Modelling the interaction processes between nanoparticles and biomacromolecules of variable hydrophobicity: Monte Carlo simulations

Abstract

The conformational properties and formation of a complex between a weak flexible biomacromolecule chain of variable hydrophobicity and one negatively charged nanoparticle in the presence of explicit counterions are investigated here using Monte Carlo simulations. The influence of the charge distribution and hydrophobicity, monomer distribution of the chain as well as the pH of the solution are systematically investigated. It is shown that the isolated chain conformations, built with random and block distribution of carboxylic, amino and hydrophobic groups, are the result of the subtle competition between intrachain attractive and repulsive electrostatic interactions as well as intrachain attractive short-range interactions due to hydrophobic properties. Extended conformations are found at low and high pH and folded conformations at physiological pH when hydrophilic and block polymer chains are considered. On the other hand, hydrophobic chain conformations do not show pH dependency and remain folded. The intrachain attractive electrostatic interactions clearly promote the deprotonation of carboxylic groups at low pH and the protonation of amino groups at high pH with higher efficiency for hydrophilic chains. The additional set of electrostatic interactions due to the presence of one negatively charged nanoparticle limits the deprotonation of carboxylic groups at low pH. Moreover, the attractive interactions between the biomacromolecule and the nanoparticle allow to observe the formation of a complex considering intermediate and hydrophilic chains even close to the chain isoelectric point due to the charge inhomogeneity distribution. Hydrophobic chain segments are not affected by the presence of the nanoparticle and remain desorbed. In all cases, the presence of one nanoparticle influences the biomacromolecule structures and acid/base properties, leading to more stretched conformations

Development of constant-pH simulation methods in implicit solvent and applications in biomolecular systems

“…An interesting characteristic of their CG model is the inclusion of some internal degrees of freedom of the protein allowing this computer model to explore the pH effects on the macromolecular (flexible) chain. Doing so, they could observe extended and folded conformations as a function of the solution pH (Stoll 2014;Carnal et al 2015).…”

“…Corrections to this titration model were proposed later (Labbez and Jönsson 2007). Other models in the same lines with improved features were developed by Stoll and collaborators (Stoll 2014;Carnal et al 2015). An interesting characteristic of their CG model is the inclusion of some internal degrees of freedom of the protein allowing this computer model to explore the pH effects on the macromolecular (flexible) chain.…”

“…The interplay of so many titratable sites of several ionizable objects will slow down even more the already difficult sampling. The best Krieger et al (2006), Burger andAyers (2011), andOlsson et al (2011) b See Bashford (1997), Baker et al (2001), Anandakrishnan et al (2012), Wang et al (2015), and Sakalli and Knapp (2015) c See Svensson et al (1990), Kesvatera et al (1996Kesvatera et al ( , 1999Kesvatera et al ( , 2001, Teixeira et al (2010), Carnal et al (2015), Barroso da Silva et al (2017a), and Barroso da Silva and MacKernan (2017b) d See Baptista et al (1997, Baptista and Soares (2001), Machuqueiro and Baptista (2007), and Santos et al (2015) e See Lee et al (2004), Shen (2009), Dashti et al (2012), Goh et al (2013a), Chen et al (2013Chen et al ( , 2014Chen et al ( , 2016, Chen and Roux (2015), Socher and Stich (2016), and Donnini et al (2016) f See Tummanapelli and Vasudevan (2015), Kamerlin et al (2009), andLi et al (2002) alternative in this case is the MC titration schemes, particularly the FPTS. From this mesoscopic scheme, other intermediate models can also be derived in order to improve accuracy for specific tasks at higher CPU expenses.…”

“…The CpH MC schemes in implicit solvents discussed above meet well all these requirements. They have been intensively and successfully applied in several biomolecular systems: (a) protein-protein interactions Jönsson 2003, 2005;Jönsson et al 2007;Persson et al 2010;Kurut et al 2012Kurut et al , 2015Delboni and Barroso da Silva 2016;Barroso da Silva et al 2016), (b) protein-polyelectrolyte interactions (Barroso da Jönsson et al 2007; Barroso da Silva and Jönsson 2009;Barroso da Silva 2013;Srivastava et al 2017), (c) protein-peptide interactions (André et al 2004;Jönsson et al 2007), (d) protein-surface interactions (Nylander et al 2017;Hyltegren and Skepö 2017), and (e) protein-nanoparticle interactions (Barroso da Silva et al 2014;Carnal et al 2015). There is also ongoing work on protein-RNA interactions at our laboratory together with Profs.…”

Dissipative particle dynamic simulation and experimental assessment of the impacts of humic substances on aqueous aggregation and dispersion of engineered nanoparticles

“…There is a growing interest in exploring various computational approaches to better understand and generalize the fate and behavior of NPs in aqueous suspensions [17][18][19][20][21][22][23][24][25]. Brownian dynamics, molecular dynamics, and Monte Carlo simulations have been applied to analyze the details of NP agglomeration kinetics and agglomerate morphology [17,26,27].…”