FPDock: Protein–protein docking using flower pollination algorithm

Sunny, Sharon; Jayaraj, P. B.

doi:10.1016/j.compbiolchem.2021.107518

Cited by 8 publications

(5 citation statements)

References 38 publications

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Section: Experimental and Parameters Settingmentioning

confidence: 99%

“…The experimental results obtained from the proposed MHHO and different evolutionary-based ELM trainers on breast dataset. (SSA) (Tubishat et al, 2021), Harris hawks optimization (HHO), particle swarm optimization algorithm (PSO) (Song et al, 2021), Differential evolution (DE) (Deng et al, 2021), and gray wolf optimization algorithm (GWO) (Dadashzadeh et al, 2021). To ensure a fair comparison, we kept the population size consistent at 100 individuals for all algorithms.…”

Section: Experimental and Parameters Settingmentioning

confidence: 99%

“…To assess the performance of our proposed MHHO, we conducted an extensive set of experiments by comparing it with seven widely used metaheuristics, including flower pollination algorithm (FPA) (Sunny & Jayaraj, 2021), Bat algorithm (Alsalibi et al, 2021), salp swarm algorithm (SSA) (Tubishat et al, 2021), Harris hawks optimization (HHO), particle swarm optimization algorithm (PSO) (Song et al, 2021), Differential evolution (DE) (Deng et al, 2021), and gray wolf optimization algorithm (GWO) (Dadashzadeh et al, 2021). To ensure a fair comparison, we kept the population size consistent at 100 individuals for all algorithms.…”

Section: Experimental Settingsmentioning

confidence: 99%

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An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis

Al Bataineh,

Jalali,

Mousavirad

et al. 2024

Expert Systems

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Integrating machine learning techniques into medical diagnostic systems holds great promise for enhancing disease identification and treatment. Among the various options for training such systems, the extreme learning machine (ELM) stands out due to its rapid learning capability and computational efficiency. However, the random selection of input weights and hidden neuron biases in the ELM can lead to suboptimal performance. To address this issue, our study introduces a novel approach called modified Harris hawks optimizer (MHHO) to optimize these parameters in ELM for medical classification tasks. By applying the MHHO‐based method to seven medical datasets, our experimental results demonstrate its superiority over seven other evolutionary‐based ELM trainer models. The findings strongly suggest that the MHHO approach can serve as a valuable tool for enhancing the performance of ELM in medical diagnosis.

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Section: Experimental and Parameters Settingmentioning

confidence: 99%

Section: Experimental and Parameters Settingmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

See 1 more Smart Citation

An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis

Al Bataineh,

Jalali,

Mousavirad

et al. 2024

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Computational docking methods can be generally divided into classical docking software and deep learning-based methods (Biesiada et al, 2011; Torchala et al, 2013; Vakser, 2014; Schindler et al, 2017; Weng et al, 2019; Yan et al, 2020; Sunny and Jayaraj, 2021; Christoffer et al, 2021). Specifically, classical docking software is typically very slow and follows a three-step framework of candidate sampling, ranking (Launay et al, 2020; Eismann et al, 2021), and refinement (Verburgt and Kihara, 2022).…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

xTrimoDock: Rigid Protein Docking via Cross-Modal Representation Learning and Spectral Algorithm

Luo¹,

Li²,

Sun³

et al. 2023

Preprint

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Protein-protein interactions are the basis for the formation of protein complexes which are essential for almost all cellular processes. Knowledge of the structures of protein complexes is of major importance for understanding the biological function of these protein-protein interactions and designing protein drugs. Here we address the problem of rigid protein docking which assumes no deformation of the involved proteins during interactions. We develop a method called, xTrimoDock, which leverages a cross-modal representation learning to predict the protein distance map, and then uses a spectral initialization and gradient descent to obtain the roto-translation transformation for docking. We show that, on antibody heavy-chain and light-chain docking, and antibody-antigen docking, xTrimoDock consistently outperforms the state-of-the-art such as AlphaFold-Multimer and HDock, and can lead to as much as a 10% improvement in DockQ metric. xTrimoDock has been applied as a useful tool in protein drug design at BioMap.

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“…Notably, approaches for efficient execution of this global search are key in rigid-body docking. While algorithms based on fast-Fourier-transform (FFT) − are commonly utilized for this purpose; the Monte Carlo method , or genetic algorithms , are also sometimes employed. Another vital aspect of protein–protein docking is the criterion for judging the appropriateness of the complex structure, which is also referred to as the scoring function.…”

Section: Introductionmentioning

confidence: 99%

Quantum Chemistry-Based Protein–Protein Docking without Empirical Parameters

Kousaka,

Ishikawa

2024

J. Chem. Theory Comput.

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This study developed a novel protein−protein docking approach based on quantum chemistry. To judge the appropriateness of complex structures, we introduced two criterion values, EV1 and EV2, computed using the fragment molecular orbital method without any empirical parameters. These criterion values enable us to search complex structures in which patterns of the electrostatic potential of the two proteins are optimally aligned at their interface. The performance of our method was validated using 53 complexes in a benchmark set provided for protein−protein docking. When employing bound state structures, docking success rates reached 64% for EV1 and 76% for EV2. On the other hand, when employing unbound state structures, docking success rates reached 13% for EV1 and 17% for EV2.

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FPDock: Protein–protein docking using flower pollination algorithm

Cited by 8 publications

References 38 publications

An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis

An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis

xTrimoDock: Rigid Protein Docking via Cross-Modal Representation Learning and Spectral Algorithm

Quantum Chemistry-Based Protein–Protein Docking without Empirical Parameters

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