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27
Constraint logic programming approach to protein structure prediction
- BMC Bioinformatics
, 2004
"... Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization prob ..."
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Cited by 42 (18 self)
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Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Results Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. Conclusions The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The
PSICO: Solving Protein Structures with Constraint Programming and Optimisation
- Constraints
, 2002
"... Abstract. In this paper we propose PSICO (Processing Structural Information with Constraint programming and Optimisation) as a constraint-based approach to determining protein structures compatible with distance constraints obtained from Nuclear Magnetic Resonance (NMR) data. We compare the performa ..."
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Abstract. In this paper we propose PSICO (Processing Structural Information with Constraint programming and Optimisation) as a constraint-based approach to determining protein structures compatible with distance constraints obtained from Nuclear Magnetic Resonance (NMR) data. We compare the performance of our proposed algorithm with DYANA ("Dynamics algorithm for NMR applications”) an existing commercial application based on simulated annealing. On a test case with experimental data on the dimeric protein Desulforedoxin, the method proposed here supplied similar results in less than 10 minutes compared to approximately 10 hours of computation time for DYANA. Although the quality of results can still be improved, this shows that CP technology can greatly reduce computation time, a major advantage because structural NMR technique generally demands multiple runs of structural computation.
Combinatorial Algorithms for Protein Folding in Lattice Models: A Survey of Mathematical Results
, 2009
"... “... a very nice step forward in the computerology of proteins. ” Ken Dill 1995[1] We present a comprehensive survey of combinatorial algorithms and theorems about lattice protein folding models obtained in the almost 15 years since the publication in 1995 of the first protein folding approximation ..."
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“... a very nice step forward in the computerology of proteins. ” Ken Dill 1995[1] We present a comprehensive survey of combinatorial algorithms and theorems about lattice protein folding models obtained in the almost 15 years since the publication in 1995 of the first protein folding approximation algorithm with mathematically guaranteed error bounds [60]. The results presented here are mainly about the HP-protein folding model introduced by Ken Dill in 1985 [37]. The main topics of this survey include: approximation algorithms for linear-chain and side-chain lattice models, as well as off-lattice models, NP-completeness theorems about a variety of protein folding models, contact map structure of self-avoiding walks and HP-folds, combinatorics and algorithmics of side-chain models, bi-sphere packing and the Kepler conjecture, and the protein side-chain self-assembly conjecture. As an appealing bridge between the hybrid of continuous mathematics and discrete mathematics, a cornerstone of the mathematical difficulty of the protein folding problem, we show how work on 2D self-avoiding walks contact-map decomposition [56] can build upon the exact RNA contacts counting
Optimally Compact Finite Sphere Packings - Hydrophobic Cores in the FCC
- In Proc. of CPM2001
, 2001
"... . Lattice protein models are used for hierarchical approaches to protein structure prediction, as well as for investigating principles of protein folding. The problem is that there is so far no known lattice that can model real protein conformations with good quality, and for which there is an e ..."
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Cited by 11 (5 self)
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. Lattice protein models are used for hierarchical approaches to protein structure prediction, as well as for investigating principles of protein folding. The problem is that there is so far no known lattice that can model real protein conformations with good quality, and for which there is an ecient method to prove whether a conformation found by some heuristic algorithm is optimal. We present such a method for the FCC-HP-Model [3]. For the FCC-HP-Model, we need to nd conformations with a maximally compact hydrophobic core. Our method allows us to enumerate maximally compact hydrophobic cores for suciently great number of hydrophobic amino-acids. We have used our method to prove the optimality of heuristically predicted structures for HP-sequences in the FCC-HP-model. 1
Computing Approximate Solutions of the Protein Structure Determination Problem using Global Constraints on Discrete Crystal Lattices
, 2008
"... Abstract: Crystal lattices are discrete models of the three-dimensional space that have been effectively employed to facilitate the task of de-termining proteins ’ natural conformation. This paper investigates alter-native global constraints that can be introduced in a constraint solver over discret ..."
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Cited by 10 (6 self)
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Abstract: Crystal lattices are discrete models of the three-dimensional space that have been effectively employed to facilitate the task of de-termining proteins ’ natural conformation. This paper investigates alter-native global constraints that can be introduced in a constraint solver over discrete crystal lattices. The objective is to enhance the efficiency of lattice solvers in dealing with the construction of approximate solu-tions of the protein structure determination problem. Some of them (e.g., self-avoiding-walk) have been explicitly or implicitly already used in previous approaches, while others (e.g., the density constraint) are new. The intrinsic complexities of all of them are studied and prelimi-nary experimental results are discussed.
Heuristics, optimizations, and parallelism for protein structure prediction
- in CLP(FD). Principles and Practice of Declarative Programming, ACM
, 2005
"... The paper describes a constraint-based solution to the protein folding problem on face-centered cubic lattices—a biologically meaningful approximation of the general protein folding problem. The paper improves the results presented in [15] and introduces new ideas for improving efficiency: (i) prope ..."
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Cited by 6 (1 self)
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The paper describes a constraint-based solution to the protein folding problem on face-centered cubic lattices—a biologically meaningful approximation of the general protein folding problem. The paper improves the results presented in [15] and introduces new ideas for improving efficiency: (i) proper reorganization of the constraint structure; (ii) development of novel, both general and problem-specific, heuristics; (iii) exploitation of parallelism. Globally, we obtain a speed up in the order of 60 w.r.t. [15]. We show how these results can be employed to solve the folding problem for large proteins containing subsequences whose conformation is already known.
A new constraint solver for 3d lattices and its application to the protein folding problem
- In Geoff Sutcliffe and Andrei Voronkov, editors, Logic for Programming, Artificial Intelligence, and Reasoning, 12th International Conference, LPAR 2005, Montego
, 2005
"... Abstract. The paper describes the formalization and implementation of an efficient constraint programming framework operating on 3D crystal lattices. The framework is motivated and applied to address the problem of solving the abinitio protein structure prediction problem—i.e., predicting the 3D str ..."
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Abstract. The paper describes the formalization and implementation of an efficient constraint programming framework operating on 3D crystal lattices. The framework is motivated and applied to address the problem of solving the abinitio protein structure prediction problem—i.e., predicting the 3D structure of a protein from its amino acid sequence. Experimental results demonstrate that our novel approach offers up to a 3 orders of magnitude of speedup compared to other constraint-based solutions proposed for the problem at hand. 1
Protein folding simulation in CCP
- In Proceedings of BioConcur2004
, 2004
"... Abstract. A protein is identified by a finite sequence of amino acids, each of them chosen from a set of 20 elements. The Protein Structure Prediction Problem is the problem of predicting the 3D native conformation of a protein, when its sequence of amino acids is known. This problem is fundamental ..."
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Abstract. A protein is identified by a finite sequence of amino acids, each of them chosen from a set of 20 elements. The Protein Structure Prediction Problem is the problem of predicting the 3D native conformation of a protein, when its sequence of amino acids is known. This problem is fundamental for biological and pharmaceutical research. All current mathematical models of the problem are affected by intrinsic computational limits. In particular, simulation-based techniques that handle every chemical interaction between all atoms in the amino acids (and the solvent) are not feasible due to the huge amount of computations involved. These programs are typically written in imperative languages and hard to be parallelized. Moreover, each approach is based on a particular energy function. In the literature there are various proposals and there is no common agreement on which is the most reliable. In this paper we present a novel framework for ab-initio simulations using Concurrent Constraint Programming. We are not aware of any other similar proposals in the literature. Each amino acid of an input protein is viewed as an independent process that communicates with the others. The framework allows a modular representation of the problem and it is easily extensible for further refinements. Simulations at this level of abstraction allow faster calculation. We provide a first preliminary working example in Mozart, to show the feasibility and the power of the method. The code is intrinsically concurrent and thus easy to be parallelized. 1
The algorithmics of folding proteins on lattices
, 2003
"... It should be possible to predict the fold of a protein into its native conformation, once we are given the sequence of the constituent amino acids. This is known as the protein structure prediction problem and is sometimes referred to as the problem of deciphering the second half of the genetic code ..."
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Cited by 3 (0 self)
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It should be possible to predict the fold of a protein into its native conformation, once we are given the sequence of the constituent amino acids. This is known as the protein structure prediction problem and is sometimes referred to as the problem of deciphering the second half of the genetic code. While large proteins fold in nature in seconds, computational chemists and biologists have found that folding proteins to their minimum energy conformations is a challenging unsolved optimization problem. Computational complexity theory has been useful in explaining, at least partially, this (Levinthal’s) paradox. The pedagogic cross-disciplinary survey by Ngo, Marks and Karplus (Computational Complexity, Protein Structure Prediction and the Levinthal Paradox, Birkhauser, Basel, 1994) provides an excellent starting point for non-biologists to take a plunge into the problem of folding proteins. Since then, there has been remarkable progress in the algorithmics of folding proteins on discrete lattice models, an account of which is presented herein.
A Filter-and-Fan Approach to the 2D Lattice Model of the Protein Folding Problem
, 2006
"... We examine a prominent and widely-studied model of the protein folding problem, the two-dimensional (2D) HP model, by means of a filter-and-fan (F&F) solution approach. Our method is designed to generate compound moves that explore the solution space in a dynamic and adaptive fashion. Computat ..."
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We examine a prominent and widely-studied model of the protein folding problem, the two-dimensional (2D) HP model, by means of a filter-and-fan (F&F) solution approach. Our method is designed to generate compound moves that explore the solution space in a dynamic and adaptive fashion. Computational results for a standard set of benchmark problems show that the F&F algorithm performs more robustly and efficiently than the current leading algorithms, requiring only a single solution trial to obtain best known solutions to all but two of the benchmark problems, in contrast to a hundred or more trials required in the typical case by the best of the alternative methods. On the remaining problems a single trial of our method obtains a solution one unit away from the best known solution.
