Identifying Affinity Classes of Inorganic Materials Binding Sequences via a Graph-Based Model

Sequence rules for gold-binding peptides

Metal-binding peptides play a central role in bionanotechnology, wherein they are responsible for directing growth and influencing the resulting properties of inorganic nanomaterials. One of the key advantages of using peptides to create nanomaterials is their versatility, wherein subtle changes in the sequence can have a dramatic effect on the structure and properties of the nanomaterial. However, precisely knowing which position and which amino acid should be modified within a given sequence to enhance a specific property can be a daunting challenge owing to combinatorial complexity. In this study, classification based on association rules was performed using 860 gold-binding peptides. Using a minimum support threshold of 0.035 and confidence of 0.9, 30 rules with confidence and lift values greater than 0.9 and 1, respectively, were extracted that can differentiate high-binding from low-binding peptides. The test performance of these rules for categorizing the peptides was found to be satisfactory, as characterized by accuracy = 0.942, F₁ = 0.941, MCC = 0.884. What stands out from the extracted rules are the importance of tryptophan and arginine residues in differentiating peptides with high binding affinity from those with low affinity. In addition, the association rules revealed that positions 2 and 4 within a decapeptide are frequently involved in the rules, thus suggesting their importance in influencing peptide binding affinity to AuNPs. Collectively, this study identified sequence rules that may be used to design peptides with high binding affinity.

Machine Learning Model for Biomimetic Chromatography Peptide Ligands

Purification is an essential part of antibody production, which are important therapeutic biomolecules. Common methods of antibody purification rely on affinity chromatography (AC), wherein whole proteins are oftentimes used as ligands to catch the antibodies to be purified. While AC has been successful in purifying antibodies, it is associated with multiple challenges such as high cost and low stability, among others. A promising alternative is using short peptide sequences in place of whole proteins as the stationary phase for the chromatographic separation of the antibodies. In an effort to accelerate the discovery and development of short peptides for biomimetic chromatography, this study reports the creation of a machine learning classification which was trained and tested on 480 tetrapeptides. The optimized logistic regression model uses Cruciani properties as the input variables and can categorize peptides into one of two classes based on their binding affinity with immunoglobulin G (IgG). The externally validated model demonstrates satisfactory predictive performance and excellent discrimination as demonstrated by performance metrics such as AUC = 0.874, Balanced Accuracy = 0.874, F1 = 0.871, Precision = 0.884, and Recall = 0.859. Apart from this, the classifier has also provided valuable insights into important variables that influence the classification, such as electrostatic and hydrophobic interactions. Overall, the classifier can be regarded as a welcome development for biomimetic chromatography and is the first study that aims to integrate machine learning in the biomimetic chromatography peptide development process.

A Machine Learning Classification Model for Gold-Binding Peptides

There has been growing interest in using peptides for the controlled synthesis of nanomaterials. Peptides play a crucial role not only in regulating the nanostructure formation process but also in influencing the resulting properties of the nanomaterials. Leveraging machine learning (ML) in the biomimetic workflow is anticipated to accelerate peptide discovery, make the process more resource-efficient, and unravel associations among attributes that may be useful in peptide design. In this study, a binary ML classifier is formulated that was trained and tested on 1720 peptide examples. The support vector machine classifier uses Kidera factors to categorize peptides into one of two groups based on their binding ability. The classifier exhibits satisfactory performance, as demonstrated by various performance metrics. In addition, key variables that bear a huge impact on the model were identified, such as peptide hydrophobicity. As these trends were derived from a large and diverse dataset, the insights drawn from the data are expected to be generalizable and robust. Thus, the presented ML model is an important step toward the rational and predictive peptide design.

Tuning Materials-Binding Peptide Sequences toward Gold- and Silver-Binding Selectivity with Bayesian Optimization

Peptide sequence engineering can potentially deliver materials-selective binding capabilities, which would be highly attractive in numerous biotic and abiotic nanomaterials applications. However, the number of known materials-selective peptide sequences is small, and identification of new sequences is laborious and haphazard. Previous attempts have sought to use machine learning and other informatics approaches that rely on existing data sets to accelerate the discovery of materials-selective peptides, but too few materials-selective sequences are known to enable reliable prediction. Moreover, this knowledge base is expensive to expand. Here, we combine a comprehensive and integrated experimental and modeling effort and introduce a Bayesian Effective Search for Optimal Sequences (BESOS) approach to address this challenge. Through this combined approach, we significantly expand the data set of Au-selective peptide sequences and identify an additional Ag-selective peptide sequence. Analysis of the binding motifs for the Ag-binders offers a roadmap for future prediction with machine learning, which should guide identification of further Ag-selective sequences. These discoveries will enable wider and more versatile integration of Ag nanoparticles in biological platforms.

Enhanced Hyperbox Classifier Model for Nanomaterial Discovery

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.