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Protein Sci. Feb 2002; 11(2): 322–331.
PMCID: PMC2373451

Side-chain modeling with an optimized scoring function


Modeling side-chain conformations on a fixed protein backbone has a wide application in structure prediction and molecular design. Each effort in this field requires decisions about a rotamer set, scoring function, and search strategy. We have developed a new and simple scoring function, which operates on side-chain rotamers and consists of the following energy terms: contact surface, volume overlap, backbone dependency, electrostatic interactions, and desolvation energy. The weights of these energy terms were optimized to achieve the minimal average root mean square (rms) deviation between the lowest energy rotamer and real side-chain conformation on a training set of high-resolution protein structures. In the course of optimization, for every residue, its side chain was replaced by varying rotamers, whereas conformations for all other residues were kept as they appeared in the crystal structure. We obtained prediction accuracy of 90.4% for χ1, 78.3% for χ1 + 2, and 1.18 Å overall rms deviation. Furthermore, the derived scoring function combined with a Monte Carlo search algorithm was used to place all side chains onto a protein backbone simultaneously. The average prediction accuracy was 87.9% for χ1, 73.2% for χ1 + 2, and 1.34 Å rms deviation for 30 protein structures. Our approach was compared with available side-chain construction methods and showed improvement over the best among them: 4.4% for χ1, 4.7% for χ1 + 2, and 0.21 Å for rms deviation. We hypothesize that the scoring function instead of the search strategy is the main obstacle in side-chain modeling. Additionally, we show that a more detailed rotamer library is expected to increase χ1 + 2 prediction accuracy but may have little effect on χ1 prediction accuracy.

Keywords: Parameter optimization, scoring function, side-chain rotamer, Monte Carlo simulation

Side-chain modeling plays an important role in molecular docking and protein structure prediction. Protein side chains make a dominant contribution to molecular recognition (Vasquez 1996). Homology modeling of a protein from its sequence using the structure of its homolog is widely used in structure-based drug design (Lybrand 1995). Detailed information about the binding site of the target protein is essential to generate new lead compounds. The ab initio protein folding problem can be divided into two sequential tasks of approximately equal computational complexity: the generation of nativelike backbone folds and the positioning of side chains on these backbones (Huang et al. 1998). The combinatorial complexity of the entire problem is merely additive for the two steps, rather than multiplicative, which makes this task computationally feasible.

Protein side chains tend to exist in a limited number of low energy conformations called rotamers (Ponder and Richards 1987). Instead of considering the full geometrically possible conformational space, only a small number of rotamers can be used to describe most naturally occurring conformers of a side chain. Growth of the Protein Data Bank (PDB, Berman et al. 2000) provides more high-quality protein structures for statistical analysis, which increases the reliability and completeness of rotamer libraries. Two types of rotamer libraries have been developed, namely, a backbone-independent library (Ponder and Richards 1987; Tuffery et al. 1991; De Maeyer et al. 1997; Lovell et al. 2000) and a backbone-dependent library (Dunbrack and Karplus 1993). Both of them have been widely used for predicting side-chain conformations. As a consequence, the speed and efficiency of finding an optimal protein conformation is dramatically enhanced compared with the continuous space methods.

Even when rotamer libraries are used, the combinatorial nature of side-chain placement on a given protein backbone has been often cited as the main obstacle to the correct prediction of side-chain conformation (Lee and Subbiah 1991; Eisenmenger et al. 1993; Petrella et al. 1998). Many strategies have been proposed to solve this problem: Monte Carlo searches (Holm and Sander 1992; Vasquez 1995), genetic algorithms (Tuffery et al. 1991), neural networks (Hwang and Liao 1995), mean-field optimization (Koehl and Delarue 1994; Mendes et al. 1999), dead-end elimination (DEE) method (Desmet et al. 1992; De Maeyer et al. 1997), and actual combinatorial searches (Dunbrack and Karplus 1993; Wilson et al. 1993; Bower et al. 1997). Although DEE is considered to be the most powerful algorithm, designed to identify global minimum energy conformations, its predictions are far from being 100% accurate even for the core residues (De Maeyer et al. 1997; Looger and Hellinga 2001). Recently, Xiang and Honig (2001) obtained the greatest accuracy for core residues with an extensive library of 7560 rotamers. However, their methods did not show advantages for all residues. Thus, a scoring function might be the real obstacle for side-chain prediction.

Unlike search strategies, relatively less attention has been paid to the scoring function. The simplest energy functions, which are limited to estimating Van der Waals interactions by a Lennard-Jones potential, appear to give excellent results for buried nonpolar amino acids (Vasquez 1996). However, these approaches do not give accurate results for exposed, partially exposed, or buried polar residues. The use of electrostatic or hydrogen-bonding terms, which are typical of commonly used force fields, have not shown a significant improvement over the simple Van der Waals potential (Vasquez 1996; Bower et al. 1997; De Maeyer et al. 1997). Wilson et al. (1991) added a desolvation energy term to the AMBER force field. The weight of the desolvation energy was derived from protein–ligand interaction. However, the combined scoring function did not prove to be successful in side-chain modeling (Wilson et al. 1993). The failure of force field applications indicates that special energy functions should be used for side-chain modeling. Samudrala and Moult (1998) used a discriminatory function based on a statistical analysis of atomic contacts in protein structures for selecting side-chain rotamers, given a protein backbone. Their program, however, does not perform better than others.

The PDB contains many high-quality protein structures for derivation or testing of scoring functions. Wilson et al. (1993) tested their scoring function by searching for an optimal conformation for a single residue. Different rotamers were checked at the position of the search while other residues were fixed in their conformations observed in the experimental structure. However, the test was done only on one protein. Petrella et al. (1998) did a similar test of CHARMM energy functions for side-chain prediction on 10 proteins.

Instead of testing existing potential functions, we developed a scoring function by minimizing the average root mean square (rms) deviation between the lowest energy rotamer and real conformation in the search for a single residue rotamer. During this minimization, the weights of different energy terms were optimized. The derived scoring function exhibited better performance than the CHARMM or AMBER force field in predicting the conformation of a single residue side chain in the tested proteins. Then we used the derived scoring function combined with a Monte Carlo algorithm to predict the side-chain conformations of an entire protein. The results are discussed and compared with other side-chain modeling programs.

Results and Discussion

The scoring function

The optimized scoring function was found to be

equation M1

where Scontact, Voverlap, and Eelec are contact surface, overlapped volume, and electrostatic interaction energy between the rotamer and other parts of the protein, respectively; f is the observed frequency of the rotamer given a backbone conformation; and Nphil is the number of totally buried nonhydrogen-bonded hydrophilic atoms at the interface. The values in the equation are the optimized weights of the energy terms (the weight for Scontact was set to –1, see Materials and Methods).

The weights for the energy terms were optimized in the following way. Starting from random parameters, the average rms deviation of the predicted side chains from the true structure was calculated for each training protein. The mean rms deviation value of the 15 training proteins was minimized. The Monte Carlo searches converged very fast. For the 20 repetitions of parameter optimization procedure, the minimized rms deviation values were in the narrow range of 0.714–0.717 Å. However, the optimized values of parameters displayed larger variance. The average values and standard errors of the weights for volume overlap, backbone dependency, electrostatic interaction, and desolvation energy were 3.912 ± 0.072, –6.427 ± 0.145, 152.1 ± 13.5, and 5.316 ± 0.385, respectively. We accepted the parameter values when the objective function value was minimized to the lowest value (0.714 Å). The derived scoring function took the form of equation (1). Table 11 lists the prediction results for the 15 training proteins.

Table 1.
Prediction results for the 15 training proteins

Furthermore, we probed the contribution of individual energy terms to the prediction of the side-chain conformation (Table 22).). For example, we compared the traditionally used Van der Waals interactions (attractive/repulsive terms) with the corresponding terms from Equation 1: contact surface/overlapped volume. It appears that contact surface/overlapped volume performs better than Van der Waals potential (Table 22).). This may be because the contact surface/overlapped volume describes the complementary packing of the rotamers more accurately. As other workers have mentioned (Vasquez 1996; Bower et al. 1997; De Maeyer et al. 1997), steric interactions play the most important role in determining side-chain conformations. It is also well known that rotamers are strongly backbone dependent (Dunbrack and Cohen 1997). Thus, it is not surprising that a combination of contact surface, volume overlap, and backbone dependency results in 89.2% accuracy for χ1 and 74.6% for χ1 + 2. The prediction results show only moderate improvement when electrostatic interactions are added. Since electrostatic interactions mainly affect conformations of polar residues, the improvements for some polar residues are significant. For example, χ1 + 2 prediction accuracy of Asn is improved from 41.6% to 53.0%. Addition of the desolvation energy term (the buried surface of nonhydrogen-bonded polar atoms) results in only a small improvement of the predictions (Table 22),), but the predicted structures contain fewer clearly incorrect conformations with totally buried nonhydrogen-bonded polar atoms. We have probed other forms of desolvation energy potential, such as atomic contact energy (Zhang et al. 1997) or buried surfaces of hydrophobic and hydrophilic atoms at the interface, but the prediction results showed no apparent improvement (Table 22).

Table 2.
The roles of different energy items in the scoring function

Testing of the derived scoring function

The derived scoring function was tested with the 15 proteins selected as described in Materials and Methods. Single residue conformations were predicted. The prediction results of the testing proteins are slightly different from those of the training proteins (Table 33).). We believe these differences are due to the properties of the set of testing proteins. Specifically, the training proteins are on average larger than the testing proteins and have a higher percentage of core residues, which are easier to correctly predict than are surface residues. Thus the prediction accuracy of the training proteins is slightly better than that of testing proteins (Tables 1 and 33).). When the testing proteins are predicted by a scoring function derived from themselves, the results are very similar to those predicted by the scoring function derived from the training proteins (Tables 3 and 44).). This indicates that the scoring function derived from the training proteins performs well on other proteins.

Table 3.
Testing of the derived scoring function on 15 proteins
Table 4.
Comparison of the prediction results for the 15 testing proteins calculated by scoring functions derived from different data sets

The strategy of searching for a single residue conformation has been used by Wilson et al. (1993) to test the AMBER nonbonded energy plus a weighted solvation term. Petrella et al. (1998) used the same strategy to test the CHARMM22 energy function. Instead of using a rotamer library, Petrella et al. rotated χ1 and χ2 of side chains at the intervals of 5° or 10°, which made the prediction results less feasible computationally to model side chains simultaneously for an entire protein. Here, the protein used by Wilson et al. (PDB code 2alp) and the 10 proteins of Petrella et al. (PDB code 5pti, 1crn, 2cro, 1ctf, 4fxn, 1hiv, 1lz1, 3app, 3rn3, 3tln) were also used to test our scoring function (2fox and 4tln were used here instead of 4fxn and 3tln, which have been updated in the March 2001 release of PDB). The results calculated by our scoring function were compared with those listed by Wilson et al. and Petrella et al. (Table 55).). Our scoring function achieves better results than that of the CHARMM22 or AMBER force field. These results may indicate that force fields that are widely used in molecular mechanics calculations may not necessarily be the best for side-chain modeling.

Table 5.
Comparison of potential energy functions in searching a single residue

The predicted results of 18 residue types were analyzed for the 30 training and testing proteins (Table 66).). In general, the percentages of correctly predicted hydrophobic residues were much larger than those of hydrophilic residues. This is expected because more hydrophobic residues are buried compared with hydrophilic residues. Surprisingly, the conformations of most buried hydrophilic residues, except χ1 of Ser and χ1 + 2 of Asp, Asn, and His, are predicted, as well as those for buried hydrophobic residues. Serine may be too small to be affected by steric conflicts. Similarly, carboxylate group of aspartic acid is not sensitive to χ2 rotation concerning steric or electrostatic interactions. The poor χ1 + 2 prediction of Asn and His may be partly due to the fact that the observed frequency of a rotamer, given backbone conformation, is not correctly evaluated (see Materials and Methods). The two aromatic residues, Phe and Tyr, were predicted accurately (χ1 correct >97%; χ1 + 2 correct >93%). Pro was poorly predicted (χ1 correct = 85%; χ1 + 2 correct = 78%). The two rotamers of Pro are rather similar in shape and do not depend on the backbone conformation significantly. Cys side chain was 100% accurately predicted for both core and surface residues, which indicated that our simple strategy to manipulate disulfide bridges (see Materials and Methods) was successful. The average percentage of crystal structure side chains within 40° of any rotamer in the library is 99.1% for χ1 and 97.2% for χ1 + 2 (Table 66).). However, the average prediction accuracy is only 91.1% for χ1, and 77.6% for χ1 + 2. For core residues, the corresponding values are 99.5%, 98.2%, 97.0%, and 87.5%, respectively. Thus, it should be possible to further increase prediction accuracy by adopting better scoring functions.

Table 6.
Prediction results of 30 high-quality proteins arranged by residue types

Modeling the side chains for a whole protein

DEE, which detects and eliminates rotamers that cannot be the members of global minimum energy conformation, is the most powerful algorithm in side-chain modeling (Desmet et al. 1992; Voigt et al. 2000); however, it cannot be used together with our scoring function. DEE assumes that the total rotamer–rotamer interaction energy is the sum of the interaction energy between any two rotamers. This is not true for contact surface, volume overlap, or the number of totally buried nonhydrogen-bonded polar atoms, which can only be calculated when conformations of all side chains are known. Thus, we used the Monte Carlo-simulated annealing method to model the side-chain conformations of a whole protein. Because the derived scoring function performed equally well on the training and testing proteins, both sets were combined and used to test the program. For the 30 resulting proteins, we obtained average predictions of 87.9% for χ1, 73.2% for χ1 + 2, and 1.34 Å for rms deviation (Table 77).). These results are clearly inferior to the differences between the experimental structure and the model built from the most similar rotamers, which indicates that we are still far from the maximal prediction accuracy possible with the current rotamer set (Table 88).

Table 7.
Side-chain construction on the 30 high-quality proteins
Table 8.
Comparison of the native and predicted structures with the structure built from rotamers most similar to real conformation

We compared our program with the torso program from the MAXSPROUT package (Holm and Sander 1991), SCWRL2.2 (Bower et al. 1997), and that of Mendes et al. (1999). Like our method, torso was based on the Monte Carlo algorithm. The other two programs are the best available side-chain modeling programs developed in the last several years (Mendes et al. 1999). Mendes et al. used self-consistent mean field theory and a flexible rotamer model that handled a continuous ensemble of conformations around the classic rigid rotamer. SCWRL initializes a structure with residues in their most favorable backbone-dependent rotamers and systematically resolves steric clashes. Among the 30 selected proteins, the terminal carbonyl oxygen named "OXT" was not found in the PDB files of five proteins (1cem, 1nar, 1vjs, 1arb, and 1mml) and they could not be operated by the Mendes program. Twenty-five other proteins were used in comparison (Table 99 and Figure 1 [triangle]). The prediction accuracy of SCWRL is similar to torso but lower than the Mendes algorithm. Compared with the program of Mendes et al., our program has an improvement of 4.4% in average χ1 prediction, 4.7% in average χ1 + 2 prediction, and 0.21 Å in average global rms deviation. For core residues, the differences are small: 1.8% improvement in χ1 prediction and 3.3% improvement in χ1 + 2 prediction. Our method has a more significant advantage for surface residues. SCWRL and torso run much faster than our program and the Mendes algorithm. However, our program is two times faster than the Mendes algorithm. Both SCWRL and our program use the rotamer library of Dunbrack (Dunbrack and Karplus 1993). Our program shows an advantage over SCWRL in average χ1 prediction, χ1 + 2 prediction, and rms deviation for all residue types. SCWRL predicted χ1 + 2 of Asn and His poorly because it does not contain a mechanism to distinguish θ and 180° + θ of χ2 angles of the two residues. The Mendes algorithm, using the Tuffery rotamer library (Tuffery et al. 1991) shows obvious disadvantages for small polar residues such as Ser, Thr, and Asp. It predicted χ1 more accurately for Pro, Cys, and His, and χ1 + 2 for Gln, Met, Tyr, and Pro. The Mendes algorithm also predicted χ1 of Tyr and Met with the same accuracy as our methods. Cysteines were predicted with a high correct percentage by the Mendes algorithm partly because the program takes the disulfide bridge pairings as input.

Table 9.
Comparison of our side-chain modeling program with other methods
Fig. 1.Fig. 1.Fig. 1.
Comparison of prediction results over different residue types. Results of Holm and Sander (1991) are shown in white, the results of SCWRL (Bower et al. 1997) are in light gray, the results of Mendes et al. (1999) are in dark gray, and the results of this ...

We also compared our program with the Mendes algorithm on the Mendes et al. testing proteins. Five of the 20 high-quality protein structures used by Mendes et al. were also included in our training and testing proteins. Thus the comparison was done on the other 15 proteins: 2erl, 1cbn, 5rxn, 1bpi, 1igd, 1ptx, 1ctj, 1plc, 9rnt, 1aac, 256b, 1isu, 2ihl, 2hbg, and 1xnb. Among them, 12 proteins contain ligands. We removed all ligands in the calculation, which affected the performance of the Mendes algorithm. Because Mendes et al. included ligands in their calculation, the calculated results here are not as accurate as those presented by Mendes et al. (1999). Our method shows a significant advantage over the Mendes algorithm: 3.7% in average χ1 prediction, 6.1% in average χ1 + 2 prediction, and 0.12 Å in average global rms deviation (Table 1010).). We then investigated the effect of protein resolution on prediction accuracy. Because the Mendes algorithm is only effective on very high quality proteins and very time consuming, we compared our methods with SCWRL. The prediction ability of the two programs deteriorates as the resolution of a crystal structure decreases (Table 1111).). Bower et al. (1997) noted that the lower resolution structures might be poorly predicted because they contained errors in side-chain assignments. Our methods show an advantage over SCWRL for both high and low resolution structures.

Table 10.
Comparison of our program with that of Mendes et al. on their testing proteins
Table 11.
Effect of resolution on prediction accuracy

The prediction results for modeling of the whole protein simultaneously are inferior to those of searching for a single residue conformation (Tables 1, 3, and 77).). For the 30 tested proteins, the prediction accuracy decreases 2.5% for χ1 and 4.1% for χ1 + 2. The decreased accuracy of the prediction results for the whole protein modeling may be due to the errors caused by rotamer approximation. Compared with searches for a single residue conformation, the positional errors double when both interacted residues are represented by rotamers. To eliminate the rotamer approximation effect, we included the real conformation to the rotamer library to substitute for the rotamer with the lowest rms deviation. The scoring function was reoptimized. For the 30 selected proteins, the average accuracy was 92.2% for χ1 and 84.2% for χ1 + 2 when a single residue conformation was predicted. The prediction accuracy in this case depends on the scoring function only. Thus our scoring function can potentially be significantly improved. Then we modeled all side chains simultaneously. The average accuracy was 91.1% for χ1 and 82.6% for χ1 + 2. These values represent improvements of 3.2% for χ1 and 9.4% for χ1 + 2 compared with predictions that used standard rotamer library. The improvements in χ1 + 2 prediction are larger than the improvements in χ1 prediction. Thus a more detailed rotamer library is expected to increase χ1 + 2 prediction accuracy; however, it should have little effect on χ1 accuracy. The prediction accuracy decreases by 1.1% for χ1 and 1.6% for χ1 + 2 compared with the single residue predictions. These small decreases might be caused by the search strategy or occur for other reasons.


We have developed a new and simple scoring function for side-chain modeling. Compared with the CHARMM and AMBER force fields, our scoring function shows clear advantages in predicting the conformation of a single residue. Our scoring function was combined with a Monte Carlo algorithm to place all the side chains onto a protein backbone. The prediction results compared favorably with existing methods. It appears that the search strategy is not the main obstacle in side-chain modeling, but better scoring function and more detailed rotamer library are needed to achieve higher accuracy. A detailed rotamer library is expected to increase χ1 + 2 prediction accuracy; however, it will have little effect on χ1 accuracy.

Materials and methods

Scoring function

Five energy terms are considered in the scoring function: backbone dependency, contact surface, overlapped volume, electrostatic interactions, and desolvation energy.

The backbone-dependent rotamer library and rotamer energies

The backbone-dependent rotamer library of Dunbrack is used in this study (Dunbrack and Cohen 1997). The intrinsic energies of rotamers are represented by their expected frequencies (f), given a backbone conformation, which are derived by Bayesian statistical analysis of protein side-chain rotamer preferences (Dunbrack and Cohen 1997). Here, lnf is considered an energy term and is called backbone dependency. The Dunbrack library is modified as follows. (1) Polar hydrogen atoms, which are absent in the Dunbrack library, are added for the convenience of calculating electrostatic interactions. Each χ2 for Ser and Thr and χ3 for Tyr are assigned three possible values: –60°, 60°, and 180°. The frequency of the new rotamers is set to one-third of the observed frequency of their parent rotamer. (2) Three protonation states of His with the same expected frequencies are considered, Nδ1 protonated, Nepsilon2 protonated, and both. (3) We supplemented additional rotamers to correct for the lack of defined rotameric states for the amide planes of Asn and Gln and for the aromatic plane of His in the Dunbrack library. χ2 of Asn and His and χ3 of Gln are flipped 180° to make new rotamers. Thus the rotamer numbers of these residues are doubled and the expected frequencies are correspondingly reduced by one-half. Bond lengths and angles from Engh and Huber (1991) are used to build the rotamer library. The rotamers with standard geometries are placed on the protein backbone by superimposing N, C, and Cα atoms.

Contact surface and volume overlap

The contact surface and overlapped volume between the selected rotamer and other parts of the protein (termed protein environment, which consists of all atoms in a protein that do not belong to the selected rotamer) are calculated by the grid-based method. CHARMM22 atom radii are used (Brooks et al. 1983; Mackerell et al. 1998). The grid step is set to 0.6 Å. The selected rotamer and the protein environment are mapped using the same strategy. The grid points within the Van der Waals radius (r) of an atom are labeled as interior points. The first layer of grid points on the atom surface (between r and r+0.6 Å) are labeled as surface points. In case of a conflict, for example, if a grid point is an interior point of one atom but is a surface point of another atom, the interior points override surface points. The overlapped volume (Å3) is counted according to the number of grid points that belong to the interior points of the rotamer and protein environment simultaneously. Each co-occupied grid point corresponds to 0.216 Å3 volume overlap. The contact surface (Å3) is counted as the number of grid points that belong to the surface points of the rotamer and interior points of the protein environment, the interior points of the rotamer and surface points of the protein environment, or the surface points of both sides. Interactions between the rotamer and local backbone, which starts from the Cα of the last residue to the Cα of the next residue at the searched position, are not considered. They are assumed to be included in the backbone-dependent rotamer energy. Special attention is paid to the joint between the local backbone and other parts of the protein. A plane cuts the joining bond perpendicularly at the middle point to separate the surface and interior grid points of the two joined atoms. The grid points on the side of the local backbone are not considered. For two cysteine residues (residue 1 and residue 2) that form a disulfide bridge, the overlapped volume of Sγ1– Sγ2, Sγ1–Cβ2, or Cβ1–Sγ2, is not counted. We consider that two cysteine residues form a disulfide bridge when the distance between the two sulfur atoms is within 2.09 ± 1 Å and both angles of Cβ–S–S are within 104.2° ± 30°. Here, 2.09 Å and 104.2° are CHARMM22 parameters for a disulfide bridge.

Electrostatic interactions

The electrostatic interactions between the modeled rotamer and the protein environment are calculated as follows:

equation M2

where indices i and j refer to the atoms of the rotamer and the environment, respectively, qi and qj are partial charges, and ri and rj are atom radii from CHARMM22. Rij is the distance between the two atoms. The summation is over all atoms i and j for which Rij ≤ 12. Similar to the calculation of contact surface and volume overlap, the electrostatic interactions between the selected rotamer and the local backbone are not considered.

Desolvation energy

Desolvation energy is evaluated as the number of totally buried (<5% solvent accessible surface) nonhydrogen-bonded hydrophilic atoms. Polar H and O and nonprotonated N of His that can be an acceptor of a hydrogen bond are considered as hydrophilic atoms. Solvent-accessible surface area is calculated as described by Zou et al. (1999). The probe radius is set to 1.2 Å. The radii of polar hydrogen atoms are set to 1.0Å. The radii of other atoms are taken from CHARMM and are scaled by 0.8. The definition of hydrogen bonds is similar to that of Dahiyat et al. (1997):

equation M3

where R is the distance between donor and acceptor of a hydrogen bond, θ is the donor-hydrogen acceptor angle, and ξ is the hydrogen-acceptor base angle (the base is the atom attached to the acceptor).

Minimization methods

Continuous minimization methods by simulated annealing are used (Press et al. 1992). The basic ideas follow the Metropolis Monte Carlo simulation except that a modified downhill simplex method is used to generate random changes (Metropolis et al. 1953; Nelder and Mead 1965). The "moves" include reflections, expansions, and contractions of the simplex. −T × lnepsilon [T is the temperature; epsilon is a small random number in the range of (0,1 )] is added to the stored function value associated with every vertex of the simplex, and a similar random variable is subtracted from the function value of every new point that is tried as a replacement point. The modified function values of the new and old points are compared. This procedure takes a downhill step while sometimes takes an uphill step and converges to a local minimum in the limit T → 0. In this study, the weight of the contact surface is set to –1 (because favorable interactions are defined as having negative energy) and those of the other four energy terms are subject to optimization. For the training protein, a single residue is checked for different rotamers at each trial, and other residues are unchanged from the experimental structure. The rms difference between the lowest-energy rotamer and the real conformation is calculated and averaged for all the residues of the protein. The mean value of the averaged rms deviations for the training proteins is the objective function value to be minimized. Initial values of the parameters to be optimized are set to ±lnepsilon (epsilon is a random number as was the case earlier). The simulated annealing temperature starts from 0.01 and is gradually reduced to 0 with the step of 0.001. Two thousand moves are made at each temperature.

Training and testing protein sets

The proteins for training and testing sets were chosen according to the following criteria. Sequence identity cutoff was set to 50%, the resolution cutoff was set to 1.8 Å, and the R-factor cutoff was set to 0.2. A total of 761 chains that met the criteria were downloaded from ftp://fccc.edu/dunbrack/pub/culledpdb on March 8, 2001. Only single-chain proteins with 100–500 monomers and containing no incomplete side chains or ligands were kept. A total of 30 proteins meeting all the requirements were selected: 1a8q, 1amm, 1bd8, 1cem, 1chd, 1edg, 1ifc, 1mla, 1nar, 1npk, 1thv, 1vjs, 2baa, 2end, 2pth, 153l, 1ako, 1arb, 1bj7, 1cex, 1dhn, 1hcl, 1koe, 1mml, 1noa, 1thx, 1whi, 2cpl, 2hvm, 2rn2. The first 15 proteins were used to derive the scoring function and the remaining proteins were used for testing. The program REDUCE (Word et al. 1999) was used to add hydrogen atoms to all proteins. Nonpolar hydrogen atoms were deleted. The amide plane of Asn or Gln and the aromatic ring of His were flipped if needed to form more hydrogen bonds. When a residue had multiple conformations, only the one with the highest occupancy was used.

Modeling the side chains for an entire protein

Metropolis Monte Carlo-simulated annealing methods (Metropolis et al. 1953) with the rotamer library of Dunbrack (Dunbrack and Cohen 1997) are used to predict side-chain conformations, given a protein backbone conformation and sequence. Initially, the rotamers for the sequence are selected at random. Then, a rotamer substitution is made at a selected position. The frequency to select a position is proportional to the number of rotamers for the residue in the position. One rotamer is selected at random and the interaction energy with the other parts of the protein Enew is calculated using the derived scoring function. If the energy value is lower than the previous energy Eold, the move is accepted, or the move is accepted with the probability exp[(EoldEnew)/T]. The initial temperature T is set to 50 and is scaled by 0.8 after each cycle. A total of 25 cycles are repeated. We hold the temperature constant at each cycle for 10,000 substitutions or 1,000 successful substitutions, whichever comes first.

Evaluation methods

Several evaluation methods for side-chain modeling programs have been proposed (De Maeyer et al. 1997). We make sure that the evaluation methods obey the same standards when the results obtained by different programs are compared. Unless specifically indicated, all computational results in this work are evaluated as the following. Cβ is included in rms deviation calculation and hydrogen atoms are excluded. Incomplete residues, Ala, or residues with alternative conformation are not evaluated. Residues with <20% solvent accessibility are considered as core residues. If the χ1 angle of a predicted residue is within 40° of the experimental value, the residue is considered correctly predicted until χ1. χ1 + 2 only refers to residues that have more than one side-chain dihedral angle (not including Ser, Thr, Val, and Cys). χ1 + 2 is considered correctly predicted when both χ1 and χ2 are within 40° of their experimental values. For residues with a rotational symmetry axis (Asp, Glu, Phe, and Tyr), we consider the torsion angle corresponding to this axis correct if either of the symmetric conformations obeys the above criteria, and the rms deviation is calculated from the closest symmetric conformation. Asn, Gln, and His especially are compared with the structures resulting from running REDUCE.


The authors thank Jamie Wrabl for critical reading of the manuscript and helpful comments. The work was supported in part by the Welch foundation grant I-1505 to N.V.G.

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.


Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.24902.


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