Predictive analytics for biomineralization peptide binding affinity
The rational design of biomineralization peptides for the synthesis of inorganic nanomaterials remains a challenging endeavor in biomimetics. The difficulty arises from the multiple factors that influence the affinity of the peptide towards a particular surface. This study presents classification an...
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oai:animorepository.dlsu.edu.ph:faculty_research-33042021-08-23T08:39:35Z Predictive analytics for biomineralization peptide binding affinity Janairo, Jose Isagani B. The rational design of biomineralization peptides for the synthesis of inorganic nanomaterials remains a challenging endeavor in biomimetics. The difficulty arises from the multiple factors that influence the affinity of the peptide towards a particular surface. This study presents classification and regression models of biomineralization peptide binding affinity for a gold surface using support vector machine. It was found that the Kidera factors, in particular those related to the extended structure preference, partial specific volume, flat extended preference, and pK-C of the peptide, are important descriptors to predict biomineralization peptide binding affinity. The classification model exhibited an overall prediction accuracy of 90% and 83% for the regression model. This highlights the reliability and accuracy of the formulated models, while requiring a reasonable number of descriptors. The created predictive models are steps in the right direction towards the further development of rational biomineralization peptide design. © 2018, Springer Science+Business Media, LLC, part of Springer Nature. 2019-03-15T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/2305 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3304/type/native/viewcontent Faculty Research Work Animo Repository Biomimetics Biomineralization Peptides Support vector machines Chemical Engineering |
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Biomimetics Biomineralization Peptides Support vector machines Chemical Engineering Janairo, Jose Isagani B. Predictive analytics for biomineralization peptide binding affinity |
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The rational design of biomineralization peptides for the synthesis of inorganic nanomaterials remains a challenging endeavor in biomimetics. The difficulty arises from the multiple factors that influence the affinity of the peptide towards a particular surface. This study presents classification and regression models of biomineralization peptide binding affinity for a gold surface using support vector machine. It was found that the Kidera factors, in particular those related to the extended structure preference, partial specific volume, flat extended preference, and pK-C of the peptide, are important descriptors to predict biomineralization peptide binding affinity. The classification model exhibited an overall prediction accuracy of 90% and 83% for the regression model. This highlights the reliability and accuracy of the formulated models, while requiring a reasonable number of descriptors. The created predictive models are steps in the right direction towards the further development of rational biomineralization peptide design. © 2018, Springer Science+Business Media, LLC, part of Springer Nature. |
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Janairo, Jose Isagani B. |
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Janairo, Jose Isagani B. |
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Janairo, Jose Isagani B. |
title |
Predictive analytics for biomineralization peptide binding affinity |
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Predictive analytics for biomineralization peptide binding affinity |
title_full |
Predictive analytics for biomineralization peptide binding affinity |
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Predictive analytics for biomineralization peptide binding affinity |
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Predictive analytics for biomineralization peptide binding affinity |
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predictive analytics for biomineralization peptide binding affinity |
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2019 |
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https://animorepository.dlsu.edu.ph/faculty_research/2305 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3304/type/native/viewcontent |
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