New optimization method for conformational energy calculations on polypeptides: Conformational space annealing

Abstract

A new optimization method is presented to search for the global minimum-energy conformations of polypeptides. The method combines essential aspects of the build-up procedure and the genetic algorithm, and it introduces the important concept of ''conformational space annealing.'' Instead of considering a single conformation, attention is focused on a population of conformations while new conformations are obtained by modifying a ''seed conformation.'' The annealing is carried out by introducing a distance cutoff, D , which is defined in the conformational space; D effectively divides the cut cut whole conformational space of local minima into subdivisions. The value of D cut is set to a large number at the beginning of the algorithm to cover the whole conformational space, and annealing is achieved by slowly reducing it. Many distinct local minima designed to be distributed as far apart as possible in conformational space are investigated simultaneously. Therefore, the new method finds not only the global minimum-energy conformation but also many other distinct local minima as by-products. The method is tested on Metenkephalin, a 24-dihedral angle problem. For all 100 independent runs, the accepted global minimum-energy conformation was obtained after about 2600 minimizations on average.

Re‐examination of structure optimization of off‐lattice protein AB models by conformational space annealing

“…6, a global optimization method called conformational space annealing (CSA) [10][11][12] is applied to find ground-state conformations of protein AB models. At that time, optimization by CSA was terminated when a new structure was obtained with lower energy than reported in the literature.…”

Folding of small proteins using a single continuous potential

“…The parameters of the UNRES force field were simultaneously optimized [16] for four proteins of betanova [11,17] (20 residues, three-stranded β-sheet), 1fsd [18] (28 residues, one β-hairpin and one α-helix), HP-36 [1,12] (36 residues, three-helix bundle), and fragment B of staphylococcal protein A [5,7,9,10,13,19] (46 residues, three-helix bundle). The low-lying local-energy minima for these proteins were found by the conformational space annealing [20] method. The parameters were modified in such a way that the native-like conformations are energetically more favored than the others.…”

Protein structure prediction based on fragment assembly and parameter optimization

“…The CSA [29][30][31] is a powerful global optimization algorithm that has played the integral role in the recent success of the energy-based method for protein structure prediction [13][14][15][16]. A population of local minimum-energy conformations is maintained in the CSA method, which is called the bank.…”

“…In a previous paper [25], we have introduced a fragment-based protein structure prediction method PROFESY (PROFile Enumerating SYstem), which utilizes the fragment library obtained from the secondary structure prediction method PREDICT (PRofile Enumeration DICTionary) [27,28]. In contrast to earlier methods where only simple sampling algorithms such as the simulated annealing method are used for generating low-energy conformations, PROFESY applies a powerful global optimization algorithm, conformational space annealing (CSA) [29][30][31][32][33], for sampling low-energy conformations. However, the energy function used was rather primitive partly due to the fact that the solvent effect was not properly incorporated.…”