Application of the parallel fast messy genetic algorithm to the protein folding problem (original) (raw)
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Journal of Computational Chemistry, 1997
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.
Polypeptide structure prediction
Proceedings of the 1997 ACM symposium on Applied computing - SAC '97, 1997
C h a r l e s E . K a i s e r , J r . , G a r y B . L a m o n t , L a u r e n c e D . M e r k l e D e p a r t m e n t of Electrical and C o m p u t e r Engineering G r a d u a t e School of Engineering Air Force Institute of Technology lamont @afit. af.mil G e o r g e H. Gates, Jr., Ruth Pachter Wright L a b o r a t o r y 3005 P St., Ste. 1 W r i g h t -P a t t e r s o n A F B , O H 45433-7702 {gatesgh, pachterr} @ml.wpafb.af.mil
Calculation of protein conformation by global optimization of a potential energy function
Proteins-structure Function and Bioinformatics, 1999
A novel hierarchical approach to protein folding has been applied to compute the unknown structures of seven target proteins provided by CASP3. The approach is based exclusively on the global optimization of a potential energy function for a united-residue model by conformational space annealing, followed by energy refinement using an all-atom potential. Comparison of the submitted models for five globular proteins with the experimental structures shows that the conformations of large fragments (ϳ60 aa) were predicted with rmsds of 4.2-6.8 Å for the C ␣ atoms. Our lowest-energy models for targets T0056 and T0061 were particularly successful, producing the correct fold of approximately 52% and 80% of the structures, respectively. These results support the thermodynamic hypothesis that protein structure can be computed solely by global optimization of a potential energy function for a given amino acid sequence.
A fast conformational search strategy for finding low energy structures of model proteins
Protein Science, 1996
We describe a new computer algorithm for finding low-energy conformations of proteins. It is a chain-growth method that uses a heuristic bias function to help assemble a hydrophobic core. We call it the Core-directed chain Growth method (CG). We test the CG method on several well-known literature examples of HP lattice model proteins [in which proteins are modeled as sequences of hydrophobic (H) and polar (P) monomers], ranging from 20-64 monomers in two dimensions, and up to 88-mers in three dimensions. Previous nonexhaustive methods-Monte Carlo, a Genetic Algorithm, Hydrophobic Zippers. and Contact
Proceedings of The National Academy of Sciences, 2001
Recent improvements of a hierarchical ab initio or de novo approach for predicting both ␣ and  structures of proteins are described. The united-residue energy function used in this procedure includes multibody interactions from a cumulant expansion of the free energy of polypeptide chains, with their relative weights determined by Z-score optimization. The critical initial stage of the hierarchical procedure involves a search of conformational space by the conformational space annealing (CSA) method, followed by optimization of an all-atom model. The procedure was assessed in a recent blind test of protein structure prediction (CASP4). The resulting lowest-energy structures of the target proteins (ranging in size from 70 to 244 residues) agreed with the experimental structures in many respects. The entire experimental structure of a cyclic ␣-helical protein of 70 residues was predicted to within 4.3 Å ␣-carbon (C ␣ ) rms deviation (rmsd) whereas, for other ␣-helical proteins, fragments of roughly 60 residues were predicted to within 6.0 Å C ␣ rmsd. Whereas  structures can now be predicted with the new procedure, the success rate for ␣͞and -proteins is lower than that for ␣-proteins at present. For the  portions of ␣͞ structures, the C ␣ rmsd's are less than 6.0 Å for contiguous fragments of 30 -40 residues; for one target, three fragments (of length 10, 23, and 28 residues, respectively) formed a compact part of the tertiary structure with a C ␣ rmsd less than 6.0 Å. Overall, these results constitute an important step toward the ab initio prediction of protein structure solely from the amino acid sequence.
Determining minimum energy conformations of polypeptides by dynamic programming
Biopolymers, 1990
A combinatorial optimization approach is used for solving the multiple-minima problem when determining the low-energy conformations of short polypeptides. Each residue is represented by a finite number of discrete states corresponding to single residue local minima of the energy function. These precomputed values constitute a search table and define the conformational space for discrete minimization by a generalized dynamic programming algorithm that significantly limits the number of intermediate conformations to be generated during the search. Since dynamic programming involves stagewise decisions, it results in buildup-type procedures implemented in two different forms. The first procedure predicts a number of conformations by a completely discrete search and these are subsequently refined by local minimization. The second involves limited continuous local minimization within the combinatorial algorithm, generally restricted to two dihedral angles in a buildup step. Both procedures are tested on 17 short peptides previously studied by other global minimization methods but involving the same potential energy function. The discrete method is extremely fast, but proves to be successful only in 1 4 of the 17 test problems. The version with limited local minimization finds, however, conformations in all the 17 examples that are close to the ones previously presented in the literature or have lower energies. In addition, results are almost independent of the cutoff energy, the most important parameter governing the search. Although the limited local minimization increases the number of energy evaluations, the method still offers substantial advantages in speed.
Protein Structure Prediction by Global Optimization of a Potential Energy Function
Proceedings of The National Academy of Sciences, 1999
An approach based exclusively on finding the global minimum of an appropriate potential energy function has been used to predict the unknown structures of five globular proteins with sizes ranging from 89 to 140 amino acid residues. Comparison of the computed lowest-energy structures of two of them (HDEA and MarA) with the crystal structures, released by the Protein Data Bank after the predictions were made, shows that large fragments (61 residues) of both proteins were predicted with rms deviations of 4.2 and 6.0 Å for the C ␣ atoms, for HDEA and MarA, respectively. This represents 80% and 53% of the observed structures of HDEA and MarA, respectively. Similar rms deviations were obtained for ϳ60-residue fragments of the other three proteins. These results constitute an important step toward the prediction of protein structure based solely on global optimization of a potential energy function for a given amino acid sequence.
A global optimization approach for searching low energy conformations of proteins
2010
De novo protein structure prediction and understanding the protein folding mechanism is an outstanding challenge of Biological Physics. Relying on the thermodynamic hypothesis of protein folding it is expected that the native state of a protein can be found out if the global minimum of the free energy surface is found. To understand the energy landscape or the free energy surface is challenging. The structure and dynamics of proteins are the manifestations of the underlying potential energy surface. Here the potential energy function stands on a framework of all-atom representation and uses purely physics-based interactions. For the solvated proteins the effective free energy is defined as an implicit solvation model which includes the solvation free energy, along with a standard all-atom biomolecular forcefield. A major challenge is to search for the global minimum on this effective free energy surface. In this work the Minima Hopping Algorithm (MHOP) to find global minima on poten...