Conformational analysis of the 20-residue membrane-bound portion of melittin by conformational space annealing

The Journal of Chemical Physics

2003

Self Cite

A global optimization method, conformational space annealing (CSA), is applied to study a 46-residue protein with the sequence B 9 N 3 (LB) 4 N 3 B 9 N 3 (LB) 5 L, where B, L and N designate hydrophobic, hydrophilic, and neutral residues, respectively. The 46-residue BLN protein is folded into the native state of a four-stranded β-barrel. It has been a challenging problem to locate the global minimum of the 46-residue BLN protein since the system is highly frustrated and consequently its energy landscape is quite rugged. The CSA successfully located the global minimum of the 46-mer for all 100 independent runs. The CPU time for CSA is about seventy times less than that for simulated annealing (SA), and its success rate (100%) to find the global minimum is about eleven times higher. The amount of computational efforts used for CSA is also about ten times less than that of the best global optimization method yet applied to the 46-residue BLN protein, the quantum thermal annealing with renormalization. The 100 separate CSA runs produce the global minimum 100 times as well as other 5950 final conformations corresponding to a total of 2361 distinct local minima of the protein. Most of the final conformations have relatively small RMSD values from the global minimum, independent of their diverse energy values. Very close to the global minimum, there exist quasi-global-minima which are frequently obtained as one of the final answers from SA runs. We find that there exist two largest energy gaps between the quasi-global-minima and the other local minima. Once a SA run is trapped in one of these quasiglobal-minima, it cannot be folded into the global minimum before crossing over the two large energy barriers, clearly demonstrating the reason for the poor success rate of SA.2

Section: Introductionmentioning

confidence: 99%

Conformational space annealing and an off-lattice frustrated model protein

Kim

The Journal of Chemical Physics

2003

Self Cite

Proc. Natl. Acad. Sci. U.S.A.

“…To search conformational space efficiently, we developed a global optimization method, CSA (33). The CSA method is based on a genetic algorithm in which populations are generated by a set of crossover operators.…”

Section: Methodsmentioning

confidence: 99%

“…To locate global energy minima, we developed the Conformational Space Annealing (CSA) (29,33,34) method, which is based on a genetic algorithm and local energy minimization. The energy is the only scoring function used in the search and to rank the resulting structures; no filters, based on the similarity to known structures or common features of folds characteristic of a given part of the sequence under study (such as, e.g., the handedness of loops, the directions and packing of ␤-strands in composite ␤-sheets, etc.…”

mentioning

confidence: 99%

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests

Ołdziej

Czaplewski

Liwo

et al. 2005

136

119

Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energyminimized with the ECEPP͞3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of ␣ and ␣؉␤ proteins [60 -70 residues with C ␣ rms deviation (rmsd) <6 Å]. However, for ␣؉␤ proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two ␣ and three ␣؉␤, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue ␣؉␤ protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-Å C ␣ rmsd. So far this protein is the largest ␣؉␤ protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly ␣-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 Å rmsd, except the 32 C-terminal residues, most of which form a ␤-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.global optimization ͉ thermodynamic hypothesis T o date, the great majority of successful algorithms for proteinstructure prediction are knowledge-based approaches; they make explicit use of homology modeling (1, 2) or fold recognition methods (2-6). This feature even pertains to most of the methods considered as ab initio (7,8), which, in theory, should not make explicit use of structural databases. However, in-depth understanding of the physical principles of formation of protein structure requires the development of physics-based methods for proteinstructure prediction (9). Moreover, such methods will be independent of structural databases used in the training of knowledgebased methods. Furthermore, physics-based methods will enable us to study the structures of proteins that seem to possess a degenerate native state, such as the prion proteins, to simulate protein-folding pathways, to understand the mechanisms of protein folding, and to study interactions of proteins with other biomacromolecules and their assemblies (e.g., nucleic acids, polysaccharides, lipids, etc.). The underlying principle of physics-based methods for proteinstructure prediction is Anfinsen's thermodynamic hypothesis (10), according to which protein molecules adopt the conformations that are the global minima of their potential-energy surfaces. The methods based on this hypoth...

“…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.…”

Section: Conformational Samplingmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Protein structure prediction based on fragment assembly and parameter optimization

Kim

2005

Biophysical Chemistry

Self Cite

We propose a novel method for ab-initio prediction of protein tertiary structures based on the fragment assembly and global optimization. Fifteen residue long fragment libraries are constructed using the secondary structure prediction method PREDICT, and fragments in these libraries are assembled to generate full-length chains of a query protein. Tertiary structures of 50 to 100 conformations are obtained by minimizing an energy function for proteins, using the conformational space annealing method that enables one to sample diverse low-lying local minima of the energy. Then in order to enhance the performance of the prediction method, we optimize the linear parameters of the energy function, so that the native-like conformations become energetically more favorable than the non-native ones for proteins with known structures. We test the feasibility of the parameter optimization procedure by applying it to the training set consisting of three proteins: the 10-55 residue fragment of staphylococcal protein A (PDB ID 1bdd), a designed protein betanova, and 1fsd. D