Predictive analytics for biomineralization peptide binding affinity

College

Gokongwei College of Engineering

Department/Unit

Chemical Engineering

Document Type

Article

Source Title

BioNanoScience

Volume

9

Issue

1

First Page

74

Last Page

78

Publication Date

3-15-2019

Abstract

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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Digitial Object Identifier (DOI)

10.1007/s12668-018-0578-4

Disciplines

Chemical Engineering

Keywords

Biomimetics; Biomineralization; Peptides; Support vector machines

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