Machine Learning‐Assisted Clustering of Nanoparticle‐Binding Peptides and Prediction of Their Properties

Abstract

Bioinspired and biomimetic nanostructures have attracted tremendous interest for theranostic and nanomedicine applications. Among the strategies employed to synthesize these nanostructures, surface functionalization and biomineralization of nanomaterials using peptides stand out due to the wide availability of peptides and their variations as well as the ease of modification process. Effective peptide‐based modification of nanomaterials relies on preferential and strong binding between peptides and target nanomaterials. Therefore, the discovery and design of specific peptides with high binding affinity to nanomaterials are essential. Unfortunately, conventional peptide screening methods suffer from shortcomings which render peptide discovery time‐consuming, expensive, and cumbersome. Herein, leveraging unsupervised and supervised machine learning, a framework to accelerate peptide analysis is presented. Specifically, more than 1700 nanoparticle‐binding peptides are classified into peptide clusters to identify important peptide features to realize higher‐affinity binding. In addition, the binding and biomineralization properties of peptides are predicted with high classification accuracy, precision, and recall. This work then proposes the use of unsupervised k‐means clustering and supervised k‐nearest neighbors algorithms for grouping peptides and predicting their properties, respectively. It is anticipated that the framework originated from this study will further facilitate the rational selection and design of peptides for engineering functional bioinspired and biomimetic nanostructures.

Peptide-Hitchhiking for the Development of Nanosystems in Glioblastoma

“…The approach developed in this study has the potential for future applications in targeting diseases such as GBM by enabling precise peptide selection and design. 371 Furthermore, AI techniques have the potential to extract data-driven insights from omics data and analyze patientspecific information. This can assist in designing peptides that are customized to individual patients' tumor profiles.…”

“…Therefore, the performance of the supervised models was assessed in terms of metrics, demonstrating that supervised learning models such as notably logistic regression, decision tree, random forest, k-nearest neighbors, naïve Bayes, support vector machine, and neural network could effectively predict peptide binding properties. The approach developed in this study has the potential for future applications in targeting diseases such as GBM by enabling precise peptide selection and design …”

“…The approach developed in this study has the potential for future applications in targeting diseases such as GBM by enabling precise peptide selection and design. 371 …”