Huan-Xiang Zhou

Macromolecular Crowding and Confinement: Biochemical, Biophysical, and Potential Physiological Consequences*

Expected and observed effects of volume exclusion on the free energy of rigid and flexible macromolecules in crowded and confined systems, and consequent effects of crowding and confinement on macromolecular reaction rates and equilibria are summarized. Findings from relevant theoretical/simulation and experimental literature published from 2004 onward are reviewed. Additional complexity arising from the heterogeneity of local environments in biological media, and the presence of nonspecific interactions between macromolecules over and above steric repulsion, are discussed. Theoretical and experimental approaches to the characterization of crowding- and confinement-induced effects in systems approaching the complexity of living organisms are suggested.

Fundamental Aspects of Protein-Protein Association Kinetics

The structure of a protein complex, together with information about its affinity and other thermodynamic characteristics, provide a “frozen” view of the complex. This picture ignores the kinetic nature of protein-protein association and dissociation, which are of major biological and biophysical interest. This review focuses on recent advances in deciphering the kinetic pathway of protein complex formation, the nature of the pre-complex formed through diffusion (which we have termed the “transient complex”1), the transition state, and other intermediates (such as the so-called encounter complex) along the association pathway. Protein-protein association is at the center of diverse biological processes ranging from enzyme catalysis/inhibition to regulation of immune response by cytokines. The association rates often play a critical role in such processes, as in situations where speed is of essence.2 For example, the purple cone snail and other venomous animals capture prey with remarkable efficiency and speed by releasing toxins that rapidly bind to ion channels;3 the green mamba achieves a similar feat by targeting acetylcholinesterase (AChE), an enzyme essential for the integrity of neural transmission.4 Bacteria such as Escherichia coli and Bacillus amyloliquefaciens excrete nucleases as weapons against competitors or predators. Defense of the producing cells from damage to their own DNA or RNA by such nucleases requires rapid association with cognate inhibitors.5,6 Indeed, in the last example rapid association is such a priority that the inhibitor barstar has a cluster of acidic residues that facilitate association with the nuclease barnase, even though the clustered charges reduce folding stability.7 In the ruminant gut, RNase A is required for degrading accumulated RNA; potential toxicity of leaked nuclease is prevented by rapid association with a ribonuclease inhibitor.8,9 Reorganization of the actin cytoskeleton provides yet another illustration of the importance of rapid protein association. Reorganization is attained through actin polymerization, which is nucleated by the Arp2/3 complex. The latter is activated by the Wiskott-Aldrich Syndrome protein (WASp), which in turn is released from the auto-inhibited state by the Rho GTPase Cdc42.10 As actin polymerization is initiated with a nucleation process, the speed of upstream signaling has a critical impact on the rate of polymer formation. It is thus not surprising that high association rate constants have been observed between partners along the signaling pathway.11,12 The high association rate constant between Cdc42 and WASp has been found to be essential for the latter to stimulate actin polymerization, as another Rho GTPase sharing 70% sequence identity, TC10, with an identical dissociation rate constant but a 1000-fold lower association rate constant, failed to stimulate actin polymerization.11 The failure to stimulate actin polymerization in patients carrying mutant WAS genes is the root cause of the Wiskott-Aldrich Syndrome. Several other compelling arguments can be made for the biological roles of rapid protein association.13 (a) Fast association may enhance binding affinity. High affinity can also be achieved through slow dissociation; however, for proteins involved in signaling, slow dissociation is not an option, since it implies a long-lasting bound state, which effectively corresponds to a permanent off- or on-switch. A good example for this is the binding of Ras to its natural affector Raf. This protein dissociates within a fraction of a second, but maintains an affinity in the nM range through fast association. Moreover, the difference between the natural effector, Raf, and the non-natural effector, Ral, lies in their rates of association with Ras.14 Therefore, even if not for a direct reason (such as in stimulation of actin polymerization), the affinity requirement alone may call for fast association. (b) Enzyme-substrate binding is a determining factor for the overall turnover rate and becomes the rate-limiting step for catalytically “perfect” enzymes. Substrate-binding rate constants of such enzymes reach 108 M−1s−1 and beyond, as found for the ribotoxin restrictocin and RNase A.15,16 (c) When several proteins compete for the same receptor or when one protein is faced with alternative pathways, kinetic control, not thermodynamic control, dominates in many cases; this is especially true when dissociation is slow. For example, during protein synthesis cognate and noncognate aminoacyl-tRNA synthetases can potentially compete for the same tRNA. As an additional example, consider newly synthesized proteins, which potentially face aggregation if not isolated by a chaperone. From the point of view of kinetic control, it is easy to see why rapid binding of denatured proteins to the chaperonin GroEL has been observed.17 (d) Differences in binding rate between related proteins may serve as an additional mechanism for specificity, as can be suggested for Rho GTPases Cdc42 and TC10 and for Ras effectors Raf and Ral. The examples and arguments presented above suggest that rapid binding is as important as high affinity in the proper functioning of proteins. It is now increasingly recognized that proteins function in the context of multi-component complexes. Manipulating association rate constants of various components presents unique opportunities for the control of protein functions. Many interactions between proteins are also targeted for drug development; in designing such drugs, both high affinity and rapid binding should be taken into consideration. 1.1. Overview of Protein Association Kinetics The observed rate constants of protein association span a wide range, from 109 M−1s−1 (Figure 1). In comprehending these values, a basic fact is that, for two proteins to recognize each other, their interfaces have to be oriented with high specificity. A relative rotation of as little as a few degrees or a relative translation by a few Angstroms is sufficient to break all specific interactions between the two proteins.18 The rate of association of a protein complex is limited by diffusion and geometric constraints of the binding sites, and may be further reduced by subsequent chemical processes.19 Figure 1 The wide spectrum of association rate constants. The red vertical line marks the start of the diffusion-controlled regime. The shaded range marks the absence of long-range forces. Adapted with permission from Ref. 1. Copyright 2008 Wiley Interscience.. ... To better understand the kinetics of association of two proteins (A and B), it is useful to consider the process as going through an intermediate state (A*B), in which the two proteins have near-native separations and orientations.1,20–23 We refer to this intermediate state as the transient complex,1,20 noting that is sometimes also termed the encounter complex.24 A more detailed discussion of terminology, as well as the specification of the ensemble of configurations making up the transient complex is provided in Section 3. From this ensemble, conformational rearrangement can lead to the native complex (C). Accordingly we have the kinetic scheme A+B⇄k−DkDA∗B→kcC (1) While the first step of this scheme depends on relative diffusion between the protein molecules, the second step is akin to an intramolecular chemical reaction, and can therefore be described by the classical transition-state theory25 (with the transition state located at the top of the free energy barrier separating A*B from C26) or by Kramers’ theory.27 The latter theory accounts for barrier recrossing and models motion along the reaction coordinate as diffusive. The overall rate constant of association is ka=kDkck−D+kc (2) which is bounded by the diffusion-controlled rate constant, kD, for reaching the transient complex. This limit is reached when conformational rearrangement is fast relative to the dissociation of the transient complex (i.e., kc ≫ k−D), leading to

Insight into the mechanism of the influenza A proton channel from a structure in a lipid bilayer.

M2 Out of the Envelope The M2 protein from influenza A virus forms an acid-activated tetrameric proton channel in the viral envelope and is essential for viral replication. Two manuscripts shed light on the functional mechanism of this channel. Sharma et al. (p. 509; see the Perspective by Fiorin et al.) determined the structure of the conductance domain in a lipid bilayer and propose that a histidine and tryptophan from each monomer form a cluster that guides protons through the channel in a mechanism that involves forming and breaking hydrogen bonds between adjacent pairs of histidines. Hu et al. (p. 505; see the Perspective by Fiorin et al.) focused on the structure and dynamics of the proton-selective histidine at high and low pH, proposing that proton conduction involves histidine deprotonation and reprotonation. A tetrameric cluster of histidine and tryptophan residues, through its unique chemistry, shepherds protons through the M2 channel. The M2 protein from the influenza A virus, an acid-activated proton-selective channel, has been the subject of numerous conductance, structural, and computational studies. However, little is known at the atomic level about the heart of the functional mechanism for this tetrameric protein, a His37-Trp41 cluster. We report the structure of the M2 conductance domain (residues 22 to 62) in a lipid bilayer, which displays the defining features of the native protein that have not been attainable from structures solubilized by detergents. We propose that the tetrameric His37-Trp41 cluster guides protons through the channel by forming and breaking hydrogen bonds between adjacent pairs of histidines and through specific interactions of the histidines with the tryptophan gate. This mechanism explains the main observations on M2 proton conductance.

Prediction of protein interaction sites from sequence profile and residue neighbor list.

Protein–protein interaction sites are predicted from a neural network with sequence profiles of neighboring residues and solvent exposure as input. The network was trained on 615 pairs of nonhomologous complex‐forming proteins. Tested on a different set of 129 pairs of nonhomologous complex‐forming proteins, 70% of the 11,004 predicted interface residues are actually located in the interfaces. These 7732 correctly predicted residues account for 65% of the 11,805 residues making up the 129 interfaces. The main strength of the network predictor lies in the fact that neighbor lists and solvent exposure are relatively insensitive to structural changes accompanying complex formation. As such, it performs equally well with bound or unbound structures of the proteins. For a set of 35 test proteins, when the input was calculated from the bound and unbound structures, the correct fractions of the predicted interface residues were 69 and 70%, respectively. Proteins 2001;44:336–343.

Prediction of interface residues in protein-protein complexes by a consensus neural network method: test against NMR data.

The number of structures of protein–protein complexes deposited to the Protein Data Bank is growing rapidly. These structures embed important information for predicting structures of new protein complexes. This motivated us to develop the PPISP method for predicting interface residues in protein–protein complexes. In PPISP, sequence profiles and solvent accessibility of spatially neighboring surface residues were used as input to a neural network. The network was trained on native interface residues collected from the Protein Data Bank. The prediction accuracy at the time was 70% with 47% coverage of native interface residues. Now we have extensively improved PPISP. The training set now consisted of 1156 nonhomologous protein chains. Test on a set of 100 nonhomologous protein chains showed that the prediction accuracy is now increased to 80% with 51% coverage. To solve the problem of over‐prediction and under‐prediction associated with individual neural network models, we developed a consensus method that combines predictions from multiple models with different levels of accuracy and coverage. Applied on a benchmark set of 68 proteins for protein–protein docking, the consensus approach outperformed the best individual models by 3–8 percentage points in accuracy. To demonstrate the predictive power of cons‐PPISP, eight complex‐forming proteins with interfaces characterized by NMR were tested. These proteins are nonhomologous to the training set and have a total of 144 interface residues identified by chemical shift perturbation. cons‐PPISP predicted 174 interface residues with 69% accuracy and 47% coverage and promises to complement experimental techniques in characterizing protein–protein interfaces. Proteins 2005.

Influences of Membrane Mimetic Environments on Membrane Protein Structures

The number of membrane protein structures in the Protein Data Bank is becoming significant and growing. Here, the transmembrane domain structures of the helical membrane proteins are evaluated to assess the influences of the membrane mimetic environments. Toward this goal, many of the biophysical properties of membranes are discussed and contrasted with those of the membrane mimetics commonly used for structure determination. Although the mimetic environments can perturb the protein structures to an extent that potentially gives rise to misinterpretation of functional mechanisms, there are also many structures that have a native-like appearance. From this assessment, an initial set of guidelines is proposed for distinguishing native-like from nonnative-like membrane protein structures. With experimental techniques for validation and computational methods for refinement and quality assessment and enhancement, there are good prospects for achieving native-like structures for these very important proteins.

Influence of Solubilizing Environments on Membrane Protein Structures

Membrane protein structures are stabilized by weak interactions and are influenced by additional interactions with the solubilizing environment. Structures of influenza virus A M2 protein, a proven drug target, have been determined in three different environments, thus providing a unique opportunity to assess environmental influences. Structures determined in detergents and detergent micelles can have notable differences from those determined in lipid bilayers. These differences make it imperative to validate membrane protein structures.

Community-wide assessment of protein-interface modeling suggests improvements to design methodology

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

From Induced Fit to Conformational Selection: A Continuum of Binding Mechanism Controlled by the Timescale of Conformational Transitions

In receptor-ligand binding, a question that generated considerable interest is whether the mechanism is induced fit or conformational selection. This question is addressed here by a solvable model, in which a receptor undergoes transitions between active and inactive forms. The inactive form is favored while unbound but the active form is favored while a ligand is loosely bound. As the active-inactive transition rates increase, the binding mechanism gradually shifts from conformational selection to induced fit. The timescale of conformational transitions thus plays a crucial role in controlling binding mechanisms.

Electrostatic rate enhancement and transient complex of protein–protein association

The association of two proteins is bounded by the rate at which they, via diffusion, find each other while in appropriate relative orientations. Orientational constraints restrict this rate to ∼105–106 M−1 s−1. Proteins with higher association rates generally have complementary electrostatic surfaces; proteins with lower association rates generally are slowed down by conformational changes upon complex formation. Previous studies (Zhou, Biophys J 1997;73:2441–2445) have shown that electrostatic enhancement of the diffusion‐limited association rate can be accurately modeled by