Protein folding

 

We are using cellular automata (CA) for the modeling of the temporal folding of proteins [2][3][6]. Unlike the focus of the vast research already done on the direct prediction of the final folded conformations [1][5], we model the temporal and dynamic folding process. The CA model defines how the amino acids interact through time to obtain a folded conformation. We employed the HP model to represent the protein conformations in a lattice, using evolutionary computing to automatically obtain the models.

 

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Figure: Example of different temporal steps in the folding process with protein sequence HPPHPPHPPHPPHPPHPPHPPH.

 

 

 

animated_sequence_2Danimated_sequence_3D

 

 

 

 

 

 

 

 

 

Animated sequences: Folding process with a protein in a 2D HP lattice and a protein in a 3D HP lattice.

 

 

 

643d-3no6_crob

Figure: Final folding, using the HP model, with protein 3no6 from PDB (Protein Data Bank).

 

 

References with more information

[1]   Santos. J. and Diéguez, M. (2011), “Differential evolution for protein structure prediction using the HP model”, Inter. Work-Conf. on the Interplay between Natural and Artificial Comp., LNCS 6686:323-333.

[2]   Santos, J., Villot, P., Diéguez, M. (2013), “Cellular automata for modeling protein folding using the HP model”, Proc. IEEE Congress on Evolutionary Comp. - IEEE-CEC 2013, 1586-1593.

[3]   Santos, J., Villot, P., Diéguez, M. (2013), “Protein folding with cellular automata in the 3D HP model”, Proc. Intern. Workshop Evolutionary Comp. in Bioinformatics - Genetic and Evol. Comp. Conf. (GECCO 2013), 1595-1602.

[4]   Santos, J., Villot, P., Diéguez, M. (2014), “Emergent protein folding modeled with evolved neural cellular automata using the 3D HP model”, Journal of Computational Biology 21(11):823-845.Varela, D. and Santos, J. (2016), “Protein folding modeling with neural cellular automata using Rosetta”, GECCO 2016 Proceedings Companion, Workshop Evolutionary Computation in Computational Structural Biology, 1307- 1312.

[5]   Varela, D.  and Santos, J. (2015), “Combination of differential evolution and fragment-based replacements for protein structure prediction” GECCO 2015 Proceedings Companion, Workshop Evolutionary Computation in Computational Structural Biology, 911-914.

[6]   Varela, D. and Santos, J. (2016), “Protein folding modeling with neural cellular automata using Rosetta”, GECCO 2016 Proceedings Companion, Workshop Evolutionary Computation in Computational Structural Biology, 1307- 1312.

[7]   Zhao, X. (2008), “Advances on protein folding simulations based on the lattice HP models with natural computing”, Applied Soft Computing 8:1029-1040.

See the workshops:

Natural computing for protein structure prediction at 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014), Ljubljana (Slovenia), September 2014.

Evolutionary computation in computational structural biology at Genetic and Evolutionary Computation Conference (GECCO 2015), Madrid (Spain), July 2015.

Evolutionary computation in computational structural biology at Genetic and Evolutionary Computation Conference (GECCO 2016), Denver (USA), July 2016.

Evolutionary computation in computational biology at Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin (Germany), July 2017.