Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
On the complexity of string folding
Discrete Applied Mathematics - Special volume on computational molecular biology
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Spatial codes and the hardness of string folding problems
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
A new algorithm for protein folding in the HP model
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Genetic Algorithm for 3D Protein Folding Simulations
Proceedings of the 5th International Conference on Genetic Algorithms
Approximate Protein Folding in the HP Side Chain Model on Extended Cubic Lattices
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
Protein folding in the HP model on grid lattices with diagonals
Discrete Applied Mathematics
Iterative Lattice Protein Design Using Template Matching
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
A multiple minima genetic algorithm for protein structure prediction
Applied Soft Computing
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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 nonbiologists 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.