Protein structure prediction with local adjust tabu search algorithm

Lin, Xiao; Zhang, Xiaolong; Zhou, Fang

doi:10.1186/1471-2105-15-s15-s1

Cited by 25 publications

(4 citation statements)

References 23 publications

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“…Lin et al. (2014) introduce a combination of SA and TS algorithms in a method (SATS). Various strategies are adopted successfully for high‐speed searching the optimal conformation.…”

Section: Protein Structure Predictionmentioning

confidence: 99%

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On the role of metaheuristic optimization in bioinformatics

Calvet

Benito

Juan

et al. 2022

Int Trans Operational Res

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Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.

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“…Lin et al. (2014) introduce a combination of SA and TS algorithms in a method (SATS). Various strategies are adopted successfully for high‐speed searching the optimal conformation.…”

Section: Protein Structure Predictionmentioning

confidence: 99%

“…Generic representation of a hypothetic 9‐mer protein structure with its bended angles (Lin et al., 2014). …”

Section: Protein Structure Predictionmentioning

confidence: 99%

On the role of metaheuristic optimization in bioinformatics

Calvet

Benito

Juan

et al. 2022

Int Trans Operational Res

View full text Add to dashboard Cite

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“…Researches combined two or more algorithms in order to develop hybrid algorithms that can obtain better results in comparison with the original algorithms. In [22], the authors combined simulated annealing and the tabu search algorithm. This algorithm was improved additionally with a local adjust strategy that improves the accuracy and speed of searching.…”

Section: Metaheuristic Optimization Algorithmsmentioning

confidence: 99%

Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model

Bošković

Brest

2016

J Mol Model

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This paper presents a differential evolution algorithm that is adapted for the protein folding optimization on a three-dimensional AB off-lattice model. The proposed algorithm is based on a self-adaptive differential evolution that improves the algorithm efficiency and reduces the number of control parameters. A mutation strategy for the fast convergence is used inside the algorithm. A temporal locality is used in order to speed up the algorithm convergence additionally and to find amino-acid conformations with the lowest free energy values. Within this mechanism a new vector is calculated when the trial vector is better than the corresponding vector from the population. This new vector is likely better than the trial vector and this accelerates convergence speed. Because of the fast convergence the algorithm has some chance to be trapped into the local optima. To mitigate this problem the algorithm includes reinitialization. The proposed algorithm was tested on amino-acid sequences that are used frequently in literature. The obtained results show that the proposed algorithm is superior to the algorithms from the literature and the obtained amino-acid sequences have significantly lower free energy values. Graphical Abstract Protein folding optimization on a three-dimensional AB off-lattice model using the differential evolution algorithm.

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“…Recognizing the interaction of proteins can reveal the mechanism of protein activities and promote the development of biotechnology and life science [ 3 , 4 ]. Studying the function and structure of proteins is very important for understanding various life activities [ 5 , 6 ]. At present, the functions of many proteins are still unclear.…”

Section: Introductionmentioning

confidence: 99%

Identification of hot regions in hub protein–protein interactions by clustering and PPRA optimization

Lin

Zhang

2021

BMC Med Inform Decis Mak

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Background Protein–protein interactions (PPIs) are the core of protein function, which provide an effective means to understand the function at cell level. Identification of PPIs is the crucial foundation of predicting drug-target interactions. Although traditional biological experiments of identifying PPIs are becoming available, these experiments remain to be extremely time-consuming and expensive. Therefore, various computational models have been introduced to identify PPIs. In protein-protein interaction network (PPIN), Hub protein, as a highly connected node, can coordinate PPIs and play biological functions. Detecting hot regions on Hub protein interaction interfaces is an issue worthy of discussing. Methods Two clustering methods, LCSD and RCNOIK are used to detect the hot regions on Hub protein interaction interfaces in this paper. In order to improve the efficiency of K-means clustering algorithm, the best k value is selected by calculating the distance square sum and the average silhouette coefficients. Then, the optimization of residue coordination number strategy is used to calculate the average coordination number. In addition, the pair potentials and relative ASA (PPRA) strategy is also used to optimize the predicted results. Results DataHub dataset and PartyHub dataset were used to train two clustering models respectively. Experiments show that LCSD and RCNOIK have the same coverage with Hub protein datasets, and RCNOIK is slightly higher than LCSD in Precision. The predicted hot regions are closer to the standard hot regions. Conclusions This paper optimizes two clustering methods based on PPRA strategy. Compared our methods for hot regions prediction against the well-known approaches, our improved methods have the higher reliability and are effective for predicting hot regions on Hub protein interaction interfaces.

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Protein structure prediction with local adjust tabu search algorithm

Cited by 25 publications

References 23 publications

On the role of metaheuristic optimization in bioinformatics

On the role of metaheuristic optimization in bioinformatics

Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model

Identification of hot regions in hub protein–protein interactions by clustering and PPRA optimization

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