1International University Bremen, School of Engineering and Science, D-28759 Bremen, Germany and 2Institut für Molekulare Biotechnologie, Beutenbergstrasse 11, D-07745 Jena, Germany
3 To whom correspondence should be addressed. E-mail: m.zacharias{at}iu-bremen.de
![]() |
Abstract |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Keywords: molecular dynamics simulation/peptideprotein docking/potential smoothing/protein design/side chain prediction
![]() |
Introduction |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The situation is similar in the case of predicted proteinprotein or proteinpeptide complexes. A typical proteinprotein docking simulation to systematically search for putative interaction geometries results in a set of possible complexes that can still deviate from a realistic structure owing to approximations used during the initial docking approach (e.g. rigid protein partners). The refinement, in particular of the interface between the binding partners, is an important step in order to achieve an accurate complex structure (Najmanovich et al., 2000; Smith and Sternberg, 2002
). As for the prediction of protein core structures, a straightforward and rapid approach for interface refinement is to assume that the protein main chain structure at the interface is approximately correct and to predict side chain conformations based on the rotamer search and optimal steric fit (Gray et al., 2003
). The success of this approach may significantly depend on the accuracy of the main chain structure at the interface close to the native structure, after the initial docking attempt. In addition, water molecules, not present during standard side chain rotamer searches, might be of importance for the proteinprotein interface structure.
In principle, molecular dynamics (MD) simulations allow for simultaneous adjustment of both side chain and backbone atoms and can be performed in the presence of water molecules. However, repacking of side chain conformations during an MD simulation starting, for example, from a random initial side chain placement involves crossing of many high-energy barriers. The simulation can easily be trapped in unfavourable states that are separated from low-energy states by large energy barriers. A standard method to enhance the convergence of MD simulations is to use the simulated annealing (SA) approach, i.e. to initially use a high simulation temperature to allow for barrier crossing followed by cooling of the simulation to identify favourable low-energy states (Brunger et al., 1997). The high initial temperatures used in SA approaches may, however, interfere with the presence of explicit water molecules during the simulation and can also distort the overall protein fold. In the present study, we have developed and tested a potential scaling method that allows the specific lowering of energy barriers for residues located in the protein core or at the interface of proteinpeptide complexes during an MD simulation with a minimum perturbation of other residues and surrounding water molecules. The smooth rescaling of the potential during MD simulations allows a rapid convergence to an optimal interface structure with simultaneous adjustment of both side chain and backbone protein core or interface structure. The approach was tested on three test proteins and two proteinpeptide complexes with known structures, including a systematic evaluation and comparison with standard MD simulations and a rotamer search based method. In general, the potential scaling (PS-MD) approach was found to be much more efficient than standard MD simulations to obtain realistic side chain conformations starting from arbitrary initial placements. Depending on the accuracy of the protein main chain, the approach also performed better than search strategies based on rotamer libraries.
![]() |
Materials and methods |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
All energy minimizations (EMs) and MD simulations were performed using a modified version of the sander module of the Amber6 package (Case et al., 1999) with the Cornell et al. (1995)
force field parameter file (parm94). During this potential scaling MD run, all pairwise LennardJones and electrostatic interactions of selected side chain atoms beyond Cß were scaled. A soft-core scaling scheme of the following form was employed (Zacharias et al., 1994
) and implemented in the sander code:
![]() |
Here, corresponds to a scaling parameter that can vary between 0 (original interaction energy) and 1 (no interaction), whilst
is the shift parameter and
ij is the minimum LennardJones energy for the interaction between atoms i and j, respectively. This functional form allows a smooth transition between zero and full potential and preserves the position of the energy minimum of the LennardJones term (see Figure 1). For
several values from 10 to 30 Å2 were tested and, owing to its performance,
= 20 Å2 was finally chosen for the simulations using the ddd model, whereas
= 15 Å2 was used for simulations in the presence of explicit water molecules. During the potential scaling run the scaling parameter
was varied linearly in 21 steps.
|
Restraint MD simulation and generation of start structures
In order to allow for conformational relaxation of the protein main chain during potential scaling and standard MD simulations, positional window restraints were applied that allow for free motion within a preset range with respect to the start structure and application of a soft quadratic penalty (force constant of 5.0 kcal mol1 Å2) for deviations beyond the preset range. In general, all side chains involved in the potential scaling were completely free to move throughout all stages of the simulations.
Start structures (24) for each test case were generated using high-temperature MD simulations (900 K; 100 ps), with values close to 1 for the selected fully mobile side chains (means with reduced interaction of the selected side chains) and positional window restraints (±1 Å) with respect to the experimental structure for the rest of the protein. The structures were subsequently energy minimized (2000 steps) with the full force field followed by a short MD simulation (300 K, 3 ps) and another EM (2000 steps; both with the same positional window restraints as above). Generated structures with high steric strain and structures with an average root mean square deviation (RMSD) of <2.0 Å for buried side chains with respect to the experiment were not considered. This protocol leads to start structures with randomly misplaced buried side chains that are sterically possible and average main chain deviations from the experiment of 0.40.8 Å (RMSD of the C
atoms). The same conditions were used to generate start structures for the evaluation set of proteins (12 protein structures, see Table I) except that in this case only eight start structures per protein were used.
In the case of the peptideprotein complexes the peptide side chains were first randomized using the MD simulations, with values close to 1 for the peptide side chains (see above). In addition, ligand peptides were randomly misplaced by up to 1.3 Å (RMSD of the C
atoms) from their placement in the experimental structure to mimic an imperfect docking result. The randomized initial structures were energy minimized using the full potential and by applying window restraints (allowing free motion within ±2 Å from the start structure). This step also leads to a relaxed protein main chain in part compatible with the initial side chain placements. This step was necessary to compare the PS-MD method with standard and temperature SA MD simulations using the same start structures and the full potential during the entire simulation.
Simulations with a distance-dependent dielectric constant
Simulations in the absence of solvent were performed using a distance-dependent dielectric constant of = 4r (ddd model), a 12.0 Å cut-off and a time step of 1 fs. Bonds involving hydrogen were restrained by applying the RATTLE (Anderson, 1983
) algorithm. In addition to regular MD simulations (at 300 K with a time constant of 2.0 ps) and PS-MD simulations, the performance of SA (Kirkpatrick et al., 1983
; Brunger et al., 1997
) simulations was also tested starting at a simulation temperature of 1000 K cooled down to 300 K (within 11 steps) for the same simulation time as used for the PS-MD and regular MD runs. The start structures also served as restraining reference structures during the PS-MD, SA-MD and standard MD simulations. The total simulation length was 252 ps. All final structures were energy minimized using the full energy function.
Explicit solvent simulations
In order to systematically test the potential scaling scheme for proteinpeptide interface refinement in the presence of explicit solvent, the solvated part of the proteinpeptide complexes was restricted to a water cap around the binding site. In the case of the MDM2p53 complex, a water cap with radius 18.5 Å and 421 TIP3 water molecules (Jorgensen et al., 1983) centred at the C
atom of Leu22 was added. Beyond the radius of 18.5 Å a harmonic potential with a force constant of 1.5 kcal mol1 Å2 was applied to prevent solvent molecules from escaping out of the water cap. The cap completely embedded the peptide and a large part of the receptor protein. A similar set-up was used in the case of the GroEL fragment and the bound peptide, with a solvent cap radius of 19.5 Å (531 TIP3 water molecules) centred at the C
atom of Thr605 of the ligand peptide. All receptor and peptide atoms were mobile during the simulations; however, the motion of all non-scaled atoms was restricted to ±2 Å of the corresponding atom position in the start structure (window restraints). All simulations with explicit solvent were performed using a dielectric constant of
= 1.0. An 11.0 Å cut-off distance for non-bonded interactions was also used and the system was coupled to a heat bath of 300 K. The time step was 1 fs for all MD runs and bonds involving hydrogen have been restrained by applying the RATTLE algorithm (Andersen, 1983
).
Comparison with a side chain rotamer search method and experiment
For comparison the systematic side chain rotamer search method SCWRL3.0 (Canutescu et al., 2003) based on the dead-end elimination theorem, a rigid backbone and a simple steric packing energy function was applied on the same set of start structures. The SCWRL3.0 approach is a deterministic side chain prediction method. Therefore, differences in the final results are solely due to the differences in protein main chains of the various start structures. To allow direct comparison with the PS-MD results the same set of buried or interface side chains as used during PS-MD was rebuilt [nearly experimental conformations for the other side chains employing the s (side chain fixing) option of SCWRL3.0].
Comparison with experimental structures
The PS-MD method for predicting the conformation of buried or interface side chain allows for conformational changes of the protein backbone within preset boundaries (typically free backbone movements of up to ±1 Å were allowed). However, this complicates the comparison of predicted side chain placements with experimental reference structures in terms of a RMSD of buried side chains from the experiment, since it is not only influenced by the side chain but also by the backbone conformation. In order to allow a direct comparison of the side chain placements with experimental reference structures, the final structures of the PS-MD and MD simulations as well as the SCWRL3.0 results were energy minimized using the experimental backbone structures as restraining reference structures. This step basically eliminates the contribution of the protein main chain. At the same time the side chains stay nearly in the same orientation/conformation. Independently, final structures were also compared with the experimental structures in terms of internal coordinates (side chain dihedral torsion angles).
Smoothing of RMSD distribution curves
To allow better comparison, RMSD distribution curves were smoothed using a step-wise error function Fi(RMSD) representing a single RMSD result and the graph G(RMSD) is then the normalized sum over all N final structures, with
![]() |
![]() |
![]() |
Results |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
In a first set of simulations the PS-MD approach was used to predict the core side chain conformations of three test proteins. These test structures represent different protein folds. One structure (PDB entry: 1CWE; Mikol et al., 1995), termed 1CWE in the following, consists of four ß-strands enclosed between two
-helices in a sandwich conformation (
/ß-protein with a SH2 domain fold). The second test case, termed 1GFC, was an all ß-sheet protein (NMR structure of an SH3 domain; PDB entry: 1GFC; Kohda et al., 1994
) and the third test structure (PDB entry: 1R69; Mondragon et al., 1989
), termed 1R69, corresponds to an all
-helical protein (DNA-binding fragment of the cro repressor protein of E.coli phage lambda, see Figure 2). The approach was compared with standard MD simulations, (temperature) simulated annealing (T-SA-MD) runs and a systematic rotamer search method based on the dead-end elimination theorem and using a fixed protein main chain (SCWRL3.0). In order to test the sensitivity of the simulation results on the side chain start placement and small deviations of the protein main chain in each case, 24 start structures with random but sterically allowed initial placement of buried side chains were generated (see Materials and methods). An overview of the simulation steps of the PS-MD method is given in Figure 3. The protein main chain of these start structures deviated from the corresponding experimental main chain structure on average by 0.30.8 Å but also included larger local deviations at positions of initially misplaced side chains (of 11.5 Å). To allow conformational relaxation of the complete protein structures during the side chain building process, the protein main chain and solvent-exposed residues were allowed to move from the respective start positions by up to ±1.0 Å (positional window constraints). Larger displacements were penalized by a soft harmonic potential (force constant 5.0 kcal mol1 Å2). Initial tests indicated that the PS-MD approach is not useful for the structure prediction of solvent-exposed side chains. For example, in the case of the 1R69 test protein a set of 24 random start structures with all side chains included in the PS-MD approach was generated and showed an average side chain RMSD of
2.1 Å from the experiment. Application of the PS-MD method resulted in final structures with only slightly improved solvent-exposed side chain conformations and an average RMSD of
1.7 Å from the experiment (average over all side chains with >50% solvent accessibility). In contrast, the buried side chains (<15% solvent accessible) were on average predicted to better than 0.8 Å (Figure 4, 1r69). Also, in the absence of explicit solvent and employing a distance-dependent dielectric constant charged solvent-exposed residues are promoted to move towards the protein interior that interfere with the placement of buried non-polar residues. Therefore, the above positional window restraints were applied to both atom positions of the main chain as well as solvent-exposed residues. The purpose was to keep these residues in an extended solvent-exposed conformation. All buried side chains subject to the potential scaling approach were, however, completely mobile.
|
|
|
Each of the various start structures resulted in final structures of different force field energy. In order to check if the final energy (obtained after energy minimizing the final structure, including the positional window restraints to the start structure; see Materials and methods) could be used to select realistic predictions, the 12 lowest energy structures (out of 24) from the PS-MD and the SCWRL3.0 predictions were compared (Figure 5A). This subset has an on average lower side chain RMSD with respect to the experiment and further improves the prediction results (Figure 5A). However, it does so for both PS-MD and SCWRL3.0 approaches and results in the same trends, as seen in the comparison of all final structures (Figure 5A). To find out if the PS-MD method can improve the SCWRL3.0 predictions, the latter predicted structures were used as start structures for the PS-MD approach. A slight improvement was observed for test cases 1 (1GFC) and 2 (1CWE) and a significant improvement in the case of test structure 3 (1R69). The RMSD distributions appear very similar to those obtained starting from the original start structures (compare Figures 4 and 5B). Hence, starting from the SCWRL3.0 predictions does not further improve the results and indicates that protein main chain deviations of each start structure may limit the quality of the prediction. It is important to note that the start structures used for the above systematic test showed overall close agreement to the protein main chain of the experimental structure. However, the generation of start structures with initially misplaced side chains can lead to protein main chain deviations that are larger than the average main chain deviation. It is likely that the significant local deviation of the main chain is one reason for the relatively poor performance of both the SCWRL3.0 and the PS-MD methods for some start structures.
|
For a more reliable validation of the present PS-MD method, 12 additional high-resolution X-ray protein structures (>2.0 Å resolution) were chosen from the PDB consisting of 56124 amino acids (evaluation set, see Table I). For comparison the SCWRL3.0 side chain prediction method was applied to each target using the experimental main chain structure. As indicated in the second column of Table I, in this case SCWRL3.0 gives excellent predictions, with RMSDs of buried side chains of <1 Å (in most cases). However, this represents an ideal case and in practical applications of side chain prediction one often relies on main chain template structures that deviate from the real main chain structure (e.g. homology modelling or prediction of the structure of mutations based on the wild-type template). In order to generate template structures with small but significant main chain deviations from the experimental reference in each case, eight start structures were generated with random initial placement of buried side chains and protein main chain deviations of 0.50.8 Å from the experiment (see start structure generation in Materials and methods and Table I). The deterministic side chain prediction using SCWRL3.0 and these slightly incorrect main chains as (rigid) templates performed quite well, but significantly worse than when using the experimental main chain structure as the template (compare columns 4 and 2 in Table I). The PS-MD approach starting from the same start structures and using the same conditions as described in the previous benchmark (and the same procedure to calculate the deviation from the experiment, see above) resulted in overall more realistic side chain placement in 10 out of 12 cases compared with SCWRL3.0 (compare columns 5 and 4 in Table I).
Comparative protein modelling using PS-MD
Comparison of homologous protein structures in the range of 3050% sequence identity in most cases translates to a structural deviation of the main chain in the range of 0.52 Å (Martí-Renom et al., 2000). In the case of a relatively even distribution of sequence variation (Figure 6), it is expected that the main chain deviation between homologous protein structures may also be evenly distributed along the protein chain (except for long sequence insertions or deletions). The PS-MD approach was applied to predict the side chain conformations of buried residues in 1R69 based on the protein main chain of the two homologous proteins 2CRO and 1ADR (both bacterial DNA-binding domains). The sequence identity of the two proteins with respect to 1R69 is 57% (2CRO) and 30% (1ADR), respectively. The overall main chain RMSD of these two protein domains with respect to the known structure of 1R69 was 0.8 Å (2CRO) and 1.5 Å (1ADR), respectively. These structures have also been used previously to benchmark protein side chain predictions (Chung and Subbiah, 1995
; Desjarlais and Handel, 1999
; Schueler-Furman and Baker, 2003
). In the case of the 1GFC structure, a protein main chain of the homologous protein 1QWE (viral C-Src SH3 domain) was used for side chain prediction. The sequence identity of the two latter proteins is 34% (main chain RMSD
1.4 Å).
|
|
|
|
The refinement of side chain placements at proteinprotein and proteinpeptide interfaces is an important step during docking simulations or refinement of low-resolution structures and complexes obtained by homology modelling. Such proteinprotein interfaces are at least in part close to the solvent and it is expected that the inclusion of explicit solvent molecules and readjustment of the protein main chain are critical for a realistic interface side chain prediction. The performance of the side chain PS-MD method for interface refinement was tested on two proteinpeptide complexes with known 3D structures (PDB-entries: 1YCQ and 1DKD; Figures 8 and 9). One case consisted of the protein MDM2 from X.laevis in complex with an -helical trans-activation domain (residues 1727) of the protein p53 (PDB entry: 1YCQ). The second complex corresponds to the protein-binding domain of the chaperone protein GroEL (residues 218277) and a peptide consisting of 11 residues (PDB entry: 1DKD; residues 602612).
|
|
|
|
|
![]() |
Discussion |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Potential scaling approaches have been used previously to tackle the problem of sampling limitations of conventional MD simulations (Piela et al., 1989; van Schaik et al., 1992
; Mierke and Kessler, 1993
; Dahiyat and Mayo, 1997
; Huber et al., 1997
; Tappura et al., 2000
; Tappura, 2001
; Riemann and Zacharias, 2004
). However, the application of potential scaling during MD simulations to predict and refine side chain conformations in protein cores or at proteinprotein interfaces has so far not been evaluated.
In the case of protein side chain prediction of protein cores (in the absence of solvent) with slightly perturbed protein main chains, PS-MD showed similar prediction accuracy to SA-MD and only for one test system better performance than the dead-end elimination-theorem based rotamer search approach SQWRL3.0. The results depended on the protein type and the best results were obtained with an -helical protein domain (1R69). The approach was further tested on 12 proteins for which high-resolution X-ray structures have been determined (Table I). Generation of start structures with random initial buried side chain placements resulted in structures with main chain deviations of 0.50.8 Å from the experiment. The SQWRL3.0 approach gave excellent prediction results for buried side chains when using the experimental main chain structure. Still reasonable performance was achieved when using the generated start structures (with incorrect main chain) as templates. However, using the same start structures for the PS-MD approach and allowing main chain flexibility during PS-MD of up to ±1 Å resulted in overall better buried side chain predictions in 10 out of 12 cases compared with the deterministic approach using a rigid main chain structure.
In the case of comparative protein modelling of the 1R69 or 1GFC test cases with protein templates that deviated by 0.81.6 Å from the known target main chain, the PS-MD approach performed significantly better than the SQWRL3.0 approach (and conventional MD simulations). It is important to note that although the start structures used in the first test of the method had an overall main chain deviation of 0.40.8 Å from the experiment, considerably larger deviations of the main chain near initially misplaced side chains were present. This can strongly affect the performance of the approach and might be an explanation as to why the PS-MD method performed quite well in the case of comparative modelling and less well for some of the start structures used in the first test.
The present PS-MD method performed significantly better than conventional MD or the deterministic side chain prediction in the case of proteinpeptide interface refinement. In this case the protein main chain of the peptide was allowed to deviate from the experiment by up to 1.3 Å, including overall translational and rotational shifts of the ligand. An additional difficulty is due to the further stabilization of the misplaced peptide conformations by EM of the incorrect side chain placements before the side chain refinement. However, this corresponds to a typical docking scenario where one energy minimizes initially docked complexes to reduce steric overlap between the receptor and the ligand. Apparently, the performance of the deterministic side chain prediction approach, which does not allow overall conformational relaxation of the protein and the peptide during side chain prediction, depends sensitively on the main chain conformation of the start structure. However, conventional MD simulations that allow overall conformational adjustment of both the protein and the peptide fail because the initial side chain placement at the interface is separated from the realistic placement by high-energy barriers. The PS-MD approach allows easy crossing of the barriers during the potential scaling and final settlement of side chains close to the realistic structure for most start conformations. A disadvantage of the present approach is that it is much more time consuming than methods based on a fixed protein main chain and a rotamer search. However, even in the case of larger proteins or proteinprotein complexes it is often possible to limit the region of interest to only the interface between two proteins in the case of a proteinprotein complex or to part of the protein structure (e.g. region around a protein mutation). Especially in the case of introducing several mutations in a protein, it has been shown experimentally that conformational adjustments of the protein are important and side chain prediction based on a fixed side chain (of the wild-type structure) can fail to accurately predict the side chain conformations owing to the mutation (Baldwin et al., 1993). Besides homology modelling of proteins and refinement of peptideprotein and proteinprotein interfaces, the present PS-MD approach may also be applied in rational protein design efforts to approximately account for protein main chain relaxation to predict the effect of a protein sequence modification on the structure of the protein.
![]() |
Acknowledgements |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Arcus,V.L., Vuilleumier,S., Freund,S.M.V., Bycroft,M. and Fersht,A.R. (1995) J. Mol. Biol., 254, 305321.[CrossRef][ISI][Medline]
Baldwin,E.P., Hajiseyedjavadi,O., Baase,W.A. and Matthews,B.W. (1993) Science, 262, 17151718.[ISI][Medline]
Brunger,A.T., Adams,P.D. and Rice,L.M. (1997) Structure, 5, 325336.[CrossRef][ISI][Medline]
Canutescu,A.A., Shelenkov,A.A. and Dunbrack,R.L.,Jr (2003) Protein Sci., 12, 20012014.
Case,D.A. et al. (1999) AMBER 6, University of California, San Francisco.
Chen,L. and Sigler,P.B. (1999) Cell, 99, 757768.[CrossRef][ISI][Medline]
Chung,S.Y. and Subbiah,S. (1995) Protein Sci., 4, 23002309.
Cornell,W.D., Cieplak,P., Bayly,C.I., Gould,I.R., Merz,K.M.,Jr, Ferguson,D.M., Spellmeyer,D.C., Fox,T., Caldwell,J.W. and Kollman,P.A. (1995) J. Am. Chem. Soc., 117, 51795197.[CrossRef][ISI]
Dahiyat,B.I. and Mayo,S.L. (1997) Proc. Natl Acad. Sci. USA, 94, 1017210177.
Desjarlais,J.R. and Handel,T.M. (1999) J. Mol. Biol., 290, 305318.[CrossRef][ISI][Medline]
Dunbrack,R.L. and Karplus,M. (1993) J. Mol. Biol., 230, 543574.[CrossRef][ISI][Medline]
Dunbrack,R.L.,Jr (2002) Curr. Opin. Struct. Biol., 12, 431440.[CrossRef][ISI][Medline]
Gray,J.J., Moughon,S.E., Kotemme,T., Schueler-Furman,O., Misura,K.M.S., Morozov,A.V. and Baker,D. (2003) Proteins, 52, 118122.[CrossRef][ISI][Medline]
Guex,N. and Peitsch,M.C. (1997) Electrophoresis, 18, 27142723.[ISI][Medline]
Halperin,I., Ma,B., Wolfson,H. and Nussinov,R. (2002) Proteins, 47, 409443.[CrossRef][ISI][Medline]
Harbury,P.B., Tidor,B. and Kim,P.S. (1995) Proc. Natl Acad. Sci. USA, 92, 84088412.
Holm,L. and Sander,C. (1991) J. Mol. Biol., 218, 183194.[CrossRef][ISI][Medline]
Huber,T., Torda,A.E. and van Gunsteren,W.F. (1997) J. Phys. Chem. A, 101, 59265930.[CrossRef][ISI]
Jorgensen,W.L., Chandrasekhar,J., Madura,J.D., Impey,R.W. and Klein,M.L. (1983) J. Chem. Phys., 79, 926935.[CrossRef][ISI]
Källblad,P. and Dean,P.M. (2003) J. Mol. Biol., 326, 16511665.[CrossRef][ISI][Medline]
Kirkpatrick,S., Gelatti,C.D. and Vecchi,M.P. (1983) Science, 220, 671680.[ISI]
Koehl,P. and Delarue,M. (1994) J. Mol. Biol., 239, 249275.[CrossRef][ISI][Medline]
Kohda,D., Terasawa,H., Ichikawa,S., Ogura,K., Hatanaka,H., Mandiyan,V., Ullrich,A., Schlessinger,J. and Inagaki,F. (1994) Structure, 2, 10291040.[CrossRef][ISI][Medline]
Lee,C. and Subbiah,S. (1991) J. Mol. Biol., 217, 373388.[CrossRef][ISI][Medline]
Martí-Renom,M.A., Ashley,C.S., Fiser,A., Sánchez,R., Melo,F. and ali,A. (2000) Annu. Rev. Biophys. Biomol. Struct., 29, 291325.[CrossRef][ISI][Medline]
Mierke,D.F. and Kessler,H. (1993) Biopolymers, 33, 10031017.[CrossRef][ISI][Medline]
Mikol,V., Baumann,G., Keller,T.H., Manning,U. and Zurini,M.G. (1995) J. Mol. Biol., 246, 344355.[CrossRef][ISI][Medline]
Mondragon,A., Subbiah,S., Almo,S.C., Drottar,M. and Harrison,S.C. (1989) J. Mol. Biol., 205, 189200.[ISI][Medline]
Najmanovich,R., Kuttner,J., Sobolev,V. and Edelman,M. (2000) Proteins, 39, 261268.[CrossRef][ISI][Medline]
Piela,L., Kostrowicicli,J. and Scheraga,H.A. (1989) J. Phys. Chem., 93, 33393346.[CrossRef][ISI]
Riemann,N. and Zacharias,M. (2004) J. Pept. Res., 63, 354364.[CrossRef][ISI][Medline]
Samudrala,R. and Moult,J. (1998) Protein Eng., 11, 991997.[CrossRef][ISI][Medline]
Schueler-Furman,O. and Baker,D. (2003) Proteins, 52, 225235.[CrossRef][ISI][Medline]
Smith,G.R. and Sternberg,M.J.E. (2002) Curr. Opin. Struct. Biol., 12, 2835.[CrossRef][ISI][Medline]
Summers,N.L. and Karplus,M. (1989) J. Mol. Biol., 210, 785811.[CrossRef][ISI][Medline]
Swain,M.T. and Kemp,G.J.L. (2001) In Walsh,T. (ed.), A CLP Approach to the Protein Side Chain Placement Problem. Springer-Verlag, Berlin, CP 2001, LNCS 2239, pp. 479493.
Tappura,K. (2001) Proteins, 44, 167179.[CrossRef][ISI][Medline]
Tappura,K., Lahtela-Kakkonen,M. and Teleman,O. (2000) J. Comput. Chem., 21, 388397.[CrossRef][ISI]
Tufféry,P., Etchebest,C. and Hazout,S. (1997) Protein Eng., 10, 361372.[CrossRef][ISI][Medline]
van Schaik,R.C., van Gunsteren,W.F. and Berendsen,H.J.C. (1992) J. Comput. Aided Mol. Des., 6, 97112.[CrossRef][ISI][Medline]
Wright,J.D. and Lim,C. (2001) Protein Eng., 14, 479486.[CrossRef][ISI][Medline]
Xiang,Z. and Honig B. (2001) J. Mol. Biol., 311, 421430.[CrossRef][ISI][Medline]
Zacharias,M., Straatsma,T.P. and McCammon,J.A. (1994) J. Chem. Phys., 100, 90259031.[CrossRef][ISI]
Received January 17, 2005; revised April 21, 2005; accepted July 11, 2005.
Edited by Fred Cohen
|