, Volume 9, Issue 1, pp 74–78 | Cite as

Predictive Analytics for Biomineralization Peptide Binding Affinity

  • Jose Isagani B. JanairoEmail author


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.


Support vector machine Classification and regression algorithm Biomimetics Metal-binding peptides 



This study was made possible through Data Science Track of the Department of Science and Technology–Innovation Council (DOST-PCIEERD).

Supplementary material

12668_2018_578_MOESM1_ESM.xlsx (15 kb)
ESM 1 (XLSX 15 kb)


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Biology DepartmentDe La Salle UniversityManilaPhilippines

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