RMSD Protein Tertiary Structure Prediction with Soft Computing

Iraji, Mohammad Saber; Ameri, Hakimeh

doi:10.5815/ijmsc.2016.02.03

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Apart from being used to predict the protein structure, RMSD can also predict the structure of non-protein molecules. RMSD is an indicator in the structure prediction algorithm ( Mohammad and Hakimeh, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism

Liu

Yang

et al. 2022

Front. Genet.

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Background: Macromolecule structure prediction remains a fundamental challenge of bioinformatics. Over the past several decades, the Rosetta framework has provided solutions to diverse challenges in computational biology. However, it is challenging to model RNA tertiary structures effectively when the de novo modeling of RNA involves solving a well-defined small puzzle.Methods: In this study, we introduce a stepwise Monte Carlo parallelization (SMCP) algorithm for RNA tertiary structure prediction. Millions of conformations were randomly searched using the Monte Carlo algorithm and stepwise ansatz hypothesis, and SMCP uses a parallel mechanism for efficient sampling. Moreover, to achieve better prediction accuracy and completeness, we judged and processed the modeling results.Results: A benchmark of nine single-stranded RNA loops drawn from riboswitches establishes the general ability of the algorithm to model RNA with high accuracy and integrity, including six motifs that cannot be solved by knowledge mining–based modeling algorithms. Experimental results show that the modeling accuracy of the SMCP algorithm is up to 0.14 Å, and the modeling integrity on this benchmark is extremely high.Conclusion: SMCP is an ab initio modeling algorithm that substantially outperforms previous algorithms in the Rosetta framework, especially in improving the accuracy and completeness of the model. It is expected that the work will provide new research ideas for macromolecular structure prediction in the future. In addition, this work will provide theoretical basis for the development of the biomedical field.

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Section: Methodsmentioning

confidence: 99%

Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism

Liu

Yang

et al. 2022

Front. Genet.

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“…Root mean squared deviation (RMSD) is widely used as a metric for measuring the deviation of protein structures from their native protein structures (Iraji and Ameri, 2016). In this analysis, our goal is to construct a classifier and predict whether the root mean squared deviation is greater than ten or not.…”

Section: Real Data Analysismentioning

confidence: 99%

Leverage Classifier: Another Look at Support Vector Machine

Han,

Yu,

Zhang

et al. 2025

STAT SINICA

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Support vector machine (SVM) is a popular classifier known for accuracy, flexibility, and robustness. However, its intensive computation has hindered its application to large-scale datasets. In this paper, we propose a new optimal leverage classifier based on linear SVM under a nonseparable setting. Our classifier aims to select an informative subset of the training sample to reduce data size, enabling efficient computation while maintaining high accuracy. We take a novel view of SVM under the general subsampling framework and rigorously investigate the statistical properties. We propose a two-step subsampling procedure consisting of a pilot estimation of the optimal subsampling probabilities and a subsampling step to construct the classifier. We develop a new Bahadur representation of the SVM coefficients and derive unconditional asymptotic distribution and optimal subsampling probabilities without giving the full sample. Numerical results demonstrate that our classifiers outperform the existing methods in terms of estimation, computation, and prediction.

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