Computational Study of the Forces Driving Aggregation of Ultrasmall Nanoparticles in Biological Fluids

Abstract

Nanoparticle (NP) aggregation can lead to prolonged retention in tissues or embolism, among other adverse effects. Successful use in biomedicine thus requires the capability to make NPs with limited aggregative : potential. Rational design is presently a challenge due to incomplete knowledge of their interactions in biofluids. Recently, ultrasmall gold NPs passivated with endogenous antioxidant glutathione have shown promise for use in vivo. Computer simulations are here conducted to identify the forces underlying aggregation (or lack thereof) of these NPs in a cell culture. Electrostatic interactions are insufficient to induce association, but the van der Waals forces exerted by cations, anions, and net-neutral polar species can promote the formation of stable dimers. The entropic effects of depletion are negligible, but the combined effect of depletion and macromolecular crowding at physiological concentrations can stabilize aggregates containing just a few NPs. Interparticle interactions are controlled by modest changes in both the structure and dynamic of the interfacial liquid. The molecular origin of these effects and their dependence on NP size are described. The liquid is shown to be highly structured, with large and long-lived hydrogen-bonded water clusters developing often in the interparticle space; their potential role as transient, long-range proton wires connecting and enveloping neighboring NPs is discussed. The basis for a parsimonious theory of ultrasmall NPs in complex fluids is established.

Binding kinetics of ultrasmall gold nanoparticles with proteins

“…This situation is indeed akin to the current view of the protein complexation mechanism 58-59 (These simulations cannot fully represent the water desolvation barrier, which is modulated by liquid-structure forces and require atomic resolution; these forces would destabilize both potentials in Fig. 6 and introduce a barrier between state 1 and the dissociated state 60 , but such modulations do not change the kinetics mechanism discussed. )…”

“…The calculations were carried out with canonical Monte Carlo simulations in a biased potential η , so V ( r ) = − kT ln P ( r ) + η ( r ) + c , where P are the weighted probability distributions, and r is the distance between the centers of mass of the NP and the protein. A nonharmonic potential of the form 60 η ( r ) = a 1 ( r 2 – r i 2 ) + a 2 ( r – r i ) 2 – a 3 ln{[1 + exp(– a 4 ( r – r i ) 2 )]/2} was applied at intervals of 1 Å, where a 1 = −9×10 −4 ; a 2 = 4×10 −3 ; a 3 = 2; a 4 = 0.05 were determined in test simulations. A total of 10 6 trial moves were performed for each r i consisting of rotations and/or translations.…”

Determination of the local density of polydisperse nanoparticle assemblies

“…Such interactions may be temperature-dependent, as in hybrid functional polymer-NP systems aiming at a control of optical properties via aggregation, 9 or dominated by biologically relevant ions in systems mimicking NP assembly in cells 10 . Particular interactions may lead to specific aggregates, like the formation of NP chains which has been related to the combined presence of hydrogen bonding and dipolar interactions.…”

Self-adaptive multiscaling algorithm for efficient simulations of many-protein systems in crowded conditions

“…The latter is desirable when simulating protein–protein interactions where structural details at the interfaces are generally important, but it may not be necessary when modeling protein–surface interactions, 44 including protein–membrane, protein–NP, or NP–NP associations. 2,41,42…”

“…A simplified, non-adaptive version of the multiscaling method presented here was used earlier to simulate a system of ultrasmall gold nanoparticles in an aqueous solution of albumin at concentrations comparable to those in blood serum 41 and in other crowded solutions. 2,42 For a protein I , the algorithm first identifies the atom i 1 closest to the protein center of mass (COM), and all the protein atoms { I } 1 within a distance λ from i 1 are represented by a spherical particle of radius scriptR1, mass m 1 , and charge q 1 , centered at the COM of the set { I } 1 ; then, the atom i2∉{I}1 closest to the COM is identified, and all atoms {I}2⊂{I}1 within a distance λ from i 2 are represented by a spherical particle with parameters scriptR2, m 2 , and q 2 centered at the COM of { I } 2 ; the process continues so that in step n the atom in∉{I}1∪{I}2∪…∪{I}n−1 closest to the protein COM is identified, and the atoms {I}n⊂{I}1∪{I}2∪…∪{I}n−1 within a distance λ from i n are represented by a sphere with scriptRn…”