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Energy Functions that Discriminate Xray and Nearnative Folds from Wellconstructed Decoys
, 1996
"... this paper is concerned, have been derived in several ways. Levitt (1976) generated potentials of mean force by averaging energies over all relative orientations of pairs of sidechains. More recently these kinds of energy functions have been derived as potentials of mean force from the evergrowing ..."
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Cited by 106 (8 self)
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this paper is concerned, have been derived in several ways. Levitt (1976) generated potentials of mean force by averaging energies over all relative orientations of pairs of sidechains. More recently these kinds of energy functions have been derived as potentials of mean force from the evergrowing database of known protein structures (see the references in Sippl, 1995). Huang et al. (1995) have devised a potential which does not explicitly use the database of known structures; they use only a simple classification of different residues as hydrophobic or hydrophilic, reminiscent of the theoretical energy models of Dill et al. (reviewed by Dill et al., 1995; Yue & Dill, 1995). Maiorov & Crippen (1992) generated a potential function by an optimization procedure which sought to maximize the difference in energy between correct and incorrect protein conformations.
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Ja ..."
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Cited by 73 (8 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Fast protein folding in the hydrophobichydrophilic model within threeeights of optimal
 1ZDD 34 1045 4.0 2.703 3.12 1Q2N 0.66 0.61 1VII 36 14280 7.4 3.047 12.59 1UNC 0.74 0.70 1EOM 37 36000 3.4 3.093 17.41 1I5H 0.47 0.49 1EDO 46 36000 7.2 3.656 11.54 1NBL 0.55 0.56 2IGD 61 174960 11.5 7.469 8.01 1MVK 0.79 0.74 1YPA 64 420840 9.4 6.687 0.34 2
, 1996
"... We present performanceguaranteed approximation algorithms for the protein folding problem in the hydrophobichydrophilic model (Dill, 1985). Our algorithms are the first approximation algorithms in the literature with guaranteed performance for this model (Dill, 1994). The hydrophobichydrophilic m ..."
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Cited by 68 (4 self)
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We present performanceguaranteed approximation algorithms for the protein folding problem in the hydrophobichydrophilic model (Dill, 1985). Our algorithms are the first approximation algorithms in the literature with guaranteed performance for this model (Dill, 1994). The hydrophobichydrophilic model abstracts the dominant force of protein folding: the hydrophobic interaction. The protein is modeled as a chain of amino acids of length n that are of two types; H (hydrophobic, i.e., nonpolar) and P (hydrophilic, i.e., polar). Although this model is a simplification of more complex protein folding models, the protein folding structure prediction problem is notoriously difficult for this model. Our algorithms have conformation that has linear (3n) or quadratic time and achieve a threedimensional protein a guaranteed free energy no worse than threeeighths of optimal. This result answers the open problem of Ngo et al. (1994) about the possible existence of an efficient approximation algorithm with guaranteed performance for protein structure prediction in any wellstudied model of protein folding. By achieving speed and nearoptimality simultaneously, our algorithms rigorously capture salient features of the recently proposed framework of protein folding by Sali et al. (1994). Equally important, the final conformations of our algorithms have significant secondary structure (antiparallel sheets, ^sheets, compact hydrophobic core). Furthermore, hypothetical folding pathways can be described for our algorithms that fit within the framework of diffusioncollision protein folding proposed by Karplus and Weaver (1979). Computational limitations of algorithms that compute the optimal conformation have restricted their applicability to short sequences (length < 90). Because our algorithms trade computational accuracy for speed, they can construct nearoptimal conformations in linear time for sequences of any size. 1.
Folding and Unfolding in Computational Geometry
"... Three open problems on folding/unfolding are discussed: (1) Can every convex polyhedron be cut along edges and unfolded at to a single nonoverlapping piece? (2) Given gluing instructions for a polygon, construct the unique 3D convex polyhedron to which itfolds. (3) Can every planar polygonal chain ..."
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Cited by 54 (4 self)
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Three open problems on folding/unfolding are discussed: (1) Can every convex polyhedron be cut along edges and unfolded at to a single nonoverlapping piece? (2) Given gluing instructions for a polygon, construct the unique 3D convex polyhedron to which itfolds. (3) Can every planar polygonal chain be straightened?
A graphtheoretic algorithm for comparative modeling of protein structure
 J Mol Biol
, 1998
"... The rapidly increasing number of known protein structures has resulted in a situation where approximate structures corresponding to new sequences are often available from one of two ..."
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Cited by 32 (9 self)
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The rapidly increasing number of known protein structures has resulted in a situation where approximate structures corresponding to new sequences are often available from one of two
Multimeme Algorithms for Protein Structure
 In: Proceedings of the Parallel Problem Solving from Nature VII. Lecture Notes in Computer Science
, 2002
"... Despite intensive studies during the last 30 years researchers are yet far from the \holy grail" of blind structure prediction of the three dimensional native state of a protein from its sequence of amino acids. ..."
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Cited by 27 (15 self)
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Despite intensive studies during the last 30 years researchers are yet far from the \holy grail" of blind structure prediction of the three dimensional native state of a protein from its sequence of amino acids.
Protein Structure Prediction With Evolutionary Algorithms
, 1999
"... Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithm ..."
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Cited by 26 (8 self)
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Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the conformational representation, the energy formulation and the way in which infeasible conformations are penalized. Further we empirically evaluate the impact of these factors on a small set of polymer sequences. Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model. 1 INTRODUCTION A protein is a chain of amino acid residues that folds into a specific native tertiary structure under certain physiological conditions. A protein's structure determines its biological function. Consequently, methods for solving protein structure prediction (PSP) problems are valuable tools for modern molecula...
A Standard GA Approach to Native Protein Conformation Prediction
 Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... Finding the 3D geometry or tertiary structure of an arbitrary protein is vital to understanding the functionality of that protein. The prediction of this structure, known as the protein folding problem, is very difficult and has been labeled one of the "grand challenge problems" for the scientific ..."
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Cited by 26 (1 self)
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Finding the 3D geometry or tertiary structure of an arbitrary protein is vital to understanding the functionality of that protein. The prediction of this structure, known as the protein folding problem, is very difficult and has been labeled one of the "grand challenge problems" for the scientific community. We report here on further work to determine tertiary structures via genetic algorithms. We build on work done first by Unger and Moult using a simplified protein model but improve on the application of GAs to this model. We show, using the same simplified model, that the genetic algorithm indeed appears effective for determining tertiary structure with far fewer computational steps than first reported. 1. INTRODUCTION In this paper we explore the applicability of a standard GA approach to the problem of protein structure prediction. This section presents the problem of protein structure prediction in detail. For the reader with a basic understanding of biochemistry and the natur...
Evolutionary Monte Carlo: Applications to C_p Model Sampling and Change Point Problem
 STATISTICA SINICA
, 2000
"... Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm so called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms ..."
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Cited by 25 (5 self)
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Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm so called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. It works by simulating a population of Markov chains in parallel, where each chain is attached to a different temperature. The population is updated by mutation (Metropolis update), crossover (partial state swapping) and exchange operators (full state swapping). The algorithm is illustrated through examples of the Cpbased model selection and changepoint identification. The numerical results and the extensive comparisons show that evolutionary Monte Carlo is a promising approach for simulation and optimization.
An immune algorithm for protein structure prediction on lattice models
 IEEE Transactions on Evolutionary Computation
"... by the clonal selection principle, which has been designed for the protein structure prediction problem (PSP). The proposed IA employs two special mutation operators, hypermutation and hypermacromutation, to allow for effective searching, and an aging mechanism, a new immune inspired operator, devis ..."
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Cited by 22 (6 self)
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by the clonal selection principle, which has been designed for the protein structure prediction problem (PSP). The proposed IA employs two special mutation operators, hypermutation and hypermacromutation, to allow for effective searching, and an aging mechanism, a new immune inspired operator, devised to enforce diversity in the population during evolution. When cast as an optimization problem, the PSP can be seen as discovering a protein conformation with minimal energy. The proposed IA was tested on well known PSP lattice models, the HP model in 2D and 3D square latticesâ€™, and the functional model protein, which is a more realistic biological model. Our experimental results demonstrate that the proposed Immune Algorithm is very competitive with existing stateofart algorithms for the PSP on lattice models. Index Terms â€” Immune algorithms, clonal selection algorithms, hypermutation operator, hypermacromutation operator, aging operator, protein structure prediction problem, 2D HP model, 3D HP model, functional model proteins. I.