Dynamic Behavior and Interaction Mechanism of Soil Organic Matter in Water Systems: A Coarse-Grained Molecular Dynamics Study.

Investigating soil organic matter's (SOM) microscale assembly and functionality is challenging due to its complexity. This study constructs comparatively realistic SOM models, including diverse components such as Leonardite humic acid (LHA), lipids, peptides, carbohydrates, and lignin, to unveil their spontaneous self-assembly behavior at the mesoscopic scale through microsecond coarse-grained molecular dynamics simulations. We discovered an ordered SOM aggregation creating a layered phase from its hydrophobic core to the aqueous phase, resulting in an increasing O/C ratio and declining structural amphiphilicity. Notably, the amphiphilic lipids formed a bilayer membrane, partnering with lignin to constitute SOM's hydrophobic core. LHA, despite forming a layer, was embedded within this structure. The formation of such complex architectures was driven by nonbonded interactions between components. Our analysis revealed component-dependent diffusion effects within the SOM system. Lipids, peptides, and lignin showed inhibitory effects on self-diffusion, while carbohydrates facilitated diffusion. This study offers novel insights into the dynamic behavior and assembly of SOM components, introducing an effective approach for studying dynamic SOM mechanisms in aquatic environments.

New Mechanism for the Apoptosis of Human Neuroblastoma Cells by the Interaction between Fluorene-9-Bisphenol and the G Protein-Coupled Estrogen Receptor 1.

Fluorene-9-bisphenol (BHPF) is an emerging contaminant. Presently, there is no report on its interaction with G protein-coupled estrogen receptor 1 (GPER). By using an integrated toxicity research scenario that combined theoretical study with experimental methods, BHPF was found to inhibit the GPER-mediated effect via direct receptor binding. Molecular dynamics simulations found that Trp2726.48 and Glu2756.51 be the key amino acids of BHPF binding with GPER. Moreover, the calculation indicated that BHPF was a suspected GPER inhibitor, which neither can activate GPER nor is able to form water channels of GPER. The role of two residues was successfully verified by following gene knockout and site-directed mutagenesis assays. Further in vitro assays showed that BHPF could attenuate the increase in intracellular concentration of free Ca2+ induced by G1-activated GPER. Besides, BHPF showed an enhanced cytotoxicity compared with G15, indicating that BHPF might be a more potent GPER inhibitor than G15. In addition, a statistically significant effect on the mRNA level of GPER was observed for BHPF. In brief, the present study proposes that BHPF be a GPER inhibitor, and its GPER molecular recognition mechanism has been revealed, which is of great significance for the health risk and assessment of BHPF.

First-principles study on the heterogeneous formation of environmentally persistent free radicals (EPFRs) over α-Fe2O3(0001) surface: Effect of oxygen vacancy.

Metal oxides with oxygen vacancies have a significant impact on catalytic activity for the transformation of organic pollutants in waste-to-energy (WtE) incineration processes. This study aims to investigate the influence of hematite surface oxygen point defects on the formation of environmentally persistent free radicals (EPFRs) from phenolic compounds based on the first-principles calculations. Two oxygen-deficient conditions were considered: oxygen vacancies at the top surface and on the subsurface. Our simulations indicate that the adsorption strength of phenol on the α-Fe2O3(0001) surface is enhanced by the presence of oxygen vacancies. However, the presence of oxygen vacancies has a negative impact on the dissociation of the phenol molecule, particularly for the surface with a defective point at the top layer. Thermo-kinetic parameters were established over a temperature range of 300-1000 K, and lower reaction rate constants were observed for the scission of phenolic O-H bonds over the oxygen-deficient surfaces compared to the pristine surface. The negative effects caused by the oxygen-deficient conditions could be attributed to the local reduction of FeIII to FeII, which lower the oxidizing ability of surface reaction sites. The findings of this study provide us a promising approach to regulate the formation of EPFRs.

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.

Generation Mechanism of Perfluorohexanesulfonic Acid from Polyfluoroalkyl Sulfonamide Derivatives During Chloramination in Drinking Water.

Per- and polyfluoroalkyl substances (PFASs), including perfluorohexanesulfonic acid (PFHxS), as emerging persistent organic pollutants widely detected in drinking water, have drawn increasing concern. The PFHxS contamination of drinking water always results from direct and indirect sources, especially the secondary generations through environmental transformations of precursors. However, the mechanism of the transformation of precursors to PFHXS during the drinking water treatment processes remains unclear. Herein, the potential precursors and formation mechanisms of PFHxS were explored during drinking water disinfection. Simultaneously, the factors affecting PFHxS generation were also examined. This study found PFHxS could be generated from polyfluoroalkyl sulfonamide derivatives during chlorination and chloramination. The fate and yield of PFHxS varied from different precursors and disinfection processes. In particular, monochloramine more favorably formed PFHxS. Several perfluoroalkyl oxidation products and decarboxylation intermediates were detected and identified in the chloraminated samples using Fourier-transform ion cyclotron resonance mass spectrometry. Combined with density functional theory calculations, the results indicated that the indirect oxidation via the attack of the nitrogen atom in sulfonamide groups might be the dominant pathway for generating PFHxS during chloramination, and the process could be highly affected by the monochloramine dose, pH, and temperature. This study provides important evidence of the secondary formation of PFHxS during drinking water disinfection and scientific support for chemical management of PFHxS and PFHxS-related compounds.

Computational Insights into the Allosteric Effect and Dynamic Structural Features of the SARS-COV-2 Spike Protein.

Invited for the cover of this issue are Aiqian Zhang, Jianjie Fu, Guibin Jiang, and co-workers at the Chinese Academy of Sciences. The image depicts the molecular recognition of human angiotensin-converting enzyme 2 by the SARS-COV-2 spike protein. Read the full text of the article at 10.1002/chem.202104215.

Computational Insights into the Allosteric Effect and Dynamic Structural Features of the SARS-COV-2 Spike Protein.

COVID-19 caused by SARS-COV-2 is continuing to surge globally. The spike (S) protein is the key protein of SARS-COV-2 that recognizes and binds to the host target ACE2. In this study, molecular dynamics simulation was used to elucidate the allosteric effect of the S protein. Binding of ACE2 caused a centripetal movement of the receptor-binding domain of the S protein. The dihedral changes in Phe329 and Phe515 played a key role in this process. Two potential cleavage sites S1/S2 and S2' were exposed on the surface after the binding of ACE2. The binding affinity of SARS-COV-2 S protein and ACE2 was higher than that of SARS-COV. This was mainly due to the mutation of Asp480 in SARS-COV to Ser494 in SARS-COV-2, which greatly weakened the electrostatic repulsion. The result provides a theoretical basis for the SARS-COV-2 infection and aids the development of biosensors and detection reagents.

Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock.

Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.

Data-driven coarse-grained modeling of non-equilibrium systems.

Modeling a high-dimensional Hamiltonian system in reduced dimensions with respect to coarse-grained (CG) variables can greatly reduce computational cost and enable efficient bottom-up prediction of main features of the system for many applications. However, it usually experiences significantly altered dynamics due to loss of degrees of freedom upon coarse-graining. To establish CG models that can faithfully preserve dynamics, previous efforts mainly focused on equilibrium systems. In contrast, various soft matter systems are known to be out of equilibrium. Therefore, the present work concerns non-equilibrium systems and enables accurate and efficient CG modeling that preserves non-equilibrium dynamics and is generally applicable to any non-equilibrium process and any observable of interest. To this end, the dynamic equation of a CG variable is built in the form of the non-stationary generalized Langevin equation (nsGLE), where the two-time memory kernel is determined from the data of the auto-correlation function of the observable of interest. By embedding the nsGLE in an extended dynamics framework, the nsGLE can be solved efficiently to predict the non-equilibrium dynamics of the CG variable. To prove and exploit the equivalence of the nsGLE and extended dynamics, the memory kernel is parameterized in a two-time exponential expansion. A data-driven hybrid optimization process is proposed for the parameterization, which integrates the differential-evolution method with the Levenberg-Marquardt algorithm to efficiently tackle a non-convex and high-dimensional optimization problem.

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics.

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Long-Range Transport, Trophic Transfer, and Ecological Risks of Organophosphate Esters in Remote Areas.

Organophosphate esters (OPEs) have been a focus in the field of environmental science due to their large volume production, wide range of applications, ubiquitous occurrence, potential bioaccumulation, and worrisome ecological and health risks. Varied physicochemical properties among OPE analogues represent an outstanding scientific challenge in studying the environmental fate of OPEs in recent years. There is an increasing number of studies focusing on the long-range transport, trophic transfer, and ecological risks of OPEs. Therefore, it is necessary to conclude the OPE pollution status on a global scale, especially in the remote areas with vulnerable and fragile ecosystems. The present review links together the source, fate, and environmental behavior of OPEs in remote areas, integrates the occurrence and profile data, summarizes their bioaccumulation, trophic transfer, and ecological risks, and finally points out the predominant pollution burden of OPEs among organic pollutants in remote areas. Given the relatively high contamination level and bioaccumulation/biomagnification behavior of OPEs, in combination with the sensitivity of endemic species in remote areas, more attention should be paid to the potential ecological risks of OPEs.

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved.

We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system. The CG modeling is built upon the framework of the generalized Langevin equation (GLE). The key is to determine each term in the GLE by directly linking it to atomistic data. In particular, we propose a two-stage Gaussian process-based Bayesian optimization method to infer the non-Markovian memory kernel from the data of the velocity autocorrelation function (VACF). Considering that the long-time behaviors of the VACF and memory kernel for polymer solutions can exhibit hydrodynamic scaling (algebraic decay with time), we further develop an active learning method to determine the emergence of hydrodynamic scaling, which can accelerate the inference process of the memory kernel. The proposed methods do not rely on how the mean force or CG potential in the GLE is constructed. Thus, we also compare two methods for constructing the CG potential: a deep learning method and the iterative Boltzmann inversion method. With the memory kernel and CG potential determined, the GLE is mapped onto an extended Markovian process to circumvent the expensive cost of directly solving the GLE. The accuracy and computational efficiency of the proposed CG modeling are assessed in a model star-polymer solution system at three representative concentrations. By comparing with the reference atomistic simulation results, we demonstrate that the proposed CG modeling can robustly and accurately reproduce the dynamic and structural properties of polymers in solution.

The Effect of Structural Diversity on Ligand Specificity and Resulting Signaling Differences of Estrogen Receptor α.

Numerous chemicals have been reported to exert estrogen-like endocrine disrupting effects via a receptor binding mechanism that directly interacts with the ligand binding domain of estrogen receptor α (ERα). However, not only their binding affinities to ERα but also their interference in specific cell and tissue functions are clearly different. In this regard, significant regulation differences among three representative estrogenic chemicals (diethylstilbestrol (DES), bisphenol A (BPA), and diarylpropionitrile (DPN)), well-known ERα agonists with very similar structures, have been observed. Molecular dynamics simulation is used to explore the underlying mechanism of different regulation effects induced by the similar estrogen-like chemicals. The DES-induced 12 Å motion of the H9-H10 loop markedly expands the negative electrostatic potential surface of the AF-2 domain, which is consistent with the over-regulation effect of the agonist. In comparison, the 3 Å motion induced by BPA and DPN corresponds to the low-regulation effect of the chemicals. Cross-correlation analysis indicates that the different ERα motions and resulting surface feature of AF-2 domain are brought by the distinguished binding modes of the agonists. Moreover, only hydrophobic DES with estrogen-like size and flexibility has a high binding affinity of -23.47 kcal/mol binding free energy. Both the hydrophilic group in DPN and the small molecular size of BPA dramatically decrease the agonist binding ability, and their binding free energies are only -12.43 kcal/mol and -11.82 kcal/mol, respectively. Our study demonstrates that similar chemicals interact differently with ERα and induce different allosteric effects, which explains the observed regulation diversity.

Correction: Implicit-solvent coarse-grained modeling for polymer solutions via Mori-Zwanzig formalism.

Correction for 'Implicit-solvent coarse-grained modeling for polymer solutions via Mori-Zwanzig formalism' by Shu Wang et al., Soft Matter, 2019, DOI: .

Implicit-solvent coarse-grained modeling for polymer solutions via Mori-Zwanzig formalism.

We present a bottom-up coarse-graining (CG) method to establish implicit-solvent CG modeling for polymers in solution, which conserves the dynamic properties of the reference microscopic system. In particular, tens to hundreds of bonded polymer atoms (or Lennard-Jones beads) are coarse-grained as one CG particle, and the solvent degrees of freedom are eliminated. The dynamics of the CG system is governed by the generalized Langevin equation (GLE) derived via the Mori-Zwanzig formalism, by which the CG variables can be directly and rigorously linked to the microscopic dynamics generated by molecular dynamics (MD) simulations. The solvent-mediated dynamics of polymers is modeled by the non-Markovian stochastic dynamics in GLE, where the memory kernel can be computed from the MD trajectories. To circumvent the difficulty in direct evaluation of the memory term and generation of colored noise, we exploit the equivalence between the non-Markovian dynamics and Markovian dynamics in an extended space. To this end, the CG system is supplemented with auxiliary variables that are coupled linearly to the momentum and among themselves, subject to uncorrelated Gaussian white noise. A high-order time-integration scheme is used to solve the extended dynamics to further accelerate the CG simulations. To assess, validate, and demonstrate the established implicit-solvent CG modeling, we have applied it to study four different types of polymers in solution. The dynamic properties of polymers characterized by the velocity autocorrelation function, diffusion coefficient, and mean square displacement as functions of time are evaluated in both CG and MD simulations. Results show that the extended dynamics with auxiliary variables can construct arbitrarily high-order CG models to reproduce dynamic properties of the reference microscopic system and to characterize long-time dynamics of polymers in solution.

Interfacial formation of environmentally persistent free radicals-A theoretical investigation on pentachlorophenol activation on montmorillonite in PM2.5.

Environmentally persistent free radicals (EPFRs) in atmospheric fine particulate matters (PM2.5) possess high bioactivity and result in severe health problems. The facile transformation of aromatic pollutants into EPFRs on montmorillonite (MMT), an important solid component in PM2.5, is an activation of air pollutants into more toxic chemical species and also attributes to the secondary source of EPFRs in PM2.5. In this study, the interfacial reactions of pentachlorophenol (PCP), a typical EPFR precursor in air pollution, on the Fe(III)-, Ca- and Na-MMT surfaces have been explored by the density functional theory (DFT) calculations using the periodic slab models. The PCP molecule is found to be exothermically adsorbed on the three MMT surfaces. Moreover, significant charge transfer from PCP to Fe takes place and finally leads to the surface-bound phenoxyl radical formation on the Fe(III)-MMT surface since the half-filled 3d orbital of Fe3+ in Fe(III)-MMT could act as electron acceptor allowing the electron transferring from the 2p orbital of the phenolic O in PCP to Fe ion. However, similar charge transfer is not found in the Ca- and Na-MMTs, and the PCP transformation reaction is hindered on the Ca- and Na-MMT surfaces. Namely, the PCP activation to the corresponding EPFRs is impossible on the Ca-MMT and Na-MMT surfaces, while the catalytically active Fe(III)-MMT in PM2.5 can transform the chlorinated phenols into more toxic phenoxy-type EPFRs at ambient temperatures. Accordingly, more attention should be paid on the effect of MMT with catalytical capacity on the toxicity of PM2.5.

A novel computational solution to the health risk assessment of air pollution via joint toxicity prediction: A case study on selected PAH binary mixtures in particulate matters.

Regional haze episode has already caused overwhelming public concern. Unraveling the health effects of the representative composition mixtures of atmospheric fine particulate matters (PM2.5) becomes a top priority. In this study, a novel computational solution integrating chemical-induced genomic residual effect prediction with in vitro-based risk assessment is proposed to obtain the cumulative health risk of typical chemical mixtures of particulate matters (PM). The joint toxicity of binary mixtures is estimated by analyzing both genomic similarity and dose-response curve of relevant pollutants for the chemical-induced genomic residual effect. Specifically, the modified relative potency factor (mRPF) of mixtures is introduced for this purpose, and the ratio of activation (RA) value is defined to assess the corresponding health risks of the mixtures. As a methodology demonstration, the health risk of typical binary polycyclic aromatic hydrocarbon (PAH) mixtures in PM, containing Benzo[a]pyrene (BaP) as a component, is assessed using the proposed solution. Our results indicate that the combined effect of pairwise PAHs of BaP with Benzo[b]fluoranthene (BbF) and Benz[a]anthracene (BaA) is synergistic on p53 pathway, and that the health risk of the such mixtures increases compared to that of the individual ones. Obviously, the cumulative health risk of environmental mixtures will be underestimated when the synergistic effect is wrongly assumed to be additive. To our knowledge, this is the first study ever report on a computational solution to the health risk assessment of environmental pollution via joint toxicity prediction. The novel methodology proposed here makes full use of the open-access in vitro assay data and transcriptomic information in literatures and provides a successful demonstration of the concept of systems biology and translational science.

Quantum Chemical and Kinetic Study on Radical/Molecule Formation Mechanism of Pre-Intermediates for PCTA/PT/DT/DFs from 2-Chlorothiophenol and 2-Chlorophenol Precursors.

Polychlorinated phenoxathiins (PCPTs), polychlorinated dibenzothiophenes (PCDTs), and polychlorinated thianthrenes (PCTAs) are sulfur analogues of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/DFs). Chlorothiophenols (CTPs) and chlorophenols (CPs) are key precursors for the formation of PCTA/PT/DTs, which can react with H or OH to form chloro(thio)phenoxy radical, sulfydryl/hydroxyl-substituted phenyl radicals, and (thio)phenoxyl diradicals. However, previous radical/radical PCTA/DT formation mechanisms in the literature failed to explain the higher concentration of PCDTs than that of PCTAs under the pyrolysis or combustion conditions. In this work, a detailed thermodynamics and kinetic calculations were carried out to investigate the pre-intermediate formation for PCTA/PT/DTs from radical/molecule coupling of the 2-C(T)P with their key radical species. Our study showed that the radical/molecule coupling mechanism explains the gas-phase formation of PCTA/PT/DTs in both thermodynamic and kinetic perspectives. The S/C coupling modes to form thioether-(thio)enol intermediates are preferable over the O/C coupling modes to form ether-(thio)enol intermediates. Thus, although the radical/molecule coupling of chlorophenoxy radical with 2-C(T)P has no effect on the PCDD/PT formation, the radical/molecule coupling of chlorothiophenoxy radical with 2-C(T)P plays an important role in the PCTA/PT formation. Most importantly, the pre-PCDT intermediates formation pathways from the couplings of sulfydryl/hydroxyl-substituted phenyl radical with 2-C(T)P and (thio)phenoxyl diradicals with 2-C(T)P are more favorable than pre-PCTA/PT intermediates formation pathways from the coupling of chlorothiophenoxy radical with 2-C(T)P, which provides reasonable explanation for the high PCDT-to-PCTA ratio in the environment.

Theoretical study on the formation mechanism of polychlorinated dibenzothiophenes/thianthrenes from 2-chlorothiophenol molecules.

Homogeneous formation of polychlorinated dibenzothiophenes/thianthrenes (PCDT/TAs), sulfurated compounds analogous to polychlorinated dibenzo-p-dioxin/dibenzofurans (PCDD/Fs), has been well-documented to occur via radical-radical coupling reactions from chlorinated thiophenol precursors. However, the current understanding of the formation mechanism of PCDT/TAs is exclusively limited to the inherent point of view that chlorothiophenoxy radicals act as the only required intermediates for PCDT/TAs. This study investigates reaction pathways for the formation of PCDT/TAs involving two new types of radical species, i.e., substituted phenyl radicals and substituted thiophenoxyl diradicals. Taking 2-chlorothiophenol (2-CTP) as a model compound for chlorothiophenols, we found that apart from the mostly discussed chlorothiophenoxy radicals, substituted phenyl radicals and substituted thiophenoxyl diradicals could also be readily formed via the reaction of 2-CTP with H radicals. Furthermore, direct self- and cross-coupling of these radicals can result in the formation of PCDT/TAs, including 1-monochlorothianthrene (1-MCTA), 1,6-dichlorothianthrene (1,6-DCTA), 4,6-dichlorodibenzothiophene (4,6-DCDT) and 1,6-dichlorodibenzothiophene (1,6-DCDT). The pathways proposed in this work are proven to be both thermodynamically and kinetically favorable. Particularly, comparisons were made between the formation mechanisms of sulfurated and oxygenated dioxin systems from an energetic point view, showing that replacing oxygen with sulfur atoms greatly reduces the activation barriers of the rate-controlling steps involved in the PCDT/TA formation processes compared with those involved for PCDD/Fs. The calculated results in this work may improve our understanding of the formation mechanism of PCDT/TAs from chlorothiophenol precursors and should be informative to environmental scientists.

Inhibition of thrombin by functionalized C60 nanoparticles revealed via in vitro assays and in silico studies.

The studies on the human toxicity of nanoparticles (NPs) are far behind the rapid development of engineered functionalized NPs. Fullerene has been widely used as drug carrier skeleton due to its reported low risk. However, different from other kinds of NPs, fullerene-based NPs (C60 NPs) have been found to have an anticoagulation effect, although the potential target is still unknown. In the study, both experimental and computational methods were adopted to gain mechanistic insight into the modulation of thrombin activity by nine kinds of C60 NPs with diverse surface chemistry properties. In vitro enzyme activity assays showed that all tested surface-modified C60 NPs exhibited thrombin inhibition ability. Kinetic studies coupled with competitive testing using 3 known inhibitors indicated that six of the C60 NPs, of greater hydrophobicity and hydrogen bond (HB) donor acidity or acceptor basicity, acted as competitive inhibitors of thrombin by directly interacting with the active site of thrombin. A simple quantitative nanostructure-activity relationship model relating the surface substituent properties to the inhibition potential was then established for the six competitive inhibitors. Molecular docking analysis revealed that the intermolecular HB interactions were important for the specific binding of C60 NPs to the active site canyon, while the additional stability provided by the surface groups through van der Waals interaction also play a key role in the thrombin binding affinity of the NPs. Our results suggest that thrombin is a possible target of the surface-functionalized C60 NPs relevant to their anticoagulation effect.