Protein Structure Prediction by Global Optimization of a Potential Energy Function (original) (raw)
Related papers
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.
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.
Protein Structure Evaluation using an All-Atom Energy Based Empirical Scoring Function
Journal of Biomolecular Structure and Dynamics, 2006
Arriving at the native conformation of a polypeptide chain characterized by minimum most free energy is a problem of long standing interest in protein structure prediction endeavors. Owing to the computational requirements in developing free energy estimates, scoring functions-energy based or statistical-have received considerable renewed attention in recent years for distinguishing native structures of proteins from non-native like structures. Several cleverly designed decoy sets, CASP (Critical Assessment of Techniques for Protein Structure Prediction) structures and homology based internet accessible three dimensional model builders are now available for validating the scoring functions. We describe here an all-atom energy based empirical scoring function and examine its performance on a wide series of publicly available decoys. Barring two protein sequences where native structure is ranked second and seventh, native is identified as the lowest energy structure in 67 protein sequences from among 61,659 decoys belonging to 12 different decoy sets. We further illustrate a potential application of the scoring function in bracketing native-like structures of two small mixed alpha/beta globular proteins starting from sequence and secondary structural information. The scoring function has been web enabled at www.scfbio-iitd.res.in/utility/proteomics/energy.jsp
Proteins: Structure, Function, and Genetics, 2001
This study describes a computational method for ab inito protein structure prediction. Protein conformation has been modeled by using six optimized backbone torsion angles and fixed side chains approximating rotationally averaged real side chains. The approximations aim to keep complexity of the structure description to a minimum without seriously compromising the accuracy of the structural representation. An evolutionary Monte Carlo algorithm has been developed to search through this restricted conformational space to locate low-energy protein structures. A simple physicochemical force field has been developed to assess the energies of different conformations within this structural description. The corresponding residue interaction energies are based on hydrophobic, hydrophilic, steric, and hydrogen-bonding potentials. The search procedure has been used to locate native energy minima from primary sequence alone. The 3-D structures of polypeptides up to 38 residues with both  and ␣ secondary structural elements have been accurately predicted. The search procedure has been found to be highly efficient and follows an energetically and structurally plausible pathway to locate native populations. The simple force field described in the study has been compared with a more complex all-atom model and been found to be similarly effective in predicting the structures of proposed independent folding units. Proteins 2001;43:186 -202.
Molecular structure prediction by global optimization
1997
The CGU (convex global underestimator) global optimization method is used to predict the minimum energy structures, i.e. folded states, of small protein sequences. Computational results obtained from the CGU method applied to actual protein sequences using a detailed polypeptide model and a differentiable form of the Sun/Thomas/Dill potential energy function are presented. This potential function accounts for steric repulsion, hydrophobic attraction, and ϕ/ψ pair restrictions imposed by the so called Ramachandran maps. Furthermore, it is easily augmented to accommodate additional known data such as the existence of disulphide bridges and any other a priori distance data. The Ramachandran data is modeled by a continuous penalty term in the potential function, thereby permitting the use of continuous minimization techniques.
A Physical Approach to Protein Structure Prediction
Biophysical Journal, 2002
We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4 th Critical Assessment of Techniques for Protein Structure Prediction competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.
Predicting Protein Tertiary Structure using a Global Optimization
1999
We present a global optimization algorithm and demonstrate its effectiveness in solving the protein structure prediction problem for a 70 amino-acid helical protein, the A-chain of uteroglobin. This is a larger protein than solved previously by our global optimization method or most other optimization-based protein structure prediction methods. Our approach combines techniques that "smooth" the potential energy surface being minimized with methods that do a global search in selected subspaces of the problem in addition to locally minimizing in the full parameter space. Neural network predictions of secondary structure are used in the formation of initial structures.
A global optimization strategy for predicting protein tertiary structure: α-helical proteins
Comput. …, 2000
We present a global optimization strategy that incorporates predicted soft constraints in both a local optimization context and as directives for global optimization approaches, to tackle the protein structure prediction problem. Specifically, neural networks are used to predict the secondary structure of a protein, soft-constraints are defined that are manifestations of the network predicted secondary structure, and the secondary structure is formed using local minimizations on a protein energy surface in the presence of these soft-constraints. In addition, those residues predicted to be coil by the network define a conformational sub-space that is subject to optimization using a global approach based on sampling and perturbation that has been quite successful working directly on the potential energy surface of homo-polypeptides. We demonstrate this global optimization strategy by predicting the tertiary structure of the 70 amino acid A-chain of the α-helical protein, uteroglobin.