Computational Analysis of the Binding Mechanism of GenX and HSA

Rank-In Integrated Machine Learning and Bioinformatic Analysis Identified the Key Genes in HFPO-DA (GenX) Exposure to Human, Mouse, and Rat Organisms

Hexafluoropropylene Oxide Dimer Acid (HFPO-DA or GenX) is a pervasive perfluorinated compound with scant understood toxic effects. Toxicological studies on GenX have been conducted using animal models. To research deeper into the potential toxicity of GenX in humans and animals, we undertook a comprehensive analysis of transcriptome datasets across different species. A rank-in approach was utilized to merge different transcriptome datasets, and machine learning algorithms were employed to identify key genetic mechanisms common among various species and humans. We identified seven genes-TTR, ATP6V1B1, EPHX1, ITIH3, ATXN10, UBXN1, and HPX-as potential variables for classification of GenX-exposed samples, and the seven genes were verified in separate datasets of human, mouse, and rat samples. Bioinformatic analysis of the gene dataset further revealed that mitochondrial function and metabolic processes may be modulated by GenX through these key genes. Our findings provide insights into the underlying genetic mechanisms and toxicological impacts of GenX exposure across different species and offer valuable references for future studies using animal models to examine human exposure to GenX.

Insights into PFAS environmental fate through computational chemistry: A review

Per- and polyfluoroalkyl substances (PFAS) are widely used chemicals that exhibit exceptional chemical and thermal stability. However, their resistance to degradation has led to their widespread environmental contamination. PFAS also negatively affect the environment and other organisms, highlighting the need for effective remediation methods to mitigate their presence and prevent further contamination. Computational chemistry methods, such as Density Functional Theory (DFT) and Molecular Dynamics (MD) offer valuable tools for studying PFAS and simulating their interactions with other molecules. This review explores how computational chemistry methods contribute to understanding and tackling PFAS in the environment. PFAS have been extensively studied using DFT and MD, each method offering unique advantages and computational limitations. MD simulates large macromolecules systems however it lacks the ability model chemical reactions, while DFT provides molecular insights however at a high computational cost. The integration of DFT with MD shows promise in predicting PFAS behavior in different environments. This work summarizes reported studies on PFAS compounds, focusing on adsorption, destruction, and bioaccumulation, highlighting contributions of computational methods while discussing the need for continued research. The findings emphasize the importance of computational chemistry in addressing PFAS contamination, guiding risk assessments, and informing future research and innovations in this field.

Quantitative cross-species comparison of serum albumin binding of per- and polyfluoroalkyl substances from five structural classes

Per- and polyfluoroalkyl substances (PFAS) are a class of over 8000 chemicals, many of which are persistent, bioaccumulative, and toxic to humans, livestock, and wildlife. Serum protein binding affinity is instrumental in understanding PFAS toxicity, yet experimental binding data is limited to only a few PFAS congeners. Previously, we demonstrated the usefulness of a high-throughput, in vitro differential scanning fluorimetry assay for determination of relative binding affinities of human serum albumin for 24 PFAS congeners from 6 chemical classes. In the current study, we used this assay to comparatively examine differences in human, bovine, porcine, and rat serum albumin binding of 8 structurally informative PFAS congeners from 5 chemical classes. With the exception of the fluorotelomer alcohol 1H, 1H, 2H, 2H-perfluorooctanol (6:2 FTOH), each PFAS congener bound by human serum albumin was also bound by bovine, porcine, and rat serum albumin. The critical role of the charged functional headgroup in albumin binding was supported by the inability of albumin of each species tested to bind 6:2 FTOH. Significant interspecies differences in serum albumin binding affinities were identified for each of the bound PFAS congeners. Relative to human albumin, perfluoroalkyl carboxylic and sulfonic acids were bound with greater affinity by porcine and rat serum albumin, and the perfluoroalkyl ether acid congener bound with lower affinity to porcine and bovine serum albumin. These comparative affinity data for PFAS binding by serum albumin from human, experimental model, and livestock species reduce critical interspecies uncertainty and improve accuracy of predictive bioaccumulation and toxicity assessments for PFAS.

Adsorption of Diclofenac and PFBS on a Hair Keratin Dimer

Environmental pollution by man-made toxic and persistent organic compounds, found throughout the world in surface and groundwater, has various negative effects on aquatic life systems and even humans. Therefore, it is important to develop and improve water treatment technologies capable of removing such substances from wastewater and purifying drinking water. The two substances investigated are the widely used painkiller diclofenac and a member of the class of "forever chemicals", perfluorobutanesulfonate. Both are known to have serious negative effects on living organisms, especially under long-term exposure, and are detectable in human hair, suggesting adsorption to a part of the hair fiber complex. In this study, a human hair keratin dimer is investigated for its ability to absorb diclofenac and perfluorobutanesulfonate. Initial predictions for binding sites are obtained via molecular docking and subjected to molecular dynamics simulations for more than 1 μs. The binding affinities obtained by the linear interaction energy method are high enough to motivate further research on human hair keratins as a sustainable, low-cost, and easily allocatable filtration material.

In Vitro and In Silico Analysis of the Bindings between Legacy and Novel Per- and Polyfluoroalkyl Substances and Human Serum Albumin

Per- and polyfluoroalkyl substances (PFASs) are emerging contaminants of concern that can enter the human body through a variety of pathways and thereby cause harmful effects. Exposure of pregnant women to PFASs could even affect both the mother and the child. Human serum albumin (HSA) is considered to be the primary transport protein for a variety of substances in body fluids. It can be bound to different contaminants and might result in possible effects on human health. Yet, few studies are available on the binding affinity of legacy PFASs and their novel alternatives to HSA. In this study, the binding mechanisms of HSA to both legacy PFASs and their novel alternatives were investigated using fluorescence spectroscopy, together with further molecular docking. The results show that all the target PFASs were statically quenched against HSA with binding ratios of 1:1. The binding constants of long-chain PFASs and novel alternatives of perfluoroalkanesulfonic acids (PFSAs) were greater than 102, whereas those of short-chain PFASs alternatives and novel alternatives of perfluorocarboxylic acids (PFCAs) were less than 102. In general, the binding affinities of PFCAs on HSA were less than that of PFSAs, while the binding affinities of short-chain PFASs alternatives on HSA were smaller than those of long-chain PFASs and their novel alternatives. Therefore, bindings to HSA could be considered as an important influencing factor for the bioaccumulation of legacy and novel PFASs in the human body.

Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation

More than 7000 per- and polyfluorinated alkyl substances (PFAS) have been documented in the U.S. Environmental Protection Agency's CompTox Chemicals database. These PFAS can be used in a broad range of industrial and consumer applications but may pose potential environmental issues and health risks. However, little is known about emerging PFAS bioaccumulation to assess their chemical safety. This study focuses specifically on the large and high-quality data set of fluorochemicals from the related environmental and pharmaceutical chemicals databases, and machine learning (ML) models were developed for the classification prediction of the unbound fraction of compounds in plasma. A comprehensive evaluation of the ML models shows that the best blending model yields an accuracy of 0.901 for the test set. The predictions suggest that most PFAS (∼92%) have a high binding fraction in plasma. Introduction of alkaline amino groups is likely to reduce the binding affinities of PFAS with plasma proteins. Molecular dynamics simulations indicate a clear distinction between the high and low binding fractions of PFAS. These computational workflows can be used to predict the bioaccumulation of emerging PFAS and are also helpful for the molecular design of PFAS to prevent the release of high-bioaccumulation compounds into the environment.

Computational Modeling of Human Serum Albumin Binding of Per- and Polyfluoroalkyl Substances Employing QSAR, Read-Across, and Docking

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals in widespread use that have been shown to be toxic to wildlife and humans. Human serum albumin (HSA) is a known transport protein that binds PFAS at various sites, leading to bioaccumulation and long-term toxicity. In silico tools like quantitative structure-activity relationship (QSAR), read-across, and quantitative read-across structure-property relationship (q-RASPR) are proven techniques for modeling chemical toxicity based on experimental data which can be used to predict the toxicity of untested and new chemicals, while at the same time, help to identify the major features responsible for toxicity. Classification-based and regression-based QSAR models are employed in the present study to predict the binding affinities of 24 PFAS to HSA. Regression-based QSAR models revealed that the packing density index (PDI) and quantitative estimation of drug-likeness (QED) descriptors were both positively correlated with higher binding affinity, while the classification-based QSAR model showed the average connectivity index of order 4 (X4A) descriptor was inversely correlated with binding affinity. Whereas molecular docking studies suggested that PFAS with the highest binding affinity to HSA create hydrogen bonds with Arg348 and salt bridges with Arg348 and Arg485, PFAS with lower binding affinity either showed no interactions with either amino acid or only interactions with Arg348. Among the studied PFAS, perfluoroalkyl acids (PFAA) with large carbon chain length (>C10) have one of the lowest binding affinities, compared to PFAA with carbon chain length ranging from 7 to 9, which showed the highest affinity to HSA. Generalized Read-Across (GenRA) was used to predict toxicity outcomes for the top five highest binding affinity PFAS based on 10 structural analogs for each and found that all are predicted as being chronic to sub-chronically toxic to HSA. The developed in silico models presented in this work can provide a framework for designing PFAS alternatives, screening compounds currently in use, and for the study of PFAS mixture toxicity, which is an area of intense research.

A Review on Per- and Polyfluoroalkyl Substances in Pregnant Women: Maternal Exposure, Placental Transfer, and Relevant Model Simulation

Per- and polyfluoroalkyl substances (PFASs) are important and ubiquitous environmental contaminants worldwide. These novel contaminants can enter human bodies via various pathways, subsequently posing risks to the ecosystem and human health. The exposure of pregnant women to PFASs might pose risks to the health of mothers and the growth and development of fetuses. However, little information is available about the placental transfer of PFASs from mothers to fetuses and the related mechanisms through model simulation. In the present study, based upon a review of previously published literature, we initially summarized the exposure pathways of PFASs in pregnant women, factors affecting the efficiency of placental transfer, and mechanisms associated with placental transfer; outlined simulation analysis approaches using molecular docking and machine learning to reveal the mechanisms of placental transfer; and finally highlighted future research emphases that need to be focused on. Consequently, it was notable that the binding of PFASs to proteins during placental transfer could be simulated by molecular docking and that the placental transfer efficiency of PFASs could also be predicted by machine learning. Therefore, future research on the maternal-fetal transfer mechanisms of PFASs with the benefit of simulation analysis approaches is warranted to provide a scientific basis for the health effects of PFASs on newborns.

Insight into the binding model of per- and polyfluoroalkyl substances to proteins and membranes

Legacy per- and polyfluoroalkyl substances (PFASs) have elicited much concern because of their ubiquitous distribution in the environment and the potential hazards they pose to wildlife and human health. Although an increasing number of effective PFAS alternatives are available in the market, these alternatives bring new challenges. This paper comprehensively reviews how PFASs bind to transport proteins (e.g., serum albumin, liver fatty acid transport proteins and organic acid transporters), nuclear receptors (e.g., peroxisome proliferator activated receptors, thyroid hormone receptors and reproductive hormone receptors) and membranes (e.g., cell membrane and mitochondrial membrane). Briefly, the hydrophobic fluorinated carbon chains of PFASs occupy the binding cavities of the target proteins, and the acid groups of PFASs form hydrogen bonds with amino acid residues. Various structural features of PFAS alternatives such as chlorine atom substitution, oxygen atom insertion and a branched structure, introduce variations in their chain length and hydrophobicity, which potentially change the affinity of PFAS alternatives for endogenous proteins. The toxic effects and mechanisms of action of legacy PFASs can be demonstrated and compared with their alternatives using binding models. In future studies, in vitro experiments and in silico quantitative structure-activity relationship modeling should be better integrated to allow more reliable toxicity predictions for both legacy and alternative PFASs.

Probing the Interactions of Perfluorocarboxylic Acids of Various Chain Lengths with Human Serum Albumin: Calorimetric and Spectroscopic Investigations

Despite an exponential increase in PFAS research over the past two decades, the mechanisms behind how PFAS cause adverse health effects are still poorly understood. Protein interactions are considered a significant driver of bioaccumulation and subsequent toxicity from re-exposure; however, most of the available literature is limited to legacy PFAS. We utilized microcalorimetric and spectroscopic methods to systematically investigate the binding between human serum albumin (HSA) and perfluorocarboxylic acids (PFCAs) of varying chain lengths and their nonfluorinated fatty acid (FA) counterparts. The results reveal the optimal chain length for significant PFCA-HSA binding and some fundamental interactions, i.e., the polar carboxylic head of PFCA is countered by ionizable amino acids such as arginine, and the fluorocarbon tails stabilized by hydrophobic residues like leucine and valine. Additionally, fluorine's unique polarizability contributes to PFCA's stronger binding affinities relative to the corresponding fatty acids. Based on these observations, we posit that PFCAs likely bind to HSA in a "cavity-filling" manner, provided they have an appropriate size and shape to accommodate the electrostatic interactions. The results reported herein widen the pool of structural information to explain PFAS bioaccumulation patterns and toxicity and support the development of more accurate computational modeling of protein-PFAS interactions. TOC graphic created with Biorender.com.

Investigation of the Interaction between Human Serum Albumin and Branched Short-Chain Perfluoroalkyl Compounds

The current trend dealing with the production of per- and polyfluoroalkyl substances (PFASs) involves the shifting toward branched short-chain fluorinated compounds known as new-generation PFASs. A key aspect to be clarified, to address the adverse health effects associated with the exposure to PFASs, is their binding mode to human serum albumin (hSA), the most abundant protein in plasma. In this study, we investigated the interaction between hSA and two representative branched short-chain PFASs, namely, HPFO-DA and C6O4. In-solution studies revealed that both compounds bind hSA with affinities and stoichiometries lower than that of the legacy long-chain perfluoroalkyl compound PFOA. Competition experiments using hSA-binding drugs with known site-selectivity revealed that both HPFO-DA and C6O4 bound to pockets located in subdomain IIIA. The crystal structure of hSA in complex with HPFO-DA unveiled the presence of two binding sites. The characterization and direct comparison of hSA interactions with new-generation PFASs may be key elements for the understanding of the toxicological impact of these compounds.

Insights into glyphosate removal efficiency using a new 2D nanomaterial

Glyphosate (GLY) is a nonselective herbicide that has been widely used in agriculture for weed control. However, there are potential genetic, development and reproduction risks to humans and animals associated with exposure to GLY. Therefore, the removal of this type of environmental pollutants has become a significant challenge. Some of the two-dimensional nanomaterials, due to the characteristics of hydrophilic nature, abundant highly active surficial sites and, large specific surface area are showed high removal efficiency for a wide range of pollutants. The present study focused on the adsorption behavior of GLY on silicene nanosheets (SNS). In order to provide more detailed information about the adsorption mechanism of contaminants on the adsorbent's surface, molecular dynamics (MD) and well-tempered metadynamics simulations are performed. The MD results are demonstrated that the contribution of the L-J term in pollutant/adsorbent interactions is more than coulombic energy. Furthermore, the simulation results demonstrated the lowest total energy value for system-A (with the lowest pollutant concentration), while system-D (contains the highest concentration of GLY) had the most total energy (E _tot: -78.96 vs. -448.51 kJ mol^-1). The well-tempered metadynamics simulation is accomplished to find the free energy surface of the investigated systems. The free energy calculation for the SNS/GLY system indicates a stable point in which the distance of GLY from the SNS surface is 1.165 nm.