Enhanced Hyperbox Classifier Model for Nanomaterial Discovery

Abstract

Machine learning tools can be applied to peptide-mediated biomineralization, which is an emerging biomimetic technique of creating functional nanomaterials. In particular, they can be used for the discovery of biomineralization peptides, which currently relies on combinatorial enumeration approaches. In this work, an enhanced hyperbox classifier is developed which can predict if a given peptide sequence has a strong or weak binding affinity towards a gold surface. A mixed-integer linear program is formulated to generate the rule-based classification model. The classifier is optimized to account for false positives and false negatives, and clearly articulates how the classification decision is made. This feature makes the decision-making process transparent, and the results easy to interpret for decision support. The method developed can help accelerate the discovery of more biomineralization peptide sequences, which may expand the utility of peptide-mediated biomineralization as a means for nanomaterial synthesis.

Sequence rules for gold-binding peptides

“…Thus, past studies that created ML models for metal-binding peptides used datasets composed of less than 100 peptides. 18–20…”

“…Thus, past studies that created ML models for metal-binding peptides used datasets composed of less than 100 peptides. [18][19][20] The goal of CBA is to identify a rule in the form of an association that is valuable, providing insights about the dataset that were previously unknown and likely difficult to explicitly express. Contextualizing this goal into the present research translates into searching the dataset of decapeptides to nd patterns in the amino acid composition and the peptide position that are associated with strong binding affinity.…”

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

“…At the molecular level, helical protein conformations can form interval distributions of acidic and basic residues that enhance clustering of calcium and phosphate ions to induce precipitation . Machine learning models have also been applied toward investigation of biomineralization processes, such as in prediction of biomineralization proteins. , For example, an SVR was trained to predict peptide binding affinity for a gold surface based on three classes of peptide descriptors, ultimately finding that the Kidera factors (helix preference, side chain size, extended structure preference, etc.) were the most important descriptors and achieving 83% prediction accuracy .…”

A Machine Learning Classification Model for Gold-Binding Peptides

“…Previous works have reported the creation of ML models that aim to assist the peptide screening process by predicting the peptide binding affinity toward a particular substrate, 7 classifying a given sequence if it is a strong binder or not, 8 , 9 and the creation of novel algorithms to predict the peptide binding phenomenon. 10 However, these studies have used relatively small datasets, wherein the number of peptides used for training and testing the algorithm is less than 50. Considering that the predictive success of such ML models relies on the quality and quantity of the data used for training, 11 , 12 utilizing a larger dataset for training may lead to the creation of ML models that are more robust and the observed trends more generalizable.…”

“…ML can minimize trial-and-error by quickly identifying potential sequences that can be used for the biomimetic nanomaterial synthesis. Previous works have reported the creation of ML models that aim to assist the peptide screening process by predicting the peptide binding affinity toward a particular substrate, classifying a given sequence if it is a strong binder or not, , and the creation of novel algorithms to predict the peptide binding phenomenon . However, these studies have used relatively small datasets, wherein the number of peptides used for training and testing the algorithm is less than 50.…”