Circular dichroism spectroscopic assessment of structural changes upon protein thermal unfolding at contrasting pH: Comparison with molecular dynamics simulations.

In most instances, the usual fastness of protein unfolding events hinders determining changes in secondary structures associated with this process because these determinations rely on the recording of high-resolution circular dichroism (CD) spectra. In this work, far-UV CD spectra, recorded at ten-minute intervals, were used to evaluate the time course followed by four classes of secondary structures in the slow temperature-induced unfolding of yeast triosephosphate isomerase (yTIM) under distinct pH conditions. CONTIN-LL and SELCON3 algorithms were used for the deconvolution of spectra. Both algorithms furnished helix and unordered structure contents that changed according to first-order kinetics, agreeing with the behavior shown by CD data at specific wavelengths. Analyses of unfolded yTIM spectra, using a dataset that includes spectra of unfolded proteins and either one of the two algorithms, clearly showed a more unordered protein structure at high pH; this finding was corroborated with analysis of the difference spectra. Molecular dynamics (MD) simulations performed with AMBER and OPLS force fields resulted in more extensive loss of helices and gain in coils at high pH, in agreement with spectroscopic results. However, structural differences between low- and high-pH unfolded yTIM were relatively small. Comparison of results from CD and MD thus point to the need of fine-tuning of MD procedures.

Validation of DBFOLD: An efficient algorithm for computing folding pathways of complex proteins.

Atomistic simulations can provide valuable, experimentally-verifiable insights into protein folding mechanisms, but existing ab initio simulation methods are restricted to only the smallest proteins due to severe computational speed limits. The folding of larger proteins has been studied using native-centric potential functions, but such models omit the potentially crucial role of non-native interactions. Here, we present an algorithm, entitled DBFOLD, which can predict folding pathways for a wide range of proteins while accounting for the effects of non-native contacts. In addition, DBFOLD can predict the relative rates of different transitions within a protein's folding pathway. To accomplish this, rather than directly simulating folding, our method combines equilibrium Monte-Carlo simulations, which deploy enhanced sampling, with unfolding simulations at high temperatures. We show that under certain conditions, trajectories from these two types of simulations can be jointly analyzed to compute unknown folding rates from detailed balance. This requires inferring free energies from the equilibrium simulations, and extrapolating transition rates from the unfolding simulations to lower, physiologically-reasonable temperatures at which the native state is marginally stable. As a proof of principle, we show that our method can accurately predict folding pathways and Monte-Carlo rates for the well-characterized Streptococcal protein G. We then show that our method significantly reduces the amount of computation time required to compute the folding pathways of large, misfolding-prone proteins that lie beyond the reach of existing direct simulation. Our algorithm, which is available online, can generate detailed atomistic models of protein folding mechanisms while shedding light on the role of non-native intermediates which may crucially affect organismal fitness and are frequently implicated in disease.

On-Chip Acousto Thermal Shift Assay for Rapid and Sensitive Assessment of Protein Thermodynamic Stability.

Thermal shift assays (TSAs) have been extensively used to study thermodynamics of proteins and provide an efficient means to assess protein-ligand binding or protein-protein interactions. However, existing TSAs have limitations, such as being time consuming, labor intensive, or having low sensitivity. Herein, an acousto thermal shift assay (ATSA), the first ultrasound enabled TSA, is reported for real-time analysis of protein thermodynamic stability. It capitalizes the coupling of unique acoustic mechanisms to achieve protein unfolding, concentration, and measurement on a single microfluidic chip within minutes. Compared to conventional TSA methods, the ATSA technique enables ultrafast (at least 30 times faster), highly sensitive (7-34 folds higher), and label-free monitoring of protein-ligand interactions and protein stability. ATSA paves new avenues for protein analysis in biology, medicine, and fast diagnosis.

An application of Nano Differential Scanning Fluorimetry for Higher Order Structure assessment between mAb originator and biosimilars: Trastuzumab and Rituximab as case studies.

Differential scanning fluorimetry (DSF) or thermal shift has emerged in recent years as a high-throughput screening method in biotherapeutic formulation studies. The present article reports on a fast-track assessment platform for rapid investigation of therapeutic proteins such as monoclonal antibodies (mAb) with minimal sample concentration, volume, and preparation. The proposed nanoDSF platform has been demonstrated for rapid assessment of two commercial IgG 1 drug products (DP), trastuzumab and rituximab, and their biosimilars with respect to their conformational and colloidal stability. Domain specific differences for each of the IgGs have been elucidated with respect to onset of domain unfolding (Tonset) and melting temperatures. These thermal unfolding and transition midpoint (Tm) measurements are based on the intrinsic aromatic amino acid residue fluorescence of proteins. Moreover, to understand the possibility of nanoDSF as a predictive tool, data from nanoDSF has been correlated with accelerated stability studies. Melting temperatures across brands were found to be highly comparable to the rate of heating, thereby exhibiting a significant domain specific effect on melting temperatures for both trastuzumab and rituximab. Conservation of higher order structure (HOS) through reversible unfolding was also examined and both the mAbs were found to regain tertiary structure up till the first transition midpoint. No clear correlation was found between formation of higher molecular weight species (HMWS) and unfolding parameters (Tonset and Tagg) for accelerated stability studies. Finally, a discussion on the need for fast predictive assessment of conformation and colloidal stability as well as a comparison of advantages and limitations of the technique with routine/classical tools such as circular dichroism spectrophotometry and differential scanning calorimetry has been presented.

pH and Charged Mutations Modulate Cold Shock Protein Folding and Stability: A Constant pH Monte Carlo Study.

The folding and stability of proteins is a fundamental problem in several research fields. In the present paper, we have used different computational approaches to study the effects caused by changes in pH and for charged mutations in cold shock proteins from Bacillus subtilis (Bs-CspB). First, we have investigated the contribution of each ionizable residue for these proteins to their thermal stability using the TKSA-MC, a Web server for rational mutation via optimizing the protein charge interactions. Based on these results, we have proposed a new mutation in an already optimized Bs-CspB variant. We have evaluated the effects of this new mutation in the folding energy landscape using structure-based models in Monte Carlo simulation at constant pH, SBM-CpHMC. Our results using this approach have indicated that the charge rearrangements already in the unfolded state are critical to the thermal stability of Bs-CspB. Furthermore, the conjunction of these simplified methods was able not only to predict stabilizing mutations in different pHs but also to provide essential information about their effects in each stage of protein folding.

Thermal versus mechanical unfolding in a model protein.

Force spectroscopy techniques are often used to learn about the free energy landscape of single biomolecules, typically by recovering free energy quantities that, extrapolated to zero force, are compared to those measured in bulk experiments. However, it is not always clear how the information obtained from a mechanically perturbed system can be related to the information obtained using other denaturants since tensioned molecules unfold and refold along a reaction coordinate imposed by the force, which is not likely to be meaningful in its absence. Here, we explore this dichotomy by investigating the unfolding landscape of a model protein, which is unfolded first mechanically through typical force spectroscopy-like protocols and next thermally. When unfolded by nonequilibrium force extension and constant force protocols, we recover a simple two-barrier landscape as the protein reaches the extended conformation through a metastable intermediate. Interestingly, folding-unfolding equilibrium simulations at low forces suggested a totally different scenario, where this metastable state plays little role in the unfolding mechanism, and the protein unfolds through two competing pathways [R. Tapia-Rojo et al., J. Chem. Phys. 141, 135102 (2014)]. Finally, we use Markov state models to describe the configurational space of the unperturbed protein close to the critical temperature. The thermal dynamics is well understood by a one-dimensional landscape along an appropriate reaction coordinate, however it is very different from the mechanical picture. In this sense, the results of our protein model for the mechanical and thermal descriptions provide incompatible views of the folding/unfolding landscape of the system, and the estimated quantities to zero force result are hard to interpret.

Stability and Local Unfolding of SOD1 in the Presence of Protein Crowders.

Using NMR and Monte Carlo (MC) methods, we investigate the stability and dynamics of superoxide dismutase 1 (SOD1) in homogeneous crowding environments, where either bovine pancreatic trypsin inhibitor (BPTI) or the B1 domain of streptococcal protein G (PGB1) serves as a crowding agent. By NMR, we show that both crowders, and especially BPTI, cause a drastic loss in the overall stability of SOD1 in its apo monomeric form. Additionally, we determine chemical shift perturbations indicating that SOD1 interacts with the crowder proteins in a residue-specific manner that further depends on the identity of the crowding protein. Furthermore, the specificity of SOD1-crowder interactions is reciprocal: chemical shift perturbations on BPTI and PGB1 identify regions that interact preferentially with SOD1. By MC simulations, we investigate the local unfolding of SOD1 in the absence and presence of the crowders. We find that the crowders primarily interact with the long flexible loops of the folded SOD1 monomer. The basic mechanisms by which the SOD1 ß-barrel core unfolds remain unchanged when adding the crowders. In particular, both with and without the crowders, the second ß-sheet of the barrel is more dynamic and unfolding-prone than the first. Notably, the MC simulations (exploring the early stages of SOD1 unfolding) and the NMR experiments (under equilibrium conditions) identify largely the same set of PGB1 and BPTI residues as prone to form SOD1 contacts. Thus, contacts stabilizing the unfolded state of SOD1 in many cases appear to form early in the unfolding reaction.

Dissociation Process of a MDM2/p53 Complex Investigated by Parallel Cascade Selection Molecular Dynamics and the Markov State Model.

Recently, we efficiently generated dissociation pathways of a protein-ligand complex without applying force bias with parallel cascade selection molecular dynamics (PaCS-MD) and showed that PaCS-MD in combination with the Markov state model (MSM) yielded a binding free energy comparable to experimental values. In this work, we applied the same procedure to a complex of MDM2 protein and the transactivation domain of p53 protein (TAD-p53), the latter of which is known to be very flexible in the unbound state. Using 30 independent MD simulations in PaCS-MD, we successfully generated 25 dissociation pathways of the complex, which showed complete or partial unfolding of the helical region of TAD-p53 during the dissociation process within an average simulation time of 154.8 ± 46.4 ns. The standard binding free energy obtained in combination with one-dimensional-, three-dimensional (3D)- or Cα-MSM was in good agreement with those determined experimentally. Using 3D-MSM based on the center of mass position of TAD-p53 relative to MDM2, the dissociation rate constant was calculated, which was comparable to those measured experimentally. Cα-MSM based on all Cα coordinates of TAD-p53 reproduced the experimentally measured standard binding free energy, and dissociation and association rate constants. We conclude that the combination of PaCS-MD and MSM offers an efficient computational procedure to calculate binding free energies and kinetic rates.

Maximizing Kinetic Information Gain of Markov State Models for Optimal Design of Spectroscopy Experiments.

Spectroscopic techniques such as Trp-Tyr quenching, luminescence resonance energy transfer, and triplet-triplet energy transfer are widely used for understanding the dynamic behavior of proteins. These experiments measure the relaxation of a particular labeled set of residue pairs, and the choice of residue pairs requires careful thought. As a result, experimentalists must pick residue pairs from a large pool of possibilities. In the current work, we show that molecular simulation datasets of protein dynamics can be used to systematically select an optimal set of residue positions to place probes for conducting spectroscopic experiments. The method described in this work, called Optimal Probes, can be used to rank trial sets of residue pairs in terms of their ability to capture the conformational dynamics of the protein. Optimal probes ensures two conditions: residue pairs capture the slow dynamics of the protein and their dynamics is not correlated for maximum information gain to score each trial set. Eventually, the highest scored set can be used for biophysical experiments to study the kinetics of the protein. The scoring methodology is based on kinetic network models of protein dynamics and a variational principle for molecular kinetics to optimize the hyperparameters used for the model. We also discuss that the scoring strategy used by Optimal Probes is the best possible way to ensure the ideal choice of residue pairs for experiments. We predict the best experimental probe positions for proteins λ-repressor, ß2-adrenergic receptor, and villin headpiece domain. These proteins have been well-studied and allow for a rigorous comparison of Optimal Probes predictions with already available experiments. Additionally, we also illustrate that our method can be used to predict the best choice for experiments by including any previous experiment choices available from other studies on the same protein. We consistently find that the best choice cannot be based on intuition or structural information such as distance difference between few known stable structures of the protein. Therefore, we show that incorporating protein dynamics could be used to maximize the information gain from experiments.

Nonproductive Binding Modes as a Prominent Feature of Aß40 Fiber Elongation: Insights from Molecular Dynamics Simulation.

The formation of amyloid fibers has been implicated in a number of neurodegenerative diseases. The growth of amyloid fibers is strongly thermodynamically favorable, but kinetic traps exist where the incoming monomer binds in an incompatible conformation that blocks further elongation. Unfortunately, this process is difficult to follow experimentally at the atomic level. It is also too complex to simulate in full detail and to date has been explored either through coarse-grained simulations, which may miss many important interactions, or full atomic simulations, in which the incoming peptide is constrained to be near the ideal fiber geometry. Here we use an alternate approach starting from a docked complex in which the monomer is from an experimental NMR structure of one of the major conformations in the unbound ensemble, a largely unstructured peptide with the central hydrophobic region in a 310 helix. A 1000 ns full atomic simulation in explicit solvent shows the formation of a metastable intermediate by sequential, concerted movements of both the fiber and the monomer. A Markov state model shows that the unfolded monomer is trapped at the end of the fiber in a set of interconverting antiparallel ß-hairpin conformations. The simulation here may serve as a model for the binding of other non-ß-sheet conformations to amyloid fibers.

Rare Dissipative Transitions Punctuate the Initiation of Chemical Denaturation in Proteins.

Protein unfolding dynamics are bound by their degree of entropy production, a quantity that relates the amount of heat dissipated by a nonequilibrium process to a system's forward and time-reversed trajectories. We here explore the statistics of heat dissipation that emerge in protein molecules subjected to a chemical denaturant. Coupling large molecular dynamics datasets and Markov state models with the theory of entropy production, we demonstrate that dissipative processes can be rigorously characterized over the course of the urea-induced unfolding of the protein chymotrypsin inhibitor 2. By enumerating full entropy production probability distributions as a function of time, we first illustrate that distinct passive and dissipative regimes are present in the denaturation dynamics. Within the dissipative dynamical region, we next find that chymotrypsin inhibitor 2 is strongly driven into unfolded states in which the protein's hydrophobic core has been penetrated by urea molecules and disintegrated. Detailed analyses reveal that urea's interruption of key hydrophobic contacts between core residues causes many of the protein's native structural features to dissolve.

Development of isothermal-isobaric replica-permutation method for molecular dynamics and Monte Carlo simulations and its application to reveal temperature and pressure dependence of folded, misfolded, and unfolded states of chignolin.

We developed a two-dimensional replica-permutation molecular dynamics method in the isothermal-isobaric ensemble. The replica-permutation method is a better alternative to the replica-exchange method. It was originally developed in the canonical ensemble. This method employs the Suwa-Todo algorithm, instead of the Metropolis algorithm, to perform permutations of temperatures and pressures among more than two replicas so that the rejection ratio can be minimized. We showed that the isothermal-isobaric replica-permutation method performs better sampling efficiency than the isothermal-isobaric replica-exchange method and infinite swapping method. We applied this method to a ß-hairpin mini protein, chignolin. In this simulation, we observed not only the folded state but also the misfolded state. We calculated the temperature and pressure dependence of the fractions on the folded, misfolded, and unfolded states. Differences in partial molar enthalpy, internal energy, entropy, partial molar volume, and heat capacity were also determined and agreed well with experimental data. We observed a new phenomenon that misfolded chignolin becomes more stable under high-pressure conditions. We also revealed this mechanism of the stability as follows: TYR2 and TRP9 side chains cover the hydrogen bonds that form a ß-hairpin structure. The hydrogen bonds are protected from the water molecules that approach the protein as the pressure increases.

Conformational Heterogeneity and FRET Data Interpretation for Dimensions of Unfolded Proteins.

A mathematico-physically valid formulation is required to infer properties of disordered protein conformations from single-molecule Förster resonance energy transfer (smFRET). Conformational dimensions inferred by conventional approaches that presume a homogeneous conformational ensemble can be unphysical. When all possible-heterogeneous as well as homogeneous-conformational distributions are taken into account without prejudgment, a single value of average transfer efficiency 〈E〉 between dyes at two chain ends is generally consistent with highly diverse, multiple values of the average radius of gyration 〈Rg〉. Here we utilize unbiased conformational statistics from a coarse-grained explicit-chain model to establish a general logical framework to quantify this fundamental ambiguity in smFRET inference. As an application, we address the long-standing controversy regarding the denaturant dependence of 〈Rg〉 of unfolded proteins, focusing on Protein L as an example. Conventional smFRET inference concluded that 〈Rg〉 of unfolded Protein L is highly sensitive to [GuHCl], but data from SAXS suggested a near-constant 〈Rg〉 irrespective of [GuHCl]. Strikingly, our analysis indicates that although the reported 〈E〉 values for Protein L at [GuHCl] = 1 and 7 M are very different at 0.75 and 0.45, respectively, the Bayesian Rg2 distributions consistent with these two 〈E〉 values overlap by as much as 75%. Our findings suggest, in general, that the smFRET-SAXS discrepancy regarding unfolded protein dimensions likely arise from highly heterogeneous conformational ensembles at low or zero denaturant, and that additional experimental probes are needed to ascertain the nature of this heterogeneity.

Peptide-Level Interactions between Proteins and Small-Molecule Drug Candidates by Two Hydrogen-Deuterium Exchange MS-Based Methods: The Example of Apolipoprotein E3.

We describe a platform utilizing two methods based on hydrogen-deuterium exchange (HDX) coupled with mass spectrometry (MS) to characterize interactions between a protein and a small-molecule ligand. The model system is apolipoprotein E3 (apoE3) and a small-molecule drug candidate. We extended PLIMSTEX (protein-ligand interactions by mass spectrometry, titration, and H/D exchange) to the regional level by incorporating enzymatic digestion to acquire binding information for peptides. In a single experiment, we not only identified putative binding sites, but also obtained affinities of 6.0, 6.8, and 10.6 µM for the three different regions, giving an overall binding affinity of 7.4 µM. These values agree well with literature values determined by accepted methods. Unlike those methods, PLIMSTEX provides site-specific binding information. The second approach, modified SUPREX (stability of unpurified proteins from rates of H/D exchange) coupled with electrospray ionization (ESI), allowed us to obtain detailed understanding about apoE unfolding and its changes upon ligand binding. Three binding regions, along with an additional site, which may be important for lipid binding, show increased stability (less unfolding) upon ligand binding. By employing a single parameter, ΔC1/2%, we compared relative changes of denaturation between peptides. This integrated platform provides information orthogonal to commonly used HDX kinetics experiments, providing a general and novel approach for studying protein-ligand interactions.

All-Atom Simulations Reveal Protein Charge Decoration in the Folded and Unfolded Ensemble Is Key in Thermophilic Adaptation.

Thermophilic proteins denature at much higher temperature compared to their mesophilic homologues, in spite of high structural and sequential similarity. Computational approaches to understand this puzzle face three major challenges: (i) unfolded ensembles are usually neglected, (ii) simulation studies of the folded states are often too short, and (iii) the majority of investigations focus on a few protein pairs, obscuring the prevalence of different strategies across multiple protein systems. We address these concerns by carrying out all-atom simulations to characterize physicochemical properties of both the folded and the disordered ensemble in multiple (12) thermophilic-mesophilic homologous protein pairs. We notice two clear trends in most pairs (10 out of 12). First, specific distribution of charges in the native basin-sampled from multimicrosecond long Molecular Dynamics (MD) simulation trajectories-leads to more favorable electrostatic interaction energy in thermophiles compared to mesophiles. Next, thermophilic proteins have lowered electrostatic interaction in their unfolded state-generated using Monte Carlo (MC) simulation-compared to their mesophilic counterparts. The net contribution of interaction energy to folding stability, however, remains more favorable in thermophiles compared to mesophiles. The overall contribution of electrostatics quantified by combining the net interaction energy and the solvation penalty of folding-due to differential charge burial in the folded and the unfolded ensemble-is also mostly favorable in thermophilic proteins compared to mesophiles. The systems that deviate from this trend provide interesting test cases to learn more about alternate design strategies when modification of charges is not viable due to functional reasons. The unequal contribution of the unfolded state to the stability in thermophiles and mesophiles highlights the importance of modeling the disordered ensemble to understand thermophilic adaptation as well as protein stability, in general. Our integrated approach-combining finite element analysis with MC and MD-can be useful in designing charge mutations to alter protein stability.

Comparing pairwise-additive and many-body generalized Born models for acid/base calculations and protein design.

Generalized Born (GB) solvent models are common in acid/base calculations and protein design. With GB, the interaction between a pair of solute atoms depends on the shape of the protein/solvent boundary and, therefore, the positions of all solute atoms, so that GB is a many-body potential. For compute-intensive applications, the model is often simplified further, by introducing a mean, native-like protein/solvent boundary, which removes the many-body property. We investigate a method for both acid/base calculations and protein design that uses Monte Carlo simulations in which side chains can explore rotamers, bind/release protons, or mutate. The fluctuating protein/solvent dielectric boundary is treated in a way that is numerically exact (within the GB framework), in contrast to a mean boundary. Its originality is that it captures the many-body character while retaining the residue-pairwise complexity given by a fixed boundary. The method is implemented in the Proteus protein design software. It yields a slight but systematic improvement for acid/base constants in nine proteins and a significant improvement for the computational design of three PDZ domains. It eliminates a source of model uncertainty, which will facilitate the analysis of other model limitations. © 2017 Wiley Periodicals, Inc.

Reconciling Intermediates in Mechanical Unfolding Experiments with Two-State Protein Folding in Bulk.

Most experimentally well-characterized single domain proteins of less than 100 residues have been found to be two-state folders. That is, only two distinct populations can explain both equilibrium and kinetic measurements. Results from single molecule force spectroscopy, where a protein is unfolded by applying a mechanical pulling force to its ends, have largely confirmed this description for proteins found to be two-state in ensemble experiments. Recently, however, stable intermediates have been reported in mechanical unfolding experiments on a cold-shock protein previously found to be a prototypical two-state folder. Here, we tackle this discrepancy using free energy landscapes and Markov state models derived from coarse-grained molecular simulations. We show that protein folding intermediates can be selectively stabilized by the pulling force and that the populations of these intermediates vary in a force-dependent manner. Our model qualitatively captures the experimental results and suggests a possible origin of the apparent discrepancy.

A Simple Model of Protein Domain Swapping in Crowded Cellular Environments.

Domain swapping in proteins is an important mechanism of functional and structural innovation. However, despite its ubiquity and importance, the physical mechanisms that lead to domain swapping are poorly understood. Here, we present a simple two-dimensional coarse-grained model of protein domain swapping in the cytoplasm. In our model, two-domain proteins partially unfold and diffuse in continuous space. Monte Carlo multiprotein simulations of the model reveal that domain swapping occurs at intermediate temperatures, whereas folded dimers and folded monomers prevail at low temperatures, and partially unfolded monomers predominate at high temperatures. We use a simplified amino acid alphabet consisting of four residue types, and find that the oligomeric state at a given temperature depends on the sequence of the protein. We also show that hinge strain between domains can promote domain swapping, consistent with experimental observations for real proteins. Domain swapping depends nonmonotonically on the protein concentration, with domain-swapped dimers occurring at intermediate concentrations and nonspecific interactions between partially unfolded proteins occurring at high concentrations. For folded proteins, we recover the result obtained in three-dimensional lattice simulations, i.e., that functional dimerization is most prevalent at intermediate temperatures and nonspecific interactions increase at low temperatures.

Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models.

Because standard molecular dynamics (MD) simulations are unable to access time scales of interest in complex biomolecular systems, it is common to "stitch together" information from multiple shorter trajectories using approximate Markov state model (MSM) analysis. However, MSMs may require significant tuning and can yield biased results. Here, by analyzing some of the longest protein MD data sets available (>100 µs per protein), we show that estimators constructed based on exact non-Markovian (NM) principles can yield significantly improved mean first-passage times (MFPTs) for protein folding and unfolding. In some cases, MSM bias of more than an order of magnitude can be corrected when identical trajectory data are reanalyzed by non-Markovian approaches. The NM analysis includes "history" information, higher order time correlations compared to MSMs, that is available in every MD trajectory. The NM strategy is insensitive to fine details of the states used and works well when a fine time-discretization (i.e., small "lag time") is used.

Conformational heterogeneity of the calmodulin binding interface.

Calmodulin (CaM) is a ubiquitous Ca(2+) sensor and a crucial signalling hub in many pathways aberrantly activated in disease. However, the mechanistic basis of its ability to bind diverse signalling molecules including G-protein-coupled receptors, ion channels and kinases remains poorly understood. Here we harness the high resolution of molecular dynamics simulations and the analytical power of Markov state models to dissect the molecular underpinnings of CaM binding diversity. Our computational model indicates that in the absence of Ca(2+), sub-states in the folded ensemble of CaM's C-terminal domain present chemically and sterically distinct topologies that may facilitate conformational selection. Furthermore, we find that local unfolding is off-pathway for the exchange process relevant for peptide binding, in contrast to prior hypotheses that unfolding might account for binding diversity. Finally, our model predicts a novel binding interface that is well-populated in the Ca(2+)-bound regime and, thus, a candidate for pharmacological intervention.