Producción de snacks de papas

While at present many of the difficulties discussed above in Section 1.2.3 still hin- der experimental determination of peptide conformation and/or aggregation upon adsorption, such information is, in principle, readily attainable from molecular simu- lation. Numerous studies modeling a range of different substrates have been carried out in recent years–see Sections 1.3 and 1.4 for a review of recent quartz and gold biointerfacial simulations. However, the ‘success’ of each–determined by the accu- racy with which the essential physics and chemistry of the system are reproduced– depends critically on the model and simulation protocol adopted [Latour [2008]].

First-principles, quantum mechanical methods can be used to study a system at the most fundamental level with no (or in the case of Density Functional Theory , DFT, minimal) empirically fitted parameters (Section 2.1.1). These calculations can be highly accurate but are as a consequence, intrinsically very computation- ally demanding. This therefore limits their use to static calculations or simulations on the order of ps (Section 2.2.4), for systems of small dimension (_≪1000 atoms) only. Both the small size and short duration of a first-principles simulation impose implementation and interpretation difficulties–namely, the potential to introduce unphysical artifacts due to cell periodicity; and the ability adequately to equilibrate and sample different conformational states of a system, respectively. In addition many biointerfacial electronic structure theory (EST) calculations in the past have been performed in vacuo or implicit solvent. However, it is widely thought that the structure of water at an interface, neglected by both approximations, can play a critical role in peptide binding [Jena and Hore [2010]; Skelton et al. [2009]; Schneider and Ciacchi [2012]]. In the case of DFT, the performance of the exchange-correlation

functional used must also be considered: without corrective terms, dispersion inter- actions (such as those between an amino-acid with a hydrophobic side-chain and a surface) are poorly described. Despite these caveats, first-principles simulation is the only method in which electrons are explicitly accounted. As a consequence of all the possible levels of theory, they give the most faithful representation of atomic polar- ization and charge transfer, both of which are especially important when modeling metallic interfaces or chemisorption events.

Atomistic models employing empirical force-fields (FFs) to describe the inter- actions between ‘atoms’ (entities of point mass and charge) are more computation- ally accessible, enabling scientists to study larger systems (on the order of millions of atoms) over longer time-scales (up to 1µs). Although over recent years this type of simulation has become the method of choice for many in the field, a number of challenges remain.

Many well-established FFs exist for solid inorganic materials (for example, see Ref [Herzbach et al. [2005]] for a review of silica potentials), while those parame- terised to be compatible with existing biomolecular FFs are fewer in number. In the latter, the description of the bulk phase of the solid substrate is often compromised in favour of compatibility and accurate representation of the surface itself [Lopes et al. [2006]]. To overcome this issue, some either employ methods such as those outlined by Schroderet al. [Schroder et al. [1992]] to fit cross-terms between organic and inorganic phases [Freeman et al. [2007]] or derive parameters for a biointerfacial FF in which the co-ordinates of the substrate are held fixed in space [Iori et al. [2009]; Iori and Corni [2008]; Wright et al. [2013b,c]]. The latter approach may introduce artifacts into the model [Phan et al. [2012]; Wright et al. [2013a]], however.

Like in solution, the choice of biomolecular FF employed to model the or- ganic component of the system and with which the interfacial FF is designed to be compatible, is also critical, especially for peptide conformational studies. Although refinement of bio-organic FFs is a rapidly advancing area of research, a number of re- ports have shown that the most recent version of CHARMM (CHARMM22* [Piana et al. [2011]]) performs well for predicting the structure of proteins and peptides in solution [Piana et al. [2011]; Collier et al. [2012]; Lindorff-Larson et al. [2012]; Cino et al. [2012]]. Either CHARMM27 [MacKerell et al. [1998, 2004]] or CHARMM22* [Piana et al. [2011]] were used in all the simulations reported here. Questions remain, however, over the transferability of standard biomolecular FF parameters (derived for solution-based studies) to bio-interfacial simulations [Vellore et al. [2010]; Collier et al. [2012]]. To tackle this issue, Latour and co-workersvery recently proposed a dual parameter protocol in which a refined set of intra-peptide interaction terms are

used when the peptide is within the interfacial region, while standard CHARMM parameters are adopted in solution [Snyder et al. [2012]].

The ability adequately to sample all the relevant conformational states of a system during a molecular simulation poses one of the biggest challenges for the future of both bio-interfacial and solution-based studies. The need explicitly to account for water in the former case intrinsically makes the systems both large and complex. Thus, in many cases, unless advanced sampling techniques are employed, it is not possible to explore all of the important low energy states of the system within the time-scale of a simulation. As a worst case scenario, the system may remain trapped in a single deep well in its potential energy landscape throughout an entire simulation. Unless these difficulties are overcome the use of computational modeling to probe both the hypothesis of peptide-substrate binding induced peptide-folding [Evans et al. [2008]; Capriotti et al. [2007]; Collino and Evans [2008]; Rimola et al. [2012]]–thoroughly, and calculate peptide binding affinities accurately, is hindered.