Oligomerization enhances the binding affinity of a silver biomineralization peptide and catalyzes nanostructure formation

Biomineralization through a Symmetry-Controlled Oligomeric Peptide

Biomineralization peptides are versatile tools for generating nanostructures since they can make specific interactions with various inorganic metals, which can lead to the formation of intricate nanostructures. Previously, we examined the influence that multivalency has on inorganic structures formed by p53 tetramer-based biomineralization peptides and noted a connection between the geometry of the peptide and its ability to regulate nanostructure formation. To investigate the role of multivalency in nanostructure formation by biomineralization peptides more thoroughly, silver biomineralization peptides were engineered by linking them to additional self-assembling molecules based on coiled-coil peptides and multistranded DNA oligomers. Under mild reducing conditions at room temperature, these engineered biomineralization peptides self-assembled and formed silver nanostructures. The trimeric forms of the biomineralization peptides were the most efficient in forming a hexagonal disk nanostructure, with both the coiled-coil peptide and DNA-based multimeric forms. Together, the results suggest that the spatial arrangement of biomineralization peptides plays a more important role in regulating nanostructure formation than their valency.

Applications of Material-Binding Peptides: A Review

Material-binding peptides (MBPs) are functionalized adhesive materials consisting of a few to several dozen amino acids. This affinity between MBPs and materials is regulated by multiple interactions, including hydrogen bonding, electrostatic, hydrophobic interactions, and π-π stacking. They show selective binding and high affinity to a diverse range of inorganic and organic materials, such as silicon-based materials, metals, metal compounds, carbon materials, and polymers. They are used to improve the biocompatibility of materials, increase the efficiency of material synthesis, and guide the controlled synthesis of nanomaterials. In addition, these can be used for precise targeting of proteins by conjugating to target biomolecules. In this review, we summarize the main designs and applications of MBPs in recent years. The discussions focus on more efficient and functional peptides, including evolution and overall design of MBPs. We have also highlighted the recent applications of MBPs, such as functionalization of material surfaces, synthesis of nanomaterials, drug delivery, cancer therapy, and plastic degradation. Besides, we also discussed the development trend of MBPs. This interpretation will accelerate future investigations to bottleneck the drawbacks of available MBPs, promoting their commercial applications.

Microbe-mediated transformation of metal sulfides: Mechanisms and environmental significance

Microorganisms play a key role in the natural circulation of various constituent elements of metal sulfides. Some microorganisms (such as Thiobacillus ferrooxidans) can promote the oxidation of metal sulfides to increase the release of heavy metals. However, other microorganisms (such as Desulfovibrio vulgaris) can transform heavy metals into metal sulfides crystals. Therefore, insight into the metal sulfides transformation mediated by microorganisms is of great significance to environmental protection. In this review, first, we discuss the mechanism and influencing factors of microorganisms transforming heavy metals into metal sulfides crystals in different environments. Then, we explore three microbe-mediated transformation forms of heavy metals to metal sulfides and their environmental applications: (1) transformation to metal sulfides precipitation for metal resource recovery; (2) transformation to metal sulfides nanoparticles (NPs) for pollutant treatment; (3) transformation to "metal sulfides-microbe" biohybrid system for clean energy production and pollutant remediation. Finally, we further provide critical views on the application of microbe-mediated metal sulfides transformation in the environmental field and discuss the need for future research.

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.

Analysis of binding modes of biopanning heptapeptides with specific affinity to chitosan wrapped MSN

In this work, we demonstrated for the first time that six short heptapeptides screened out by biopanning strategy show affinity binding to chitosan wrapped mesoporous silica nanoparticle (CS/MSN). The interaction between peptide and chitosan-wrapped MSN (CS/MSN) was carefully analyzed with the aid of DLS and ELISA characterization. The results show that π-π stacking and hydrogen bonds account for the strong adsorption under neutral as well as acidic conditions. While hydrophilic and electrostatic interaction are the major modes of the peptide under alkaline condition. These different interaction modes involved in the adsorption of peptides onto CS/MSN could vividly mimic the binding of peptide with chitosan in physiological conditions and help to deeply understand the process of protein adsorption on nanomaterials.

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.

The tetramerization domain of the tree shrew p53 protein displays unique thermostability despite sharing high sequence identity with the human p53 protein

The p53 protein plays a number of roles in protecting organisms from different genotoxic stresses and this includes DNA damage induced by acetaldehyde, a metabolite of alcohol. Since the common tree shrew ingests high levels of alcohol as part of its normal diet, this suggests that its p53 protein may possess unique properties. Using a combination of biophysical and modeling studies, we demonstrate that the tetramerization domain of the tree shrew p53 protein is considerably more stable than the corresponding domain from humans despite sharing almost 90% sequence identity. Based on modeling and mutagenesis studies, we determine that a glutamine to methionine substitution at position 354 plays a key role in this difference. Given the link between stability of the p53 tetramerization domain and its transcriptional activity, the results suggest that this enhanced stability could lead to important consequences at p53-regulated genes in the tree shrew.

Synthetic Genetic Circuits for Self-Actuated Cellular Nanomaterial Fabrication Devices

Genetically controlled synthetic biosystems are being developed to create nanoscale materials. These biosystems are modeled on the natural ability of living cells to synthesize materials: many organisms have dedicated proteins that synthesize a wide range of hard tissues and solid materials, such as nanomagnets and biosilica. We designed an autonomous living material synthesizing system consisting of engineered cells with genetic circuits that synthesize nanomaterials. The circuits encode a nanomaterial precursor-sensing module (sensor) coupled with a materials synthesis module. The sensor detects the presence of cadmium, gold, or iron ions, and this detection triggers the synthesis of the related nanomaterial-nucleating extracellular matrix. We demonstrate that when engineered cells sense the availability of a precursor ion, they express the corresponding extracellular matrix to form the nanomaterials. This proof-of-concept study shows that endowing cells with synthetic genetic circuits enables nanomaterial synthesis and has the potential to be extended to the synthesis of a variety of nanomaterials and biomaterials using a green approach.

Experimental and theoretical tools to elucidate the binding mechanisms of solid-binding peptides

The interactions between biomolecules and solid surfaces play an important role in designing new materials and applications which mimic nature. Recently, solid-binding peptides (SBPs) have emerged as potential molecular building blocks in nanobiotechnology. SBPs exhibit high selectivity and binding affinity towards a wide range of inorganic and organic materials. Although these peptides have been widely used in various applications, there is a need to understand the interaction mechanism between the peptide and its material substrate, which is challenging both experimentally and theoretically. This review describes the main characterisation techniques currently available to study SBP-surface interactions and their contribution to gain a better insight for designing new peptides for tailored binding.