采用高斯过程模拟预测域/肽识别和相互作用

Ren, Yanrong; Chen, Shaocheng; Xiaochuan, Zou; Tian, Feifei; Zhou, Peng

doi:10.1360/032012-335

Cited by 3 publications

(1 citation statement)

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“…The domain-peptide binding affinity data used in this study were collected from a number of published works (Landgraf et al, 2004;Kofler et al, 2005;Vouilleme et al, 2010;Panni et al, 2011;Ren et al, 2012a). The primary sequences of these peptides and their binding strength (quantified by logBLU) to corresponding domains are summarized in Supporting Information (SI) Tables S1-S4.…”

Section: Peptide Data Setsmentioning

confidence: 99%

Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models

et al. 2013

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Section: Peptide Data Setsmentioning

confidence: 99%

Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models

et al. 2013

Self Cite

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Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression

Uslan

Şeker

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domain-mediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings. In this study, domain-peptide binding affinity prediction models are proposed based on support vector regression. Proposed models are applied to yeast bmh 14-3-3 and syh GYF peptide-recognition domains. The cross validated results of the domain-peptide binding affinity data sets show that predictive performance of the support vector based models are efficient.

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Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

Miao

Yan

et al. 2019

CDM

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Background: Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention. Methods: We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods. Results: Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed. Conclusion: There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

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采用高斯过程模拟预测域/肽识别和相互作用

Cited by 3 publications

References 0 publications

Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models

Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models

Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

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