Theoretical studies on solvation contribution to the thermodynamic stability of mutants of lysozyme T4

Shashank Deep1 and J.C. Ahluwalia

Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India

1 To whom correspondence should be addressed. Present address: Center for Biomolecular Structure Analysis, Department of Biochemistry, Allied Health Building, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229-3900, USA. e-mail: sdeep{at}instinct.v24.uthscsa.edu


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 Conclusions
 References
 
Atomic solvation parameters (ASPs) are widely used to estimate the solvation contribution to the thermodynamic stability of proteins as well as the free energy of association for protein–ligand complexes. In view of discrepancies in the results of free energies of solvation of folding for various proteins obtained using different atomic solvation parameter sets, systematic studies have been carried out for the calculation of accessible surface area and the changes in free energy of solvation of folding ({Delta}Gs,f) for mutants of lysozyme T4 where threonine 157 is replaced by amino acids: cysteine, aspartate, glutamate, phenylalanine, glycine, histidine, isoleucine, leucine, asparagine, arginine, serine and valine. The deviations of the calculated results from the experimental results are discussed to highlight the discrepancies in the atomic solvation parameter sets and possible reasons for them. The results are also discussed to throw light on the effect of chain free energy and hydrogen bonding on the stability of mutants. The octanol to water-based ASP sets ‘Sch1’ and ‘EM’ perform better than the vacuum to water-based ASP sets. The vacuum to water-based ASP sets ‘Sch3’ and ‘WE’ can be used to predict the stability of mutants if a proper method to calculate the hydrogen bond contribution to overall stability is in place.

Keywords: atomic solvation parameter sets/chain free energy/free energy of solvation of folding/hydrogen bonding/solvent-accessible surface area


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 Conclusions
 References
 
The free energy of folding of a protein consists of contributions from the energy of its intramolecular interactions and the free energy of interaction of the protein with the surrounding solvent molecule. The intramolecular energy of a protein can be computed more easily in the approximation of theoretically based atom–atom potential functions with terms such as electrostatic energy, van der Waals energy and bond and angle distortion energies (Wesson and Eisenberg, 1992Go) and monopole charges at atomic positions. Some of the common molecular simulation programs that use this approach are CHARMm (Brooks et al., 1983Go), GROMOS (van Gunsteren and Berendsen, 1987Go), AMBER (Weiner et al., 1984Go), ECEPP/2 (Momany et al., 1975Go; Némethy et al., 1983Go; Sippl et al., 1984Go) and OPLS modification of AMBER (Jorgensen and Tirado-Rives, 1988Go). It is generally accepted that protein–solvent interactions play a major role to the extent that the solvent hydrophobic effect is considered to be the driving force for folding and binding (Chothia, 1974Go, 1975; Chothia and Janin, 1975Go; Dill, 1990Go; Honig and Yang, 1995Go). Inclusion of solvent effects is essential in calculations involving docking of proteins (Korn and Burnett, 1991Go; Young et al., 1994Go; Jackson and Sternberg, 1995Go) and ligand binding (Leckband et al., 1994Go; Jones et al., 1995Go), as well as protein recognition, protein modeling, engineering and design (Eisenhaber et al., 1995Go). However, the calculation of the solvent contribution by a molecular simulation program is still a problem because of the large number of degrees of configurational freedom of water molecules, making the explicit simulation of water difficult. Also, the structure of water is less well understood than the structure of proteins and water interacts with proteins through hydrophobic forces in addition to electrostatic and van der Waals forces. The hydrophobicity is more difficult than the other forces to represent effectively with simple atom–atom energy potentials.

The alternative approach to calculate the solvent contribution is based on the Kauzmann formalism (Kauzmann, 1959Go). To quantify the contributions of hydrophobic free energy ({Delta}GH{phi}) to the observed standard free energy change for the folding of proteins, most work has been focused on analyzing the free energy of transfer ({Delta}G°tr) of amino acids or their analogues from water to an organic solvent (Nozaki and Tanford, 1971Go) or the gas phase (Wolfenden et al., 1981Go). Various hydrophobicity scales have been proposed that rank amino acids according to either their experimental transfer behavior or their observed distribution in protein crystal structures between exterior and interior of the folded form (Bull and Breese, 1974Go; Fendler et al., 1975Go; Tanford, 1978Go; Janin, 1979Go; Rose et al., 1985Go; Damodaran and Song, 1986Go). However a comparison of the hydrophobicity scales reveals that in general, values of {Delta}G°tr from different scales do not correlate well with each other and that even the relative ranking of amino acid varies from one scale to another.

Hermann (Hermann, 1972Go) developed an implicit method for treating water molecules, whereby the area of various hydrocarbons could be quantitatively related to their solubilities. Chothia (Chothia, 1974Go) found a linear relationship between surface areas of amino acid residues and free energy changes associated with the transfer of amino acids from organic solvent to water. The slope of this line corresponds to a free energy change of about 25 cal/mol.Å2 for non-polar residues.

Assuming that the thermodynamic component or the free energy of the folded protein molecule is proportional to the total protein surface that is accessible to water, regardless of whether the exposed surface is apolar, polar or charged, is an oversimplification. Eisenberg and McLachlan (Eisenberg and McLachlan, 1986Go) computed the free energy of interaction of water with the protein as the sum of energies of atomic groups. They considered the process of transferring atoms or the groups from the interior of a protein to aqueous solution and used transfer free energies of amino acids from n-octanol to water as reported by Fauchere and Plisca (Fauchere and Plisca, 1983Go). The solvent contribution to the free energy of folding of a protein ({Delta}Gs,f) can be expressed simply in terms of reduction in its solvent-accessible area of folding multiplied by the solvation free energy per unit area. , where {sigma}i is the atomic solvation parameter of the atom i of the given type, {Delta}Ai is the change in solvent-accessible surface area upon folding and {Delta}Gs,f is the change in solvation energy of folding (Eisenberg and McLachlan, 1986Go).

Atomic solvation parameters (ASPs) are widely used to estimate the solvation contribution to the thermodynamic stability of proteins and the results obtained are generally promising (Wesson and Eisenberg, 1992Go; Williams et al., 1992Go; Schiffer et al., 1993Go; Stouten et al., 1993Go). These parameters are used to rationalize the changes in protein stability that result from single-site mutations (Kellis et al., 1989Go; Eriksson et al., 1992Go; Lee, 1993Go; Blaber et al., 1994Go; Huang et al., 1995Go) and to predict thermodynamic properties of compact denatured states and partially folded states or molten-globule states of proteins (Xie and Freire, 1994Go; Freire, 1995Go; Hilser and Freire, 1996Go).

Many workers have pursued this approach, which led to a dissemination/proliferation of several different atomic solvation parameter sets; some of them are listed by Juffer et al. (Juffer et al., 1995Go). All ASP sets are derived by a least-squares fitting of experimentally observed changes in free energies upon transfer of a simple model compound from an organic solvent to water.

However, recent reports showed that large discrepancies exist between results using different atomic solvation parameter sets. It has also been shown that the use of atomic solvation parameters often does not contribute to the ability to estimate accurately the energetics of ligand binding or protein association (Horton and Lewis, 1992Go; von Freyberg et al., 1993Go; Janin, 1995Go; Karplus, 1997Go).

In view of discrepancies in the results of free energies of solvation for various proteins obtained using different atomic solvation parameter sets, it was felt desirable to carry out systematic studies on the calculation of accessible surface area and the changes in free energy of folding of a large number of proteins. Since experimental values for free energy of solvation of proteins are not available for comparison with the values calculated using ASPs, it was thought worthwhile to calculate the changes in free energy of folding of proteins on mutations, where the calculated results can be compared with the experimental results. This would give a better perception of the suitability and limitations of the various ASP sets for modeling folding processes and protein–ligand binding.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 Conclusions
 References
 
Atomic coordinates of wild-type and mutant proteins were taken from the Brookhaven protein data bank (Bernstein et al., 1977Go). They represent well-defined high-resolution (2 Å or better) X-ray structures of proteins. Models of unfolded proteins were generated using InsightII (Biosym) on a Silicon Graphics workstation. The dihedral angles of amino acid residues, representing the unfolded chain, were taken from the minimum energy conformations of the N-acetyl-N'-methylamides of the residue near the extended conformations. Accessible surface areas of the native and denatured conformation of globular proteins were calculated using Lee and Richards’ analytical molecular surface algorithm ACCESS (Lee and Richards, 1971Go), with a probe radius of 1.4 Å, a slice width of 0.1 Å and atomic radii with two different radii sets. The atom types defined in the first radius set (Table I) are C (all carbon), N/O (all uncharged nitrogens and oxygens), N+ (charged nitrogens), O (charged oxygens) and S (all sulfurs). The resonance distribution of side-chain charges by describing the relevant heteroatoms as linear combinations of two atom types are taken into account while assigning the charge to heteroatoms of glutamate, aspartate, arginine and histidine (Cummings et al., 1995Go). For glutamate and aspartate, each side-chain O was described as 50% N/O and 50% O. For arginine, each of three guanidino Ns was described as 67% N/O and 33% N+. For histidine, each imidazole N was described as 10% N+ and 90% N/O. The atom types defined in the second radius set (Table II) are aliphatic carbon, aromatic carbon, carbonyl or carboxyl carbon, any nitrogen, carbonyl or carboxylic oxygen, hydroxylic oxygen and all sulfur. The changes in non-polar accessible surface area ({Delta}AC), aromatic accessible surface area ({Delta}AAr), carbonyl carbon accessible area ({Delta}ACC), carbonyl oxygen accessible surface area ({Delta}ACO), uncharged oxygen accessible surface area ({Delta}AO), charged oxygen accessible surface area ({Delta}AChO), sulfur accessible surface area ({Delta}AS), uncharged nitrogen accessible surface area ({Delta}AN) and charged nitrogen accessible surface area ({Delta}AChN) were calculated for wild-type and mutants of lysozyme T4 where threonine 157 is replaced by amino acids: cysteine (PDB1L03), aspartate (PDB1L04 and PDB1L05), glutamate (PDB1L06), phenyalanine (PDB1L07), glycine (PDB1L08), histidine (PDB1L09), isoleucine (PDB1L10), leucine (PDB1L11), asparagine (PDB1L12), arginine (PDB1L13), serine (PDB1L14) and valine (PDB1L15) using radii listed for different atoms as given in set 1 and set 2. These mutants were chosen since the crystal structure and calorimetrically obtained values of {Delta}mw{Delta}Gf are reported in the literature (Alber et al., 1987Go).


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Table I. Radii set 1: atomic solvation parameters (cal/mol.Å2) with six atom types
 

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Table II. Radii set 2: atomic solvation parameters (cal/mol.Å2) with seven atom types
 
The ASP sets can be classified on the basis of two radii sets of atoms and on the basis of the transfer free energy of small solute molecules from vacuum to water (v/w) and from octanol to water (o/w). The first radius set of atoms (Table I) constitutes six atom type solvation parameters for atoms C (any carbon), O (non-charged), O (charged), N (non-charged), N (charged) and S (any). This includes octanol to water-based ASP sets of Eisenberg and McLachlan (EM86) (Eisenberg and McLachlan, 1986Go), Eisenberg et al. (Sch1) (Eisenberg et al., 1989Go) and Kim (Sch2) (Kim, 1990Go) and vacuum to water-based ASP sets of Wesson and Eisenberg (WE and Sch3) (Wesson and Eisenberg, 1992Go) and Schiffer et al. (Sch4) (Schiffer et al., 1993Go). The second radius set of atoms (Table II) constitutes seven atom type solvation parameters for atoms C (aliphatic), C (aromatic), C (carbonyl and carboxyl), N (any), O (carbonyl or carboxyl), O (other) and S (any). This includes the vacuum to water-based ASP set of Ooi et al. (Oons) (Ooi et al., 1987Go) and octanol to water-based ASP set of Vila et al. (Jrf) (Vila et al., 1991Go).

The Sch3 and WE sets were derived by Wesson and Eisenberg (Wesson and Eisenberg, 1992Go) from Wolfenden’s measurement data on transfer free energies of small solutes from vacuum to water, which were adjusted for the entropy of mixing by Kyte and Doolittle (Kyte and Doolittle, 1982Go) and Sharp et al. (Sharp et al., 1991aGo,b), respectively. A solvent potential function was introduced that describes protein–water interactions based solely on the position of protein atoms and on atomic solvation parameters, as an alternative to including explicit water molecules in a simulation. The potential function and its derivative are added to the CHARMm force field (Brooks et al., 1983Go) for simulation of protein in a vacuum; the total potential describes a protein solvated in water.

The Sch4 set was derived by Schiffer et al. (Schiffer et al., 1993Go) by comparison of simulations using an ASP solvation term based on different octanol–water transfer data as complements to the AMBER potential function in a protein folding/molecular dynamics study.

The Oons atomic solvation parameter set was established by Ooi et al. (Ooi et al., 1987Go) to supplement the ECEPP/2 (empirical conformational energy programs for peptides) algorithm (Momany et al., 1975Go; Némethy et al., 1983Go) that computes the intramolecular energy of the folded protein molecule. It was derived from the transfer free energies of small solute molecules from vacuum to water, as given by Cabani et al. (Cabani et al., 1981Go) and supplemented with data from Wolfenden et al. (Wolfenden et al., 1981Go) on transfer energies for the partition of amino acids between water and vapour phase (vacuum). While the EM set was derived by assuming that there are no significant changes in conformation of amino acids during the transfer process, possible shifts of the distribution among various conformations were taken into account in the Oons set.

In a similar approach, the Jrf set was derived by Vila et al. (Vila et al., 1991Go) by adding a solvation potential function based on the Connolly molecular surface areas and its derivative to the ECEPP/2 program. The solvation models were evaluated by the concordance between solvation free energy and root mean square deviation from the crystal structure in 39 near-native conformations of bovine pancreatic trypsin inhibitor.


    Results and discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 Conclusions
 References
 
The changes in the standard free energy of folding of phage T4 lysozyme and its mutants, {Delta}mw{Delta}Gf, as obtained calorimetrically (Alber et al., 1987Go) are listed in Table III.


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Table III. Experimental values of the change in standard free energy of folding of the mutant and wild-type lysozyme T4a
 
                            {Delta}mw{Delta}Gf = {Delta}Gmf{Delta}Gwf

where {Delta}Gmf and {Delta}Gwf are the free energies of folding of the mutant and wild-type lysozyme T4, respectively.

Since ({Delta}Gmf{Delta}Gwf) > 0, it means that all the mutants of T4 lysozyme are less stable than the wild-type lysozyme T4. In order to quantify the relative contribution of the hydrophobic interactions, hydrogen bonding, etc., to the stability of these mutants, the changes in accessible surface areas of folding and the changes in free energies of solvation of folding of mutants of lysozyme T4 were calculated.

The values of {Delta}mw{Delta}A of folding calculated using two radii sets are listed in Table IV and Table V and are found to be negative for all mutants except for the mutant where threonine is replaced by cysteine. The negative values of {Delta}mw{Delta}A of folding appear to arise from the increase in the extent of the burial of polar nitrogen atoms (both charged and uncharged) in mutants leading to a decrease in stability, although it is partly compensated by exposure of oxygen atoms.


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Table IV. Change in accessible surface area of folding, {Delta}mw{Delta}A2), of T4 lysozyme on mutation (radii set 1)
 

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Table V. Change in accessible surface area of folding, {Delta}mw{Delta}A2), of T4 lysozyme on mutation (radii set 2)
 
The free energies of solvation of folding ({Delta}Gs,f) were calculated for the mutants of lysozyme T4 by multiplying the change in accessible surface area of each group with its ASP assigned by different ASP sets. The values of changes in total free energies of solvation of folding of mutants and wild-type lysozyme T4, {Delta}mw{Delta}Gs,f, are compared with the experimental values of {Delta}mw{Delta}Gf in Table VI.


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Table VI. Comparison of calculated values of change in free energy of solvation, {Delta}mw{Delta}Gs,f (cal/mol), with experimental values of free energy of folding, {Delta}mw{Delta}Gf (cal/mol), of T4 lysozyme on mutation
 
All the ASP sets except ‘Jrf’ predict the mutants of lysozyme T4 to be less stable than wild-type lysozyme T4 as seen from the positive calculated values of {Delta}mw{Delta}Gs,f, which is consistent with the experimental results for {Delta}mw{Delta}Gf, indicating that the solvation contribution plays a dominant role. However, the values of {Delta}mw{Delta}Gs,f obtained from the octanol to water-based ASP sets ‘EM’ and ‘Sch1’ are relatively closer to the experimental values of free energy of folding whereas the values of {Delta}mw{Delta}Gs,f obtained from other ASP sets differ considerably from the experimental values of {Delta}mw{Delta}Gf. The difference in the experimental value of free energy of folding ({Delta}mw{Delta}Gf) and the calculated values of free energy of solvation of folding could be due to the contribution of intramolecular forces of interaction and deficiency in ASP sets.

A protein structure is stabilized not only by buried hydrophobic residues but also by buried polar residues with hydrogen bonds. The X-ray crystal structure of lysozyme T4 shows that Thr157 is located in an irregular loop on the surface of the protein and is involved in hydrogen bonding with Thr155 and Asp159 as shown in Figure 1. The substitution of Thr157 by amino acid alters the geometry of the hydrogen bonding network (as shown in Figure 2 for Ile), resulting in a change of several interactions in the folded state, i.e. hydrogen bonding, van der Waals, hydrophobic and electrostatic interactions. When Thr157 is replaced by Asn (T157N), Ser (T157S), Asp (T157D) or Arg (T157R), the buried backbone amide of Asp159 is hydrogen bonded (Alber et al., 1987Go). The substitution of Thr157 by Phe (T157F), I1e (T157I), Leu (T157L), Val (T157V), Cys (T157C), Glu (T157E) or His (T157H) results in the loss of the hydrogen bond to the amide of Asp159 while the hydroxyl oxygen of Thr155 forms a new hydrogen bond with a water molecule in place of the hydroxyl hydrogen of Thr157. The mutants with or without a hydrogen bond to the amide of Asp159 will be subsequently referred to as type A and type B mutants. The loss of one hydrogen bond destabilizes type B mutants by 1 kcal (Fersht et al., 1985Go; Shirley et al., 1992Go).



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Fig. 1. Schematic diagram showing the geometry of the hydrogen bonding network around position 157 of lysozyme T4.

 


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Fig. 2. Schematic diagram showing the local environment of Ile157 in the mutant of lysozyme T4.

 
Introduction and removal of water molecules from the interior of mutant proteins can also lead to changes in protein stability. X-ray structures of wild-type and mutant lysozyme T4 show that there is an extra water molecule in the interior of the wild-type lysozyme T4. This corresponds to destabilization of wild-type lysozyme T4 by 2 kcal due to loss of the entropy of trapped water (Dunitz, 1994Go). This extra water molecule in wild-type lysozyme T4 can form three hydrogen bonds. However, two hydrogen bonds can be formed only at the expense of the other hydrogen bonds in the interior, only one hydrogen bond contributing 1 kcal to the stability of protein. Thus, loss of water molecule from the interior of mutants stabilizes both type A and type B mutants by 1 kcal.

The most striking X-ray crystal structure of this series is that of T157G. The absence of a side chain on glycine leaves a gap on the surface of the enzyme that is filled by a new bound water molecule (Alber et al., 1987Go). The water molecule is positioned to interact with the same hydrogen-bonded partners as Thr157 in wild-type protein. The average geometry of the hydrogen-bonded network is improved, apparently because the solvent molecule is not covalently bonded to the protein. With two protons to donate, the new water can form a hydrogen bond with the side-chain carboxylate of Asp159. Similarly, Asn has two protons to donate and hence can form an extra hydrogen bond with the side-chain carboxylate of Asp159. The formation of one extra hydrogen bond is correlated with the decrease in {Delta}mw{Delta}Gf, leading to higher stability of mutants T157G and T157E.

The major force opposing protein folding is the loss of conformational entropy (Doig and Sternberg, 1995Go). Since, in the wild-type lysozyme T4, Thr157 is conformationally restricted owing to its ß-branched side chain compared with most of the amino acids, it is expected that the contribution of the chain entropy term will be significant. The values of {Delta}mw{Delta}Gc,f were calculated using the value of side-chain entropy for different amino acids (Pickett and Sternberg, 1993Go).

Another factor affecting the stability of mutants is the change in the volume of the cavity. The change in the volume of the cavity upon mutation of lysozyme T4 is essentially very small so it was not included in the calculation of {Delta}mw{Delta}Gf.

Table VII summarizes the contributions of various stabilization factors to the overall stability of mutants relative to the wild-type lysozyme T4. The values of the calculated {Delta}mw{Delta}Gf, the sum of {Delta}mw{Delta}Gs,f, {Delta}mw{Delta}GHB, {Delta}mw{Delta}GH2O and {Delta}mw{Delta}Gc, for different mutants are given in Table VIII. All atomic solvation parameter sets except ‘Jrf’ predict wild-type lysozyme to be more stable than its mutants. However, the values of {Delta}mw{Delta}Gf differ for different ASP sets.


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Table VII. Changes in different components of standard free energy of folding of the mutant and wild-type lysozyme T4a
 

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Table VIII. Comparison between calculated {Delta}mw{Delta}Gf (cal/mol) and experimental {Delta}mw{Delta}Gf (cal/mol)
 
Evaluation of different ASP sets

The correlation coefficients between the experimental and calculated {Delta}mw{Delta}Gf data sets are listed in Table VIII. It is clear that octanol to water-based atomic solvation parameter sets ‘Sch1’ and ‘EM’ are more consistent than other atomic solvation parameter sets in predicting {Delta}mw{Delta}Gf. ‘EM’ gives a slightly higher value of {Delta}mw{Delta}Gf. Figure 3 shows the correlation between the experimental and calculated {Delta}mw{Delta}Gf values based on the ‘Sch1’ set. The only mutant for which the experimental and calculated {Delta}mw{Delta}Gf values differ considerably is T157I. The bulkier ethyl substituent of Ile forces the side chain of Asp 159 to move 1.1 Å away from its position in the wild-type protein (Alber et al., 1987Go). The unusually large motion of Asp159 disturbs the intramolecular network of hydrogen bonding in that region, leading to a large reduction in stability.



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Fig. 3. Correlation of calculated {Delta}mw{Delta}Gf using the ‘Sch1’ set and experimental {Delta}mw{Delta}Gf.

 
The values of {Delta}mw{Delta}Gf obtained from the octanol to water atomic solvation parameter set ‘Sch2’ are higher than the experimental {Delta}mw{Delta}Gf values. Particularly, mutants T157N, T157H and T157R are predicted to be least and T157C to be most stable among the mutants. However, the predicted order of stability of other mutants is in same order as expected based on the experimental values. Also, the correlation coefficient between the experimental and predicted {Delta}mw{Delta}Gf values improves from 0.59 to 0.91 when the data for T157A, T157H, T157R and T157C are excluded from the analysis. A cursory look at Table IV shows that T157N, T157H and T157R have higher values of {Delta}mw{Delta}AN + {Delta}mw{Delta}AChN and T157C has the lowest. Since charged N/O atoms have been assigned a higher ASP, the calculated {Delta}mw{Delta}Gf is high for T157N, T157H and T157R and low for T157C. These observations indicate that the high ASP assigned to charged/uncharged N/O is the reason for the ‘not so good’ correlation between the experimental and calculated {Delta}mw{Delta}Gf values.

The octanol to water-based ASP set ‘Jrf’ predicts all mutants to be more stable than wild-type lysozyme T4. Mutations of lysozyme T4 are accompanied by burial of charged/uncharged N that is partly compensated for by exposure of O atoms. Most ASP sets assign almost equal ASP to N and O. Since ‘Jrf’ assigns ASP of O three times ASP of N, the solvation free energy will be determined more or less by the changes in the accessible surface area of the O atoms and hence mutants are predicted to be more stable than wild-type lysozyme T4.

Vacuum to water-based ASP sets ‘WE’ and ‘Sch3’ predict all mutants to be less stable than the wild-type lysozyme T4. However, the {Delta}mw{Delta}Gf values obtained are higher in magnitude, which can be attributed to the higher ASP assigned to charged nitrogen atoms. If vacuum to water transfer is used to define hydrophobicity, then the solvation enthalpy makes a large contribution to the net free energy change, partially cancelling the solvation entropy. As a result, the hydrophobic effect becomes smaller than when defined in terms of octanol to water transfer. This will result in a higher ASP of polar atoms than non-polar atoms. Based on the definition involving vacuum to water transfer, Privalov and co-workers have argued that the hydrophobic effect makes only a small contribution to protein stability (Privalov and Gill, 1988Go; Makhatdze and Privalov, 1993Go; Privalov and Makhatdze, 1993Go). However, exclusion of T157N, T157H and T157R and T157C from the experimental data does not improve the correlation coefficient significantly. This clearly indicates that factors other than the hydrophobic effect are responsible for the poor performance of vacuum to water-based ASP sets. The change of mode to define hydrophobicity also affects the contribution of hydrogen bonds to the overall stability. Since vacuum has a lower dielectric constant than octanol, the hydrogen bond contribution to {Delta}mw{Delta}Gf is expected to be higher in the case of vacuum to water-related ASP sets. This can explain why type B mutants are predicted to be more stable than type A mutants. Also, the contribution of hydrogen bonding to {Delta}mw{Delta}Gf will vary significantly owing to the changes in hydrogen bond geometry and type of hydrogen bonds. Burial of polar and charged polar atoms in the interior of the mutants may result in good geometry of the already existing hydrogen-bond network of the protein and therefore a higher negative value of {Delta}mw{Delta}GHB, which will lower the predicted large value of {Delta}mw{Delta}Gf for mutants. Similarly, the calculated higher value of {Delta}mw{Delta}Gf for T157R, T157H and T157N can be compensated. The strength of protein–protein and protein–water hydrogen bonds will be considerably different. The low strength of protein–water hydrogen bonds compared with protein–protein hydrogen bonds can explain the calculated low value of {Delta}mw{Delta}Gf for T157G compared with T157D and T157S.

The ‘WE’ and ‘Sch3’ ASP sets have been derived using the transfer free energy that was corrected to exclude the effect of differences in molar volume between solute and solvent. The volume effect is not considered when deriving other ASP sets. The volume effect is particularly large upon transfer of the solute from vacuum to water. This is one of the reasons for the poor performance of ASP sets ‘Sch4’ and ‘Oons’. The values of the calculated {Delta}mw{Delta}Gf using ‘Sch4’ and ‘Oons’ defy the logic that has been used to describe the difference in the experimental and calculated values using ‘WE’ and ‘Sch3’. The ASP set ‘Sch4’ assigns higher ASP to charged N/O than uncharged N/O and predicts T157R and T157D to be the least stable mutants. Similarly, the ASP set ‘Oons’ assigns higher ASP to carbonyl carbon and predicts T157D to be more stable than T157S.


    Conclusions
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 Conclusions
 References
 
It is clear from the results that ASP sets derived from octanol to water transfer energies ‘Sch1’ and ‘EM’ are more consistent in predicting the stability of mutants. It indicates that the octanol environment resembles protein interior. This seems reasonable given the presence of the alcohol hydroxyls and the significant amount of water present in wet octanol (Radzicka and Wolfenden, 1988Go). Thus, bulk octanol contains a significant proportion of a hydrophobic, hydrogen bond-donating and hydrogen bond-accepting surface. However, the other octanol to water transfer energy-based ASP sets ‘Sch2’ and ‘Jrf’ are not consistent. ‘Sch2’ assigns high ASP to charged/uncharged polar atoms. ‘Jrf’ assigns ASP of the charged/uncharged O three times that of the charged/uncharged N, therefore making the prediction of the stability of mutants totally dependent on the exposure/burial of O atoms.

The vacuum to water-based atomic solvation parameter sets ‘WE’ and ‘Sch3’ predict a higher magnitude of the hydrophobic contribution to {Delta}mw{Delta}Gf, leading to a higher value of {Delta}mw{Delta}Gf. This is due to the different definition used to describe hydrophobicity. However, a higher {Delta}mw{Delta}Gs,f is partly compensated by negative {Delta}mw{Delta}GHB. The contribution of hydrogen bonds to the stability of proteins is much higher when defined in terms of vacuum to water transfer. Also, the contribution of hydrogen bonds to {Delta}mw{Delta}Gf will vary significantly owing to the changes in hydrogen bond geometry and type of hydrogen bonds. Hence the use of vacuum to water-based atomic solvation parameter sets should be avoided for the prediction of the stability of mutants until a more accurate method to determine the strength of hydrogen bond is in place. The ASP sets ‘WE’ and ‘Sch3’ sets are better than ‘Sch4’ and ‘Oons’ because they are derived from the transfer free energies that are corrected to exclude the volume effect. The ASP set ‘Sch4’ assigns higher ASP to charged N/O relative to uncharged N/O whereas ‘Oons’ assigns a higher ASP to carbonyl carbon.


    Acknowledgements
 
We thank Dr B.Jayaram, Department of Chemistry, and his students A.Das and S.Dixit for helpful discussions. J.C.Ahluwalia is grateful to the Indian National Science Academy for the award of an INSA Senior Scientist and Shashank Deep is grateful to the University Grant Commission (UGC) for the award of a Senior Research Fellowship.


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results and discussion
 Conclusions
 References
 
Alber,T., Sun,D.P., Wilson,K., Wozniac,J.A., Cook,S.P. and Matthews,B.W. (1987) Nature, 330, 41–46.[CrossRef][ISI][Medline]

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Received February 26, 2002; revised December 5, 2002; accepted April 15, 2003.





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