Development and optimisation of a novel genetic algorithm for studying model protein folding

Cox, Graham A.; Mortimer-Jones, Thomas V.; Taylor, Robert P.; Johnston, Roy L.

doi:10.1007/s00214-004-0601-4

Cited by 25 publications

(11 citation statements)

References 1 publication

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“…In their work, when operators such as mutation and crossover were applied, the GA iterated until a "nonlethal" conformation was generated. Similarly, Rylance [12] obtained the initial population using a recoil growth algorithm and Song et al [13] applied the genetic operators used in [14], with 6 types of mutations.…”

Section: Previous Work For the Direct Prediction Of The Final Promentioning

confidence: 99%

Cellular automata for modeling protein folding using the HP model

Santos¹,

Villot²,

Diéguez³

2013

2013 IEEE Congress on Evolutionary Computation

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We used cellular automata (CA) for the modeling of the temporal folding of proteins. Unlike the focus of the vast research already done on the direct prediction of the final folded conformations, we will 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, we extended the classical CA models using artificial neural networks for their implementation, and we used evolutionary computing to automatically obtain the models by means of Differential Evolution. Moreover, the modeling of the folding provides the final protein conformation.

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Section: Previous Work For the Direct Prediction Of The Final Promentioning

confidence: 99%

Cellular automata for modeling protein folding using the HP model

Santos¹,

Villot²,

Diéguez³

2013

2013 IEEE Congress on Evolutionary Computation

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“…An ant colony optimisation algorithm (ACO) for the PSP using both 2D and 3DHP models was presented by Shmygelska and Hoos (2005). Local search methods such as Monte Carlo, tabu search and hill-climbing were used as genetic operators for genetic algorithms by Cox et al (2004), Jiang et al (2003) and Tantar et al (2007), respectively.…”

Section: Protein Structure Predictionmentioning

confidence: 99%

Population-based harmony search using GPU applied to protein structure prediction

Scalabrin

Parpinelli

Benítez

et al. 2014

IJCSE

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This work presents a new evolutionary algorithm based on the standard harmony search strategy, called population-based harmony search (PBHS). Also, this work provides a parallelisation method for the proposed PBHS by using graphical processing units (GPU), allowing multiple function evaluations at the same time. Experiments were done using a benchmark of a hard scientific problem: protein structure prediction with the AB-2D off-lattice model. The performance and the solution quality were evaluated and compared using four implementations: two concerning the standard HS, one running in CPU and another running in GPU, and two implementations concerning the PBHS, also running in CPU and in GPU. Results show that the quality of solutions and speed-ups achieved by the PBHS is significantly better than the HS.

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“…[27] Predictions are also made of molecular structure, from nanoclusters[21, 28–30] through to protein folding. [31, 32]…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Development and optimization of a novel genetic algorithm for identifying nanoclusters from scanning transmission electron microscopy images

Logsdail

Johnston

2011

J Comput Chem

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Whilst technological advancements have allowed imaging at atomic resolution using scanning transmission electron microscopy (STEM), identification of nanocluster structures has proven difficult due to their low thermal stability, and often resultant low-symmetry. In this work, we look at a novel solution to this problem using a genetic algorithm (GA). GAs are search methods for the minimization of statistical problems based on natural evolution. We develop a STEM model first described by Curley et al. (2007) and, using high-symmetry cluster structures as test subjects, look at the effectiveness and efficiency of the GA at optimizing orientation parameters for a cluster when compared to a model solution. We find for a 309-atom icosahedron that a random minimizing search would prove more efficient than a GA; however, for a 309-atom decahedron the GA becomes more effective and efficient than a random search. We predict that as we continue to lower symmetry of our test cases, we will find the GA becomes even more efficient at optimizing this otherwise computationally expensive problem.

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Development and optimisation of a novel genetic algorithm for studying model protein folding

Cited by 25 publications

References 1 publication

Cellular automata for modeling protein folding using the HP model

Cellular automata for modeling protein folding using the HP model

Population-based harmony search using GPU applied to protein structure prediction

Development and optimization of a novel genetic algorithm for identifying nanoclusters from scanning transmission electron microscopy images

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