|
|
||||||||
Department of Biomolecular Sciences, University of Manchester Institute of Science and Technology (UMIST), Manchester M60 1QD, United Kingdom
Reprint requests to: Jim Warwicker, Department of Biomolecular Sciences, UMIST, P.O. Box 88, Manchester M60 1QD, UK; e-mail: jim. warwicker{at}umist.ac.uk; fax: +44-(0)161-236-0409.
(RECEIVED April 2, 2004; FINAL REVISION June 30, 2004; ACCEPTED July 6, 2004)
| Abstract |
|---|
|
|
|---|
Keywords: protein electrostatics; pKas; ionization entropy; side-chain packing; active-site identification; structural genomics
Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.04785604.
| Introduction |
|---|
|
|
|---|
p = 4; Gilson and Honig 1986) have been applied to pKa calculations (Bashford and Karplus 1990). These can be useful when applied to regions with limited solvent accessibility (SA; Demchuk and Wade 1996; Warwicker 1998), but
p = 4 calculations based on a single conformer have been largely unreliable for overall pKa analysis. Generally
p = 20 performs better (Antosiewicz et al. 1994, 1996), presumably accounting to some degree for other factors (Schutz and Warshel 2001) such as conformational variation (Simonson and Perahia 1995), proton/hydrogen-bond network relaxation (Nielsen et al. 1999), or specific internal water binding (Fitch et al. 2002). Indeed, a Debye-Hückel (DH) model with water dielectric also gives reasonable agreement over-all for pKas, and for the pH dependence of folding energy when combined with a simple model for ionizable group interactions in the unfolded state (Warwicker 1999). A Tanford-Kirkwood model gives reasonable estimates for carboxylate pKas in ubiquitin (Sundd et al. 2002). As with FDPB/
p = 20, the DH and Tanford-Kirkwood models fail to generate the large
pKas often associated with functional groups (Warwicker 1998).
The problem of accounting for large and small
pKas in one scheme is being addressed with inclusion of conformational and proton configurational relaxation in
p = 4 calculations. Multiple conformers are generally sampled from molecular dynamics simulations (You and Bashford 1995; Zhou and Vijayakumar 1997; van Vlijmen et al. 1998; Koumanov et al. 2001; Gorfe et al. 2002), with some improvement in match to experiment and significant increase in computational requirement. A key issue is to address those conformational adjustments of most relevance to a pH titration. Multiconformation continuum electrostatics (MCCE) samples side-chain ionization and conformation (Alexov and Gunner 1997; Georgescu et al. 2002; Alexov 2003), yielding a pKa root-mean-square (RMS) error of 0.83. An approximation in this model is that all conformers are combined to produce a single dielectric boundary. A method that assigns higher or lower electrostatic screening functions, (applied to Coulomb potentials), according to the hydrophobicity/hydrophilicity of residue microenvironments performs well, giving an RMS error of 0.5 for a large pKa set (Mehler and Guarnieri 1999).
The current work also pursues two interaction schemes, the DH model that is effective for flexible groups, and FDPB/
p = 4, which can give large
pKas of functional interest, with combination in a framework of conformational relaxation. FD (for FDPB/
p = 4) interactions are always sampled for the experimental conformer, and DH interactions are selectively introduced as a mimic for relaxation from this conformer. Two examples illustrate this idea. First, a buried lysine, neutral at pH 7 due to dehydration (lower residue in Fig. 1
). The DH model omits the Born term and underestimates such a
pKa, whereas FD with the Born term provides the required destabilization. Because DH would give a higher statistical weight than FD, a combined scheme must exclude DH and reflect the restricted conformation. Secondly (top of Fig. 1
), in a relatively flexible salt bridge the Born contributions are balanced by favorable chargecharge interactions. For a pH shift that neutralizes one of the partners, without conformational relaxation FD will record a Born term for the remaining ionized group that is not balanced by chargecharge interactions (other than potential hydrogen bonds). The ionized partner is likely to seek a more solvent accessible conformation, reducing the Born energy penalty. Interactions in this water-dominated situation could be estimated by DH modeling of the salt bridge, as an alternative to FD calculations on a range of generated conformers.
|
pKas over a wide range, and is compared with other techniques. The contribution of ionizable groups to folded state stability is discussed, with FD/DH delivering overall stabilization in contrast to much FD/
p = 4 single conformer work. The utility of a distinction between flexible and buried groups is considered in the context of active-site finding for structural genomics, and with regard to automation of active-site subset selection for detailed electrostatic analysis (Nielsen and McCammon 2003).
An earlier empirical analysis of hydration entropy and pKa calculations (Warwicker 1997) has been extended with study of relatively buried ionizable groups in the FD/
p = 4 model, and comparison made to small molecule ionization entropies. It is concluded that pKa adjustments due to hydration entropy are relatively small, except in the case of cysteine. Changes in hydration entropy that can be important in binding energetics (Jung et al. 2002) are discussed in the framework of the empirical analysis.
| Results and Discussion |
|---|
|
|
|---|
pKas and surrounding ionizations that can be reasonably assigned at a pH around the pKa of interest are studied, partitioning the analysis from the wider prediction of pH dependence (Table 1
p = 4 model. Polar hydrogen optimization is applied for the hydroxyl groups of serine and threonine. Tyrosine hydroxyls are included where the unionized form is used, which for entropic term modeling is in all calculations other than those of Figure 2C
pKas, and includes polar hydrogen optimization.
|
|
|
pKa contributions and the measured
pKa, with a burial fraction (Vf) of 0.57, gives Ns = 0.7. A second calculation, with partially charged forms for ionized and neutral E172 and torsioning of the Y80 hydroxyl to point away from neutral E172, gives Ns = 1.9. Without Ns modification, partial charge analysis gives
pKa = 3.7 versus 2.8 for the net charge model and 2.3 experimentally. Both calculations qualitatively capture the pKa shift.
Hen egg white lysozyme is a well-known model for electrostatic calculations, particularly E35 (pKa = 6.1; Kuramitsu and Hamaguchi 1980). Figure 2A
shows the restricted environment of E35 and the elements that give Ns = 1.5, again with qualitative agreement to the experiment without Ns modification (Warwicker 1997). If discrepancies in the carboxylate group calculations of Figure 2A
are approximated as hydration entropy within the FD framework, then a related single Ns value is relatively small and positive (Table 2
; Warwicker 1997).
|
pKas require a high value of Ns to match experiment (Table 2
Calculations (Fig. 2B
) with a reduced DsbA crystal structure (Guddat et al. 1998) give Ns = 4.2, where H60, E24, and E38 were assigned neutral (Warwicker 1998), and the measured C30 pKa is 3.4 (Grauschopf et al. 1995). A reduced model of the D26N mutant was made from oxidized, wild-type thioredoxin, uncoupling the similar pKas of C32 and D26, with C32 pKa = 7.5 (Chivers et al. 1997) and Ns = 4.9. Calculated Ns of 4.5, 4.2, and 4.9 imply significant discrepancies in FD/
p = 4 calculations of pKas for relatively buried cysteines, without the Ns term.
For tyrosine, the active-site residue Y9 of glutathione S-transferase A11 has a low pKa (8.1) in the substrate-free enzyme (Björnestedt et al. 1995). Calculations with a monomer from the dimeric crystal structure (Cameron et al. 1995) give Ns = 1.4 (Fig. 2C
). A pKa of 6.1 has been measured for Y149 in the active site of UDP-galactose-4-epimerase with NAD+ bound (Liu et al. 1997). Removing UDP, but not NAD+, from the crystal structure (Thoden et al. 1996) gives Ns = 0.8 (Fig. 2C
), with a strong interaction to the positively charged side chain of K153 and a hydrogen bond between the deprotonated Y149 side chain and a ribose hydroxyl group.
Hydration entropy and pKas: Lysine, histidine, and arginine
Figure 2D
shows the environment of K229 in rabbit muscle aldolase (Blom and Sygusch 1997), implicated in Schiff base formation, with pKa probably matching the decline of activity around pH 6.5 (Morris and Tolan 1994). A monomer from the tetramer (with product removed) gives Ns = 0.2. The network of ionizable groups around K229 (Fig. 2D
) were assigned unperturbed charges for neutral pH calculations. A catalytic antibody with aldolase activity has an active-site lysine (H chain K93) with a pKa of 5.5 estimated from the pH dependence of enamine formation (Barbas et al. 1997). Figure 2D
shows the enclosed environment of K93(H), with W103(H) adjacent and H27(L) distant, but displayed to facilitate the view. NZ atom locations are shown from 1axt
[PDB]
and modeled with more solvent exposure, because electron density for K93(H) does not extend beyond CE (Barbas et al. 1997). These give Ns = 2.0 (1axt
[PDB]
, quoted in Fig. 2D
) and Ns = 0.8 (model), with the low pKa dominated by the Born term.
Histidine 48 of porcine pancreatic phospholipase A2 is catalytic (Thunnissen et al. 1990), with a pKa of 5.5 in the calcium-bound form (Verheij et al. 1980). Calculation gives Ns = 2.0 (Table 2
). In chymotrypsin, interactions between partially charged/protonated or partially charged/neutral forms of H57, and other catalytic residues D102 and S195 have been evaluated, with S195 hydroxyl directed toward neutral H57 and away from protonated H57. Comparison with the measured pKa of 6.8 (Fersht and Renard 1974) gives Ns = 0.9 (Table 2
). For T4 lysozyme, the noncatalytic residue H31 (pKa = 9.1) is significantly buried in a salt bridge with D70 (Anderson et al. 1990), giving Ns = 3.1 (Table 2
). This Ns-based study of buried side chains with known pKas has excluded histidine residues with complicating factors such as strongly coupled titrations and ligand involvement. Variability in Ns values for histidine (prior to averaging) probably reflects relatively mixed environments and the omission of local relaxations (Edgcomb and Murphy 2002).
There is some evidence for reduced arginine pKas (Lehoux and Mitra 1999; Morillas et al. 1999), but not a clear example coupled to an atomic structure. Arginine side chain and N-terminal groups are assigned (Ns) as the lysine side-chain average, and C-terminal groups follow the average of the carboxylate containing side chains.
Comparison with ionization data for amino acids and analogs
The empirical modification for hydration entropy is generally small in
pKa terms compared with the Born and chargecharge contributions, with the exception of cysteine (Fig. 2
; Table 2
). The sense of the modification is in line with water molecule release upon ionizable group burial being greater (albeit slightly in most cases) for ionized versus unionized forms. Table 3
shows the enthalpies and entropies of ionization for amino acid side chains and molecular analogs (Izatt and Christensen 1976). Hydration entropies make the major contributions to ionization entropies (Alberty 1983), which show a wide variation for acidic and basic side chains in amino acids, partially due to overlapping proton binding equilibria. In contrast, the analog data show similar ionization
S within acidic and within basic groups, distinguishing between dissociation from one neutral to two charged species (acidic, larger
S), and from one charged to one charged and one neutral species (basic, smaller
S). Differencing between average
S values for each grouping (103.5 J/deg/mole for AH
A B + H+ and 17.0 J/deg/mole for BH+
B + H+), cancelling the proton term, gives
S =86.5 J/deg/mole for AH
A summed with B
BH+. Approximating equal
S for each of these unionized to ionized form transitions,
S = 43.3 J/deg/ mole is obtained, equivalent to Ns = 1.7 (300 K) in the hydration shell model and consistent overall with the pKa calculations (other than cysteine). This favorable comparison supports use of the empirical model.
|
S values measured for small carboxylic acids and thiols (Irving et al. 1964). Consistently more negative
S for thiol group dissociation compared to carboxylic acids qualitatively matches the results of the hydration shell model, supporting a significant role for hydration entropy in cysteine pKa calculation.
|
Go =52.7 kJ/mole) is entropically driven (T
So = 61.9 kJ/mole), and alanine mutations demonstrate that one side chain (R135) makes a major entropic contribution (Table 5
|
(T
S)QWAT for burial of a full ionizable group hydration shell in the E3 dimer:PSBD interface. The fractions of hydration shell occluded upon complexation are summed and multiplied by the 36 kJ/mole of a full shell. Mutant complexes were modelled with arginine side chain reduction to alanine (Fig. 4
|
Combination of FDPB and DH models: The FD/DH interaction scheme
The empirical modification of the FD/
p = 4 single conformer pKa calculations for hydration entropy is applied to the FD component of the FD/DH scheme. Figure 3
schematizes side-chain flexibility in different environments, and the derivation of SAmax and access to DH interactions. A VdW clash tolerance (VdWtol) is included in mean-field calculations that determine allowed rotamers for SA analysis. This parameter is varied to approximate conformational relaxation not explicitly included (nonlibrary rotamer packing and main-chain movement), and the best value determined with respect to experimental pKas (Fig. 5B
). VdWtol of around 0.8 Å is typically required to give a packing solution for united atom VdW radii, so that VdWtol > 0.8 Å represents additional flexibility that will lead to greater SAmax and successive entry of more ionizable groups to the DH calculation regime.
|
Figure 5B
shows that for the 110 groups, excluding most of the large active-site
pKas, FD performs poorly and DH well in comparison to the null hypothesis. For the 117 set, the large
pKas impair DH performance, but have relatively little impact overall on FD, which remains dominated by inaccuracy within the 110 set. A broad region of optimal fit to experiment is found as VdWtol is varied for FD/DH. Increasing VdWtol adds flexibility that reduces the erroneously large
pKas for surface groups in the 110 set (schematically at the top of Fig. 1
), while too much flexibility (beyond VdWtol = 1.4 Å) allows buried group relaxation (bottom of Fig. 1
) to an extent that is inconsistent with experiment. From the optimal region, VdWtol = 1.4 Å is taken for FD/DH pKa calculations.
Comparison of pKa calculation methods
Table 6
gives overall RMS pKa errors for various methods. FD/
p = 20 (Antosiewicz et al. 1994) is similar to DH, consistent with a water-like environment (Warwicker 1999). Similarly, FD/
p = 4 with SA derived from VdW radii rather than a probe reentrant surface (Dong and Zhou 2002), has a large degree of internal water, and is comparable overall to DH. FD/DH performs moderately better with hydration entropy modification than without, with the largest difference for the active-site cysteine pKas of papain and DsbA (Table 7
). Overall results are improved somewhat with the simple polar hydrogen optimization algorithm, consistent with previous observations (Nielsen et al. 1999). Differences associated with application of either the hydration entropy or polar hydrogen placement schemes are generally smaller than those from FD/DH introduction relative to FD (Table 6
). The MCCE method gives an RMS pKa error for the 110 groups of 0.92 (Georgescu et al. 2002), compared with 0.58 for DH and 0.79 for FD/DH, while the single conformer FDPB method used by these authors gives 2.05, close to the current FD value of 2.10.
|
|
pKas and pKas (Fig. 6
pKas is evident. These trends are corrected to a large extent in the FD/DH model, although several groups remain significantly in error.
|
pKa > 1, despite derivation of Ns values from experimental pKas (Table 7
For 3rn3
[PDB]
, groups with
pKa to experiment >1.5 are H48 and H119. There are alternate locations for H119 ("A" was used), and a sulphate ion that may give overestimation of stabilizing interactions (FD/DH pKa = 8.3 and measured pKa = 6.1). H48 of 3rn3
[PDB]
has an FD/DH pKa of 4.6 and a measured pKa of 6.3. H48 is relatively buried and excluded from the DH scheme. FD interactions are dominated by an ion pair with D14 that varies between ribonuclease A structures (Georgescu et al. 2002).
Calculations for 2rn2 [PDB] were made without magnesium to match experiment. FD/DH discrepancies with experiment >1.5 pKa units are E48 and H114. For E48 (1.7 calculated and 4.4 measured), DH is incorporated at VdWtol = 1.4 Å but not at VdWtol = 1.2 Å. However, the calculated pKa remains the same, because interactions are dominated by hydrogen bonding to background charges in the FD scheme. It is possible that unfavorable interactions with the D10, D70, and D134 charge cluster surrounding E48 have been underestimated. These groups are DH accessible, so that relatively weak DH interactions dominate. For H114 an FD/DH pKa at VdWtol = 1.4 Å of 1.0 compares with a measured pKa of 5.0. This residue is relatively buried, with an enforced FD-only scheme. The key (FD) interaction is a hydrogen bond to the peptide NH of C63, so that H114 protonation must involve some change in structure.
Of the seven additional proteins with individual groups, 1xnb
[PDB]
/E172 and 9pap
[PDB]
/C25 give the worst results for FD/DH (Table 7
). For 1xnb
[PDB]
, this discrepancy (calculated pKa = 4.4, measured = 6.7) is a failure of the FD/DH scheme, because FD alone gives a large positive
pKa for E172 with or without hydration entropy modification. Again, more detailed FD analysis of coupled clusters may improve the FD/DH model, a conclusion supported for xylanase by pKa calculations using subsets of ionizable groups (Nielsen and McCammon 2003).
The FD/DH pKa for C25 in papain (9pap [PDB] ) is 5.9, compared with 3.3 by experiment, and in contrast the FD pKa is a good match. Both C25 and H159 are accessible to the DH scheme at VdWtol = 1.4 Å. Because the FD result corresponds to significant stabilization of C25, it might be expected to figure more strongly in FD/DH calculation at VdWtol = 1.4 Å. Of the four possible samplings of C25 and H159 net charged forms, only one (both FD) will give a large favorable interaction. Alteration from FD to FD/DH may therefore result from relative weighting toward weak (DH) interactions. Active-site residue Y9 of 1gsd [PDB] is allowed access to the DH scheme at VdWtol = 1.4 Å, (FD/DH pKa = 9.5 and measured 8.1), but not at VdWtol = 1.3 Å (calculated pKa = 8.7). It is tightly coupled to R20, which also has a DH accessibility transition over this VdWtol range.
Ionizable group electrostatic energy contributions
A move away from neutral pH generally destabilizes the folded state (Fink et al. 1994), consistent with a stabilizing network of positive and negative charges (Wada and Nakamura 1981), and with destabilization by charge reversal of surface amino groups (Hollecker and Creighton 1982). Computational methods with FD/
p = 20 or DH interactions accommodate such pH dependence, albeit with adjustment for pH-dependent effects in unfolded states (Schaefer et al. 1997; Warwicker 1999).
However, use of single conformer FD/
p = 4 has given rise to questions of salt-bridge stability (Hendsch and Tidor 1994; Dong and Zhou 2002), as is shown with the mostly unfavorable contributions of ionizable group interactions to stability at pH 7 (FD column of Table 8
). In contrast to the DH column, FD calculations suggest that most of these folded proteins would be more stable with global replacement of ionizable groups, at variance with the prior discussion. The FD/DH method recovers favorable contributions to neutral pH stability. In many cases FD/DH matches DH closely, with DH interactions dominating FD overall. The particularly favorable values in Table 8
for 3icb
[PDB]
/calbindin with FD and FD/DH calculation result from the strong binding of calcium ions by acidic side chains.
|
pKas in a background of smaller
pKas. Four representative examples are shown, with ionizable groups disallowed from DH interactions (VdWtol = 1.4 Å), further restricted to those with calculated pKas between 3 and 10. This latter feature excludes side chains such as tyrosine or histidine that are largely buried but remain neutral over a pH range around physiological, and buried groups in particularly stable charge networks. In Figure 7
|
pKas (Fig. 2
The effectiveness of FD/DH calculations in combining FD/
p = 4 and DH computation is illustrated in Figure 5B
. The simple DH model is relatively good for the mostly small
pKas of the 110 groups set (Table 6
), with an RMS pKa error of 0.58, matching the performance of more complex methods (Mehler and Guarnieri 1999; Georgescu et al. 2002). However, DH performance is eroded when larger
pKas are included (117 groups). The FD/
p = 4 single-conformer method fails to consistently predict
pKas in the 110 set. As the system is relaxed in the FD/DH model, with selective access to DH interactions mimicking side-chain and possibly main-chain readjustment on a limited scale, an optimal relaxation is evident before key groups are erroneously solvent-exposed. Large-scale conformational change cannot be reliably predicted, and is not the subject of this work. A VdWtol parameter controls conformational relaxation through mean-field rotamer packing and estimation of SAmax for each ionizable group (Figs. 3
, 5A
). At VdWtol = 1.4 Å, FD/DH gives an RMS pKa error of 0.86 for the 117 groups, compared with null, DH and FD values of 1.24, 1.18, and 2.06, respectively. The tendencies of FD to overestimate small
pKas and DH to underestimate large
pKas are clear in Figure 6
. Factors that could further improve the FD/DH scheme include modeling of larger scale flexibility and pH-dependent ion binding, and detailed water structure (Koumanov et al. 2002). Tightly coupled clusters, with groups that are borderline for access to the DH scheme at VdWtol = 1.4 Å, may benefit from FD analysis of individual rotamer combinations. This refers to relatively few groups; the majority would be included in FD/DH sampling.
Generally, the FD/DH model is improved by hydration entropy modification and polar hydrogen optimization, but not to the extent of FD/DH improvement over FD (Tables 6
,7
). Two areas further demonstrate potential. First (Table 8
), FD/DH recovers overall stabilizing contributions for ionizable group interactions at pH 7, in contrast to the de-stabilization of the FD/
p = 4 single-conformer model. Second, Figure 7
demonstrates active-site identification, which could be used in a structural genomics context or provide subsets of residues to seed active-sitecentered electrostatics calculations (Nielsen and McCammon 2003). The computational speed of the FD/DH method, coupled to accuracy over large and small pKa deviations, make it suitable for large-scale database analysis.
| Materials and methods |
|---|
|
|
|---|
This set of 110 groups was supplemented by a further seven active-site groups, with large
pKas for FD/DH analysis, forming the "117" set. Groups and coordinates used for development of the empirical hydration model (Warwicker 1997), including these seven active-site residues, are listed in Table 1
. In addition, coordinate set 1ebd
[PDB]
was used to study interactions within the pyruvate dehydrogenase complex.
Electrostatics calculations (FD and DH)
DH (Warwicker 1999) and FD (
p = 4, single conformer) (Warwicker 1998) calculations followed previous work, except for hydration entropy and polar hydrogen optimization modifications (next section). Model compound pKas used and charge assignment for ionizable group atoms were Arg 12.0 0.5/NH1 0.5/NH2; Lys 10.4 1.0/NZ; His 6.3 0.5/ND1 0.5/NE2; Asp 4.00.5/OD10.5/ OD2; Glu 4.40.5/OE10.5/OE2; Cys 8.31.0/SG (where not disulfide bonded); Tyr 10.21.0/OH; N-t 7.5 1.0/N; C-t 3.80.5/O0.5/OXT. Cysteine side chains were excluded from pKa calculations, except for papain C25, DsbA C30 and C33, and thioredoxin C32 and C35. Partial charges were assigned from the GROMOS library (van Gunsteren and Berendsen 1987). Where specified,
p = 20 was used in place of
p = 4 for the FD model. A water relative dielectric of 78.4 was used throughout. An alternative to a solvent-probed (probe radius =1.4 Å) reentrant surface was tested, a VdW sphere derived surface giving greater water penetration into the protein. All DH and FD computations were made with a linear ion response at 0.15 Molar.
The energy of a protonation microstate relative to the fully unprotonated state for M titratable groups is (modified from Bashford and Karplus 1990):
![]() |
where xm,xn are members of vectors (lengths M) taking the values 0 (unprotonated) and 1 (protonated), qmo,qno are the charges of the unprotonated sites m,n, and pKm,model is the model compound pKa for site m. Interactions of group m with the protein dielectric and nonionizable (background) charge environments are differenced to calculations for each ionizable group extracted from the protein, but remaining on the same FD grid (
GBorn,
Gback), while
Gmn gives the interaction between ionizable groups. The term
G(T
S)QWAT arises from the hydration entropy model discussed in the next section. This definition of Gs applies to either FD or DH calculations, but in the DH case
GBorn and
G(T
S)QWAT terms are zero. In addition
G(T
S)QWAT is set to zero for
p = 20 calculations. Average fractional protonation for site m is derived as:
![]() |
where a full evaluation of the partition function (Z) is possible for small numbers of groups using the reduced sites method (Bashford and Karplus 1990) at pH extremes, or alternatively monte carlo sampling of lowest energy states is used (Beroza et al. 1991). The calculated pKa of site m is that pH for which
xm
=0.5.
Electrostatic energy contributions from ionizable groups were calculated by summing increments over pH according to
(
G)/
pH =2.303RT
Q, where
G,
Q differences are relative to pH titration of the same set of ionizable groups with model compound pKas (Antosiewicz et al. 1994). These sums were extended to an extreme pH at which a full evaluation of ionization states was possible, G =-RTlnZ, again with differencing to a calculation for the same groups titrating with model compound pKas.
Hydration entropy modification and polar hydrogen placement
An empirical estimate of the contribution that water ordering makes to the change in ionization energy upon transfer into protein is written as
G(T
S)QWAT = VfEs (Warwicker 1997), where Vf is the fractional change in first hydration shell volume, and Es is a free energy associated with water ordering for the complete shell. Vf is calculated from FD grids for a group in the protein relative to that whole amino acid extracted from the protein. It is convenient to consider Es in terms of a notional number of water molecules (Warwicker 1997) in the first hydration shell (Ns), using 25 J/K/ mole entropy cost of immobilization of a single water molecule or 7.5 kJ/mole at 300 K. In pKa calculations, Es and Ns correspond to differences between water structure in ionized and unionized forms. Averages of Ns are taken over groups within a class, and individual Ns values derived with charge environments fixed according to expected ionization at physiological pH (see Results and Discussion). Thus, Ns derivation was separated from global pKa computation in the FD/DH method through the use of separate calculations with fixed ionization on all groups but one. The Ns parameter gives the empirically derived difference in hydration numbers between ionized and unionized forms, for a complete hydration shell. It is uniform for a class of ionizable group, but each individual modification is proportional to burial (Vf).
Optimization of polar hydrogen locations can improve pKa calculations (Nielsen et al. 1999; Koumanov et al. 2003). A simple procedure was implemented which optimizes polar hydrogen positions for the OH groups of Ser and Thr side chains (and Tyr if present in its neutral form). Torsions are sampled, with 12° resolution, against coulombic interactions from charges other than those belonging to the set of OH groups being optimized. Ionizable groups are specified in their ionized forms, to focus pKa calculations on the net difference upon titration.
Mean-field packing algorithm and access to the DH interaction scheme
A mean-field algorithm (Koehl and Delarue 1994), adjusted with hard sphere clashes replacing Lennard-Jones interactions (Cole and Warwicker 2002), establishes a set of allowed rotamers from a standard library (Tuffery et al. 1997) as well as those in the experimental structure. The conformational matrix (CM) or weight for rotamer k of side chain i (of N side chains), depends on clashes with fixed atoms and the rotamers of other side chains j:
![]() |
Weights for rotamers l =1 to Kj of side chain j are summed, and the multiplication of these side-chain and fixed-atom weights requires that each be nonzero for a packing solution for rotamer k of residue i. CM values are iterated from an initial assignment of equal weights. These mean-field methods were used previously to assess the entropy associated with side-chain rotamer distributions, but are used here simply to estimate possible rotamers for a given structure and packing tolerance (VdWtol).
Hydrogen atoms are absent in the packing calculations, which use united atom VdW radii, so that a VdWtol of about 0.8 Å is typically required to repack rotamers of the experimental structure, due particularly to side-chain/main-chain clashes. Above this value clashes may partially account for conformational relaxation, such as variation from library side-chain rotamers or small main-chain movements.
An estimate of maximal SA (SAmax) is obtained for each ionizable group. For each neighboring side chain, the (allowed) rotamer that provides the most SA for the charged atom of the ionizable group under study is used to mark SA information on a spherical polar grid around that atom. This grid is scanned after complete neighbor analysis for the overall SAmax. Maximal SA estimates for an ionizable group with more than one charged atom in our model are averaged. The likelihood that small conformational adjustment could lead to substantial solvent exposure of each group is assessed with SAmax (Fig. 3
). If SAmax/SAfixed-atoms
0.75, where SAfixed-atoms is calculated in the context of fixed atoms only (i.e., excluding atoms that move in the rotamer library), then that group is allowed to sample DH (water-dominated) interactions.
Combined FD/DH calculations
In the FD/DH combination, each site m in the equation for microstate energy Gs is sampled as protonated or unprotonated in each of FD and DH (if allowed) schemes, with the component energies of Gs assigned appropriately. Thus, a standard single conformer pKa calculation with 2M states for M groups, approaches 4M states in FD/DH, because most sites are relatively flexible and have access to the DH scheme. Note that for any group m sampled as DH, all mn (and nm) interactions are DH whether group n is sampled as FD or DH, that is, the water-dominated scheme persists for any interaction involving a group sampled in a presumed water-rich environment. Ionizable group array energies and pKas in FD/DH calculations are derived as for FD or DH alone. No attempt is made to assign a density of conformational states, other than DH access or not, so that electrostatic energy determines the relative weightings of FD and DH. A moderate energy (favorable or unfavorable) from DH interaction would outweigh a highly unfavorable FD interaction, while a large and favorable FD term will dominate DH.
| Acknowledgments |
|---|
The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
| References |
|---|
|
|
|---|
Alexov, E. 2003. Role of the protein side-chain fluctuations on the strength of pair-wise electrostatic interactions: Comparing experimental with computed pKas. Proteins 50: 94103.[CrossRef][Medline]
Alexov, E. and Gunner, M.R. 1997. Incorporating protein conformational flexibility into the calculation of pH-dependent protein properties. Biophys. J. 74: 20752093.
Anderson, D.E., Becktel, W.J., and Dahlquist, F.W. 1990. pH-induced denaturation of proteins: A single salt bridge contributes 35 kcal/mol to the free energy of folding of T4 lysozyme. Biochemistry 29: 24032408.[CrossRef][Medline]
Antosiewicz, J., McCammon, J.A., and Gilson, M.K. 1994. Prediction of pH-dependent properties in proteins. J. Mol. Biol. 238: 415436.[CrossRef][Medline]
. 1996. The determinants of pKas in proteins. Biochemistry 35: 78197833.[CrossRef][Medline]
Barbas III, C.F., Heine, A., Zhong, G., Hoffmann, T., Gramatikova, S., Björnestedt, R., List, B., Anderson, J., Stura, E.A., Wilson, I.A., et al. 1997. Immune versus natural selection: Antibody aldolases with enzymic rates but broader scope. Science 278: 20852092.
Bashford, D. and Karplus, M. 1990. pKas of ionizable groups in proteins: Atomic detail from a continuum electrostatic model. Biochemistry 29: 1021910225.[CrossRef][Medline]
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., and Bourne, P.E. 2000. The Protein Data Bank. Nucleic Acids Res. 28: 235242.
Beroza, P., Fredkin, D.R., Okamura, M.Y., and Feher, G. 1991. Protonation of interacting residues in a protein by a Monte Carlo method: Application to lysozyme and the photosynthetic reaction center of Rhodobacter sphaeroides. Proc. Natl. Acad. Sci. 88: 58045808.
Björnestedt, R., Stenberg, G., Widersten, M., Board, P.G., Sinning, I., Jones, T.A., and Mannervik, B. 1995. Functional significance of arginine 15 in the active site of human class
glutathione transferase A11. J. Mol. Biol. 247: 765773.[CrossRef][Medline]
Blom, N. and Sygusch, J. 1997. Product binding and role of the C-terminal region in class I D-fructose 1,6-bisphosphate aldolase. Nat. Struct. Biol. 4: 3639.[CrossRef][Medline]
Cameron, A.D., Sinning, I., LHermite, G., Olin, B., Board, P.G., Mannervik, B., and Jones, T.A. 1995. Structural analysis of human
-class glutathione transferase A11 in the apo-form and in complexes with ethacrynic acid and its glutathione conjugate. Structure 3: 717727.[Medline]
Chivers, P.T., Prehoda, K.E., Volkman, B.F., Kim, B.M., Markley, J.L., and Raines, R.T. 1997. Microscopic pKa values of Escherichia coli thioredoxin. Biochemistry 36: 1498514991.[CrossRef][Medline]
Cole, C. and Warwicker, J. 2002. Side-chain conformational entropy at proteinprotein interfaces. Protein Sci. 11: 28602870.
Demchuk, E. and Wade, R.C. 1996. Improving the continuum dielectric approach to calculating pKas of ionizable groups in proteins. J. Phys. Chem. 100: 1737317387.[CrossRef]
Dong, F. and Zhou, H.X. 2002. Electrostatic contributions to T4 lysozyme stability: Solvent-exposed charges versus semi-buried salt bridges. Biophys. J. 83: 13411347.
Edgcomb, S.P. and Murphy, K.P. 2002. Variability in the pKa of histidine side-chains correlates with burial within proteins. Proteins 49: 16.[CrossRef][Medline]
Elcock, A.H. 2001. Prediction of functionally important residues based solely on the computed energetics of protein structure. J. Mol. Biol. 312: 885896.[CrossRef][Medline]
Fersht, A.R. and Renard, M. 1974. pH-dependence of chymotrypsin catalysis. Appendix: Substrate binding to dimeric
chymotrypsin studied by x-ray diffraction and equilibrium method. Biochemistry 13: 14161426.[CrossRef][Medline]
Fink, A.L., Calciano, L.J., Goto, Y., Kurotsu, T., and Palleros, D.R. 1994. Classification of acid denaturation of proteins: Intermediates and unfolded states. Biochemistry 33: 1250512511.
Fitch, C.A., Karp, D.A., Lee, K.K., Stites, W.E., Lattman, E.E., and Garcia- Moreno, E.B. 2002. Experimental pKa values of buried residues: Analysis with continuum methods and role of water penetration. Biophys. J. 82: 32893304.
Georgescu, R.E., Alexov, E., and Gunner, M.R. 2002. Combining conformational flexibility and continuum electrostatics for calculating pKas in proteins. Biophys. J. 83: 17311748.