nach oben

Natural Computing

Erschienen in:

24.08.2018

Automatically obtaining a cellular automaton scheme for modeling protein folding using the FCC model

verfasst von: Daniel Varela, José Santos

Erschienen in: Natural Computing | Ausgabe 2/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper proposes to model protein folding as an emergent process, using machine learning to infer the folding modeling only from information of known protein structures. Using the face-centered cubic lattice for protein conformation representation, the dynamic nature of protein folding is captured with an evolved neural cellular automaton that defines the amino acids moves along the protein chain and across time. The results of the final folded conformations are compared, using different protein benchmarks, with other methods used in the traditional protein structure prediction problem, highlighting the capabilities and problems found with this modeling.

Vorheriger Artikel KLN, a new biological koniocortex based unsupervised neural network: competitive results on credit scoring

Nächster Artikel Preface

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

The acronym CA will be used either for Cellular Automata or for Cellular Automation.

Anfinsen CB (1973) Principles that govern the folding of proteins. Science 181(96):223–230CrossRef

Backofen R, Will S (2006) A constraint-based approach to fast and exact structure prediction in three-dimensional protein models. Constraints 11(1):5–30MathSciNetCrossRefMATH

Backofen R, Will S, Clote P (2000) Algorithmic approach to quantifying the hydrophobic force contribution in protein folding. In: Proceedings of the Pacific symposium on biocomputing. Citeseer, pp 92–103

Calabretta R, Nolfi S, Parisi D (1995) An artificial life model for predicting the tertiary structure of unknown proteins that emulates the folding process. In: Proceedings of European conference on advances in artificial life—LNCS, vol 929, pp 862–875

Conway JH, Sloane NJA (1998) Sphere packings, lattices and groups. Springer, Berlin

Cutello V, Nicosia G, Pavone M, Timmis J (2007) An immune algorithm for protein structure prediction on lattice models. IEEE Trans Evolut Comput 11(1):101–117CrossRef

Danks G, Stepney S, Caves L (2008) Protein folding with stochastic L-systems. In: Proceedings of artificial life XI, pp 150–157

Dill KA (1990) Dominant forces in protein folding. Biochemestry 29:7133–7155CrossRef

Dotu I, Cebrián M, Van Hentenryck PV, Clote P (2011) On lattice protein structure prediction revisited. IEEE/ACM Trans Comput Biol Bioinform 8(6):1620–1632CrossRef

Feig M, Mirjalili V (2016) Protein structure refinement via molecular-dynamics simulations: what works and what does not? Proteins Suppl 1:282–292CrossRef

Hirst JD (1999) The evolutionary landscape of functional model proteins. Protein Eng 12(9):721–726CrossRef

Ilachinski A (2001) Cellular automata, a discrete universe. World Scientific, SingaporeCrossRefMATH

Krasnogor N, Hart WE, Smith J, Pelta DA (1999) Protein structure prediction with evolutionary algorithms. In: Proceedings of the genetic and evolutionary computation conference—GECCO’99, pp 1596–1601

Krasnogor N, Terrazas G, Pelta DA, Ochoa G (2002) A critical view of the evolutionary design of self-assembling systems. In: Proceedings of the 2005 conference on artificial evolution, LNCS, vol 3871, pp 179–188

Langton CG (1992) Life at the edge of chaos. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life II. Addison-Wesley, Reading, pp 41–49

Levinthal C (1968) Are there pathways for protein folding? J Chim Phys 65:44–45CrossRef

Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092CrossRef

Olson B, De-Jong K, Shehu A (2013) Off-lattice protein structure prediction with homologous crossover. In: Proceedings conference on genetic and evolutionary computation—GECCO 2013, pp 287–294

Patton WP, Punch WF, Goldman E (1995) A standard genetic algorithm approach to native protein conformation prediction. In: Proceedings of 6th international conference on genetic algorithms, pp 574–581

Price KV, Storn RM, Lampinen JA (2005) Differential evolution. A practical approach to global optimization. Natural computer series. Springer, BerlinMATH

Rashid MA, Hoque MT, Newton MH, Pham DN, Sattar A (2012) A new genetic algorithm for simplified protein structure prediction. In: Proceedings Australasian joint conference on advances in artificial intelligence—AI 2012, LNCS, vol 7691, pp 107–119

Rosetta system. http://www.rosettacommons.org

Santos J, Diéguez M (2011) Differential evolution for protein structure prediction using the HP model. In: Lecture Notes in Computer Science, vol 6686, pp 323–323

Santos J, Villot P, Diéguez M (2013) Cellular automata for modeling protein folding using the HP model. In: Proceedings IEEE congress on evolutionary computation—IEEE-CEC 2013, pp 1586–1593

Santos J, Villot P, Diéguez M (2014) Emergent protein folding modeled with evolved neural cellular automata using the 3D HP model. J Comput Biol 21(11):823–845CrossRef

Shatabda S, Newton MH, Pham DN, Sattar A (2012) Memory-based local search for simplified protein structure prediction. In: Proceedings of ACM conference on bioinformatics, computational biology and biomedicine—BCB’12, pp 345–352

Shatabda S, Newton MH, Rashid MA, Sattar A (2013) An efficient encoding for simplified protein structure prediction using genetic algorithms. In: Proceedings of IEEE congress on evolutionary computation—IEEE-CEC 2013, pp 1217–1224

Shmygelska A, Hoos HH (2005) An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. Bioinformatics 6:30

Tsay J-J, Su S-C (2013) An effective evolutionary algorithm for protein folding on 3D FCC HP model by lattice rotation and generalized move sets. Proteome Sci 11(1):S19CrossRef

Unger R, Moult J (1993) Genetic algorithms for protein folding simulations. J Mol Biol 231(1):75–81CrossRef

Varela D, Santos J (2017) Protein folding modeling with neural cellular automata using the face-centered cubic model. In: Proceedings international work-conference on the interplay between natural and artificial computation. LNCS, vol 10337, pp 125–134

Yue K, Fiebig KM, Thomas PD, Chan HS, Shakhnovich EI, Dill KA (1995) A test of lattice protein folding algorithms. In: Proceedings of the pacific symposium on biocomputing, vol 92, no 1, p 325

Zhao X (2008) Advances on protein folding simulations based on the lattice HP models with natural computing. Appl Soft Comput 8:1029–1040CrossRef

Titel: Automatically obtaining a cellular automaton scheme for modeling protein folding using the FCC model
verfasst von: Daniel Varela
José Santos
Publikationsdatum: 24.08.2018
Verlag: Springer Netherlands
Erschienen in: Natural Computing / Ausgabe 2/2019
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI: https://doi.org/10.1007/s11047-018-9705-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2019

Discretization of continuous-time quantum walks via the staggered model with Hamiltonians

Simplifying the role of signals in tile self-assembly

Preface

KLN, a new biological koniocortex based unsupervised neural network: competitive results on credit scoring

Discovery of conserved regions in DNA sequences by Artificial Bee Colony (ABC) algorithm based methods

Accelerating bioinspired lateral interaction in accumulative computation for real-time moving object detection with graphics processing units