Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties

Does the surface charge of the nanoparticles drive nanoparticle-cell membrane interactions?

Classical Coulombic interaction, characterized by electrostatic interactions mediated through surface charges, is often regarded as the primary determinant in nanoparticles' (NPs) cellular association and internalization. However, the intricate physicochemical properties of particle surfaces, biomolecular coronas, and cell surfaces defy this oversimplified perspective. Moreover, the nanometrological techniques employed to characterize NPs in complex physiological fluids often exhibit limited accuracy and reproducibility. A more comprehensive understanding of nanoparticle-cell membrane interactions, extending beyond attractive forces between oppositely charged surfaces, necessitates the establishment of databases through rigorous physical, chemical, and biological characterization supported by nanoscale analytics. Additionally, computational approaches, such as in silico modeling and machine learning, play a crucial role in unraveling the complexities of these interactions.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Unveiling the challenges of engineered protein corona from the proteins' perspective

It is well known that protein corona affects the "biological identity" of nanoparticles (NPs), which has been seen as both a challenge and an opportunity. Approaches have moved from avoiding protein adsorption to trying to direct it, taking advantage of the formation of a protein corona to favorably modify the pharmacokinetic parameters of NPs. Although promising, the results obtained with engineered NPs still need to be completely understood. While much effort has been put into understanding how the surface of nanomaterials affects protein absorption, less is known about how proteins can affect corona formation due to their specific physicochemical properties. This review addresses this knowledge gap, examining key protein factors influencing corona formation, highlighting current challenges in studying protein-protein interactions, and discussing future perspectives in the field.

Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.

Biomolecular Corona of Gold Nanoparticles: The Urgent Need for Strong Roots to Grow Strong Branches

Gold nanoparticles (GNPs) are largely employed in diagnostics/biosensors and are among the most investigated nanomaterials in biology/medicine. However, few GNP-based nanoformulations have received FDA approval to date, and promising in vitro studies have failed to translate to in vivo efficacy. One key factor is that biological fluids contain high concentrations of proteins, lipids, sugars, and metabolites, which can adsorb/interact with the GNP's surface, forming a layer called biomolecular corona (BMC). The BMC can mask prepared functionalities and target moieties, creating new surface chemistry and determining GNPs' biological fate. Here, the current knowledge is summarized on GNP-BMCs, analyzing the factors driving these interactions and the biological consequences. A partial fingerprint of GNP-BMC analyzing common patterns of composition in the literature is extrapolated. However, a red flag is also risen concerning the current lack of data availability and regulated form of knowledge on BMC. Nanomedicine is still in its infancy, and relying on recently developed analytical and informatic tools offers an unprecedented opportunity to make a leap forward. However, a restart through robust shared protocols and data sharing is necessary to obtain "stronger roots". This will create a path to exploiting BMC for human benefit, promoting the clinical translation of biomedical nanotools.

Formation of extracellular polymeric substances corona on TiO2 nanoparticles: Roles of crystalline phase and exposed facets

Nanoparticles (NPs) in the environment can interact with macromolecules in the surrounding environment to form eco-corona on their surfaces, which in turn affects the environmental fate and toxicity of nanoparticles. Wastewater treatment plants containing large amounts of microbial extracellular polymeric substances (EPS) are an important source of NPs into the environment, where the formation of EPS coronas on NPs is critical. However, it remains unclear how the crystalline phase and exposed facets, which are intrinsic properties of NPs, affect the formation of EPS coronas on NPs. This study investigated the formation of EPS corona on three TiO2 NPs (representing the most widely used engineered NPs) with different crystalline phases and exposed facets. The protein type and abundance in EPS coronas on TiO2 NPs varied depending on the crystalline phase and exposed facets. Anatase with {101} facets and {001} facets preferred to adsorb proteins with lower molecular weights and higher H-bonding relevant amino acids, respectively, while EPS corona on rutile with {110} facets had proteins with higher hydrophobicity. In addition, the selective adsorption of proteins was primarily determined by steric hindrance, hydrogen bonding, and hydrophobic interaction between TiO2 NPs and proteins, which were affected by changes in aggregation state, surface hydroxyl density, and hydrophobicity of TiO2 NPs induced by crystalline phase and exposed facets. Moreover, crystalline phase and exposed facets-induced EPS corona changes altered the aggregation state and oxidation potential of TiO2-EPS corona complexes. These findings emphasize the important role of crystalline phase and exposed facets in the environmental behavior of nanoparticles and may provide insights into the safe design of nanoparticles.

Unveiling protein corona composition: predicting with resampling embedding and machine learning

Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (R2) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an R2 of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO2), silicon dioxide (SiO2) and silver (Ag), and we achieved a prediction performance with an R2 value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.

Glyphosate Separating and Sensing for Precision Agriculture and Environmental Protection in the Era of Smart Materials

The present article critically and comprehensively reviews the most recent reports on smart sensors for determining glyphosate (GLP), an active agent of GLP-based herbicides (GBHs) traditionally used in agriculture over the past decades. Commercialized in 1974, GBHs have now reached 350 million hectares of crops in over 140 countries with an annual turnover of 11 billion USD worldwide. However, rolling exploitation of GLP and GBHs in the last decades has led to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide of farm and companies' workers. Intoxication with these herbicides dysregulates the microbiome-gut-brain axis, cholinergic neurotransmission, and endocrine system, causing paralytic ileus, hyperkalemia, oliguria, pulmonary edema, and cardiogenic shock. Precision agriculture, i.e., an (information technology)-enhanced approach to crop management, including a site-specific determination of agrochemicals, derives from the benefits of smart materials (SMs), data science, and nanosensors. Those typically feature fluorescent molecularly imprinted polymers or immunochemical aptamer artificial receptors integrated with electrochemical transducers. Fabricated as portable or wearable lab-on-chips, smartphones, and soft robotics and connected with SM-based devices that provide machine learning algorithms and online databases, they integrate, process, analyze, and interpret massive amounts of spatiotemporal data in a user-friendly and decision-making manner. Exploited for the ultrasensitive determination of toxins, including GLP, they will become practical tools in farmlands and point-of-care testing. Expectedly, smart sensors can be used for personalized diagnostics, real-time water, food, soil, and air quality monitoring, site-specific herbicide management, and crop control.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q²  = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.

Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles

Although challenging, the accurate and rapid prediction of nanoscale interactions has broad applications for numerous biological processes and material properties. While several models have been developed to predict the interaction of specific biological components, they use system-specific information that hinders their application to more general materials. Here we present NeCLAS, a general and efficient machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. NeCLAS outperforms current nanoscale prediction models for generic nanoparticles up to 10-20 nm, reproducing interactions for biological and non-biological systems. Two aspects contribute to these results: a low-dimensional representation of nanoparticles and molecules (to reduce the effect of data uncertainty), and environmental features (to encode the physicochemical neighborhood at multiple scales). This framework has several applications, from basic research to rapid prototyping and design in nanobiotechnology.

Mechanistic Understanding of Protein Corona Formation around Nanoparticles: Old Puzzles and New Insights

Although a wide variety of nanoparticles (NPs) have been engineered for use as disease markers or drug delivery agents, the number of nanomedicines in clinical use has hitherto remained small. A key obstacle in nanomedicine development is the lack of a deep mechanistic understanding of NP interactions in the bio-environment. Here, the focus is on the biomolecular adsorption layer (protein corona), which quickly enshrouds a pristine NP exposed to a biofluid and modifies the way the NP interacts with the bio-environment. After a brief introduction of NPs for nanomedicine, proteins, and their mutual interactions, research aimed at addressing fundamental properties of the protein corona, specifically its mono-/multilayer structure, reversibility and irreversibility, time dependence, as well as its role in NP agglomeration, is critically reviewed. It becomes quite evident that the knowledge of the protein corona is still fragmented, and conflicting results on fundamental issues call for further mechanistic studies. The article concludes with a discussion of future research directions that should be taken to advance the understanding of the protein corona around NPs. This knowledge will provide NP developers with the predictive power to account for these interactions in the design of efficacious nanomedicines.

The protein corona from nanomedicine to environmental science

The protein corona spontaneously develops and evolves on the surface of nanoscale materials when they are exposed to biological environments, altering their physiochemical properties and affecting their subsequent interactions with biosystems. In this Review, we provide an overview of the current state of protein corona research in nanomedicine. We next discuss remaining challenges in the research methodology and characterization of the protein corona that slow the development of nanoparticle therapeutics and diagnostics, and we address how artificial intelligence can advance protein corona research as a complement to experimental research efforts. We then review emerging opportunities provided by the protein corona to address major issues in healthcare and environmental sciences. This Review details how mechanistic insights into nanoparticle protein corona formation can broadly address unmet clinical and environmental needs, as well as enhance the safety and efficacy of nanobiotechnology products.

Aging of Nanoplastics Significantly Affects Protein Corona Composition Thus Enhancing Macrophage Uptake

Nanoplastics (NPs), as emerging contaminants, have attracted increasing attention for their effects on human exposure and potential health risks. The protein corona formed on the surface of NPs affects the biological activity and fate of the NPs in vivo. However, how environmental aging, an inevitable process once NPs enter the environment, affects the formation of protein corona on NPs is still unclear. This study investigated the changes in the compositions of protein corona formed on photo-aged polystyrene (PS) NPs in human bronchoalveolar lavage fluid (BALF), corresponding to the inhalation exposure pathway. The results demonstrated that both the species and abundance of proteins in the BALF protein corona on the surface of PS NPs were altered by aging. In addition, the aged PS NPs are more hydrophilic and less electronegative than the pristine PS NPs; hence, there is an increased sorption of more negatively charged hydrophilic proteins. Moreover, aging-induced alterations in BALF protein corona enhanced the uptake of aged PS NPs by lung macrophages J774A.1 through phagocytosis and clathrin-mediated endocytosis. These findings highlight the importance of environmental aging processes in the biosafety assessment of nanoplastics.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery

In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputs and outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in bio-pharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs-the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Hemolytic Activity of Nanoparticles as a Marker of Their Hemocompatibility

The potential use of nanomaterials in medicine offers opportunities for novel therapeutic approaches to treating complex disorders. For that reason, a new branch of science, named nanotoxicology, which aims to study the dangerous effects of nanomaterials on human health and on the environment, has recently emerged. However, the toxicity and risk associated with nanomaterials are unclear or not completely understood. The development of an adequate experimental strategy for assessing the toxicity of nanomaterials may include a rapid/express method that will reliably, quickly, and cheaply make an initial assessment. One possibility is the characterization of the hemocompatibility of nanomaterials, which includes their hemolytic activity as a marker. In this review, we consider various factors affecting the hemolytic activity of nanomaterials and draw the reader's attention to the fact that the formation of a protein corona around a nanoparticle can significantly change its interaction with the red cell. This leads us to suggest that the nanomaterial hemolytic activity in the buffer does not reflect the situation in the blood plasma. As a recommendation, we propose studying the hemocompatibility of nanomaterials under more physiologically relevant conditions, in the presence of plasma proteins in the medium and under mechanical stress.

A Nanomedicine Structure-Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

Despite their clinical success in drug delivery applications, the potential of theranostic nanomedicines is hampered by mechanistic uncertainty and a lack of science-informed regulatory guidance. Both the therapeutic efficacy and the toxicity of nanoformulations are tightly controlled by the complex interplay of the nanoparticle's physicochemical properties and the individual patient/tumor biology; however, it can be difficult to correlate such information with observed outcomes. Additionally, as nanomedicine research attempts to gradually move away from large-scale animal testing, the need for computer-assisted solutions for evaluation will increase. Such models will depend on a clear understanding of structure-activity relationships. This review provides a comprehensive overview of the field of cancer nanomedicine and provides a knowledge framework and foundational interaction maps that can facilitate future research, assessments, and regulation. By forming three complementary maps profiling nanobio interactions and pathways at different levels of biological complexity, a clear picture of a nanoparticle's journey through the body and the therapeutic and adverse consequences of each potential interaction are presented.

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives

Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.

LGB-Stack: Stacked Generalization with LightGBM for Highly Accurate Predictions of Polymer Bandgap

Recently, the Ramprasad group reported a quantitative structure-property relationship (QSPR) model for predicting the E _gap values of 4209 polymers, which yielded a test set R ² score of 0.90 and a test set root-mean-square error (RMSE) score of 0.44 at a train/test split ratio of 80/20. In this paper, we present a new QSPR model named LGB-Stack, which performs a two-level stacked generalization using the light gradient boosting machine. At level 1, multiple weak models are trained, and at level 2, they are combined into a strong final model. Four molecular fingerprints were generated from the simplified molecular input line entry system notations of the polymers. They were trimmed using recursive feature elimination and used as the initial input features for training the weak models. The output predictions of the weak models were used as the new input features for training the final model, which completes the LGB-Stack model training process. Our results show that the best test set R ² and the RMSE scores of LGB-Stack at the train/test split ratio of 80/20 were 0.92 and 0.41, respectively. The accuracy scores further improved to 0.94 and 0.34, respectively, when the train/test split ratio of 95/5 was used.

Concentration and composition of the protein corona as a function of incubation time and serum concentration: an automated approach to the protein corona

Nanoparticles in contact with proteins form a "corona" of proteins adsorbed on the nanoparticle surface. Subsequent biological responses are then mediated by the adsorbed proteins rather than the bare nanoparticles. The use of nanoparticles as nanomedicines and biosensors would be greatly improved if researchers were able to predict which specific proteins will adsorb on a nanoparticle surface. We use a recently developed automated workflow with a liquid handling robot and low-cost proteomics to determine the concentration and composition of the protein corona formed on carboxylate-modified iron oxide nanoparticles (200 nm) as a function of incubation time and serum concentration. We measure the concentration of the resulting protein corona with a colorimetric assay and the composition of the corona with proteomics, reporting both abundance and enrichment relative to the fetal bovine serum (FBS) proteins used to form the corona. Incubation time was found to be an important parameter for corona concentration and composition at high (100% FBS) incubation concentrations, with only a slight effect at low (10%) FBS concentrations. In addition to these findings, we describe two methodological advances to help reduce the cost associated with protein corona experiments. We have automated the digest step necessary for proteomics and measured the variability between triplicate samples at each stage of the proteomics experiments. Overall, these results demonstrate the importance of understanding the multiple parameters that influence corona formation, provide new tools for corona characterization, and advance bioanalytical research in nanomaterials.

Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment

The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.

Investigation of interaction between MXene nanosheets and human plasma and protein corona composition

The composition of protein corona affects the behavior and fate of nanoparticles in biological systems, which strongly relates to the intrinsic properties of nanoparticles and proteins. Here, three types of MXene Ti₃C₂T_x nanosheets are prepared by different etching methods, and certain physicochemical characteristics of the nanosheets before and after exposure to human plasma (HP) are characterized. The Ti₃C₂T_x nanosheets with protein coronas suffer more easily from aggregation than pristine Ti₃C₂T_x. The composition of protein coronas by LC-MS/MS-based label-free proteomic analysis reveals a high overlap of protein types and functions but a significant difference in relative protein abundance for the three Ti₃C₂T_x. Immunoglobulins and coagulation proteins are highly enriched while albumin is depleted in the coronas compared with their abundance in original HP. The random forest classification model predicts that the main driving forces for the adsorption of HP proteins on Ti₃C₂T_x are hydrogen bonding, steric hindrance, and hydrophobic interaction. This study provides insights into the colloidal stability of Ti₃C₂T_x nanosheets and their interaction with human plasma proteins.

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.

Nanoparticle-protein corona complex: understanding multiple interactions between environmental factors, corona formation, and biological activity

The surfaces of pristine nanoparticles become rapidly coated by proteins in biological fluids, forming the so-called protein corona. The corona modifies key physicochemical characteristics of nanoparticle surfaces that modulate its biological and pharmacokinetic activity, biodistribution, and safety. In the two decades since the protein corona was identified, the importance of nanoparticles surface properties in regulating biological responses have been recognized. However, there is still a lack of clarity about the relationships between physiological conditions and corona composition over time, and how this controls biological activities/interactions. Here we review recent progress in characterizing the structure and composition of protein corona as a function of biological fluid and time. We summarize the influence of nanoparticle characteristics on protein corona composition and discuss the relevance of protein corona to the biological activity and fate of nanoparticles. The aim is to provide a critical summary of the key factors that affect protein corona formation (e.g. characteristics of nanoparticles and biological environment) and how the corona modulates biological activity, cellular uptake, biodistribution, and drug delivery. In addition to a discussion on the importance of the characterization of protein corona adsorbed on nanoparticle surfaces under conditions that mimic relevant physiological environment, we discuss the unresolved technical issues related to the characterization of nanoparticle-protein corona complexes during their journey in the body. Lastly, the paper offers a perspective on how the existing nanomaterial toxicity data obtained from in vitro studies should be reconsidered in the light of the presence of a protein corona, and how recent advances in fields, such as proteomics and machine learning can be integrated into the quantitative analysis of protein corona components.

An EPA database on the effects of engineered nanomaterials-NaKnowBase

The US EPA Office of Research and Development (ORD) has conducted a research program assessing potential risks of emerging materials and technologies, including engineered nanomaterials (ENM). As a component of that program, a nanomaterial knowledge base, termed “NaKnowBase”, was developed containing the results of published ORD research relevant to the potential environmental and biological actions of ENM. The experimental data address issues such as ENM release into the environment; fate, transport and transformations in environmental media; exposure to ecological species or humans; and the potential for effects on those species. The database captures information on the physicochemical properties of ENM tested, assays performed and their parameters, and the results obtained. NaKnowBase (NKB) is a relational SQL database, and may be queried either with SQL code or through a user-friendly web interface. Filtered results may be output in spreadsheet format for subsequent user-defined analyses. Potential uses of the data might include input to quantitative structure-activity relationships (QSAR), meta-analyses, or other investigative approaches.

Measurement(s)	engineered nanomaterial effects
Technology Type(s)	digital curation
Factor Type(s)	physicochemical property
Sample Characteristic - Environment	nanomaterial

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.17060120

Individual and Binary Mixture Toxicity of Five Nanoparticles in Marine Microalga Heterosigma akashiwo

The investigation of the combined toxic action of different types of nanoparticles (NPs) and their interaction between each other and with aquatic organisms is an important problem of modern ecotoxicology. In this study, we assessed the individual and mixture toxicities of cadmium and zinc sulfides (CdS and ZnS), titanium dioxide (TiO₂), and two types of mesoporous silicon dioxide (with no inclusions (SMB3) and with metal inclusions (SMB24)) by a microalga growth inhibition bioassay. The counting and size measurement of microalga cells and NPs were performed by flow cytometry. The biochemical endpoints were measured by a UV-VIS microplate spectrophotometer. The highest toxicity was observed for SMB24 (EC50, 3.6 mg/L) and CdS (EC50, 21.3 mg/L). A combined toxicity bioassay demonstrated that TiO₂ and the SMB3 NPs had a synergistic toxic effect in combinations with all the tested samples except SMB24, probably caused by a “Trojan horse effect”. Sample SMB24 had antagonistic toxic action with CdS and ZnS, which was probably caused by metal ion scavenging.

Supervised learning model predicts protein adsorption to carbon nanotubes

Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

Supervised learning model predicts protein adsorption to carbon nanotubes

Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

Two-dimensional (2D) materials have emerged as an important class of nanomaterials for technological innovation due to their remarkable physicochemical properties, including sheet-like morphology and minimal thickness, high surface area, tuneable chemical composition, and surface functionalization. These materials are being proposed for new applications in energy, health, and the environment; these are all strategic society sectors toward sustainable development. Specifically, 2D materials for nano-imaging have shown exciting opportunities in in vitro and in vivo models, providing novel molecular imaging techniques such as computed tomography, magnetic resonance imaging, fluorescence and luminescence optical imaging and others. Therefore, given the growing interest in 2D materials, it is mandatory to evaluate their impact on the immune system in a broader sense, because it is responsible for detecting and eliminating foreign agents in living organisms. This mini-review presents an overview on the frontier of research involving 2D materials applications, nano-imaging and their immunosafety aspects. Finally, we highlight the importance of nanoinformatics approaches and computational modeling for a deeper understanding of the links between nanomaterial physicochemical properties and biological responses (immunotoxicity/biocompatibility) towards enabling immunosafety-by-design 2D materials.

Environmental dimensions of the protein corona

The adsorption of biomolecules to the surface of engineered nanomaterials, known as corona formation, defines their biological identity by altering their surface properties and transforming the physical, chemical and biological characteristics of the particles. In the first decade since the term protein corona was coined, studies have focused primarily on biomedical applications and human toxicity. The relevance of the environmental dimensions of the protein corona is still emerging. Often referred to as the eco-corona, a biomolecular coating forms upon nanomaterials as they enter the environment and may include proteins, as well as a diverse array of other biomolecules such as metabolites from cellular activity and/or natural organic matter. Proteins remain central in studies of eco-coronas because of the ease of monitoring and structurally characterizing proteins, as well as their crucial role in receptor engagement and signalling. The proteins within the eco-corona are optimal targets to establish the biophysicochemical principles of corona formation and transformation, as well as downstream impacts on nanomaterial uptake, distribution and impacts on the environment. Moreover, proteins appear to impart a biological identity, leading to cellular or organismal recognition of nanomaterials, a unique characteristic compared with natural organic matter. We contrast insights into protein corona formation from clinical samples with those in environmentally relevant systems. Principles specific to the environment are also explored to gain insights into the dynamics of interaction with or replacement by other biomolecules, including changes during trophic transfer and ecotoxicity. With many challenges remaining, we also highlight key opportunities for method development and impactful systems on which to focus the next phase of eco-corona studies. By interrogating these environmental dimensions of the protein corona, we offer a perspective on how mechanistic insights into protein coronas in the environment can lead to more sustainable, environmentally safe nanomaterials, as well as enhancing the efficacy of nanomaterials used in remediation and in the agri-food sector.

Quantitative Protein Corona Composition and Dynamics on Carbon Nanotubes in Biological Environments

When nanoparticles enter biological environments, proteins adsorb to form the "protein corona" which alters nanoparticle biodistribution and toxicity. Herein, we measure protein corona formation on DNA-functionalized single-walled carbon nanotubes (ssDNA-SWCNTs), a nanoparticle used widely for sensing and delivery, in blood plasma and cerebrospinal fluid. We characterize corona composition by mass spectrometry, revealing high-abundance corona proteins involved in lipid binding, complement activation, and coagulation. We investigate roles of electrostatic and entropic interactions driving selective corona formation. Lastly, we study real-time protein binding on ssDNA-SWCNTs, obtaining agreement between enriched proteins binding strongly and depleted proteins binding marginally, while highlighting cooperative adsorption mechanisms. Knowledge of protein corona composition, formation mechanisms, and dynamics informs nanoparticle translation from in vitro design to in vivo application.

Knowledge gaps in immune response and immunotherapy involving nanomaterials: Databases and artificial intelligence for material design

Exploring the interactions between the immune system and nanomaterials (NMs) is critical for designing effective and safe NMs, but large knowledge gaps remain to be filled prior to clinical applications (e.g., immunotherapy). The lack of databases on interactions between the immune system and NMs affects the discovery of new NMs for immunotherapy. Complement activation and inhibition by NMs have been widely studied, but the general rules remain unclear. Biomimetic nanocoating to promote the clearance of NMs by the immune system is an alternative strategy for the immune response mediation of the biological corona. Immune response predictions based on NM properties can facilitate the design of NMs for immunotherapy, and artificial intelligences deserve much attention in the field. This review addresses the knowledge gaps regarding immune response and immunotherapy in relation to NMs, effective immunotherapy and material design without adverse immune responses.

Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data

Nanoparticles used in biological settings are exposed to proteins that adsorb on the surface forming a protein corona. These adsorbed proteins dictate the subsequent cellular response. A major challenge has been predicting what proteins will adsorb on a given nanoparticle surface. Instead, each new nanoparticle and nanoparticle modification must be tested experimentally to determine what proteins adsorb on the surface. We propose that any future predictive ability will depend on large datasets of protein-nanoparticle interactions. As a first step towards this goal, we have developed an automated workflow using a liquid handling robot to form and isolate protein coronas. As this workflow depends on magnetic separation steps, we test the ability to embed magnetic nanoparticles within a protein nanoparticle. These experiments demonstrate that magnetic separation could be used for any type of nanoparticle in which a magnetic core can be embedded. Higher-throughput corona characterization will also require lower-cost approaches to proteomics. We report a comparison of fast, low-cost, and standard, slower, higher-cost liquid chromatography coupled with mass spectrometry to identify the protein corona. These methods will provide a step forward in the acquisition of the large datasets necessary to predict nanoparticle-protein interactions.

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.

Engineering at the nano-bio interface: harnessing the protein corona towards nanoparticle design and function

Unpredictable and uncontrollable protein adsorption on nanoparticles remains a considerable challenge to achieving effective application of nanotechnologies within biological environments. Nevertheless, engineered nanoparticles offer unprecedented functionality and control in probing and altering biological systems. In this review, we highlight recent advances in harnessing the "protein corona" formed on nanoparticles as a handle to tune functional properties of the protein-nanoparticle complex. Towards this end, we first review nanoparticle properties that influence protein adsorption and design strategies to facilitate selective corona formation, with the corresponding characterization techniques. We next focus on literature detailing corona-mediated functionalities, including stealth to avoid recognition and sequestration while in circulation, targeting of predetermined in vivo locations, and controlled activation once localized to the intended biological compartment. We conclude with a discussion of biocompatibility outcomes for these protein-nanoparticle complexes applied in vivo. While formation of the nanoparticle-corona complex may impede our control over its use for the projected nanobiotechnology application, it concurrently presents an opportunity to create improved protein-nanoparticle architectures by exploiting natural or guiding selective protein adsorption to the nanoparticle surface.

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

The protein corona affects the clinical applications, organ targeting, and safety assessment of nanomaterials, and prediction of the protein corona would be valuable for the design of ideal nanomaterials. However, no methods to predict the protein corona are available. Overcoming the numerous quantitative and qualitative factors influencing corona formation, the present work builds models that precisely predict the functional composition of the protein corona and the cell recognition of nanoparticles (NPs) integrating machine learning and meta-analysis. This workflow provides an effective method to predict the functional composition of the protein corona that determines cell recognition to guide the synthesis and applications of NPs.

Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R² (less than 0.40). Here, the performance with R² over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R² (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Bovine Serum Albumin Enhances Silver Nanoparticle Dissolution Kinetics in a Size- and Concentration-Dependent Manner

The dissolution of silver nanoparticles (AgNPs) to release Ag(I)(aq) is an important mechanism in potentiating AgNP cytotoxicity and imparting their antibacterial properties. However, AgNPs can undergo other simultaneous biophysicochemical transformations, such as protein adsorption, which can mediate AgNP dissolution behaviors. We report the comprehensive analysis of AgNP dissolution and protein adsorption behaviors with monolayer surface coverage of AgNPs by bovine serum albumin (BSA). AgNP dissolution rate constants, k_dissolution, were quantified over several particle sizes (10, 20, and 40 nm) and BSA concentrations (0-2 nM) using linear sweep stripping voltammetry. Across all particle sizes, the dissolution rate constant increased with increasing BSA concentrations. However, protein-enhanced dissolution behaviors were most pronounced for 10 nm AgNPs, which exhibited 3.6-fold and 7.7-fold relative enhancement when compared to 20 and 40 nm AgNPs, respectively. Changes to AgNP surface properties upon interaction with BSA were monitored using dynamic light scattering and zeta potential measurements, while BSA-AgNP complex formation was evaluated using UV-vis spectroscopy and circular dichroism spectroscopy. A subtle increase in the BSA-AgNP association constant was observed with an increase in the AgNP size. Together, these results suggest that the AgNP size dependence of BSA-enhanced dissolution of AgNPs is possibly mediated through both displacement of Ag(I)(aq)-loaded BSA by excess protein in the bulk solution and minimized accessibility of the AgNP surface because of BSA adsorption.

Protein Corona Composition of Silica Nanoparticles in Complex Media: Nanoparticle Size does not Matter

Biomolecules, and particularly proteins, bind on nanoparticle (NP) surfaces to form the so-called protein corona. It is accepted that the corona drives the biological distribution and toxicity of NPs. Here, the corona composition and structure were studied using silica nanoparticles (SiNPs) of different sizes interacting with soluble yeast protein extracts. Adsorption isotherms showed that the amount of adsorbed proteins varied greatly upon NP size with large NPs having more adsorbed proteins per surface unit. The protein corona composition was studied using a large-scale label-free proteomic approach, combined with statistical and regression analyses. Most of the proteins adsorbed on the NPs were the same, regardless of the size of the NPs. To go beyond, the protein physicochemical parameters relevant for the adsorption were studied: electrostatic interactions and disordered regions are the main driving forces for the adsorption on SiNPs but polypeptide sequence length seems to be an important factor as well. This article demonstrates that curvature effects exhibited using model proteins are not determining factors for the corona composition on SiNPs, when dealing with complex biological media.

Prediction of protein corona on nanomaterials by machine learning using novel descriptors

Effective in silico methods to predict protein corona compositions on engineered nanomaterials (ENMs) could help elucidate the biological outcomes of ENMs in biosystems without the need for conducting lengthy experiments for corona characterization. However, the physicochemical properties of ENMs, used as the descriptors in current modeling methods, are insufficient to represent the complex interactions between ENMs and proteins. Herein, we utilized the fluorescence change (FC) from fluorescamine labeling on a protein, with or without the presence of the ENM, as a novel descriptor of the ENM to build machine learning models for corona formation. FCs were significantly correlated with the abundance of the corresponding proteins in the corona on diverse classes of ENMs, including metal and metal oxides, nanocellulose, and 2D ENMs. Prediction models established by the random forest algorithm using FCs as the ENM descriptors showed better performance than the conventional descriptors, such as ENM size and surface charge, in the prediction of corona formation. Moreover, they were able to predict protein corona formation on ENMs with very heterogeneous properties. We believe this novel descriptor can improve in silico studies of corona formation, leading to a better understanding on the protein adsorption behaviors of diverse ENMs in different biological matrices. Such information is essential for gaining a comprehensive view of how ENMs interact with biological systems in ENM safety and sustainability assessments.

Sulfidation of Ag and ZnO Nanomaterials Significantly Affects Protein Corona Composition: Implications for Human Exposure to Environmentally Aged Nanomaterials

The physicochemical properties of engineered nanomaterials can change drastically during aging in the environment. Understanding how these changes influence protein corona formation on nanomaterials is critical for accurately predicting the human exposure risks of aged nanomaterials. Here, we show that sulfidation, a prevalently occurring environmental aging process, of Ag and ZnO nanomaterials significantly affected the protein compositions of the hard corona formed in human saliva, sweat, and bronchoalveolar lavage fluid, corresponding to three most common exposure pathways, that is, ingestion, dermal contact, and inhalation. In particular, a diverse variety of proteins selectively associated with either sulfidized or pristine nanomaterials. Random forest classification of the proteomic data revealed that this selective protein adsorption process was mainly dictated by electrostatic interaction, hydrophobic interaction, and steric hindrance between proteins and nanomaterials, which were susceptible to the changes in surface charge, hydrophobicity, and aggregation status of nanomaterials induced by sulfidation. Furthermore, even for the proteins that do not exhibit distinct adsorption selectivity between sulfidized and pristine nanomaterials, sulfidation altered the extents of impact of nanomaterials on the conformation and likely functions of the adsorbed proteins. These findings unearth a previously neglected mechanism via which environmental sulfidation process mediates the biological effects of soft-metal-containing nanomaterials.