A Machine Learning Classification Model for Gold-Binding Peptides

Engineered M13-Derived Bacteriophages Capable of Gold Nanoparticle Synthesis and Nanogold Manipulations

For years, gold nanoparticles (AuNPs) have been widely used in medicine and industry. Although various experimental procedures have been reported for their preparation and manipulation, none of them is optimal for all purposes. In this work, we engineered the N-terminus of the pIII minor coat protein of bacteriophage (phage) M13 to expose a novel HLYLNTASTHLG peptide that effectively and specifically binds gold. In addition to binding gold, this engineered phage could synthesize spherical AuNPs of 20 nm and other sizes depending on the reaction conditions, aggregate them, and precipitate gold from a colloid, as revealed by transmission electron microscopy (TEM), atomic force microscopy (AFM), and scanning electron microscopy (SEM), as well as ultraviolet-visible (UV-vis) and Fourier-transform infrared (FTIR) spectroscopic methods. We demonstrated that the engineered phage exposing a foreign peptide selected from a phage-displayed library may serve as a sustainable molecular factory for both the synthesis of the peptide and the subsequent overnight preparation of AuNPs from gold ions at room temperature and neutral pH in the absence of strong reducing agents, such as commonly used NaBH4. Taken together, the results suggest the potential applicability of the engineered phage and the new, in vitro-identified gold-binding peptide in diverse biomimetic manipulations.

Material-specific binding peptides empower sustainable innovations in plant health, biocatalysis, medicine and microplastic quantification

Material-binding peptides (MBPs) have emerged as a diverse and innovation-enabling class of peptides in applications such as plant-/human health, immobilization of catalysts, bioactive coatings, accelerated polymer degradation and analytics for micro-/nanoplastics quantification. Progress has been fuelled by recent advancements in protein engineering methodologies and advances in computational and analytical methodologies, which allow the design of, for instance, material-specific MBPs with fine-tuned binding strength for numerous demands in material science applications. A genetic or chemical conjugation of second (biological, chemical or physical property-changing) functionality to MBPs empowers the design of advanced (hybrid) materials, bioactive coatings and analytical tools. In this review, we provide a comprehensive overview comprising naturally occurring MBPs and their function in nature, binding properties of short man-made MBPs (<20 amino acids) mainly obtained from phage-display libraries, and medium-sized binding peptides (20-100 amino acids) that have been reported to bind to metals, polymers or other industrially produced materials. The goal of this review is to provide an in-depth understanding of molecular interactions between materials and material-specific binding peptides, and thereby empower the use of MBPs in material science applications. Protein engineering methodologies and selected examples to tailor MBPs toward applications in agriculture with a focus on plant health, biocatalysis, medicine and environmental monitoring serve as examples of the transformative power of MBPs for various industrial applications. An emphasis will be given to MBPs' role in detecting and quantifying microplastics in high throughput, distinguishing microplastics from other environmental particles, and thereby assisting to close an analytical gap in food safety and monitoring of environmental plastic pollution. In essence, this review aims to provide an overview among researchers from diverse disciplines in respect to material-(specific) binding of MBPs, protein engineering methodologies to tailor their properties to application demands, re-engineering for material science applications using MBPs, and thereby inspire researchers to employ MBPs in their research.

Characterization and Classification In Silico of Peptides with Dual Activity (Antimicrobial and Wound Healing)

The growing challenge of chronic wounds and antibiotic resistance has spotlighted the potential of dual-function peptides (antimicrobial and wound healing) as novel therapeutic strategies. The investigation aimed to characterize and correlate in silico the physicochemical attributes of these peptides with their biological activity. We sourced a dataset of 207 such peptides from various peptide databases, followed by a detailed analysis of their physicochemical properties using bioinformatic tools. Utilizing statistical tools like clustering, correlation, and principal component analysis (PCA), patterns and relationships were discerned among these properties. Furthermore, we analyzed the peptides' functional domains for insights into their potential mechanisms of action. Our findings spotlight peptides in Cluster 2 as efficacious in wound healing, whereas Cluster 1 peptides exhibited pronounced antimicrobial potential. In our study, we identified specific amino acid patterns and peptide families associated with their biological activities, such as the cecropin antimicrobial domain. Additionally, we found the presence of polar amino acids like arginine, cysteine, and lysine, as well as apolar amino acids like glycine, isoleucine, and leucine. These characteristics are crucial for interactions with bacterial membranes and receptors involved in migration, proliferation, angiogenesis, and immunomodulation. While this study provides a groundwork for therapeutic development, translating these findings into practical applications necessitates additional experimental and clinical research.

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.