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

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Cobalt Oxide Nanoparticle Synthesis by Cell-Surface-Engineered Recombinant Escherichia coli and Potential Application for Anticancer Treatment

Cell surface display engineering facilitated the development of a cobalt-binding hybrid Escherichia coli. OmpC served as the molecular anchor for showcasing the cobalt-binding peptides (CBPs), creating the structural model of the hybrid OmpC-CBPs (OmpC-CP, OmpC-CF). Subsequently, the recombinant peptide's cobalt adsorption and retrieval effectiveness were evaluated at various concentrations. When subjected to a pH of 7 and a concentration of 2 mM, OmpC-CF exhibited a significantly higher cobalt recovery rate (2183.87 mol/g DCW) than OmpC-CP. The strain with bioadsorbed cobalt underwent thermal treatment at varying temperatures (400 °C, 500 °C, 600 °C, and 700 °C) and morphological characterization of the thermally decomposed cobalt nanoparticle oxides using diverse spectroscopy techniques. The analysis showed that nanoparticles confined themselves to metal ions, and EDS mapping detected the presence of cobalt on the cell surface. Finally, the nanoparticles' anticancer potential was assessed by subjecting them to heating at 500 °C in a furnace; they demonstrated noteworthy cytotoxicity, as evidenced by IC50 values of 59 μg/mL. These findings suggest that these nanoparticles hold promise as potential anticancer agents. Overall, this study successfully engineered a recombinant E. coli capable of efficiently binding to cobalt, producing nanoparticles with anticancer properties. The results of this investigation could have significant implications for advancing novel cancer therapies.

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.

High-Throughput Computational Screening of Solid-Binding Peptides

Inspired by biomineralization, a naturally occurring, protein-facilitated process, solid-binding peptides (SBPs) have gained much attention for their potential to fabricate various shaped nanocrystals and hierarchical nanostructures. The advantage of SBPs over other traditionally used synthetic polymers or short ligands is their tunable interaction with the solid material surface via carefully programmed sequence and being solution-dependent simultaneously. However, designing a sequence with targeted binding affinity or selectivity often involves intensive processes such as phage display, and only a limited number of sequences can be identified. Other computational efforts have also been introduced, but the validation process remains prohibitively expensive once a suitable sequence has been identified. In this paper, we present a new model to rapidly estimate the binding free energy of any given sequence to a solid surface. We show how the overall binding of a polypeptide can be estimated from the free energy contribution of each residue based on the statistics of the thermodynamically stable structure ensemble. We validated our model using five silica-binding peptides of different binding affinities and lengths and showed that the model is accurate and robust across a wider range of chemistries and binding strengths. The computational cost of this method can be as low as 3% of the commonly used enhanced sampling scheme for similar studies and has a great potential to be used in high-throughput algorithms to obtain larger training data sets for machine learning SBP screening.

The Interaction of Amines with Gold Nanoparticles

Understanding the interactions between amines and the surface of gold nanoparticles is important because of their role in the stabilization of the nanosystems, in the formation of the protein corona, and in the preparation of semisynthetic nanozymes. By using fluorescence spectroscopy, electrochemistry, X-ray photoelectron spectroscopy, high-resolution transmission electron microscopy, and molecular simulation, a detailed picture of these interactions is obtained. Herein, it is shown that amines interact with surface Au(0) atoms of the nanoparticles with their lone electron pair with a strength linearly correlating with their basicity corrected for steric hindrance. The kinetics of binding depends on the position of the gold atoms (flat surfaces or edges) while the mode of binding involves a single Au(0) with nitrogen sitting on top of it. A small fraction of surface Au(I) atoms, still present, is reduced by the amines yielding a much stronger Au(0)-RN.+ (RN. , after the loss of a proton) interaction. In this case, the mode of binding involves two Au(0) atoms with a bridging nitrogen placed between them. Stable Au nanoparticles, as those required for robust semisynthetic nanozymes preparation, are better obtained when the protein is involved (at least in part) in the reduction of the gold ions.

Bayesian Optimization in Drug Discovery

Drug discovery deals with the search for initial hits and their optimization toward a targeted clinical profile. Throughout the discovery pipeline, the candidate profile will evolve, but the optimization will mainly stay a trial-and-error approach. Tons of in silico methods have been developed to improve and fasten this pipeline. Bayesian optimization (BO) is a well-known method for the determination of the global optimum of a function. In the last decade, BO has gained popularity in the early drug design phase. This chapter starts with the concept of black box optimization applied to drug design and presents some approaches to tackle it. Then it focuses on BO and explains its principle and all the algorithmic building blocks needed to implement it. This explanation aims to be accessible to people involved in drug discovery projects. A strong emphasis is made on the solutions to deal with the specific constraints of drug discovery. Finally, a large set of practical applications of BO is highlighted.

In Silico Design and Analysis of Plastic-Binding Peptides

Peptides that bind to inorganic materials can be used to functionalize surfaces, control crystallization, or assist in interfacial self-assembly. In the past, inorganic-binding peptides have been found predominantly through peptide library screening. While this method has successfully identified peptides that bind to a variety of materials, an alternative design approach that can intelligently search for peptides and provide physical insight for peptide affinity would be desirable. In this work, we develop a computational, physics-based approach to design inorganic-binding peptides, focusing on peptides that bind to the common plastics polyethylene, polypropylene, polystyrene, and poly(ethylene terephthalate). The PepBD algorithm, a Monte Carlo method that samples peptide sequence and conformational space, was modified to include simulated annealing, relax hydration constraints, and an ensemble of conformations to initiate design. These modifications led to the discovery of peptides with significantly better scores compared to those obtained using the original PepBD. PepBD scores were found to improve with increasing van der Waals interactions, although strengthening the intermolecular van der Waals interactions comes at the cost of introducing unfavorable electrostatic interactions. The best designs are enriched in amino acids with bulky side chains and possess hydrophobic and hydrophilic patches whose location depends on the adsorbed conformation. Future work will evaluate the top peptide designs in molecular dynamics simulations and experiment, enabling their application in microplastic pollution remediation and plastic-based biosensors.

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.

Biomedical applications of solid-binding peptides and proteins

Over the past decades, solid-binding peptides (SBPs) have found multiple applications in materials science. In non-covalent surface modification strategies, solid-binding peptides are a simple and versatile tool for the immobilization of biomolecules on a vast variety of solid surfaces. Especially in physiological environments, SBPs can increase the biocompatibility of hybrid materials and offer tunable properties for the display of biomolecules with minimal impact on their functionality. All these features make SBPs attractive for the manufacturing of bioinspired materials in diagnostic and therapeutic applications. In particular, biomedical applications such as drug delivery, biosensing, and regenerative therapies have benefited from the introduction of SBPs. Here, we review recent literature on the use of solid-binding peptides and solid-binding proteins in biomedical applications. We focus on applications where modulating the interactions between solid materials and biomolecules is crucial. In this review, we describe solid-binding peptides and proteins, providing background on sequence design and binding mechanism. We then discuss their application on materials relevant for biomedicine (calcium phosphates, silicates, ice crystals, metals, plastics, and graphene). Although the limited characterization of SBPs still represents a challenge for their design and widespread application, our review shows that SBP-mediated bioconjugation can be easily introduced into complex designs and on nanomaterials with very different surface chemistries.

Exploring the Optimal Alloy for Nitrogen Activation by Combining Bayesian Optimization with Density Functional Theory Calculations

Binary alloy catalysts have the potential to exhibit higher activity than monometallic catalysts in nitrogen activation reactions. However, owing to the multiple possible combinations of metal elements constituting binary alloys, an exhaustive search for the optimal combination is difficult. In this study, we searched for the optimal binary alloy catalyst for nitrogen activation reactions using a combination of Bayesian optimization and density functional theory calculations. The optimal alloy catalyst proposed by Bayesian optimization had a surface energy of ∼0.2 eV/Å2 and resulted in a low reaction heat for the dissociation of the N≡N bond. We demonstrated that the search for such binary alloy catalysts using Bayesian optimization is more efficient than random search.

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.

Achieving regioselective materials binding using multidomain peptides

The ability to integrate two disparate materials-binding domains into a single ligand to achieve regiospecific binding would be powerful to direct material assembly; however, this has proven challenging to achieve due to cross-materials binding. Accomplishing this goal might be achieved by harnessing the precision of biology to exploit the recognition between peptides and specific nanomaterials. Here, a designed bifunctional molecule termed Biomolecular Exfoliant and Assembly Motifs (BEAM) is introduced, featuring two different materials-binding peptide domains, one for graphene and one for hexagonal boron nitride (h-BN), at each end of the molecule, separated by a fatty acid spacer. The BEAM is demonstrated to bind strongly to both graphene and h-BN surfaces, and in each case the materials-binding peptide domain is shown to preferentially bind its target material. Critically, the two materials-binding domains exhibited limited cross-domain interaction. The BEAM design concept shows substantial potential to eventually guide self-organization of a range of materials in aqueous media.

Peptide adsorption on silica surfaces: Simulation and experimental insights

The understanding of interactions between proteins with silica surface is crucial for a wide range of different applications: from medical devices, drug delivery and bioelectronics to biotechnology and downstream processing. We show the application of EISM (Effective Implicit Surface Model) for discovering the set of peptide interactions with silica surface. The EISM is employed for a high-speed computational screening of peptides to model the binding affinity of small peptides to silica surfaces. The simulations are complemented with experimental data of peptides with silica nanoparticles from microscale thermophoresis and from infrared spectroscopy. The experimental work shows excellent agreement with computational results and verifies the EISM model for the prediction of peptide-surface interactions. 57 peptides, with amino acids favorable for adsorption on Silica surface, are screened by EISM model for obtaining results, which are worth to be considered as a guidance for future experimental and theoretical works. This model can be used as a broad platform for multiple challenges at surfaces which can be applied for multiple surfaces and biomolecules beyond silica and peptides.

Manipulation of peptide-fatty acid bioconjugates on graphene: effects of fatty acid chain length and attachment point

The non-destructive functionalisation of graphene in aqueous media is a critical process with the potential to enhance the versatility of the 2D nanosheet material as a technological enabler. This could also unlock strategies for a wider uptake of graphene in bio-related applications. Graphene functionalisation can be achieved using peptides that specifically recognise the carbon-based material, resulting in persistent non-covalent adsorption without damaging the nanosheet. Bio-conjugation of non-natural moieties with these peptides can incorporate multifunctionality, further extending the applicability of these interfaces. Here, bio-conjugates comprising a graphene-binding peptide with a fatty acid chain of varying length are investigated for their binding affinity and adsorbed structures at the aqueous graphene interface. Through an integration of quartz crystal microbalance and atomic force microscopy data with advanced sampling molecular simulations, variations in the binding of these bio-conjugates is determined. Conjugation at either terminus led to good interfacial contact, and for a given attachment point, the changes in the fatty acid length did not substantially disrupt the conformations of the adsorbed peptide domain. These findings provide a solid foundation for designing multi-functional bio-interfaces for sensing and healthcare.

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

Predicting biomolecule adsorption on MoS2 nanosheets with high structural fidelity

A new force field, MoSu-CHARMM, for the description of bio-interfacial structures at the aqueous MoS2 interface is developed, based on quantum chemical data. The force field describes non-covalent interactions between the MoS2 surface and a wide range of chemistries including hydrocarbon, alcohol, aldehyde, ketone, carboxylic acid, amine, thiol, and amino acid groups. Density functional theory (DFT), using the vdW-DF2 functional, is employed to create training and validation datasets, comprising 330 DFT binding energies for 21 organic compounds. Development of MoSu-CHARMM is guided by two criteria: (i) minimisation of energetic differences compared to target DFT data and (ii) preservation of the DFT energetic rankings of the different binding configurations. Force-field performance is validated against existing high-quality structural experimental data regarding adsorption of four 26-residue peptides at the aqueous MoS2 interface. Adsorption free energies for all twenty amino acids in liquid water are calculated to provide guidance for future peptide design, and interpret the properties of existing experimentally-identified MoS2-binding peptides. This force field will enable large-scale simulations of biological interactions with MoS2 surfaces in aqueous media where an emphasis on structural fidelity is prioritised.

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.