Open Access

Molecular dynamics simulation of humic substances

Chemical and Biological Technologies in Agriculture20141:10

https://doi.org/10.1186/s40538-014-0010-4

Received: 20 May 2014

Accepted: 15 July 2014

Published: 2 September 2014

Abstract

Humic substances (HS) are complex mixtures of natural organic material which are found almost everywhere in the environment, and particularly in soils, sediments, and natural water. HS play key roles in many processes of paramount importance, such as plant growth, carbon storage, and the fate of contaminants in the environment. While most of the research on HS has been traditionally carried out by conventional experimental approaches, over the past 20 years complementary investigations have emerged from the application of computer modeling and simulation techniques. This paper reviews the literature regarding computational studies of HS, with a specific focus on molecular dynamics simulations. Significant achievements, outstanding issues, and future prospects are summarized and discussed.

Keywords

Humic substances Natural organic matter Soil organic matter Molecular dynamics Molecular modeling Molecular simulation

Introduction

Humic substances (HS) consist of a large variety of natural organic molecules that originate from the decomposition, and related microbial activity, of dead biological material, especially plant tissues [1]. HS are ubiquitous in the natural environment where they contribute to the regulation of many crucial ecological and environmental processes. For example, HS sustain plant growth and terrestrial life in general, and control the fate of environmental contaminants by acting as sorbents for toxic metal ions, radionuclides, and organic pollutants [2]–[4]. Furthermore, HS account for most of the planet’s organic material, and represent the most abundant reservoir of carbon [1],[5]. In fact, HS are receiving growing attention in recent years because of their potential role in land management strategies aimed at promoting carbon sequestration, to ultimately reduce atmospheric CO2 and hence help tackle climate change [5].

Despite much research carried out over many decades now, the detailed nature of HS is still not fully understood. While main molecular building blocks have long been identified as hydrocarbon, quinone, phenol, catechol, and sugar groups (Figure 1), the crucial issue of how exactly these chemical moieties are organized at the molecular and supramolecular levels is still debated. A complicating factor is represented by the high variability of HS, as their composition depends on the specific ecosystem where they originate, in terms for instance of vegetation, climate, and topography. Following a polymer analogy, a popular early hypothesis described HS as collections of organic macromolecules, with molecular weights of up to several tens of thousands or even hundreds of thousands of grams per mole [1],[6],[7]. However, the current consensus, supported by the most recent experimental evidence, describes HS as heterogeneous supramolecular mixtures of relatively small molecules, with molecular weight of a few thousands of grams per mole, which associate dynamically through weak (non-covalent) interactions, especially hydrogen bonds and hydrophobic forces [8]–[14]. According to this hypothesis, HS are also capable of self-assembling into micellar structures, whereby an inner hydrophobic core is shielded from outer water through interfacial hydrophilic regions [15]–[17].
Figure 1

Common HS chemical groups. Chemical structures of the main molecular building blocks forming HS.

Over the past 20 years, traditional experimental investigation of HS has been compounded by various computer modeling and simulation approaches. This review focuses mainly on computational studies of HS conducted using the molecular dynamics method. After an introduction to the main methodological aspects, the available literature is categorized, summarized, and critically discussed.

Review

The molecular dynamics simulation method

Molecular dynamics (MD) is a computer simulation technique which is widely used in science and engineering, and is employed to obtain equilibrium and transport properties for collections of discrete particles. MD is a powerful method to simulate matter at the molecular scale; applications can be found for a wide range of systems, from simple gases and liquids [18]–[22] to various complex materials including proteins [23]–[26], lipid membranes [27]–[35], polymers [36]–[39], and carbon nanostructures [40]–[42]. Popular computer programs that implement MD include LAMMPS [43],[44], GROMACS [45], AMBER [46], GROMOS [47], DL_POLY [48], and CHARMM [49]. In this section, the main aspects of the MD method are summarized; more details can be found in dedicated books [50]–[54] and review articles [55]–[58].

The key components of the main MD algorithm are reported in Algorithm 1.

The first stage typically involves initializing the calculation by supplying the computer program with the coordinates of all atoms in the system (x), together with the models (V) which determine how the atoms interact. Such models are typically called potentials, or force fields. It should be noted that the focus of this summary is on fixed-charge biomolecular/organic force fields, as these are used predominantly in the simulation of humic substances. However, several other types of force fields exist, as documented extensively in the literature; in particular, significant progress has been recently made on polarizable models [59].

The standard form of a force field is
V = V bonded + V nonbonded
(1)
where Vbonded defines the (intramolecular) interactions between atoms covalently bonded to each other, and Vnonbonded defines the intermolecular interactions. In particular, Vbonded typically contains simple harmonic terms, whereby for instance a covalent bond is modeled with a potential representing a mechanical spring:
V ( l ) = 1 2 k ( l l eq ) 2
(2)
with k the rigidity constant, l the actual bond length, and leq the equilibrium bond length. Additional terms are used to control the angles within groups of three consecutively bonded atoms, as well as torsions involving groups of four bonded atoms. Rigidity constants and equilibrium values are typically optimized to reproduce vibrational and conformational properties from experiments or ab initio quantum mechanics calculations. The nonbonded components of a force field normally describe van der Waals and electrostatic interactions. The van der Waals interaction between each pair of atoms a distance r apart is modeled with the Lennard-Jones potential:
V ( r ) = 4 ε σ r 12 σ r 6
(3)
with σ defining the collision distance and ε the attractive energy. Atoms interacting through the Lennard-Jones potential can be thought of as spheres which repel each other at short range (a feature that mimics overlap between electron clouds) and attract each other at long range (corresponding to attractive dispersion forces). The Lennard-Jones parameters σ and ε are normally optimized to reproduce thermodynamic data from experiment, including liquid densities and enthalpies of vaporization. Electrostatic forces between each pair of atoms i,j located a distance r apart are modeled with the Coulomb potential:
V ( r ) = Q i Q j 4 π ε 0 r
(4)

with Q i and Q j the corresponding charges and ε0 the permittivity of free space. In general, charges are assigned empirically to reproduce experimental observables such as known multipole moments or thermodynamic properties.

The second stage in Box 1 represents the main computational loop of a molecular dynamics simulation. The first part of the loop involves computing the force on each atom, which is obtained from the gradient of the potential V. In the second part of the loop, the forces are used to move each atom forward in time. This is done by solving numerically Newton’s equations of motion. For example, considering one of the most widely used algorithms [60], given the force f(t) and velocity v(t) at the current time t, each atom is moved one timestep, from position x(t) at time t to position x(t+Δ t) at time t+Δ t, according to
x ( t + Δt ) = x ( t ) + Δt [ v ( t ) + Δt f ( t ) / 2 m ]
(5)

where m is the atom’s mass. Each iteration of this second stage advances the system in time by a typically small timestep (Δ t=10−15 s), and thus complete simulations normally require up to 106−109 iterations.

The third stage in Box 1 refers to the output data generated by the simulation. In particular, a trajectory is obtained consisting of consecutive snapshots of the system taken at regular time intervals during the simulation. The output trajectory is typically analyzed using statistical mechanics to obtain various thermodynamical and dynamical properties of interest, such as energy terms, average and local densities, diffusion and viscosity coefficients, mechanical parameters, and electrical potentials.

Simulations of humic substances

In this section, a number of representative molecular dynamics investigations of HS reported in the literature are reviewed. The studies considered are organized into different subsections corresponding to different types of systems investigated. It should be noted that in the literature HS are sometimes referred to as ‘NOM,’ ‘SOM,’ or ‘DOM’ [61],[62]. These acronyms stand for natural organic matter (NOM), soil organic matter (SOM), and dissolved organic matter (DOM). Specifically, NOM refers to a complex mixture of organic material that is found in water, soils, and sediments [63],[64]. SOM refers to all carbon-containing substances in soils [65]. DOM is defined as the portion of NOM which passes through a filter of 0.45- μm pore size [66]. For all of these three categories, HS represent a major constituent [7],[65].

Modeling HS and their fundamental properties

Computational molecular models for HS began to appear in the 1990s. One of the first and most significant contributions involved the development of the TNB model (from Temple-Northeastern-Birmingham) [67]–[70]. The TNB model was aimed at representing a typical, ‘average’ HS molecule, with a chemical composition determined on the basis of analytical measurements. Specifically, the TNB model comprises three carboxylic groups, three carbonyl groups, two phenolic groups, two amine groups, and four other R-OH alcohol groups, for a total chemical formula of C 38H39O16N2 and a molecular weight of 753 g mol −1. The molecular structure of the TNB molecule is reported in Figure 2. Earlier simulations of the TNB molecule were performed in vacuo, meaning that no other substance (such as water) was included in the system. In particular, these calculations aimed at finding the most realistic (optimized) geometrical arrangements by minimizing the potential energy of the system [67],[70].
Figure 2

The TNB model. Molecular structure of the Temple-Northeastern-Birmingham (TNB) model for HS. Reprinted with permission from Sein et al. [70]. Copyright 1999 American Chemical Society.

Alvarez-Puebla and Garrido [71] studied the effect of pH on the aggregation of the TNB humic model [68]–[70]. By simulating the aggregation process, it was found that the molecular size increased with increased pH values due to intramolecular electrostatic repulsion, while the size of the aggregates decreased with increased pH because of increased repulsive intermolecular interactions [71]. Alvarez-Puebla et al. [72] subsequently developed a modified version of the TNB model [68]–[70] aimed at better representing a set of experimental data on HS composition. A series of simulations were conducted to investigate HS aggregation as a function of the model’s ionic states, both in vacuo and in aqueous solution [72].

Leenheer et al. [73] developed a model of HS and used it for the interpretation of experimental data on metal-HS association. Kubicki and Apitz [69] later used the Leenheer model to predict equilibrium structures through classical molecular mechanics and quantum calculations and to test the effect of the specific computational methodology on the structures obtained. The Leenheer model [73] was also adopted by Porquet et al. [74] to investigate hydrogen bonding and clustering of neutral HS molecules in water.

While HS molecular models are typically constructed by assembling atoms manually into the desired compositions and geometries, an interesting alternative approach was developed by Diallo et al. [75], who proposed a series of structural models for soil HS by processing an extensive set of experimental data through an automated algorithm, implemented into specifically designed computational software. The model molecules obtained were relatively small, with an average molecular weight of ≈1,000 g mol −1. As opposed to the more traditional approaches, the method of Diallo et al. [75] has the advantage that only the appropriate isomers are selected when multiple structures can be deduced from the same set of analytical data.

The specific role of water in its interactions with HS was investigated by Aquino et al. [76],[77]. In this work, HS were represented by simple hydrocarbon chains containing hydrophilic (carboxyl) groups. The MD simulations showed that distant hydrophilic groups can be cross-linked by water molecular bridges [76],[77].

In general, the HS models reviewed so far include molecules characterized by relatively low numbers of atoms, on the order of 100, yielding molecular weights of ≈1,000 g mol −1. However, the development of molecular models comprising substantially larger numbers of atoms has also been reported. In particular, significant work in this context has been carried out by Schulten and coworkers [61],[65],[78], who proposed model HS molecules comprising over 1,000 atoms and corresponding molecular weights of up to and over 10,000 g mol −1.

Specifically, Schulten and Schnitzer [78] designed a SOM molecule by hydrogen bonding a humic structure to a hexapeptide and a trisaccharide, obtaining a compound with molecular formula C 342H388O124N12 and corresponding molecular weight of 6,651 g mol −1. Based on the SOM model, Schulten [61] subsequently proposed a model for DOM and investigated complexes with xenobiotic substances. In general, xenobiotics are substances such as pollutants or pesticides, which are found in the environment yet are not naturally expected to be present. Schulten [61] performed molecular simulations of systems including DOM, water, and the xenobiotic pentachlorophenol (a pesticide), atrazine (a herbicide), and DDT (an insecticide). Geometry optimization calculations were performed to analyze energetics and hydrogen bonds. It was found that van der Waals forces and hydrogen bonds were the main contributors to the temporary retention of xenobiotic substances in DOM [61].

By considering the compounds observed in pyrolysis and other experimental studies of organic matter, Schulten further refined a molecular model for DOM [65]. In particular, a prototypical DOM molecule was obtained taking into account the most frequently occurring molecular building blocks, as well as an averaged elemental composition. Various organic functional groups found in HS were employed, including aromatic, alkyl, carboxyl, ketone, quinone, phenol, alcohol, ether, amine, amide, and heterocyclic N and S functional groups. The resulting molecule contained 1,262 atoms, with molecular formula C 487H492O306N15S2 and corresponding molecular weight of 11,515 g mol −1; a 3D representation of the Schulten DOM molecule [65] is reported in Figure 3. In the simulations, 35 water molecules were also added to the surface of the DOM molecule. Energy minimization calculations were carried out to study the contributions from van der Waals and electrostatic terms, as well as to characterize hydrogen bonds [65].
Figure 3

The Schulten DOM molecule. Snapshot of a 3D representation of the Schulten DOM model [65]. Color codes for atom types are as follows: carbon (cyan), hydrogen (white), oxygen (pink), nitrogen (blue), and sulfur (yellow). Reprinted with permission from Schulten [65]. Copyright 1999 Elsevier.

Sutton et al. [62] refined the Schulten DOM molecule, obtaining a compound with molecular formula C 447H421O272N15S2 and corresponding molecular weight of 10,419 g mol −1. This model was then simulated under conditions of increased hydration, typical of natural soil and water environments. In particular, the DOM molecule was surrounded by water molecules corresponding to a hydration layer of ≈5 Å thickness. The systems were shown to reproduce experimental physical and chemical properties of HS for several characteristic environmental conditions of soil. Specifically, results were obtained for density, hydrogen bonds, radius of gyration, and the Hildebrand solubility parameter [62].

For the HS molecular models considered in this review, Table 1 reports the corresponding chemical formula and molecular weight.
Table 1

Properties of model HS molecules

Model molecule

Chemical formula

Molecular weight (g mol -1)

References

TNB

C 38H39O16N2

753

[67]–[70]

Schulten SOM

C 342H388O124N12

6,651

[61]

Schulten DOM

C 487H492O306N15S2

11,515

[65]

Sutton DOM

C 447H421O272N15S2

10,419

[62]

DOM polyanion

C 447 H 345 O 272 N 15 S 2 76

10,343

[79]

Brown humic acid

(C 38H39O16N2) 13

9,789

[86]

HS in complex with soil minerals

In general, HS are stabilized by their association with soil minerals, which prevent microbial attack and resulting rapid decomposition of HS [79],[80]. As a consequence, HS adsorption to minerals regulates the presence of carbon in soils [79]. An improved understanding of organo-mineral interactions is thus highly desirable, as it could lead to new strategies for soil carbon retention and sequestration through stabilization of HS.

A pioneering MD simulation study in this area was carried out by Teppen et al. [81], who investigated trichloroethylene (C 2HCl3), taken as a basic compound representative of organic material, adsorbed on clay mineral surfaces (kaolinite and pyrophyllite) in the presence of water. By considering different levels of hydration, it was found that water can outcompete C 2HCl3 for adsorption at the clay surface [81].

Shevchenko et al. [82] simulated organo-mineral aggregates in water using a NOM model based on an oxidized lignin-carbohydrate complex. MD simulations were conducted using the simulated annealing approach, whereby structural optimization is obtained by cooling-heating cycles which allow energy barriers to be overcome, eventually leading to optimized geometries.

To investigate further the nature of HS-mineral interactions, Sutton and Sposito [79] simulated complexes comprising the Schulten DOM molecule [65] and Ca-montmorillonite, a clay mineral of the smectite group. In particular, two DOM-Ca-montmorillonite systems were constructed. The first system included a C 447H421O272N15S2 protonated DOM molecule, a 32-unit Ca-montmorillonite clay layer, 12 Ca 2+ ions, and 543 interlayer water molecules. The second system comprised a C 447 H 345 O 272 N 15 S 2 76 DOM polyanion, a 32-unit Ca-montmorillonite clay layer, 50 Ca 2+ ions, and 852 interlayer water molecules. In both systems, the DOM molecule was inserted into one of the clay interlayers. From their simulations, Sutton and Sposito [79] were able to ascribe the stabilization of organo-mineral systems to significant direct hydrophobic and hydrogen bonding interactions between organic and mineral groups. A simulation snapshot from this work is reported in Figure 4.
Figure 4

DOM-montmorillonite system. Snapshot from a simulation of protonated DOM-montmorillonite system. Water molecules are represented with cylinders, whereas DOM and clay are represented as balls and sticks. Color codes for atom types are as follows: carbon (gray), hydrogen (white), oxygen (red), nitrogen (blue), sulfur (yellow), and calcium ions (brown). Reprinted with permission from Sutton and Sposito [79]. Copyright 2006 Elsevier.

Petridis et al. [83] modeled an Al 2O3 mineral surface in contact with the organic compounds stearic acid and glucose. The aim of this work was to study the mechanism by which glucose accumulates in a layer between Al 2O3 and stearic acid, as observed experimentally. The simulations conducted revealed that glucose deposits onto Al 2O3 driven by a lower entropic penalty with respect to stearic acid [83].

HS and metal ions

Understanding the association between HS and metal ions is a crucially important issue, as this process controls the speciation, solubility, and toxicity of trace metals [84],[85].

Sutton et al. [62] simulated systems including the Schulten DOM molecule [65], water, and the Na + and Ca 2+ ions. It was found that Ca 2+ ions associate more strongly than Na + with the carboxylate groups of the humic molecule. Moreover, Ca 2+ was shown to promote better hydration of the humic molecule [62].

Alvarez-Puebla et al. [86] studied the interaction between brown humic acid (BHA) with Cu 2+, Ni 2+, and Co 2+ ions. BHAs are the most polar and soluble components of HS, because of their high content in carboxylic and phenolic acidic groups. The BHA structure was developed based on the TNB model [68]–[70]. Specifically, a BHA polymer was obtained by concatenating 13 TNB monomer units. Due to the high computational cost of simulating such a large molecule, Alvarez-Puebla et al. [86] did not include solvating water, although its effect was approximated by introducing frictional forces through a Langevin scheme [87]. The BHA was observed to display higher affinity for Cu 2+ (most reactive), followed by Co 2+, and then by Ni 2+ (most inert). This behavior was attributed to electrostatic retention, a mechanism consistent with both experimental and simulation results [86].

Xu et al. [88] performed molecular dynamics simulations of complexes involving NOM and metal ions. In particular, they investigated the interactions between Cs + and Cl with NOM in water. For NOM, they adopted the TNB model [68]–[70]. Several simulations were performed for a range of metal ion concentrations. A representative simulation snapshot is reported in Figure 5. The data obtained showed that Cs + associates with NOM through rapid exchange with the bulk solution, whereas Cl does not significantly associate with NOM; these results were found to be consistent with nuclear magnetic resonance experiments [88].
Figure 5

Hydrated TNB molecule interacting with ions. Simulation snapshot from a system comprising the TNB molecule [70] in 0.3 M Cs + aqueous solution. Color codes for atom types are as follows: carbon (brown), hydrogen (gray), oxygen (red), nitrogen (blue), Cs + (green). Water molecules are shown in transparent representation. Reprinted with permission from Xu et al. [88]. Copyright 2006 Elsevier.

The study by Xu et al. [88] was extended by Kalinichev and Kirkpatrick [89] and by Iskrenova-Tchoukova et al. [90], who considered the Na +, Mg 2+, and Ca 2+ ions. It was found that metal-NOM binding is primarily driven by electrostatic attraction between the positive ions and the negatively charged carboxylate groups of the NOM molecule (whereas phenolic groups were not significant binding sites). Moreover, the propensity for metal-NOM aggregate formation was found to be correlated with the charge to radius ratio and the size of the ions [89].

A rather original methodological study was performed by Kalinichev et al. [91], who considered the effects of different models and system sizes on the simulation results for a NOM-Ca 2+ association process. In particular, they tested combinations of the force fields CVFF [92], CHARMM [93], and AMBER [94], with the water models SPC [95] and TIP3P [96]. The properties considered, which included radial distribution functions and potentials of mean force, were found to be fairly robust with respect to the different model parameters used [91].

HS and contaminants

Antimicrobials make up a large proportion of the contaminants detected in the environment [97]–[99]. The occurrence of antimicrobials in soil and water is caused by their widespread use in agriculture and medicine [100]–[103], as well as their presence in a wide range of healthcare and household goods [104]–[106]. The detrimental effects of antimicrobials include the disruption of key microbial processes in soil, toxicity to organisms, and the development of microbial resistance [107]–[110]. These problems are significantly mitigated when antimicrobials are adsorbed in organic matter, such as HS. To gain insights into the adsorption process, Aristilde and Sposito [111] carried out molecular dynamics simulations of the binding of the antimicrobial ciprofloxacin by HS. Ciprofloxacin is a frequently prescribed antibiotic commonly found in hospital wastewaters [112]. Regarding the HS component, Aristilde and Sposito [111] used the Schulten DOM model [62],[65]. The simulations showed that the ciprofloxacin-HS association involved the disruption of original hydrogen bonds within the DOM molecule and their replacement with intermolecular hydrogen bonds with ciprofloxacin [111].

Another class of ubiquitous contaminants is represented by polycyclic aromatic hydrocarbons (PAHs), which are highly toxic compounds that form as a result of the combustion of organic fuels such as coal, oil, and natural gas. It has been shown in a number of studies that organic matter can regulate the transport, fate, degradation, and bioavailability of PAHs [113]–[118]. Saparpakorn et al. [119] investigated by simulation the binding of PAHs to different HS models; in particular, they simulated Schulten’s SOM molecule [78] and implemented models for earlier molecules proposed by Buffle et al. [120] and by Stevenson [1]. The simulations performed aimed at quantifying the role of intermolecular interactions, as well as docking energies and binding modes [119].

Schulten et al. [121] modeled complexes of HS and the xenobiotic diethyl phtalate (DEP), with the objective of investigating the sorption process. Interactions were studied between a single HS molecule and an increasing number of DEP molecules, from 1 to 30. From their simulations, Schulten et al. [121] were able to quantify the sorption process in terms of the different contributions from electrostatic, van der Waals, and hydrogen bonding interactions. In particular, sorption inside free-volume pockets of HS was observed to take place between a single HS molecule and up to seven DEP molecules, whereas additional DEP molecules were adsorbed at the HS surface [121].

Another category of contaminants of increasing relevance is represented by carbon nanoparticles. The general use of carbon nanomaterials in industry is rapidly growing, raising health and environmental concerns which demand quantitative assessment. Wang et al. [122] used molecular simulations to investigate the interactions between DOM and fullerene (C 60), a typical carbon nanoparticle. Fullerene plays a role in a wide range of industrial applications and is known to display some degree of toxicity [123]–[126]. Wang et al. [122] selected seven small organic molecules, representative of main DOM building blocks, and characterized their interaction with C 60 in terms of adsorption energy and water solubility; it was found that the presence of DOM can stabilize C 60[122]. Further insights into DOM-C 60 systems were obtained by Sun et al. [127], who considered a range of low molecular weight organic acids as key components of DOM. By estimating adsorption energies, it was observed that aromatic acids interact more strongly with C 60 than aliphatic acids [127]. Wu et al. [128] simulated an aggregate comprising ten C 60 molecules associated with a small HS molecule. In particular, the HS model was constructed by connecting a benzoic group to a hydrocarbon tail. It was found that hydrophobic and π- π interactions were the two main mechanisms of association [128]; a simulation snapshot from this work is reported in Figure 6.
Figure 6

Interaction between a small HS molecule and fullerene. Snapshot from a MD simulation by Wu et al. [128]. Color codes for atom types are as follows: carbon (gray), hydrogen (white), oxygen (red). Reprinted with permission from Wu et al. [128]. Copyright 2006 Elsevier.

To obtain insights into the sorption of volatile organic compounds into HS, Shih et al. [129] studied the interaction between the TNB humic acid model [68]–[70] and toluene (representative volatile organic compound) in vacuo. Specifically, the diffusion coefficient of toluene was characterized as a function of temperature from 300 to 400 K. The results obtained are in qualitative agreement with experiment, in that diffusivities were observed to increase with temperature. However, the experimental data were slightly overestimated [129].

HS and water filtration

Satisfying the world’s population need for clean and drinking water is one of the greatest challenges of our time. To address this challenge, it is paramount to develop and optimize industrial processes aimed at filtering and desalinating sea water and municipal waste water. The currently most promising filtration technology relies on membranes operating in reverse osmosis plants. In these processes, the presence of HS is a fundamental aspect to consider. In fact, a key problem that greatly limits the efficiency of current filtration membranes is fouling, a phenomenon whereby particles deposit and accumulate on the membrane surface ultimately causing a reduction in the filtering performance. A major category of fouling agents is represented by organic substances, particularly HS [130]–[135].

A number of MD studies have been devoted to different aspects of the fouling process. Ahn et al. [136] investigated the effects of metal ions on the adsorption of a NOM model [68] onto the surface of polyethersulfone membranes. It was found that divalent ions (Mg 2+ and Ca 2+) induce fouling by promoting aggregation of NOM molecules [136]. However, the interactions between NOM and the filtration membrane were not explicitly investigated.

The fouling of a polyamide membrane was investigated by Hughes and Gale [137],[138]. Specifically, they considered glucose and phenol molecules as representative HS fouling agents, as both glucose and phenol are common building blocks of HS. Membrane-foulant interactions were quantified in terms of free energies and hydrogen bonding. It was found that both foulants bind strongly to the membrane surface, with phenol sometimes diffusing through the membrane pores [137],[138]. A simulation snapshot from this study is reported in Figure 7; a phenol molecule can be seen penetrating the polymeric membrane.
Figure 7

Interaction between phenol and polymeric membrane. Snapshot from a MD simulation by Hughes and Gale [138]. A phenol molecule (colored yellow) permeates into a polyamide membrane (colored purple). Reprinted with permission from Hughes and Gale [138]. Copyright 2012 Royal Society of Chemistry.

Myat et al. [139] investigated possible specific mechanisms of interaction between representative organic foulants. Specifically, they focused on the biopolymer bovine serum albumin (BSA) [140] and the polysaccharide sodium alginate, taken to be representative of high molecular weight compounds typically found in surface and waste waters. Moreover, they considered the TNB humic acid model [68]–[70] as representative of HS. No water was explicitly included. Simulations of a BSA-HS complex revealed the presence of various electrostatic and hydrophobic interactions, as well as hydrogen bonding. On the other hand, analysis of an alginate-HS complex highlighted the presence of exclusively ion-mediated interactions. The simulation results were found to be consistent with corresponding experimental data [139].

Conclusions

Achievements, issues, and future prospects

Over the past 20 years, a growing number of computer models have been developed and applied to study many important structures and processes involving humic substances (HS), including their basic molecular properties [62],[65],[67]–[70],[75], their aggregation behavior [71],[72],[74],[76],[77], their interaction with various substances including minerals [79],[81]–[83],[86], ions [62],[85],[86],[88]–[91], and contaminants [111],[119],[121],[122],[128],[129],[141],[142], and their fouling capability in relation to membrane-based water filtration technologies [136],[138],[139].

These investigations yielded considerable molecular-level insights into the structure and function of HS, as summarized in the previous sections of this review. However, a few issues should be considered. In particular, it is important to bear in mind that none of the HS models developed so far correspond to real humic molecules. Rather, the models represent putative compounds obtained by assembling molecular building blocks which are known experimentally to be most prevalent in HS. Furthermore, several investigations, especially among the earliest simulations reported, focused on energy minimization calculations, with the aim of finding the most energetically favorable (optimized) conformations for a molecule or molecular aggregate [61],[65],[67],[70],[121],[142],[143]. However, it should be noted that energy optimization methods yield properties corresponding to a temperature of 0 K, as only the potential energy is considered, while there is no kinetic energy in the system. When temperature and thermal motion are important, as is typically the case for systems of organic and biological molecules, full MD simulations, while computationally more demanding than optimizations, are to be preferred. A final issue to highlight involves the fact that many simulation studies of HS did not include hydrating water (in vacuo assumption) [67],[69]–[71],[73],[78],[82],[121],[128],[129],[139],[144]–[150]. As already pointed out elsewhere [72],[143],[149],[151], HS are hydrated in reality, and water interactions with HS are likely to influence important properties. For example, the large molecular dipole of water is expected to interact strongly with HS polar groups, and hydrogen bonds between water and HS are expected to be prevalent. The presence of appropriate amounts of water in MD simulations of HS is therefore recommended.

In terms of future prospects, there is an expectation that specific HS structures will be accurately identified from experiment, opening up opportunities for MD simulations of realistic HS compounds. As a result, simulated systems will likely become larger and more complex, and hence also more computationally expensive. While this could represent an obstacle, there are reasons to be optimistic. From a hardware perspective, the continuous increase in computational power will keep extending the attainable simulation times and sizes. Moreover, ongoing research in multiscale methods [152]–[157] promises to substantially improve simulation efficiency in the near future. Self-assembly simulations of large numbers of different HS molecules might soon become a reality, opening up the opportunity to study and quantify atomic-level properties within realistic HS supramolecular structures.

More generally, the study of HS in the foreseeable future will have great relevance for several areas of key global importance. Owing to the role of HS in controlling CO 2 in the ecosystem, advances in HS research could lead to new solutions for carbon capture and storage, thus contributing to address the urgent global challenge of increasingly rapid climate change [5]. Moreover, a better understanding of HS can be instrumental in increasing food production to satisfy the needs of a growing population [158], as well as in optimizing filtration technologies to obtain clean and drinking water [159]. While experimental research will always be essential, in the years to come, molecular simulations of HS are expected to become increasingly useful, particularly for providing a more detailed understanding of experimental observations, for guiding the design of new experiments, and for predicting properties and phenomena at the molecular scale.

Declarations

Authors’ Affiliations

(1)
School of Engineering & Materials Science, Queen Mary University of London

References

  1. Stevenson FJ: Humus chemistry: genesis, composition, reactions. 1994, Wiley, HobokenGoogle Scholar
  2. Kördel W, Dassenakis M, Lintelmann J, Padberg S: The importance of natural organic material for environmental processes in waters and soils (technical report). Pure Appl Chem. 1997, 69 (7): 1571-1600.Google Scholar
  3. Kukkonen J: Binding of organic pollutants to humic and fulvic acids: influence of ph and the structure of humic material. Chemosphere. 1997, 34 (8): 1693-1704.Google Scholar
  4. Pignatello JJ: Soil organic matter as a nanoporous sorbent of organic pollutants. Adv Colloid Interface Sci. 1998, 76: 445-467.Google Scholar
  5. Lal R: Soil carbon sequestration impacts on global climate change and food security. Science. 2004, 304 (5677): 1623-1627.PubMedGoogle Scholar
  6. Hayes MHB, MacCarthy P, Malcolm RL, Swift R: Humic substances II. In search of structure. 1989, Wiley, HobokenGoogle Scholar
  7. Hayes MH, Clapp CE: Humic substances: considerations of compositions, aspects of structure, and environmental influences. Soil Sci. 2001, 166 (11): 723-737.Google Scholar
  8. Piccolo A: The supramolecular structure of humic substances. Soil Sci. 2001, 166 (11): 810-832.Google Scholar
  9. Piccolo A: The supramolecular structure of humic substances: a novel understanding of humus chemistry and implications in soil science. Adv Agronomy. 2002, 75: 57-134.Google Scholar
  10. Piccolo A, Conte P, Cozzolino A: Chromatographic and spectrophotometric properties of dissolved humic substances compared with macromolecular polymers. Soil Sci. 2001, 166 (3): 174-185.Google Scholar
  11. Piccolo A, Conte P, Trivellone E, van Lagen B, Buurman P: Reduced heterogeneity of a lignite humic acid by preparative HPSEC following interaction with an organic acid. Characterization of size-separates by Pyr-GC-MS and 1H-NMR spectroscopy. Environ Sci Technol. 2002, 36 (1): 76-84.PubMedGoogle Scholar
  12. Piccolo A: Aggregation and disaggregation of humic supramolecular assemblies by NMR diffusion ordered spectroscopy (DOSY-NMR). Environ Sci Technol. 2007, 42 (3): 699-706.Google Scholar
  13. Nebbioso A, Piccolo A: Advances in humeomics: enhanced structural identification of humic molecules after size fractionation of a soil humic acid. Analytica Chimica Acta. 2012, 720: 77-90.PubMedGoogle Scholar
  14. Nebbioso A, Piccolo A: Basis of a humeomics science: chemical fractionation and molecular characterization of humic biosuprastructures. Biomacromolecules. 2011, 12 (4): 1187-1199.PubMedGoogle Scholar
  15. Piccolo A, Nardi S, Concheri G: Micelle-like conformation of humic substances as revealed by size exclusion chromatography. Chemosphere. 1996, 33 (4): 595-602.PubMedGoogle Scholar
  16. Nardi S, Concheri G: Macromolecular changes of humic substances induced by interaction with organic acids. Eur J Soil Sci. 1996, 47 (3): 319-328.Google Scholar
  17. Wershaw RL: Molecular aggregation of humic substances. Soil Sci. 1999, 164 (11): 803-813.Google Scholar
  18. Stoddard SD, Ford J: Numerical experiments on stochastic behavior of a Lennard-Jones gas system. Phys Rev A. 1973, 8: 1504-1512.Google Scholar
  19. Adams DJ, Adams EM, Hills GJ: The computer simulation of polar liquids. Mol Phys. 1979, 38: 387-400.Google Scholar
  20. Sokhan VP, Tildesley DJ: The free surface of water: molecular orientation, surface potential and nonlinear susceptibility. Mol Phys. 1997, 92: 625-640.Google Scholar
  21. Orsi M: Comparative assessment of the ELBA coarse-grained model for water. Mol Phys. 2014, 112: 1566-1576.Google Scholar
  22. Vega C, Abascal JL: Simulating water with rigid non-polarizable models: a general perspective. Phys Chem. 2011, 13: 19663-19688.Google Scholar
  23. Mackerell AD: Empirical force fields for biological macromolecules: overview and issues. J Comput Chem. 2004, 25: 1584-1604.PubMedGoogle Scholar
  24. Soncini M, Vesentini S, Ruffoni D, Orsi M, Deriu MA, Redaelli A: Mechanical response and conformational changes of alpha-actinin domains during unfolding: a molecular dynamics study. Biomechan Model Mechanobiol. 2007, 6: 399-407.Google Scholar
  25. Deriu MA, Soncini M, Orsi M, Patel M, Essex JW, Montevecchi FM, Redaelli A: Anisotropic elastic network modeling of entire microtubules. Biophys J. 2010, 99: 2190-2199.PubMed CentralPubMedGoogle Scholar
  26. Parton DL, Klingelhoefer JW, Sansom MSP: Aggregation of model membrane proteins, modulated by hydrophobic mismatch, membrane curvature, and protein class. Biophys J. 2011, 101: 691-699.PubMed CentralPubMedGoogle Scholar
  27. Nielsen SO, Ensing B, Ortiz V, Moore PB, Klein ML: Lipid bilayer perturbations around a transmembrane nanotube: a coarse grain molecular dynamics study. Biophys J. 2005, 88: 3822-3828.PubMed CentralPubMedGoogle Scholar
  28. Xiang T-X, Anderson BD: Liposomal drug transport: a molecular perspective from molecular dynamics simulations in lipid bilayers. Adv Drug Deliv Rev. 2006, 58: 1357-1378.PubMedGoogle Scholar
  29. Orsi M, Sanderson W, Essex JW, Kettner C (2007) Molecular interactions–bringing chemistry to life. In: Hicks MG (ed), 85–205.. Beilstein-Institut, Frankfurt.Google Scholar
  30. Orsi M, Haubertin DY, Sanderson WE, Essex JW: A quantitative coarse-grain model for lipid bilayers. J Phys Chem B. 2008, 112: 802-815.PubMedGoogle Scholar
  31. Orsi M, Essex JW (2010) Molecular simulations and biomembranes: from biophysics to function. In: Biggin PC Sansom MSP (eds), 76–90.. RSC, Cambridge.Google Scholar
  32. Orsi M, Michel J, Essex JW (2010) Coarse-grain modelling of DMPC and DOPC lipid bilayers. J Phys: Condens Matter 22: 155106.Google Scholar
  33. Lyubartsev AP, Rabinovich AL: Recent development in computer simulations of lipid bilayers. Soft Matter. 2011, 7: 25-39.Google Scholar
  34. Orsi M, Essex JW (2011) The ELBA force field for coarse-grain modeling of lipid membranes. PLoS ONE 6: 28637.Google Scholar
  35. Essex JW: Physical properties of mixed bilayers containing lamellar and nonlamellar lipids: insights from coarse-grain molecular dynamics simulations. Faraday Discuss. 2013, 161: 249-272.PubMedGoogle Scholar
  36. Kremer K, Grest GS: Dynamics of entangled linear polymer melts: a molecular-dynamics simulation. J Chem Phys. 1990, 92 (8): 5057-5086.Google Scholar
  37. Varnik F, Baschnagel J, Binder K: Molecular dynamics results on the pressure tensor of polymer films. J Chem Phys. 2000, 113: 4444-4453.Google Scholar
  38. Rapaport DC (2002) Molecular dynamics simulation of polymer helix formation using rigid-link methods. Phys Rev E 66: 011906.Google Scholar
  39. Barrat J-L, Baschnagel J, Lyulin A: Molecular dynamics simulations of glassy polymers. Soft Matter. 2010, 6 (15): 3430-3446.Google Scholar
  40. Belytschko T, Xiao S, Schatz G, Ruoff R (2002) Atomistic simulations of nanotube fracture. Phys Rev B 65(23): 235430.Google Scholar
  41. Coluci VR, Pugno NM, Dantas SO, Galvao DS, Jorio A (2007) Atomistic simulations of the mechanical properties of ’super’ carbon nanotubes. Nanotechnology 18(33): 335702.Google Scholar
  42. Zang J, Ryu S, Pugno N, Wang Q, Tu Q, Buehler MJ, Zhao X: Multifunctionality and control of the crumpling and unfolding of large-area graphene. Nat Mater. 2013, 12 (4): 321-325.PubMed CentralPubMedGoogle Scholar
  43. Plimpton S: Fast parallel algorithms for short-range molecular dynamics. J Comput Phys. 1995, 117: 1-19.Google Scholar
  44. LAMMPS molecular dynamics simulator. . Accessed 10 June 2014., [http://lammps.sandia.gov]
  45. Hess B, Kutzner C, Lindahl E: Gromacs 4, algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput. 2008, 4: 435-447.PubMedGoogle Scholar
  46. Case DA, Darden TA, Cheatham TE, Simmerling CL, Wang J, Duke RE, Luo R, Walker RC, Zhang W, Merz KM, Roberts B, Hayik S, Roitberg A, Seabra G, Swails J, Goetz AW, Kolossváry I, Wong KF, Paesani F, Vanicek J, Wolf RM, Liu J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Cai Q, Ye X, Wang J, Hsieh MJ, et al: AMBER 12. 2012, University of California, San FranciscoGoogle Scholar
  47. Kunz A-PE, Allison JR, Geerke DP, Horta BAC, Hünenberger PH, Riniker S, Schmid N, van Gunsteren WF: New functionalities in the GROMOS biomolecular simulation software. J Comput Chem. 2012, 33 (3): 340-353.PubMedGoogle Scholar
  48. Todorov IT, Smith W, Trachenko K, Dove MT: Journal of Materials Chemistry. 2006, 16: 1911-1918.Google Scholar
  49. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M: CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem. 1983, 4: 187-217.Google Scholar
  50. Allen MP, Tildesley DJ: Computer simulation of liquids. 1987, Oxford Science, OxfordGoogle Scholar
  51. Leach AR: Molecular modelling - principles and applications. 2001, Prentice Hall, HarlowGoogle Scholar
  52. Frenkel D, Smit B: Understanding molecular simulation. 2002, Academic, LondonGoogle Scholar
  53. Schlick T: Molecular modeling and simulation - an interdisciplinary guide. 2002, Springer, New YorkGoogle Scholar
  54. Rapaport DC: The art of molecular dynamics simulation. 2004, Cambridge University Press, CambridgeGoogle Scholar
  55. Sutmann G: Classical molecular dynamics. Quantum Simul Complex Many-body Syst: Theory Algorithms. 2002, 10: 211-254.Google Scholar
  56. Allen MP (2004) Introduction to molecular dynamics simulation23(Comput Soft Matter): 1–28.Google Scholar
  57. Binder K, Horbach J, Varnik F: Molecular dynamics simulations. J Phys: Condens Matter. 2004, 16: 429-453.Google Scholar
  58. van Gunsteren WF, Bakowies D, Baron R, Chandrasekhar I, Christen M, Daura X, Gee P, Geerke DP, Glaettli A, Huenenberger PH, Kastenholz MA, Ostenbrink C, Schenk M, Trzesniak D, van der Vegt NFA: Biomolecular modeling: goals, problems, perspectives. Angew Chem-Int Edit. 2006, 45: 4064-4092.Google Scholar
  59. Lopes PE, Huang J, Shim J, Luo Y, Li H, Roux B, MacKerell Jr AD: Polarizable force field for peptides and proteins based on the classical drude oscillator. J Chem Theory Comput. 2013, 9 (12): 5430-5449.PubMed CentralPubMedGoogle Scholar
  60. Swope WC, Andersen HC, Berens PH, Wilson KR: A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: application to small water clusters. J Chem Phys. 1982, 76: 637-649.Google Scholar
  61. Schulten H-R: Interactions of dissolved organic matter with xenobiotic compounds molecular modeling in water. Environ Toxicol Chem. 1999, 18 (8): 1643-1655.Google Scholar
  62. Sutton R, Sposito G, Diallo MS, Schulten H-R: Molecular simulation of a model of dissolved organic matter. Environ Toxicol Chem. 2005, 24 (8): 1902-1911.PubMedGoogle Scholar
  63. Kögel-Knabner I: The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biol Biochem. 2002, 34 (2): 139-162.Google Scholar
  64. Parsi Z, Hartog N, Górecki T: Analytical pyrolysis as a tool for the characterization of natural organic matter–a comparison of different approaches. J Anal Appl Pyrolysis. 2007, 79 (1): 9-15.Google Scholar
  65. Schulten H-R: Analytical pyrolysis and computational chemistry of aquatic humic substances and dissolved organic matter. J Anal Appl Pyrolysis. 1999, 49 (1): 385-415.Google Scholar
  66. Vepsäläinen M, Ghiasvand M, Selin J, Pienimaa J, Repo E, Pulliainen M: Investigations of the effects of temperature and initial sample pH on natural organic matter (nom) removal with electrocoagulation using response surface method (rsm). Separation Purif Technol. 2009, 69 (3): 255-261.Google Scholar
  67. Jansen SA, Malaty M, Nwabara S, Johnson E, Ghabbour E, Davies G, Varnum JM: Structural modeling in humic acids. Materials Sci Eng: C. 1996, 4 (3): 175-179.Google Scholar
  68. Davies G, Fataftah A, Cherkasskiy A, Ghabbour EA, Radwan A, Jansen SA, Kolla S, Paciolla MD, Sein Jr LT, Buermann W, Balasubramanian M, Budnick J, Xing B (1997) Tight metal binding by humic acids and its role in biomineralization. J Chem Soc Dalton Trans: 4047–4060.Google Scholar
  69. Kubicki J, Apitz S: Models of natural organic matter and interactions with organic contaminants. Org Geochem. 1999, 30 (8): 911-927.Google Scholar
  70. Sein LT, Varnum JM, Jansen SA: Conformational modeling of a new building block of humic acid approaches to the lowest energy conformer. Environ Sci Technol. 1999, 33 (4): 546-552.Google Scholar
  71. Alvarez-Puebla RA, Garrido JJ: Effect of pH on the aggregation of a gray humic acid in colloidal and solid states. Chemosphere. 2005, 59 (5): 659-667.PubMedGoogle Scholar
  72. Alvarez-Puebla R, Valenzuela-Calahorro C, Garrido J: Theoretical study on fulvic acid structure, conformation and aggregation: a molecular modelling approach. Sci Total Environ. 2006, 358 (1): 243-254.PubMedGoogle Scholar
  73. Leenheer J, Brown G, MacCarthy P, Cabaniss S: Models of metal binding structures in fulvic acid from the Suwannee River, Georgia. Environ Sci Technol. 1998, 32 (16): 2410-2416.Google Scholar
  74. Porquet A, Bianchi L, Stoll S: Molecular dynamic simulations of fulvic acid clusters in water. Colloids Surf A: Physicochem Eng Aspects. 2003, 217 (1): 49-54.Google Scholar
  75. Diallo MS, Simpson A, Gassman P, Faulon JL, Johnson JH, Goddard WA, Hatcher PG: 3-D structural modeling of humic acids through experimental characterization, computer assisted structure elucidation and atomistic simulations. 1. Chelsea soil humic acid. Environ Sci Technol. 2003, 37 (9): 1783-1793.PubMedGoogle Scholar
  76. Aquino AJ, Tunega D, Pasalic H, Schaumann GE, Haberhauer G, Gerzabek MH, Lischka H: Molecular dynamics simulations of water molecule-bridges in polar domains of humic acids. Environ Sci Technol. 2011, 45 (19): 8411-8419.PubMedGoogle Scholar
  77. Aquino AJ, Tunega D, Schaumann GE, Haberhauer G, Gerzabek MH, Lischka H: Study of solvent effect on the stability of water bridge-linked carboxyl groups in humic acid models. Geoderma. 2011, 169: 20-26.Google Scholar
  78. Schulten H-R, Schnitzer M: Chemical model structures for soil organic matter and soils. Soil Sci. 1997, 162 (2): 115-130.Google Scholar
  79. Sutton R, Sposito G: Molecular simulation of humic substance–Ca-montmorillonite complexes. Geochimica et Cosmochimica Acta. 2006, 70 (14): 3566-3581.Google Scholar
  80. von Lützow M, Kögel-Knabner I, Ekschmitt K, Flessa H, Guggenberger G, Matzner E, Marschner B: Som fractionation methods: relevance to functional pools and to stabilization mechanisms. Soil Biol Bioch. 2007, 39 (9): 2183-2207.Google Scholar
  81. Teppen BJ, Yu C-H, Miller DM, Schäfer L: Molecular dynamics simulations of sorption of organic compounds at the clay mineral/aqueous solution interface. J Comput Chem. 1998, 19 (2): 144-153.Google Scholar
  82. Shevchenko SM, Bailey GW, Akim LG: The conformational dynamics of humic polyanions in model organic and organo-mineral aggregates. J Mol Struct: THEOCHEM. 1999, 460 (1): 179-190.Google Scholar
  83. Petridis L, Ambaye H, Jagadamma S, Kilbey SM, Lokitz BS, Lauter V, Mayes M: Spatial arrangement of organic compounds on a model mineral surface: implications for soil organic matter stabilization. Environ Sci Technol. 2013, 48: 79-84.PubMedGoogle Scholar
  84. Tipping E: Cation Binding by Humic Substances. 2002, Cambridge University Press, CambridgeGoogle Scholar
  85. Cation binding by humic substances. Environ Geol. 2003, 43: 615-616.Google Scholar
  86. Alvarez-Puebla RA, Valenzuela-Calahorro C, Garrido JJ: Retention of co(ii), Ni(ii), and Cu(ii) on a purified brown humic acid. Modeling and characterization of the sorption process. Langmuir. 2004, 20 (9): 3657-3664. PMID:15875396PubMedGoogle Scholar
  87. Schneider T, Stoll E (1978) Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions17(Phys Rev B): 1302–1322.Google Scholar
  88. Xu X, Kalinichev AG: 133Cs and 35Cl NMR spectroscopy and molecular dynamics modeling of Cs + and Cl complexation with natural organic matter. Geochimica et Cosmochimica Acta. 2006, 70 (17): 4319-4331.Google Scholar
  89. Kalinichev A, Kirkpatrick R: Molecular dynamics simulation of cationic complexation with natural organic matter. Eur J Soil Sci. 2007, 58 (4): 909-917.Google Scholar
  90. Iskrenova-Tchoukova E, Kalinichev AG, Kirkpatrick RJ: Metal cation complexation with natural organic matter in aqueous solutions: molecular dynamics simulations and potentials of mean force. Langmuir. 2010, 26 (20): 15909-15919.PubMedGoogle Scholar
  91. Kalinichev AG, Iskrenova-Tchoukova E, Clark MM, Ahn W-Y, Kirkpatrick RJ: Effects of Ca 2+ on supramolecular aggregation of natural organic matter in aqueous solutions: a comparison of molecular modeling approaches. Geoderma. 2011, 169: 27-32.Google Scholar
  92. Dauber-Osguthorpe P, Roberts VA, Osguthorpe DJ, Wolff J, Genest M, Hagler AT: Structure and energetics of ligand binding to proteins: Escherichia coli dihydrofolate reductase-trimethoprim, a drug-receptor system. Proteins: Struct Funct Bioinformatics. 1988, 4 (1): 31-47.Google Scholar
  93. Bashford D, Bellott M, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WEIII, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiorkiewicz-Kuczera J, Yin D, Karplus M: All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B. 1998, 102: 3586-3616.PubMedGoogle Scholar
  94. Case DA, Darden TA, Cheatham TE, Simmerling CL, Wang J, Duke RE, Luo R, Merz KM, Pearlman DA, Crowley M, Walker RC, Zhang W, Wang B, Hayik S, Roitberg A, Seabra G, Wong KF, Paesani F, Wu X, Brozell S, Tsui V, Gohlke H, Yang L, Tan C, Mongan J, Hornak V, Cui G, Beroza P, Mathews DH, Schafmeister C, et al: Amber 9. 2006, University of California, San FranciscoGoogle Scholar
  95. Berendsen HJC, Postma JPM, van Gunsteren WF, Hermans J (1981) Intermolecular Forces(Pullman B, ed.), Reidel, Dordrecht.Google Scholar
  96. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML: Comparison of simple potential functions for simulating liquid water. J Chem Phys. 1983, 79: 926-935.Google Scholar
  97. Kolpin DW, Furlong ET, Meyer MT, Thurman EM, Zaugg SD, Barber LB, Buxton HT: Pharmaceuticals, hormones, and other organic wastewater contaminants in US streams, 1999-2000: a national reconnaissance. Environ Sci Technol. 2002, 36: 1202-1211.PubMedGoogle Scholar
  98. Halden RU, Paull DH: Co-occurrence of triclocarban and triclosan in US water resources. Environ Sci Technol. 2005, 39: 1420-1426.PubMedGoogle Scholar
  99. Higgins CP, Paesani ZJ, Chalew TEA, Halden RU: Bioaccumulation of triclocarban in Lumbriculus variegatus. Environ Toxicol Chem. 2009, 28: 2580-2586.PubMed CentralPubMedGoogle Scholar
  100. Boxall AB, Kolpin DW, Halling-Sørensen B, Tolls J: Peer reviewed: are veterinary medicines causing environmental risks?. Environ Sci Technol. 2003, 37 (15): 286-294.Google Scholar
  101. Boxall AB, Johnson P, Smith EJ, Sinclair CJ, Stutt E, Levy LS: Uptake of veterinary medicines from soils into plants. J Agric Food Chem. 2006, 54 (6): 2288-2297.PubMedGoogle Scholar
  102. Lee LS, Carmosini N, Sassman SA, Dion HM, Sepulveda MS: Agricultural contributions of antimicrobials and hormones on soil and water quality. Adv Agronomy. 2007, 93: 1-68.Google Scholar
  103. Kümmerer K: Significance of antibiotics in the environment. J Antimicrob Chemother. 2003, 52 (1): 5-7.PubMedGoogle Scholar
  104. Schweizer HP: Triclosan: a widely used biocide and its link to antibiotics. FEMS Microbiol Lett. 2001, 202: 1-7.PubMedGoogle Scholar
  105. Chalew TEA, Halden RU: Environmental exposure of aquatic and terrestrial biota to triclosan and triclocarban. J Am Water Resour Assoc. 2009, 45: 4-13.Google Scholar
  106. Orsi M, Noro MG, Essex JW: Dual-resolution molecular dynamics simulation of antimicrobials in biomembranes. J R Soc Interface. 2011, 8: 826-841.PubMed CentralPubMedGoogle Scholar
  107. Aiello AE, Larson EL, Levy SB: Consumer antibacterial soaps: effective or just risky?. Clin Infect Dis. 2007, 45: 137-147.Google Scholar
  108. Aryal N, Reinhold DM: Phytoaccumulation of antimicrobials from biosolids: impacts on environmental fate and relevance to human exposure. Water Res. 2011, 45 (17): 5545-5552.PubMedGoogle Scholar
  109. Oliver SP, Murinda SE, Jayarao BM: Impact of antibiotic use in adult dairy cows on antimicrobial resistance of veterinary and human pathogens: a comprehensive review. Foodborne Pathogens Disease. 2011, 8 (3): 337-355.PubMedGoogle Scholar
  110. Marshall BM, Levy SB: Food animals and antimicrobials: impacts on human health. Clin Microbiol Rev. 2011, 24 (4): 718-733.PubMed CentralPubMedGoogle Scholar
  111. Aristilde L, Sposito G: Binding of ciprofloxacin by humic substances: a molecular dynamics study. Environ Toxicol Chem. 2010, 29 (1): 90-98.PubMedGoogle Scholar
  112. Hartmann A, Alder AC, Koller T, Widmer RM: Identification of fluoroquinolone antibiotics as the main source of umuC, genotoxicity in native hospital wastewater. Environ Toxicol Chem. 1998, 17 (3): 377-382.Google Scholar
  113. Haigh SD: A review of the interaction of surfactants with organic contaminants in soil. Sci Total Environ. 1996, 185 (1): 161-170.Google Scholar
  114. Käcker T, Haupt ET, Garms C, Francke W, Steinhart H: Structural characterisation of humic acid-bound pah residues in soil by 13c-cpmas-nmr-spectroscopy: evidence of covalent bonds. Chemosphere. 2002, 48 (1): 117-131.PubMedGoogle Scholar
  115. Laor Y, Rebhun M: Evidence for nonlinear binding of PAHs to dissolved humic acids. Environ Sci Technol. 2002, 36 (5): 955-961.PubMedGoogle Scholar
  116. Golobočanin DD, Škrbić BD, Miljević NR (2004) Principal component analysis for soil contamination with pahs Chemometrics Intell Lab Syst72(2): 219–223.Google Scholar
  117. Zhou W, Zhu L: Distribution of polycyclic aromatic hydrocarbons in soil–water system containing a nonionic surfactant. Chemosphere. 2005, 60 (9): 1237-1245.PubMedGoogle Scholar
  118. Zhang H, Luo Y, Wong M, Zhao Q, Zhang G: Distributions and concentrations of pahs in Hong Kong soils. Environ Pollut. 2006, 141 (1): 107-114.PubMedGoogle Scholar
  119. Saparpakorn P, Kim JH, Hannongbua S: Investigation on the binding of polycyclic aromatic hydrocarbons with soil organic matter: a theoretical approach. Molecules. 2007, 12 (4): 703-715.PubMedGoogle Scholar
  120. Buffle J, Greter FL, Haerdi W: Measurement of complexation properties of humic and fulvic acids in natural waters with lead and copper ion-selective electrodes. Anal Chem. 1977, 49 (2): 216-222.PubMedGoogle Scholar
  121. Schulten H-R, Thomsen M, Carlsen L: Humic complexes of diethyl phthalate: molecular modelling of the sorption process. Chemosphere. 2001, 45 (3): 357-369.PubMedGoogle Scholar
  122. Wang Z, Chen J, Sun Q, Peijnenburg WJ: C 60-dom interactions and effects on c 60 apparent solubility A molecular mechanics and density functional theory study. Environ Int. 2011, 37 (6): 1078-1082.PubMedGoogle Scholar
  123. Bosi S, Spalluto G, Prato M: Fullerene derivatives: an attractive tool for biological applications. Eur J Med Chem. 2003, 38: 913-923.PubMedGoogle Scholar
  124. Nakamura E, Isobe H: Functionalized fullerenes in water. The first 10 years of their chemistry, biology, and nanoscience. Acc Chem Res. 2003, 36: 807-815.PubMedGoogle Scholar
  125. Oberdörster G, Sharp Z, Atudorei A, Elder V, Gelein R, Kreyling W, Cox C: Translocation of inhaled ultrafine particles to the brain. Inhal Toxicol. 2004, 16: 437-445.PubMedGoogle Scholar
  126. Sayes CM, Fortner JD, Guo W, Lyon D, Boyd AM, Ausman KD, Tao YJ, Sitharaman B, Wilson LJ, Hughes JB, West JL, Colvin VL: The differential cytotoxicity of water-soluble fullerenes. Nano Lett. 2004, 4: 1881-1887.Google Scholar
  127. Sun Q, Xie H-B, Chen J, Li X, Wang Z: Molecular dynamics simulations on the interactions of low molecular weight natural organic acids with C 60. Chemosphere. 2013, 92 (4): 429-434.PubMedGoogle Scholar
  128. Wu F, Bai Y, Mu Y, Pan B, Xing B: Fluorescence quenching of fulvic acids by fullerene in water. Environ Pollut. 2013, 172: 100-107.PubMedGoogle Scholar
  129. Molecular dynamic simulations of the sorption of toluene in a dry humic acid model: a preliminary study. Colloids Surf A: Physicochem Eng Aspects. 2006, 275 (1): 183-186.Google Scholar
  130. Lee S, Cho J, Elimelech M: Combined influence of natural organic matter (nom) and colloidal particles on nanofiltration membrane fouling. J Membrane Sci. 2005, 262 (1): 27-41.Google Scholar
  131. Wang Z, Zhao Y, Wang J, Wang S: Studies on nanofiltration membrane fouling in the treatment of water solutions containing humic acids. Desalination. 2005, 178 (1): 171-178.Google Scholar
  132. Li Q, Elimelech M: Synergistic effects in combined fouling of a loose nanofiltration membrane by colloidal materials and natural organic matter. J Membrane Sci. 2006, 278 (1): 72-82.Google Scholar
  133. Jarusutthirak C, Mattaraj S, Jiraratananon R: Influence of inorganic scalants and natural organic matter on nanofiltration membrane fouling. J Membrane Sci. 2007, 287 (1): 138-145.Google Scholar
  134. Her N, Amy G, Chung J, Yoon J, Yoon Y: Characterizing dissolved organic matter and evaluating associated nanofiltration membrane fouling. Chemosphere. 2008, 70 (3): 495-502.PubMedGoogle Scholar
  135. Xiang Y, Liu Y, Mi B, Leng Y: Hydrated polyamide membrane and its interaction with alginate: a molecular dynamics study. Langmuir. 2013, 29 (37): 11600-11608.PubMedGoogle Scholar
  136. Ahn W-Y, Kalinichev AG, Clark MM: Effects of background cations on the fouling of polyethersulfone membranes by natural organic matter: experimental and molecular modeling study. J Membrane Sci. 2008, 309 (1): 128-140.Google Scholar
  137. Hughes ZE, Gale JD: A computational investigation of the properties of a reverse osmosis membrane. J Mater Chem. 2010, 20 (36): 7788-7799.Google Scholar
  138. Hughes ZE, Gale JD: Molecular dynamics simulations of the interactions of potential foulant molecules and a reverse osmosis membrane. J Mater Chem. 2012, 22 (1): 175-184.Google Scholar
  139. Myat DT, Stewart MB, Mergen M, Zhao O, Orbell JD, Gray S: Experimental and computational investigations of the interactions between model organic compounds and subsequent membrane fouling. Water Res. 2014, 48: 108-118.PubMedGoogle Scholar
  140. Majorek KA, Porebski PJ, Dayal A, Zimmerman MD, Jablonska K, Stewart AJ, Chruszcz M, Minor W: Structural and immunologic characterization of bovine, horse, and rabbit serum albumins. Mol Immunol. 2012, 52 (3): 174-182.PubMed CentralPubMedGoogle Scholar
  141. Momany FA, Dombrink-Kurtzman MA: Molecular dynamics simulations on the mycotoxin fumonisin B1. J Agric Food Chem. 2001, 49 (2): 1056-1061.PubMedGoogle Scholar
  142. Mahfoud R, Maresca M, Santelli M, Pfohl-Leszkowicz A, Puigserver A, Fantini J: pH-dependent interaction of fumonisin B1 with cholesterol physicochemical and molecular modeling studies at the air-water interface. J Agric Food Chem. 2002, 50 (2): 327-331.PubMedGoogle Scholar
  143. Thiele-Bruhn S: Molecular modeling of soil organic matter: squaring the circle?. Geoderma. 2011, 166 (1): 1-14.Google Scholar
  144. Schulten H-R: The three-dimensional structure of humic substances and soil organic matter studied by computational analytical chemistry. Fresenius’ J Anal Chem. 1995, 351 (1): 62-73.Google Scholar
  145. Schulten H-R: The three-dimensional structure of soil organo-mineral complexes studied by analytical pyrolysis. J Anal Appl Pyrolysis. 1995, 32: 111-126.Google Scholar
  146. Schulten H-R, Leinweber P: Characterization of humic and soil particles by analytical pyrolysis and computer modeling. J Anal Appl Pyrolysis. 1996, 38 (1): 1-53.Google Scholar
  147. Shevchenko SM, Bailey GW: Non-bonded organo-mineral interactions and sorption of organic compounds on soil surfaces: a model approach. J Mol Struct: Theochem. 1998, 422 (1): 259-270.Google Scholar
  148. Schulten H, Leinweber P, Schnitzer M, Huang P, Senesi N, Buffle J: Analytical pyrolysis and computer modelling of humic and soil particles. Environmental particles: structure and surface reactions of soil particles. 1998, Wiley, Chichester, 281-324.Google Scholar
  149. Schulten H-R, Leinweber P: New insights into organic-mineral particles: composition, properties and models of molecular structure. Biol Fertil Soils. 2000, 30 (5–6): 399-432.Google Scholar
  150. Johnson J (2001) Binding of hydrophobic organic compounds to dissolved humic substances: a predictive approach based on computer assisted structure elucidation, atomistic simulations and Flory-Huggins solution theory. Humic Subst Struct Models Funct 273: 221.Google Scholar
  151. Kubicki J (2000) Molecular modeling of humic and fulvic acid. In: Abstracts of Papers of the American Chemical Society, 361–361. vol. 220.Google Scholar
  152. Ayton GS, Noid WG, Voth GA: Multiscale modeling of biomolecular systems: in serial and in parallel. Curr Opin Struct Biol. 2007, 17: 192-198.PubMedGoogle Scholar
  153. Sherwood P, Brooks BR, Sansom MSP: Multiscale methods for macromolecular simulations. Curr Opin Struct Biol. 2008, 18: 630-640.PubMedGoogle Scholar
  154. Michel J, Orsi M, Essex JW: Prediction of partition coefficients by multiscale hybrid atomic level/coarse-grain simulations. J Phys Chem B. 2008, 112: 657-660.PubMedGoogle Scholar
  155. Orsi M, Sanderson WE, Essex JW: Permeability of small molecules through a lipid bilayer: a multiscale simulation study. J Phys Chem B. 2009, 113: 12019-12029.PubMedGoogle Scholar
  156. Orsi M, Essex JW: Permeability of drugs and hormones through a lipid bilayer: insights from dual-resolution molecular dynamics. Soft Matter. 2010, 6: 3797-3808.Google Scholar
  157. Kamerlin SCL, Vicatos S, Dryga A, Warshel A: Coarse-grained (multiscale) simulations in studies of biophysical and chemical systems. Annu Rev Phys Chem. 2011, 62: 41-64.PubMedGoogle Scholar
  158. Chen Y, Aviad T (1990) Effects of humic substances on plant growth. In: Humic substances in soil and crop sciences: selected readings (humic substances), Soil Science Society of America, 161–186, USA.Google Scholar
  159. Matilainen A, Gjessing ET, Lahtinen T, Hed L, Bhatnagar A: An overview of the methods used in the characterisation of natural organic matter (nom) in relation to drinking water treatment. Chemosphere. 2011, 83 (11): 1431-1442.PubMedGoogle Scholar

Copyright

© Orsi 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

Advertisement