were obtained only between residues on Barnase and Barstar. However, introducing specific mutations that optimize the charge complementarity between BLIP and TEM1 resulted in the introduction of significant DeltaDeltaG(double dagger)(int) values also between residues of these two proteins. While electrostatic interactions make major contributions towards stabilizing the transition state, we show two examples where steric hindrance exerts an effect on the transition state as well. To model the transition-state structures from the experimental DeltaDeltaG(double dagger)(int) values, we introduced a method for structure perturbation, searching for those inter-protein orientations that best support the experimental DeltaDeltaG(double dagger)(int) values. Two types of transition states were found, specific as observed for Barnase-Barstar and the electrostatically optimized TEM1-BLIP mutants, and diffusive as shown for wild-type TEM1-BLIP and IFNalpha2-IFNAR2. The specific transition states are characterized by defined inter-protein orientations, which cannot be modeled for the diffusive transition states. Mutations introduced through rational design can change the transition state from diffusive to specific. Together, these data provide a structural view of the mechanism allowing rates of association to differ by five orders of magnitude between different protein complexes.

Binding pathways of ligands to HIV-1 protease: coarse-grained and atomistic simulations

Multiscale simulations (coarse-grained Brownian dynamics simulations and all-atom molecular dynamics simulations in implicit solvent) were applied to reveal the binding processes of ligands as they enter the binding site of the HIV-1 protease. The initial structures used for the molecular dynamics simulations were generated based on the Brownian dynamics trajectories, and this is the first molecular dynamics simulation of modeling the association of a ligand with the protease. We found that a protease substrate successfully binds to the protein when the flaps are fully open. Surprisingly, a smaller cyclic urea inhibitor (XK263) can reach the binding site when the flaps are not fully open. However, if the flaps are nearly closed, the inhibitor must rearrange or binding can fail because the inhibitor cannot attain proper conformations to enter the binding site. Both the peptide substrate and XK263 can also affect the protein's internal motion, which may help the flaps to open. Simulations allow us to efficiently study the ligand binding processes and may help those who study drug discovery to find optimal association pathways and to design those ligands with the best binding kinetics.

Prediction of protein-protein association rates from a transition-state theory

We recently developed a theory for the rates of protein-protein association. The theory is based on the concept of a transition state, which separates the bound state, with numerous short-range interactions but restricted translational and rotational freedom, and the unbound state, with, at most, a small number of interactions but expanded configurational freedom. When not accompanied by large-scale conformational changes, protein-protein association becomes diffusion limited. The association rate is then predicted as k(a)=k(a)(0)exp(-DeltaG(el)(double dagger)/k(B)T), where DeltaG(el)(double dagger) is the electrostatic interaction free energy in the transition state, k(a)(0) is the rate in the absence of electrostatic interactions, and k(B)T is thermal energy. Here, this transition-state theory is used to predict the association rates of four protein complexes. The predictions for the wild-type complexes and 23 mutants are found to agree closely with experimental data over wide ranges of ionic strength.

Inhibition of human pancreatic ribonuclease by the human ribonuclease inhibitor protein

The ribonuclease inhibitor protein (RI) binds to members of the bovine pancreatic ribonuclease (RNase A) superfamily with an affinity in the femtomolar range. Here, we report on structural and energetic aspects of the interaction between human RI (hRI) and human pancreatic ribonuclease (RNase 1). The structure of the crystalline hRI x RNase 1 complex was determined at a resolution of 1.95 A, revealing the formation of 19 intermolecular hydrogen bonds involving 13 residues of RNase 1. In contrast, only nine such hydrogen bonds are apparent in the structure of the complex between porcine RI and RNase A. hRI, which is anionic, also appears to use its horseshoe-shaped structure to engender long-range Coulombic interactions with RNase 1, which is cationic. In accordance with the structural data, the hRI.RNase 1 complex was found to be extremely stable (t(1/2)=81 days; K(d)=2.9 x 10(-16) M). Site-directed mutagenesis experiments enabled the identification of two cationic residues in RNase 1, Arg39 and Arg91, that are especially important for both the formation and stability of the complex, and are thus termed "electrostatic targeting residues". Disturbing the electrostatic attraction between hRI and RNase 1 yielded a variant of RNase 1 that maintained ribonucleolytic activity and conformational stability but had a 2.8 x 10(3)-fold lower association rate for complex formation and 5.9 x 10(9)-fold lower affinity for hRI. This variant of RNase 1, which exhibits the largest decrease in RI affinity of any engineered ribonuclease, is also toxic to human erythroleukemia cells. Together, these results provide new insight into an unusual and important protein-protein interaction, and could expedite the development of human ribonucleases as chemotherapeutic agents.

Temperature dependence of binding and catalysis for the Cdc25B phosphatase

Using a combination of steady-state and single-turnover kinetics, we probe the temperature dependence of substrate association and chemistry for the reaction of Cdc25B phosphatase with its Cdk2-pTpY/CycA protein substrate. The transition state for substrate association is dominated by an enthalpic barrier (DeltaH(++) of 13 kcal/mol) and has a favorable entropic contribution of 4 kcal/mol at 298 K. Phosphate transfer from Cdk2-pTpY/CycA to enzyme (DeltaH(++) of 12 kcal/mol) is enthalpically more favorable than for the small molecule substrate p-nitrophenyl phosphate (DeltaH(++) of 18 kcal/mol), yet entropically less favorable (TDeltaS(++) of 2 vs. -6 kcal/mol at 298 K, respectively). By measuring the temperature dependence of binding and catalysis for several hotspot mutants involved in binding of protein substrate, we determine the enthalpy-entropy compensations for changes in rates of association and phosphate transfer compared to the wild type system. We conclude that the transition state for enzyme-substrate association involves tight and specific contacts at the remote docking site and that phospho-transfer from Cdk2-pTpY/CycA to the pre-organized active site of the enzyme is accompanied by unfavorable entropic rearrangements that promote rapid product dissociation.

Massive sequence perturbation of the Raf ras binding domain reveals relationships between sequence conservation, secondary structure propensity, hydrophobic core organization and stability

The contributions of specific residues to the delicate balance between function, stability and folding rates could be determined, in part by [corrected] comparing the sequences of structures having identical folds, but insignificant sequence homology. Recently, we have devised an experimental strategy to thoroughly explore residue substitutions consistent with a specific class of structure. Using this approach, the amino acids tolerated at virtually all residues of the c-Raf/Raf1 ras binding domain (Raf RBD), an exemplar of the common beta-grasp ubiquitin-like topology, were obtained and used to define the sequence determinants of this fold. Herein, we present analyses suggesting that more subtle sequence selection pressure, including propensity for secondary structure, the hydrophobic core organization and charge distribution are imposed on the Raf RBD sequence. Secondly, using the Gibbs free energies (DeltaG(F-U)) obtained for 51 mutants of Raf RBD, we demonstrate a strong correlation between amino acid conservation and the destabilization induced by truncating mutants. In addition, four mutants are shown to significantly stabilize Raf RBD native structure. Two of these mutations, including the well-studied R89L, are known to severely compromise binding affinity for ras. Another stabilized mutant consisted of a deletion of amino acid residues E104-K106. This deletion naturally occurs in the homologues a-Raf and b-Raf and could indicate functional divergence. Finally, the combination of mutations affecting five of 78 residues of Raf RBD results in stabilization of the structure by approximately 12 kJ mol(-1) (DeltaG(F-U) is -22 and -34 kJ mol(-1) for wt and mutant, respectively). The sequence perturbation approach combined with sequence/structure analysis of the ubiquitin-like fold provide a basis for the identification of sequence-specific requirements for function, stability and folding rate of the Raf RBD and structural analogues, highlighting the utility of conservation profiles as predictive tools of structural organization.

Variations in the unstructured C-terminal tail of interferons contribute to differential receptor binding and biological activity

Type I interferons (IFNs) elicit antiviral, antiproliferative and immunomodulatory properties in cells. All of them bind to the same receptor proteins, IFNAR1 and IFNAR2, with different affinities. While the 13 known IFNalphas are highly conserved, the C-terminal unstructured tail was found to have large variation in its net charge, from neutral to +4. This led us to speculate that the tail may have a role in modulation of the IFN biological activity, through fine-tuning the binding to IFNAR2. To evaluate this hypothesis, we replaced the tail of IFNalpha2 with that of IFNalpha8 and IFNbeta tails, or deleted the last five residues of this segment. Mutations to the more positively charged tail of IFNalpha8 resulted in a 20-fold higher affinity to IFNAR2, which results in a higher antiviral and antiproliferative activity. Double and multiple mutant cycle analysis placed the tail near a negatively charged loop on IFNAR2, comprising of residues Glu 132-134. Deleting the tail resulted in only twofold reduction in binding compared to the wild-type. Next, we modeled the location of the tail using a two-step procedure: first we generated 200 models of the tail docked on IFNAR2 using HADDOCK, second the models were scored according to the fit between experimentally determined rates of association of nine mutant complexes, and their calculated rates using the PARE software. From the results we suggest that the unstructured tail of IFNalpha is gaining a specific structure in the bound state, binding to a groove below the 132-134 loop in IFNAR2.

Inhibition of protein–protein interactions: The discovery of druglike β‐catenin inhibitors by combining virtual and biophysical screening

The interaction between beta-catenin and Tcf family members is crucial for the Wnt signal transduction pathway, which is commonly mutated in cancer. This interaction extends over a very large surface area (4800 A(2)), and inhibiting such interactions using low molecular weight inhibitors is a challenge. However, protein surfaces frequently contain "hot spots," small patches that are the main mediators of binding affinity. By making tight interactions with a hot spot, a small molecule can compete with a protein. The Tcf3/Tcf4-binding surface on beta-catenin contains a well-defined hot spot around residues K435 and R469. A 17,700 compounds subset of the Pharmacia corporate collection was docked to this hot spot with the QXP program; 22 of the best scoring compounds were put into a biophysical (NMR and ITC) screening funnel, where specific binding to beta-catenin, competition with Tcf4 and finally binding constants were determined. This process led to the discovery of three druglike, low molecular weight Tcf4-competitive compounds with the tightest binder having a K(D) of 450 nM. Our approach can be used in several situations (e.g., when selecting compounds from external collections, when no biochemical functional assay is available, or when no HTS is envisioned), and it may be generally applicable to the identification of inhibitors of protein-protein interactions.

Gated binding of ligands to HIV-1 protease: Brownian dynamics simulations in a coarse-grained model

The internal motions of proteins may serve as a "gate" in some systems, which controls ligand-protein association. This study applies Brownian dynamics simulations in a coarse-grained model to study the gated association rate constants of HIV-1 proteases and drugs. The computed gated association rate constants of three protease mutants, G48V/V82A/I84V/L90M, G48V, and L90M with three drugs, amprenavir, indinavir, and saquinavir, yield good agreements with experiments. The work shows that the flap dynamics leads to "slow gating". The simulations suggest that the flap flexibility and the opening frequency of the wild-type, the G48V and L90M mutants are similar, but the flaps of the variant G48V/V82A/I84V/L90M open less frequently, resulting in a lower gated rate constant. The developed methodology is fast and provides an efficient way to predict the gated association rate constants for various protease mutants and ligands.