Rapid Calculation of Protein pK(a) Values Using Rosetta

We developed a Rosetta-based Monte Carlo method to calculate the pK(a) values of protein residues that commonly exhibit variable protonation states (Asp, Glu, Lys, His, and Tyr). We tested the technique by calculating pK(a) values for 264 residues from 34 proteins. The standard Rosetta score function, which is independent of any environmental conditions, failed to capture pK(a) shifts. After incorporating a Coulomb electrostatic potential and optimizing the solvation reference energies for pK(a) calculations, we employed a method that allowed side-chain flexibility and achieved a root mean-square deviation (RMSD) of 0.83 from experimental values (0.68 after discounting 11 predictions with an error over 2 pH units). Additional degrees of side-chain conformational freedom for the proximal residues facilitated the capture of charge-charge interactions in a few cases, resulting in an overall RMSD of 0.85 pH units. The addition of backbone flexibility increased the overall RMSD to 0.93 pH units but improved relative pK(a) predictions for proximal catalytic residues. The method also captures large pK(a) shifts of lysine and some glutamate point mutations in staphylococcal nuclease. Thus, a simple and fast method based on the Rosetta score function and limited conformational sampling produces pK(a) values that will be useful when rapid estimation is essential, such as in docking, design, and folding.     

Developing hybrid approaches to predict pKa values of ionizable groups

Accurate predictions of pKa values of titratable groups require taking into account all relevant processes associated with the ionization/deionization. Frequently, however, the ionization does not involve significant structural changes and the dominating effects are purely electrostatic in origin allowing accurate predictions to be made based on the electrostatic energy difference between ionized and neutral forms alone using a static structure and the subtle structural changes be accounted by using dielectric constant larger than two. On another hand, if the change of the charge state is accompanied by a large structural reorganization of the target protein, then the relevant conformational changes have to be explicitly taken into account in the pKa calculations. Here we report a hybrid approach that first predicts the titratable groups whose ionization is expected to cause large conformational changes, termed "problematic" residues, and then applies a special protocol on them, while the rest of the pK(a)s are predicted with rigid backbone approach as implemented in multi-conformation continuum electrostatics (MCCE) method. The backbone representative conformations for "problematic" groups are generated with either molecular dynamics simulations with charged and uncharged amino acid or with ab-initio local segment modeling. The corresponding ensembles are then used to calculate the titration curves of the "problematic" residues and then the results are averaged to obtain the corresponding pKa.     

Electrostatic environment of hemes in proteins: pK(a)s of hydroxyl ligands

The pK(a)s of ferric aquo-heme and aquo-heme electrochemical midpoints (E(m)s) at pH 7 in sperm whale myoglobin, Aplysia myoblogin, hemoglobin I, heme oxygenase 1, horseradish peroxidase and cytochrome c oxidase were calculated with Multi-Conformation Continuum Electrostatics (MCCE). The pK(a)s span 3.3 pH units from 7.6 in heme oxygenase 1 to 10.9 in peroxidase, and the E(m)s range from -250 mV in peroxidase to 125 mV in Aplysia myoglobin. Proteins with higher in situ ferric aquo-heme pK(a)s tend to have lower E(m)s. Both changes arise from the protein stabilizing a positively charged heme. However, compared with values in solution, the protein shifts the aquo-heme E(m)s more than the pK(a)s. Thus, the protein has a larger effective dielectric constant for the protonation reaction, showing that electron and proton transfers are coupled to different conformational changes that are captured in the MCCE analysis. The calculations reveal a breakdown in the classical continuum electrostatic analysis of pairwise interactions. Comparisons with DFT calculations show that Coulomb's law overestimates the large unfavorable interactions between the ferric water-heme and positively charged groups facing the heme plane by as much as 60%. If interactions with Cu(B) in cytochrome c oxidase and Arg 38 in horseradish peroxidase are not corrected, the pK(a) calculations are in error by as much as 6 pH units. With DFT corrected interactions calculated pK(a)s and E(m)s differ from measured values by less than 1 pH unit or 35 mV, respectively. The in situ aquo-heme pK(a) is important for the function of cytochrome c oxidase since it helps to control the stoichiometry of proton uptake coupled to electron transfer [Song, Michonova-Alexova, and Gunner (2006) Biochemistry 45, 7959-7975].     

MCCE analysis of the pKas of introduced buried acids and bases in staphylococcal nuclease

The pK(a)s of 96 acids and bases introduced into buried sites in the staphylococcal nuclease protein (SNase) were calculated using the multiconformation continuum electrostatics (MCCE) program and the results compared with experimental values. The pK(a)s are obtained by Monte Carlo sampling of coupled side chain protonation and position as a function of pH. The dependence of the results on the protein dielectric constant (ε(prot)) in the continuum electrostatics analysis and on the Lennard-Jones non-electrostatics parameters was evaluated. The pK(a)s of the introduced residues have a clear dependence on ε(prot,) whereas native ionizable residues do not. The native residues have electrostatic interactions with other residues in the protein favoring ionization, which are larger than the desolvation penalty favoring the neutral state. Increasing ε(prot) scales both terms, which for these residues leads to small changes in pK(a). The introduced residues have a larger desolvation penalty and negligible interactions with residues in the protein. For these residues, changing ε(prot) has a large influence on the calculated pK(a). An ε(prot) of 8-10 and a Lennard-Jones scaling of 0.25 is best here. The X-ray crystal structures of the mutated proteins are found to provide somewhat better results than calculations carried out on mutations made in silico. Initial relaxation of the in silico mutations by Gromacs and extensive side chain rotamer sampling within MCCE can significantly improve the match with experiment.     

Combining conformational flexibility and continuum electrostatics for calculating pK(a)s in proteins

Protein stability and function relies on residues being in their appropriate ionization states at physiological pH. In situ residue pK(a)s also provides a sensitive measure of the local protein environment. Multiconformation continuum electrostatics (MCCE) combines continuum electrostatics and molecular mechanics force fields in Monte Carlo sampling to simultaneously calculate side chain ionization and conformation. The response of protein to charges is incorporated both in the protein dielectric constant (epsilon(prot)) of four and by explicit conformational changes. The pK(a) of 166 residues in 12 proteins was determined. The root mean square error is 0.83 pH units, and >90% have errors of <1 pH units whereas only 3% have errors >2 pH units. Similar results are found with crystal and solution structures, showing that the method's explicit conformational sampling reduces sensitivity to the initial structure. The outcome also changes little with protein dielectric constant (epsilon(prot) 4-20). Multiconformation continuum electrostatics titrations show coupling of conformational flexibility and changes in ionization state. Examples are provided where ionizable side chain position (protein G), Asn orientation (lysozyme), His tautomer distribution (RNase A), and phosphate ion binding (RNase A and H) change with pH. Disallowing these motions changes the calculated pK(a).     

A fast and accurate computational approach to protein ionization

We report a very fast and accurate physics-based method to calculate pH-dependent electrostatic effects in protein molecules and to predict the pK values of individual sites of titration. In addition, a CHARMm-based algorithm is included to construct and refine the spatial coordinates of all hydrogen atoms at a given pH. The present method combines electrostatic energy calculations based on the Generalized Born approximation with an iterative mobile clustering approach to calculate the equilibria of proton binding to multiple titration sites in protein molecules. The use of the GBIM (Generalized Born with Implicit Membrane) CHARMm module makes it possible to model not only water-soluble proteins but membrane proteins as well. The method includes a novel algorithm for preliminary refinement of hydrogen coordinates. Another difference from existing approaches is that, instead of monopeptides, a set of relaxed pentapeptide structures are used as model compounds. Tests on a set of 24 proteins demonstrate the high accuracy of the method. On average, the RMSD between predicted and experimental pK values is close to 0.5 pK units on this data set, and the accuracy is achieved at very low computational cost. The pH-dependent assignment of hydrogen atoms also shows very good agreement with protonation states and hydrogen-bond network observed in neutron-diffraction structures. The method is implemented as a computational protocol in Accelrys Discovery Studio and provides a fast and easy way to study the effect of pH on many important mechanisms such as enzyme catalysis, ligand binding, protein-protein interactions, and protein stability.     

Electrostatic calculations for assignment of infrared difference bands to carboxyl groups getting protonated during protein reactions

Fourier transform infrared (FTIR) difference spectroscopy is predestined to monitor the protonation of carboxyl groups during protein reactions, making glutamic and aspartic amino acids unique to follow proton pathways. The absorption of the corresponding vibrations are clearly distinguishable from the absorption of other amino acids. However, the assignment to specific groups within the protein needs additional information, e.g., from induced spectral changes due to isotopic labeling or mutation. Here, the capability of electrostatic calculations to assign IR difference bands to specific carboxyl groups getting protonated is demonstrated by the ion pump mechanism of the sarcoplasmic reticulum Ca(2+)-ATPase. Active Ca(2+) transport is coupled to the hydrolysis of ATP. Two Ca(2+) ions are transported per ATP hydrolysed and two or three H(+) ions are countertransported. FTIR difference spectra show that during the Ca(2+) release step, carboxyl groups become protonated. Multiconformation continuum electrostatic calculations (MCCE) have been carried out to determine the equilibrium distribution of residue ionization and side chain conformation in dependence of pH. Available structural X-ray data from the calcium-bound and the calcium-free state allows us to simulate the transition between the two states monitored in the IR difference spectra. Exemplarily for Asp 800, ligand of both calcium ions, it is shown that MCCE calculations can identify this specific Asp to contribute to the IR bands and therefore to take part in the proton countertransport of the Ca(2+)-ATPase. In addition, an energy analysis can be performed to understand what interactions shift the pK(a).     

Optimizing pKa computation in proteins with pH adapted conformations

pK(A) in proteins are determined by electrostatic energy computations using a small number of optimized protein conformations derived from crystal structures. In these protein conformations hydrogen positions and geometries of salt bridges on the protein surface were determined self-consistently with the protonation pattern at three pHs (low, ambient, and high). Considering salt bridges at protein surfaces is most relevant, since they open at low and high pH. In the absence of these conformational changes, computed pK(A)(comp) of acidic (basic) groups in salt bridges underestimate (overestimate) experimental pK(A)(exp), dramatically. The pK(A)(comp) for 15 different proteins with 185 known pK(A)(exp) yield an RMSD of 1.12, comparable with two other methods. One of these methods is fully empirical with many adjustable parameters. The other is also based on electrostatic energy computations using many non-optimized side chain conformers but employs larger dielectric constants at short distances of charge pairs that diminish their electrostatic interactions. These empirical corrections that account implicitly for additional conformational flexibility were needed to describe the energetics of salt bridges appropriately. This is not needed in the present approach. The RMSD of the present approach improves if one considers only strongly shifted pK(A)(exp) in contrast to the other methods under these conditions. Our method allows interpreting pK(A)(comp) in terms of pH dependent hydrogen bonding pattern and salt bridge geometries. A web service is provided to perform pK(A) computations.     

Electrostatic pKa computations in proteins: role of internal cavities

The solvent accessible surface area (SASA) algorithm is conventionally used to characterize protein surfaces in electrostatic energy computations of proteins. Unfortunately, it often fails to find narrow cavities inside a protein. As a consequence pK(a) computations based on this algorithm perform badly. In this study a new cavity-algorithm is introduced, which solves this problem and provides improved pK(a) values. The procedure is applied to 20 pK(a) values of titratable groups introduced as point mutations in SNase variants, where crystal structures are available. The computations of these pK(a)s are particular challenging, since they are placed in a rather hydrophobic environment. For nine mutants, where the titratable residue is in contact with a large cavity, the RMSD(pKa) between computed and measured pK(a) values is 2.04, which is a considerable improvement as compared to the original results obtained with Karlsberg(+) (http://agknapp.chemie.fu-berlin.de/karlsberg/) that yielded an RMSD(pKa) of 8.8. However, for 11 titratable residues the agreement with experiments remains poor (RMSD(pKa) = 6.01). Considering 15 pK(a)s of SNase, which are in a more conventional less hydrophobic protein environment, the RMSD(pKa) is 2.1 using the SASA-algorithm and 1.7 using the new cavity-algorithm. The agreement is reasonable but less good than what one would expect from the general performance of Karlsberg(+) indicating that SNase belongs to the more difficult proteins with respect to pK(a) computations. We discuss the possible reasons for the remaining discrepancies between computed and measured pK(a)s.     

Who cares for the protons?

It is tempting to use standard protonation states for the analysis of protein-ligand interactions. Two different pK(a) calculation methods, PROPKA (protein pK(a)) and MCCE (multi conformation continuum electrostatics), were applied to challenge this convenient behavior. As data basis, we selected five recently approved drugs for which structural information of the protein-drug complex is available. We analyzed the pK(a) calculations in terms of a measure termed BIPS (binary protonation states) recently introduced by us. Both methods agree in detecting the majority of the sites with atypical BIPS values. However, when using only one method, some of the atypcial BIPS value would have been missed. Therefore, we recommend using both methods to set such an interpretation on a solid basis.     

Development, validation, and application of adapted PEOE charges to estimate pKa values of functional groups in protein-ligand complexes

For routine pK(a) calculations of protein-ligand complexes in drug design, the PEOE method to compute partial charges was modified. The new method is applicable to a large scope of proteins and ligands. The adapted charges were parameterized using experimental free energies of solvation of amino acids and small organic ligands. For a data set of 80 small organic molecules, a correlation coefficient of r(2) = 0.78 between calculated and experimental solvation free energies was obtained. Continuum electrostatics pK(a) calculations based on the Poisson-Boltzmann equation were carried out on a validation set of nine proteins for which 132 experimental pK(a) values are known. In total, an overall RMSD of 0.88 log units between calculated and experimentally determined data is achieved. In particular, the predictions of significantly shifted pK(a) values are satisfactory, and reasonable estimates of protonation states in the active sites of lysozyme and xylanase could be obtained. Application of the charge-assignment and pK(a)-calculation procedure to protein-ligand complexes provides clear structural interpretations of experimentally observed changes of protonation states of functional groups upon complex formation. This information is essential for the interpretation of thermodynamic data of protein-ligand complex formation and provides the basis for the reliable factorization of the free energy of binding in enthalpic and entropic contributions. The modified charge-assignment procedure forms the basis for future automated pK(a) calculations of protein-ligand complexes.     

Improving NMR protein structure quality by Rosetta refinement: a molecular replacement study

The structure of human protein HSPC034 has been determined by both solution nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography. Refinement of the NMR structure ensemble, using a Rosetta protocol in the absence of NMR restraints, resulted in significant improvements not only in structure quality, but also in molecular replacement (MR) performance with the raw X-ray diffraction data using MOLREP and Phaser. This method has recently been shown to be generally applicable with improved MR performance demonstrated for eight NMR structures refined using Rosetta (Qian et al., Nature 2007;450:259-264). Additionally, NMR structures of HSPC034 calculated by standard methods that include NMR restraints have improvements in the RMSD to the crystal structure and MR performance in the order DYANA, CYANA, XPLOR-NIH, and CNS with explicit water refinement (CNSw). Further Rosetta refinement of the CNSw structures, perhaps due to more thorough conformational sampling and/or a superior force field, was capable of finding alternative low energy protein conformations that were equally consistent with the NMR data according to the Recall, Precision, and F-measure (RPF) scores. On further examination, the additional MR-performance shortfall for NMR refined structures as compared with the X-ray structure were attributed, in part, to crystal-packing effects, real structural differences, and inferior hydrogen bonding in the NMR structures. A good correlation between a decrease in the number of buried unsatisfied hydrogen-bond donors and improved MR performance demonstrates the importance of hydrogen-bond terms in the force field for improving NMR structures. The superior hydrogen-bond network in Rosetta-refined structures demonstrates that correct identification of hydrogen bonds should be a critical goal of NMR structure refinement. Inclusion of nonbivalent hydrogen bonds identified from Rosetta structures as additional restraints in the structure calculation results in NMR structures with improved MR performance.     

Are acidic and basic groups in buried proteins predicted to be ionized?

Ionizable residues play essential roles in proteins, modulating protein stability, fold and function. Asp, Glu, Arg, and Lys make up about a quarter of the residues in an average protein. Multi-conformation continuum electrostatic (MCCE) calculations were used to predict the ionization states of all acidic and basic residues in 490 proteins. Of all 36,192 ionizable residues, 93.5% were predicted to be ionized. Thirty-five percent have lost 4.08 kcal/mol solvation energy (DeltaDeltaG(rxn)) sufficient to shift a pK(a) by three pH units in the absence of other interactions and 17% have DeltaDeltaG(rxn) sufficient to shift pK(a) by five pH units. Overall 85% of these buried residues (DeltaDeltaG(rxn)>5DeltapK units) are ionized, including 92% of the Arg, 86% of the Asp, 77% of the Glu, and 75% of the Lys. Ion-pair interactions stabilize the ionization of both acids and bases. The backbone dipoles stabilize anions more than cations. The interactions with polar side-chains are also different for acids and bases. Asn and Gln stabilize all charges, Ser and Thr stabilize only acids while Tyr rarely stabilize Lys. Thus, hydroxyls are better hydrogen bond donors than acceptors. Buried ionized residues are more likely to be conserved than those on the surface. There are 3.95 residues buried per 100 residues in an average protein.     

Predicting extreme pKa shifts in staphylococcal nuclease mutants with constant pH molecular dynamics

Accurate computational methods of determining protein and nucleic acid pK(a) values are vital to understanding pH-dependent processes in biological systems. In this article, we use the recently developed method constant pH molecular dynamics (CPHMD) to explore the calculation of highly perturbed pK(a) values in variants of staphylococcal nuclease (SNase). Simulations were performed using the replica exchange (REX) protocol for improved conformational sampling with eight temperature windows, and yielded converged proton populations in a total sampling time of 4 ns. Our REX-CPHMD simulations resulted in calculated pK(a) values with an average unsigned error (AUE) of 0.75 pK units for the acidic residues in Δ + PHS, a hyperstable variant of SNase. For highly pK(a)-perturbed SNase mutants with known crystal structures, our calculations yielded an AUE of 1.5 pK units and for those mutants based on modeled structures an AUE of 1.4 pK units was found. Although a systematic underestimate of pK shifts was observed in most of the cases for the highly perturbed pK mutants, correlations between conformational rearrangement and plasticity associated with the mutation and error in pK(a) prediction was not evident in the data. This study further extends the scope of electrostatic environments explored using the REX-CPHMD methodology and suggests that it is a reliable tool for rapidly characterizing ionizable amino acids within proteins even when modeled structures are employed.     

Remeasuring HEWL pK(a) values by NMR spectroscopy: methods, analysis, accuracy, and implications for theoretical pK(a) calculations

Site-specific pK(a) values measured by NMR spectroscopy provide essential information on protein electrostatics, the pH-dependence of protein structure, dynamics and function, and constitute an important benchmark for protein pK(a) calculation algorithms. Titration curves can be measured by tracking the NMR chemical shifts of several reporter nuclei versus sample pH. However, careful analysis of these curves is needed to extract residue-specific pK(a) values since pH-dependent chemical shift changes can arise from many sources, including through-bond inductive effects, through-space electric field effects, and conformational changes. We have re-measured titration curves for all carboxylates and His 15 in Hen Egg White Lysozyme (HEWL) by recording the pH-dependent chemical shifts of all backbone amide nitrogens and protons, Asp/Glu side chain protons and carboxyl carbons, and imidazole protonated carbons and protons in this protein. We extracted pK(a) values from the resulting titration curves using standard fitting methods, and compared these values to each other, and with those measured previously by 1H NMR (Bartik et al., Biophys J 1994;66:1180–1184). This analysis gives insights into the true accuracy associated with experimentally measured pK(a) values. We find that apparent pK(a) values frequently differ by 0.5–1.0 units depending upon the nuclei monitored, and that larger differences occasionally can be observed. The variation in measured pK(a) values, which reflects the difficulty in fitting and assigning pH-dependent chemical shifts to specific ionization equilibria, has significant implications for the experimental procedures used for measuring protein pK(a) values, for the benchmarking of protein pK(a) calculation algorithms, and for the understanding of protein electrostatics in general.     

Necessary conditions for avoiding incorrect polypeptide folds in conformational search by energy minimization

Low energy conformations have been generated for melittin, pancreatic polypeptide, and ribonuclease S-peptide, both in the vicinity of x-ray structures by energy refinement and by an unconstrained search over the entire conformational space. Since the structural polymorphism of these medium-sized peptides in crystal and solution is moderate, comparing the calculated conformations to x-ray and nmr data provides information on local and global behavior of potential functions. Local analysis includes standardization calculations, which show that models with standard geometry can approximate good resolution x-ray data with less than 0.5 Å rms deviation (RMSD). However, the atomic coordinates are shifted up to 2 Å RMSD by local energy minimization, and thus 2 Å is generally the smallest RMSD value one can target in a conformational search using the same energy evaluation models. The unconstrained search was performed by a buildup-type method based on dynamic programming. To accelerate the generation of structures in the conformational search, we used the ECEPP potential, defined in terms of standard polypeptide geometry. A number of low energy conformations were further refined by relaxing the assumption of standard bond lengths and bond angles through the use of the CHARMM potential, and the hydrophobic folding energies of Eisenberg and McLachlan were calculated. Each conformation is described in terms of the RMSD from the native, hydrogen-bonding structure, solvent-acessible surface area, and the ratio of surfaces corresponding to nonpolar and polar residues. The unconstrained search finds conformations that are different from the native, sometimes substantially, and in addition, have lower conformational energies than the native. The origin of deviations is different for each of the three peptides, but in all examples the refined x-ray structures have lower energies than the calculated incorrect folds when (1) the assumption of standard bond lengths and bond angles is relaxed; (2) a small and constant effective dielectric permittivity (ε < 10) is used; and (3) the hydrophobic folding energy is incorporated into the potential. © 1993 John Wiley & Sons, Inc.     

pKa predictions with a coupled finite difference Poisson-Boltzmann and Debye-Hückel method

Modeling charge interactions is important for understanding many aspects of biological structure and function, and continuum methods such as Finite Difference Poisson-Boltzmann (FDPB) are commonly employed. Calculations of pH-dependence have identified separate populations; surface groups that can be modeled with a simple Debye-Hückel (DH) model, and buried groups, with stronger resultant interactions that are dependent on detailed conformation. This observation led to the development of a combined FDPB and DH method for pK(a) prediction (termed FD/DH). This study reports application of this method to ionizable groups, including engineered buried charges, in staphylococcal nuclease. The data had been made available to interested research groups before publication of mutant structures and/or pK(a) values. Overall, FD/DH calculations perform as intended with low ΔpK(a) values for surface groups (RMSD between predicted and experimental pK(a) values of 0.74), and much larger ΔpK(a) values for the engineered internal groups, with RMSD = 1.64, where mutant structures were known and RMSD = 1.80, where they were modeled. The weaker resultant interactions of the surface groups are determined mostly by charge-charge interactions. For the buried groups, R(2) for correlation between predicted and measured ΔpK(a) values is 0.74, despite the high RMSDs. Charge-charge interactions are much less important, with the R(2) value for buried group ΔpK(a) values increasing to 0.80 when the term describing charge desolvation alone is used. Engineered charge burial delivers a relatively uniform, nonspecific effect, in terms of pK(a) . How the protein environment adapts in atomic detail to deliver this resultant effect is still an open question.     

Modeling the effects of mutations on the free energy of the first electron transfer from QA- to QB in photosynthetic reaction centers

Numerical calculations of the free energy of the first electron transfer in genetically modified reaction centers from Rhodobacter (Rb.) sphaeroides and Rb. capsulatus were carried out from pH 5 to 11. The multiconformation continuum electrostatics (MCCE) method allows side chain, ligand, and water reorientation to be embedded in the calculations of the Boltzmann distribution of cofactor and amino acid ionization states. The mutation sites whose effects have been modeled are L212 and L213 (the L polypeptide) and two in the M polypeptide, M43(44) and M231(233) in Rb. capsulatus (Rb. sphaeroides). The results of the calculations were compared to the experimental data, and very good agreement was found especially at neutral pH. Each mutation removes or introduces ionizable residues, but the protein maintains a net charge close to that in native RCs through ionization changes in nearby residues. This reduces the effect of mutation and makes the changes in state free energy smaller than would be found in a rigid protein. The state energy of QA-QB and QAQB- states have contributions from interactions among the residues as well as with the quinone which is ionized. For example, removing L213Asp, located in the QB pocket, predominantly changes the free energy of the QA-QB state, where the Asp is ionized in native RCs rather than the QAQB- state, where it is neutral. Side chain, hydroxyl, and water rearrangements due to each of the mutations have also been calculated showing water occupancy changes during the QA- to QB electron transfer.     

Electrostatic effects in highly charged proteins: salt sensitivity of pKa values of histidines in staphylococcal nuclease

The pK(a) values of most histidines in small peptides and in myoglobin increase on average by 0.30 unit between 0.02 and 1.5 M NaCl [Kao et al. (2000) Biophys. J. 79, 1637]. The DeltapK(a) values reflect primarily the ionic strength dependence of the solvation energy; screening of Coulombic interactions contributes only in a minor way. This implies that Coulombic interactions are weak, or that attractive and repulsive contributions to the pK(a) values are balanced. To distinguish experimentally between these two possibilities, and to further characterize the magnitude and salt sensitivity of surface electrostatic interactions in proteins, the salt dependence of pK(a) values of histidines in staphylococcal nuclease was measured by (1)H NMR spectroscopy. Three of the four histidines titrated with significantly depressed pK(a) values, and the salt sensitivity of all histidine pK(a) values was substantial. In three cases, the pK(a) values increased by a full unit between 0.01 and 1.5 M KCl. Anion-specific effects were found; the pK(a) values measured under equivalent ionic strengths in SCN(-) and SO(4)(2-) were higher than in Cl(-); the order of the sensitivity of pK(a) values to anions was SCN(-) > Cl(-) > SO(4)(2-). Structure-based pK(a) calculations with continuum methods were performed to interpret the measured effects structurally and to test their ability to capture the experimental behavior. Calculations in which the protein interior was treated empirically with a dielectric constant of 20 reproduced the pK(a) values and their dependence on the concentration of Cl(-). According to the calculations, the pK(a) values are depressed because of unfavorable self-energies and repulsive Coulombic interactions. Their striking salt sensitivity reflects screening of weak, repulsive, Coulombic interactions among charges separated by more than 10 A. Long-range Coulombic interactions on the surfaces of proteins are weak, but they can add up to produce substantial electrostatic effects when positive and negative charges are not balanced.     

Protonation states and pH titration in the photocycle of photoactive yellow protein

Photoactive yellow protein (PYP) undergoes a light-driven cycle of color and protonation states that is part of a mechanism of bacterial phototaxis. This article concerns functionally important protonation states of PYP and the interactions that stabilize them, and changes in the protonation state during the photocycle. In particular, the chromophore pK(a) is known to be shifted down so that the chromophore is negatively charged in the ground state (dark state) even though it is buried in the protein, while nearby Glu46 has an unusually high pK(a). The photocycle involves changes of one or both of these protonation states. Calculations of pK(a) values and protonation states using a semi-macroscopic electrostatic model are presented for the wild-type and three mutants, in both the ground state and the bleached (I(2)) intermediate state. Calculations allowing multiple H-bonding arrangements around the chromophore also have been carried out. In addition, ground-state pK(a) values of the chromophore have been measured by UV-visible spectroscopy for the wild-type and the same three mutants. Because of the unusual protonation states and strong electrostatic interactions, PYP represents a severe test of the ability of theoretical models to yield correct calculations of electrostatic interactions in proteins. Good agreement between experiment and theory can be obtained for the ground state provided the protein interior is assumed to have a relatively low dielectric constant, but only partial agreement between theory and experiment is obtained for the bleached state. We also present a reinterpretation of previously published data on the pH-dependence of the recovery of the ground state from the bleached state. The new analysis implies a pK(a) value of 6.37 for Glu46 in the bleached state, which is consistent with other available experimental data, including data that only became available after this analysis. The new analysis suggests that signal transduction is modulated by the titration properties of the bleached state, which are in turn determined by electrostatic interactions. Overall, the results of this study provide a quantitative picture of the interactions responsible for the unusual protonation states of the chromophore and Glu46, and of protonation changes upon bleaching.