Biology:Hydrophobic-polar protein folding model
The hydrophobic-polar protein folding model is a highly simplified model for examining protein folds in space. First proposed by Ken Dill in 1985, it is the most known type of lattice protein: it stems from the observation that hydrophobic interactions between amino acid residues are the driving force for proteins folding into their native state.[1] All amino acid types are classified as either hydrophobic (H) or polar (P), and the folding of a protein sequence is defined as a self-avoiding walk in a 2D or 3D lattice. The HP model imitates the hydrophobic effect by assigning a negative (favorable) weight to interactions between adjacent, non-covalently bound H residues. Proteins that have minimum energy are assumed to be in their native state. The HP model can be expressed in both two and three dimensions, generally with square lattices, although triangular lattices have been used as well. It has also been studied on general regular lattices.[2]
Randomized search algorithms are often used to tackle the HP folding problem. This includes stochastic, evolutionary algorithms like the Monte Carlo method, genetic algorithms, and ant colony optimization. While no method has been able to calculate the experimentally determined minimum energetic state for long protein sequences, the most advanced methods today are able to come close.[3][4] For some model variants/lattices, it is possible to compute optimal structures (with maximal number of H-H contacts) using constraint programming techniques[5][6] as e.g. implemented within the CPSP-tools webserver.[7]
Even though the HP model abstracts away many of the details of protein folding, it is still an NP-hard problem on both 2D and 3D square lattices.[8]
Recently, a Monte Carlo method, named FRESS, was developed and appears to perform well on HP models.[9]
See also
- Protein structure prediction
- Lattice proteins
References
- ↑ Dill K.A. (1985). "Theory for the folding and stability of globular proteins". Biochemistry 24 (6): 1501–9. doi:10.1021/bi00327a032. PMID 3986190.
- ↑ Bechini, A. (2013). "On the characterization and software implementation of general protein lattice models". PLOS ONE 8 (3): e59504. doi:10.1371/journal.pone.0059504. PMID 23555684. Bibcode: 2013PLoSO...859504B.
- ↑ Bui T.N.; Sundarraj G. (2005). "An efficient genetic algorithm for predicting protein tertiary structures in the 2D HP model". Proceedings of the 7th annual conference on Genetic and evolutionary computation. pp. 385–392. doi:10.1145/1068009.1068072. ISBN 978-1595930101.
- ↑ Shmygelska A.; Hoos H.H. (2003). "An Improved Ant Colony Optimisation Algorithm for the 2D HP Protein Folding Problem". Advances in Artificial Intelligence. Lecture Notes in Computer Science. 2671. pp. 400–417. doi:10.1007/3-540-44886-1_30. ISBN 978-3-540-40300-5.
- ↑ Yue K.; Fiebig K.M.; Thomas P.D.; Chan H.S.; Shakhnovich E.I.; Dill K.A. (1995). "A test of lattice protein folding algorithms". Proc Natl Acad Sci U S A 92 (1): 325–329. doi:10.1073/pnas.92.1.325. PMID 7816842. Bibcode: 1995PNAS...92..325Y.
- ↑ Mann M.; Backofen R. (2014). "Exact methods for lattice protein models". Bio-Algorithms and Med-Systems 10 (4): 213–225. doi:10.1515/bams-2014-0014.
- ↑ Mann M.; Will S.; Backofen R. (2008). "CPSP-tools - exact and complete algorithms for high-throughput 3D lattice protein studies". BMC Bioinformatics 9: 230. doi:10.1186/1471-2105-9-230. PMID 18462492.
- ↑ Crescenzi P.; Goldman D.; Papadimitriou C.; Piccolboni A.; Yannakakis M. (1998). "On the complexity of protein folding". Macromolecules 5 (1): 27–40. doi:10.1145/279069.279089. PMID 9773342.
- ↑ Jinfeng Zhang; S. C. Kou; Jun S. Liu (2007). "Polymer structure optimization and simulation via a fragment re-growth Monte Carlo". J. Chem. Phys. 126 (22): 225101. doi:10.1063/1.2736681. PMID 17581081. https://dash.harvard.edu/bitstream/handle/1/2766346/FRESS-JCP.pdf?sequence=2.
External links
- CPSP-tools webserver for optimal structure prediction in unrestricted 3D lattices
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