Protein Folding Problem in the Case of Peptides Solved by Hybrid Simulated Annealing Algorithms

2018 ◽  
pp. 141-152
Author(s):  
Anylu Melo-Vega ◽  
Juan Frausto-Solís ◽  
Guadalupe Castilla-Valdez ◽  
Ernesto Liñán-García ◽  
Juan Javier González-Barbosa ◽  
...  
Keyword(s):  
Protein Folding ◽  
Simulated Annealing ◽  
Hybrid Simulated Annealing ◽  
Annealing Algorithms

scholarly journals GRSA Enhanced for Protein Folding Problem in the Case of Peptides

Axioms ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 136
Author(s):  
Juan Frausto-Solís ◽  
Juan Paulo Sánchez-Hernández ◽  
Fanny G. Maldonado-Nava ◽  
Juan J. González-Barbosa
Keyword(s):  
Amino Acids ◽  
Protein Folding ◽  
Simulated Annealing ◽  
Optimization Problem ◽  
Golden Ratio ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
First Case ◽  
Hybrid Simulated Annealing ◽  
Annealing Algorithms

Protein folding problem (PFP) consists of determining the functional three-dimensional structure of a target protein. PFP is an optimization problem where the objective is to find the structure with the lowest Gibbs free energy. It is significant to solve PFP for use in medical and pharmaceutical applications. Hybrid simulated annealing algorithms (HSA) use a kind of simulated annealing or Monte Carlo method, and they are among the most efficient for PFP. The instances of PFP can be classified as follows: (a) Proteins with a large number of amino acids and (b) peptides with a small number of amino acids. Several HSA have been positively applied for the first case, where I-Tasser has been one of the most successful in the CASP competition. PEP-FOLD3 and golden ratio simulated annealing (GRSA) are also two of these algorithms successfully applied to peptides. This paper presents an enhanced golden simulated annealing (GRSA2) where soft perturbations (collision operators), named “on-wall ineffective collision” and “intermolecular ineffective collision”, are applied to generate new solutions in the metropolis cycle. GRSA2 is tested with a dataset for peptides previously proposed, and a comparison with PEP-FOLD3 and I-Tasser is presented. According to the experimentation, GRSA2 has an equivalent performance to those algorithms.

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (4) ◽  
pp. 373
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

CKMSA: an Anomaly Detection Process Based on K-Means and Simulated Annealing Algorithms

2016 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki
Keyword(s):  
Simulated Annealing ◽  
Anomaly Detection ◽  
Detection Process ◽  
Annealing Algorithms

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

scholarly journals A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing

2021 ◽  
Vol 26 (2) ◽  
pp. 39
Author(s):  
Juan P. Sánchez-Hernández ◽  
Juan Frausto-Solís ◽  
Juan J. González-Barbosa ◽  
Diego A. Soto-Monterrubio ◽  
Fanny G. Maldonado-Nava ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Ab Initio ◽  
Primary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Golden Ratio ◽  
Small Peptides ◽  
Hybrid Simulated Annealing ◽  
Ab Initio Models

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

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