1Department of Biology and 4Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5YW, 2Department of Biomolecular Sciences, UMIST, P.O. Box 88, Manchester M60 1QD, 5Protein Structure Division, NIMR, The Ridgeway, Mill Hill, London NW7 1AA, UK and 6Bioinformatics Institute, 30 Biopolis Way, #07-01 Matrix, Singapore 138671
3 To whom correspondence should be addressed (at the UMIST address for R.B.G.). E-mail: r.greaves{at}umist.ac.uk or chandra{at}bill.a-star.edu.sg
![]() |
Abstract |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
Keywords: B13 Glu/insulin/mutants/pKa/PoissonBoltzmann electrostatics
![]() |
Introduction |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
This paper sets out to examine this experimental observation using PoissonBoltzmann electrostatics calculations and also to probe the effect of the two mutations in different aggregation states on B13 Glu ionization. Our intention is to reproduce an experimentally observed shift and then to use the benchmarked parameters to assess mutants that have been experimentally characterized; we then hope to predict mutations that will be useful in the design of therapeutically useful insulins. We have found such calculations to be very useful in computing a subset of protein properties, notably relative binding energetics and shifts in titration characteristics (Plou et al., 1996; Gibas et al., 2000
; Hussain et al., 2003
). Of course, it has to be borne in mind that no one method of computing pKa values in proteins can claim to reproduce all the titration characteristics of proteins simultaneously with accuracy and such approaches are fraught with difficulties and limitations (see, for example, Schutz and Warshel, 2001
); however, limited success can be achieved with careful model building (Plou et al., 1996
; Mehler et al., 2002
).
![]() |
Methods |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
Models for the mutant insulins were created by mutating the appropriate residues in each monomer using Quanta (Accelrys, San Diego, CA). The conformations of the mutated side chains were investigated using a rigid body mapping. The 1 ands
2 conformations were set at the low-energy conformations determined from the mapping. The mutated residues were all modelled as charged aspartate groups. All models were then briefly energy minimized to relieve any close contacts present in the structure.
The method used for calculating pKa shifts has been outlined by several groups (see, for example, Bashford and Karplus, 1990; Yang et al., 1993
; Antosiewicz et al., 1994
; Demchuk and Wade, 1996
; Schutz and Warshel, 2001
) and hence will not be mentioned here. For our work, the point charges and van der Waals radii used in the models are those of the AMBER/OPLS parameter set (Weiner et al., 1984
). Parameters for the Zn2+ ion were derived from the CHARMM22 parameter set (Brooks et al., 1983
; Neria et al., 1996
). The solvent dielectric was set to 78 and the protein dielectric was taken to be 8.5; the reason for this choice of the protein dielectric is outlined later. Calculations were performed at 293 K using the methodology of Yang et al. (1993)
. The interaction energies used to design the mutants were computed by calculating the interactions between the charges on each atom of one monomer (of the dimer) by the potential generated at that site from the charge distribution on the other monomer (of the dimer). The program UHBD (version 6.1) (Davis et al., 1991
; Madura et al., 1995
; Accelrys, San Diego, CA) was used to perform these calculations using protocols employed previously (Plou et al., 1996
) and the ZRF methodology of Demchuk and Wade (1996)
. The ZRF methodology utilizes a modified OPLS parameter set which is calculated so as to eliminate the need to construct theoretical hydrogen atom positions which are a source of potential inaccuracy in such calculations. Therefore, hydrogen bond network optimization such as that performed by Nielsen and Vriend (2001) was not performed. However, the rotameric states of Asn, His and Gln residues were assessed visually using Quanta in each model and subsequently assessed as being the minimum energy rotamer (Plou et al., 1996
) prior to performing the calculations. The pKa calculations employed the multiple titratable site cluster methodology that is implemented in UHBD, this being the method of Gilson (Gilson, 1993
; Antosiewicz et al., 1994
) in order to calculate the titration curves for the model systems.
To examine the influence of protein fluctuations/conformational flexibility on the computed pKa shifts, several calculations were performed at set time points in a molecular dynamics trajectory. Each model used for the calculation of pKas was subjected to a 50 ps equilibration followed by 100 ps of restrained Langevin molecular dynamics. The production trajectory was sampled at 10 ps intervals and the structures derived were briefly minimized using harmonic restraints on heavy atoms prior to being used as the starting point for subsequent pKa calculations. Statistics were collected for each trajectory and the percentage variance for each ionizable group was calculated.
![]() |
Results and discussion |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
The theoretical cases of a zinc-free and a 2Zn hexamer for each mutant insulin (B9 Ser Asp, B10 His
Asp) illustrate the role of the Zn2+ ion in dissipating the concentration of negative electrostatic charge in the central solvent channel. Very large shifts in pKa are predicted for B13 in the B9 mutant hexamer, presumably because of the very high charge concentration that this would entail (12 charged groups within the solvent channel). The lower shift for B13 pKa in the putative B10 mutant hexamer would presumably be due to the fact that B10 is further away from B13 than is B9. Adding Zn2+ to the hexamer models appears to make B13 ionization in the wild-type hexamer more favourable than in the mutant species.
The influence of conformational flexibility is most apparent in the case of the monomer where the variance is the highest. A progressive decrease in the variance as the aggregation state increases reflects the increasingly constrained environment and hence a relative reduction in the flexibility of the residues. Apart from the wild-type monomer, which displays a large variance of about 20%, the other states are mostly within 10%.
Solutions to the PoissonBoltzmann equation have been successfully used to compute pKa shifts for B13 Glu in a mutant form of the insulin dimer, B9 Asp; the computed value is within 6% of the experimental value. It is clear that the proximity of B9 to B13 has the largest influence on the titration behaviour of B13. The shift in pKa for B13 is dependent on the rotameric state of the B13 side chain under consideration (data not shown).
The computed results clearly show that the wild-type monomer protein environment does not significantly shift the pKa of B13 beyond its solution pKa. The two mutant monomers show slight increases in the pKa of B13 relative to its solution value owing to the introduction of nearby charged groups. The wild-type dimer environment again only slightly perturbed the ionization of B13 Glu. The B9 Ser Asp mutation, however, caused this shift to be somewhat larger, at +2.64 and +2.45 units relative to the solution pKa of glutamic acid. Thus a maximum shift of +2.64 units has occurred relative to the pKa in the wild-type dimer, in good accord with the experimental value of +2.50 units. The effect of the B10 His
Asp mutation in the dimer was less marked, with an increase of only +0.90 units relative to solution being recorded. In the mutant B10Asp zinc-free hexamer models, ionized B13 was found to be extremely disfavoured. Inclusion of the zinc ions in the hexamer models reduced the pka of B13 groups in all the model systems studied as compared with the zinc-free models. However, B10 His
Asp hexamers cannot form because of the loss of zinc binding capability at B10 His. The charge build-up in the central hexamer channel is seen to be prohibitively large towards ionization of B13 groups in a model B9 Ser
Asp hexamer.
Design of mutants
Having successfully reproduced the experimental pKa shift in our calculations, we used this protocol to examine the influence of electrostatics in predicting mutations that would enhance the therapeutic efficacy of engineered insulin. In general, the mutational spectrum of insulin has been examined fairly exhaustively (see, for example, http://pmd.ddbj.nig.ac.jp; Nishikawa et al., 1994; or http://pmr.sdsc.edu/; Krebs and Bourne, 2003
). In an attempt to predict mutations from our computations, we examined the interactions that are made between the monomers that form a dimer. There are two aspects on which one needs to focus while suggesting a medically relevant mutation: (a) reduce the tendency to aggregate or ideally, completely abolish aggregation and (b) maintain or even enhance receptor binding. The interactions that will destabilize dimer formation were examined with the idea that any site of one monomer (say B) that has a destabilizing interaction energy with the other monomer will lead to decreased dimerization (for example, relative to the B13 Glu, the site with the strongest interactions is B9 Ser). Several studies have been devoted to engineering mutants of insulin based on residues located either in the dimer-forming or the hexamer-forming or the receptor binding surfaces (Shoelson et al., 1992
; Kaarlsholm et al., 1993
; Hua et al., 1996
) and indeed there are several factors that seem to control the structurefunction relationships of insulins (Pittman et al., 1997
; Guo et al., 2001
). Although mutations of Pro have been found to be useful (Shoelson et al., 1992
), we deliberately left them out of this study. We are interested in examining this problem from a purely electrostatic point of view; this offers the advantage of including long-range and more complex effects that would not be evident from a cursory visual examination of the structures. Our study can only suggest mutations that result in switching off a charge or changing a charge or charging an uncharged side chain. They do not take into account any conformational rearrangements or structural couplings that may be induced as a result (Zeng et al., 2000
).
The electrostatic interaction free energy between the native monomers is 3.6 kcal/mol. As a method of benchmarking, we focus our attention on some mutant form of insulin that is known to exist as a monomer. The double mutant B9 Ser Asp/B27 Thr
Glu is known to be monomeric (Ray et al., 1990
) and our calculations of the electrostatic interaction free energies between the monomers in a double mutant derived from the wild-type dimer show a 37% destabilization (2.3 kcal/mol) relative to the wild-type. Additionally, the quadruple mutant B1 Phe
Glu/B10 His
Glu/B16 Tyr
Glu/B27 Thr
Glu, which is an engineered monomer (Olsen et al., 1998
), severely destabilizes the electrostatic interactions between the monomers (the electrostatic interaction energy is +360 kcal/mol). However, this value is probably too high and illustrates the complexities of such analyses, pointing towards the need for additional considerations such as structural/energetic optimizations to relieve the strain that ensues on modelling based upon the wild-type dimer structure. In Figure 2A are shown the electrostatic potentials (the electrostatic interaction energy in kcal/mol is shown and is computed as the electrostatic potential at the site multiplied by a unit charge) generated at all the side chain positions of one monomer due to the charge changes in the other monomer in the wild-type dimer (the sequence at the top of Figure 2A lists the residues along the insulin monomer and also shows residues that are implicated in residue binding). Of course, this does not take into account any structural relaxations that will result from the incurred electrostatic changes. We list a set of mutations and the destabilization they will cause to the dimerization in Table II. Owing to the lack of consensus information on the nature of the unfolded state (Oliveberg and Fersht, 1996
), we hypothesize brief discussions of the potential effects of each proposed mutation on the stability of the monomer with respect to its unfolded state (Table III). It is clear that the A-chain residues do not contribute significantly to the electrostatic interactions between the dimers. Only the residue Asn at the C-terminal end of chain A makes a contribution. This residue incidentally is known to be conserved across all species and is thought to be dynamically coupled to B25 Phe (Zeng et al., 2000
). From this table, B9 Ser
Asp is known to be monomeric (see Introduction). B15 Leu is buried and hence introduction of a charge in this hydrophobic core may not yield a stable monomer. Only a few of these residues are conserved (somewhat) across insulins from different sources (Pierce et al., 2001
) (these are B12 Val, B15 Leu and B18 Val which, tend to be hydrophobic; B20 Gly is conserved to some degree although in some species it appears as charged). In addition, if multiple mutants are made from some combinations of these mutations (Table II), then we see destabilizations to various degrees including evidence of cooperativity. It is interesting that while the single site mutations we suggest seem stable (Table III), the multiple mutations suggest that there may be instability favouring the unfolded state. However, as pointed out earlier, careful considerations of structural relaxations need to be taken into account before any firm conclusions can be arrived for the multiple mutants. Of course, until a clear picture emerges of the conformational coordinates that characterize the states of insulin prior to binding its receptor, a cursory glance at data, such as those presented by Kaarsholm et al. (1993)
, show how complex the relationship is between designing a stable folded mutant and engineering high potency; for example, a neutral mutation (B26 Thr) is marginally more stable than the wild-type and yet is only half as potent while the triple mutant A8 HisB10 AspB25 His is destabilized by about 3 kcal/mol relative to the wild-type and yet is twice as potent. Similarly, a recent study has reported some progress in the interactions that mediate insulinreceptor interactions (Huang et al., 2004
). They report that B12 Val is an important mediator of receptor interactions and that mutation to larger side chains in general impairs activity. However, this is confounded by the fact that mutation to Glu coupled to deletion of B30 (Brange et al., 1988
) retains activity similar to that of the wild-type, once again pointing to the complexities that characterize biomolecular dynamics and functions.
|
|
|
In conclusion, we have computed and successfully reproduced the experimental pKa shift of a mutant insulin; using the parameters that we have established for the computations, we have identified residues that could potentially, upon mutation, lead to the formation of more stable monomeric insulins that might be therapeutically beneficial.
![]() |
Acknowledgments |
---|
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
Baker,E.N. et al. (1988) Philos. Trans. R. Soc. Lond. B, 319, 369456.[ISI][Medline]
Bashford,D. and Karplus,M. (1990) Biochemistry, 29, 1021910225.[ISI][Medline]
Bentley,G.A., Brange,J., Derewenda,Z., Dodson,E.J., Dodson,G.G., Markussen,J., Wilkinson,A.J., Wollmer,A. and Xiao,B. (1992) J. Mol. Biol., 228, 11631176.[ISI][Medline]
Brange,J. et al. (1988) Nature, 333, 679682.[CrossRef][ISI][Medline]
Brange,J., Owens,D.R., Kang,S. and Volund,A. (1990) Diabetes Care, 13, 923954.[Abstract]
Brooks,B.R., Bruccoleri,R.E., Olafson,B.D., States,D.J., Swaminathan,S. and Karplus,M. (1983) J. Comput. Chem., 4, 187217.[ISI]
CCP4 (Collaborative Computing Project No. 4) (1994) Acta Crystallogr. D, 50, 760763.[CrossRef][ISI][Medline]
Davis,M.E., Madura,J.D., Sines,J., Luty,B.A., Allison,S.A. and McCammon,J.A. (1991) Methods Enzymol., 202A, 473497.
Demchuk,E. and Wade,R.C. (1996) J. Phys. Chem., 100, 1737317387.[CrossRef][ISI]
Garcia-Moreno,B., Dwyer,J.J., Gittis,A.G., Lattman,E.F., Spencer,D.S. and Suites,W.E. (1997) Biophys. Chem., 64, 211224.[CrossRef][ISI][Medline]
Gibas,C.J., Jambeck,P. and Subramanian,S. (2000) Methods Companion Methods Enzymol., 20, 292309.[CrossRef][ISI]
Gilson,M.K. (1993) Proteins, 15, 266282.[ISI][Medline]
Guo,Z.Y., Tang,Y.H., Zhang,Z. and Feng,Y.M. (2001) IUBMB Life, 52, 309314.[ISI][Medline]
Hua,Q.-X., Hu,S.-Q., Frank,B.H., Jia,W., Chu,Y.-C., Wang,S.-H., Burke,G.T., Katsoyannis,P.G. and Weiss,M.A. (1996) J. Mol. Biol., 264, 390403.[CrossRef][ISI][Medline]
Huang,K. et al. (2004) J. Mol. Biol., 341, 529550.[CrossRef][ISI][Medline]
Hussain,S., Pinitglang,S., Bailey,T.S.F., Reid,J.D., Noble,M.A., Resmini,M., Thomas,E.W., Greaves,R.B., Verma,C.S. and Brocklehurst,K. (2003) Biochem. J., 372, 735746.[CrossRef][ISI][Medline]
Jorgenssen,A.M.M., Kristensen,S.M., Led,J.J. and Balschmidt,P. (1992) J. Mol. Biol., 227, 11461163.[ISI][Medline]
Kaarsholm,N.C., Havelund,S. and Hougaard,P. (1990) Arch. Biochem. Biophys., 283, 496502.[ISI][Medline]
Kaarslholm,N.C., Norris,K., Jorgensen,R.J., Mikkelsen,J., Ludvigsen,S., Olsen,O.H., Sorensen,A.R. and Havelund,S. (1993) Biochemistry, 32, 1077310778.[ISI][Medline]
Krebs,W.G. and Bourne,P.E. (2003) In Proceedings of 2003 IEEE Bioinformatics Conference: CSB2003. IEEE Computer Society Press, Los Alamitos, CA, pp. 180189.
Kristensen,S.M., Jorgenssen,A.M.M., Led,J.J., Balschmidt,P. and Hansen,F.B. (1991) J. Mol. Biol., 218, 221231.[ISI][Medline]
Ludvigsen,S., Roy,M., Thogersen,H. and Kaarsholm,N.C. (1994) Biochemistry, 33, 79988006.[ISI][Medline]
Madura,J.D. et al. (1995) Comput. Phys. Commun., 91, 5795.[CrossRef][ISI]
Mehler,E.L., Fuxreiter,M., Simon,I. and Garcia-Moreno,E.B. (2002) Proteins, 48, 283292.[CrossRef][ISI][Medline]
Neria,E., Fischer,S. and Karplus,M.J. (1996) J. Chem. Phys., 105, 19021921.[CrossRef][ISI]
Nishikawa,K., Ishino,S., Takenaka,H., Norioka,N., Hirai,T., Yao,T. and Seto,Y. (1994) Protein Eng., 7, 733733.[ISI][Medline]
Oliveberg,M. and Fersht,A.R. (1996) Biochemistry, 35, 27382749.[CrossRef][ISI][Medline]
Olsen,H.B., Ludvigsen,S. and Kaarsholm,N.C. (1998) J. Mol. Biol., 284, 477488.[CrossRef][ISI][Medline]
Pierce,S.B. et al. (2001) Genes Dev., 15, 672686.
Pittman,I.,4th, Nakagaw,S.H., Tager,H.S. and Steiner,D.F. (1997) Biochemistry, 36, 34303437.[CrossRef][ISI][Medline]
Plou,F.J. et al. (1996) J. Mol. Biol., 257, 10881111.[CrossRef][ISI][Medline]
Ray,M., Lee,R.W., Kaarsholm,N.C., Thogersen,H., Brange,J. and Dunn,M.F. (1990) Biochim. Biophys. Acta, 1053, 6373.[CrossRef][ISI][Medline]
Schutz,C.N. and Warshel,A. (2001) Proteins, 44, 400417.[CrossRef][ISI][Medline]
Shoelson,S.S., Lu,Z.-X., Parlautan,L., Lynch,C.S. and Weiss,M.A. (1992) Biochemistry, 31, 17571767.[ISI][Medline]
Shvachin, Yu,P., Nikitina,A.M., Funtova,S.M., Krasnoschchekova,S.P., Fedotov,V.P and Inanova,A.I. (1985) J. Gen. Chem. USSR, 55, 845846.
Tidor,B. and Karplus,M.J. (1994) J. Mol. Biol., 238, 405414.[CrossRef][ISI][Medline]
Turkenburg,J.P. (1992) PhD Thesis, University of York.
Weiner,S.J., Kollman,P.A., Case,D.A., Singh,U.C., Ghio,C., Alagona,G., Profeta,S.,Jr, and Weiner,P. (1984) J. Am. Chem. Soc., 106, 765784.[ISI]
Yang,A.S., Gunner,M.R., Sampogna,R., Sharp,K. and Honig,B. (1993) Proteins, 15, 252265.[ISI][Medline]
Zeng,Z.H., Liu,Y.S., Jin,L., Zhang,Y., Havelund,S., Markussen,J. and Wang,D.C. (2000) Biochim. Biophys. Acta, 1479, 225236.[ISI][Medline]
Received April 8, 2004; revised July 28, 2004; accepted August 1, 2004.
Edited by Bruce Tidor
|