Modeling of loops in protein structures

Comparative protein structure prediction is limited mostly by the errors in alignment and loop modeling. We describe here a new automated modeling tec hnique that significantly improves the accuracy of loop predictions in prot ein structures. The positions of all nonhydrogen atoms of the loop are opti mized in a fixed environment with respect to a pseudo energy function. The energy is a sum of many spatial restraints that include the bond length, bo nd angle, and improper dihedral angle terms from the CHARMM-22 force field, statistical preferences for the main-chain and side-chain dihedral angles, and statistical preferences for nonbonded atomic contacts chat depend on t he two atom types, their distance through space, and separation in sequence . The energy function is optimized with the method of conjugate gradients c ombined with molecular dynamics and simulated annealing. Typically, the pre dicted loop conformation corresponds to the lowest energy conformation amon g 500 independent optimizations. Predictions were made for 40 loops of know n structure at each length from 1 to 14 residues. The accuracy of loop pred ictions is evaluated as a Function of thoroughness of conformational sampli ng, loop length, and structural properties of native loops. When accuracy i s measured by local superposition of the model on the native loop, 100, 90, and 30% of 4-, 8-, and 12-residue loop predictions, respectively, had <2 A ngstrom RMSD error for the mainchain N, C-alpha, C, and O atoms; the averag e accuracies were 0.59 +/- 0.05, 1.16 +/- 0.10, and 2.61 +/- 0.16 Angstrom, respectively. To simulate real comparative modeling problems, the method w as also evaluated by predicting loops of known structure in only approximat ely correct environments with errors typical of comparative modeling withou t misalignment. When the RMSD distortion of the main-chain stem atoms is 2. 5 Angstrom, the average loop prediction error increased by 18, 25, and 3% f or 4-, 8-, and 12-residue loops, respectively. The accuracy of the lowest e nergy prediction for a given loop can be estimated from the structural vari ability among a number of low energy predictions. The relative value of the present method is gauged by (1) comparing it with one of the most successf ul previously described methods, and (2) describing its accuracy in recent blind predictions of protein structure. Finally, it is shown that the avera ge accuracy of prediction is limited primarily by the accuracy of the energ y function rather than by the extent of conformational sampling.