molecular modelling and simulation pdf

Molecular Modelling And Simulation Pdf

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In this paper we review the current status of high-performance computing applications in the general area of drug discovery.

Molecular Modeling and Simulation

A common consequence is the formation of the protein corona, that is, a network of adsorbed proteins that can strongly alter the surface properties of the nanoparticle. The protein corona and the resulting structural changes experienced by adsorbed proteins can lead to substantial deviations from the expected cellular uptake as well as biological responses such as NP aggregation and NP-induced protein fibrillation, NP interference with enzymatic activity, or the exposure of new antigenic epitopes.

Achieving a detailed understanding of the nano—bio interface is still challenging due to the synergistic effects of several influencing factors like pH, ionic strength, and hydrophobic effects, to name just a few. Because of the multiscale complexity of the system, modeling approaches at a molecular level represent the ideal choice for a detailed understanding of the driving forces and, in particular, the early events at the nano—bio interface.

This review aims at exploring and discussing the opportunities and perspectives offered by molecular modeling in this field through selected examples from literature. Nanomedicine is an emerging discipline that is providing novel impulses to the biomedical field thanks to the use of nanotechnologies and the continuous development of engineered nanomaterials such as polymer-, metal- or metal oxide-based nanoparticles.

Nanomaterials, by virtue of their small size 1— nm, comparable to many biological molecules like proteins and viruses open up a wide range of new opportunities and applications, for example as devices for targeted drug delivery and diagnostic purposes and as image contrast agents. However, as with every novel technology, the potential negative side effects have to be assessed early in the development process to avoid adverse social and economic effects.

Indeed, the injection of nanomaterials into an organism leads to complex interactions between the surface of the device and the components of the medium, such as proteins, carbohydrates, fatty acids, et cetera. These interactions play a key role in determining not only the fate of the nanomaterial in terms of clearance and in vivo biodistribution but also the attainment of undesired side effects.

The fundamental driving forces governing the formation of this nano-bio interface have already been identified and discussed Nel et al. The challenge lies in the rationalization of the synergistic effects of intrinsic nanomaterial properties chemical composition, size, surface functionalization, et cetera , the characteristics of the surrounding medium pH, ionic strength, et cetera , and the phenomena occurring at the interface and their impact on nanomaterial behavior.

One of the most relevant consequences is the formation of the protein corona, i. The attainment of such a network alters the surface properties of the nanomaterial, which may cause substantial deviations from the expected behavior concerning colloidal stability, cellular uptake, clearance, distribution within the organs, and immune response.

On top of that, the formation of the protein corona can lead to changes in the protein structure and thus to undesired consequences not easily predictable a priori , such as Nel et al. Experimental protocols for the investigation of the protein corona are currently well-established Walkey and Chan, ; Wei et al. Computational approaches at the molecular scale, such as molecular dynamics MD simulations, constitute the natural complement to experimental techniques.

This review aims at exploring and discussing the opportunities and limitations of nano-bio as well as giving some perspectives on the use of molecular modeling techniques for characterizing these interactions. After giving a brief theoretical background, relevant applications of simulations at the molecular scale are discussed through selected examples from the scientific literature. Molecular modeling can be seen as the sum of two components: a molecular model and a computational technique to properly characterize the behavior of the molecules.

Building a suitable molecular model, that is, how the system under investigation is rationalized and represented in the framework of a meaningful simulation, is the first fundamental step. In this framework, molecular models can be essentially divided into two categories; on the one side, full atomistic models provide the highest level of detail since all atoms considered as the smallest constitutive units of the model are explicitly accounted for.

On the other side, coarse-grained models summarize the atomic detail by enclosing groups of atoms into beads that lump the main peculiarities in terms of charge, polarity, et cetera of the atoms that they embed. Despite the loss of detail, a coarse-grained model that retains the main features of the system is able to provide meaningful insights at a reasonable computational cost vide infra. For the sake of completeness, there exist more detailed representations where electrons are the smallest constitutive units and are explicitly included.

Such models are treated with quantum chemistry methods, which are not considered or discussed here since their application in the field of nanomedicine is hindered by their computational inefficiency. In a broader sense, a molecular model also includes unavoidable simplifications that allow for the simulation of complex systems, either at a full atomistic or coarse-grained level of detail, which could not be treated otherwise.

The simulation of protein adsorption on a microparticle surface, for example, is unfeasible because of the system size. Such a system is usually simplified by adopting a molecular model that involves the adsorption of a protein on a flat surface with a suitable thickness. The second component of molecular modeling is constituted by suitable computational methods that allow the characterization of the dynamics, energetics, and conformational sampling of the system of interest.

Full atomistic models are usually treated with molecular dynamics, while other techniques such as coarse-grained molecular dynamics and dissipative particle dynamics are employed along with coarse-grained models. Each method has its own strengths and limitations, as well as characteristic accessible time and length scales, as discussed in the following paragraphs. In molecular dynamics simulations, atoms are represented as spheres that interact with each other by virtue of a potential energy function, usually called the force field FF.

Molecular coordinates and velocities as a function of simulation time can be evaluated by solving Newton's equation of motion with a suitable numerical integration scheme, as shown in Equation 1 Frenkel and Smit, :. Such an approach essentially implies a couple of assumptions, as follows.

First, the motion of electrons can be reasonably described by the dynamics of the corresponding nuclei Born—Oppenheimer approximation. Second, the motion of the atomic nuclei which are heavier than electrons can be described as point particles that follow classical mechanics; this is an acceptable approximation when quantum effects are not important Frenkel and Smit, Generally speaking, a force field takes into account both intramolecular and intermolecular interactions, in terms of bonds, angles, dihedrals, and long-range interactions, namely van der Waals and electrostatic.

The choice and the quality of the force field cannot be underestimated, since they strongly affect the reliability of the simulation outcome. MD simulations do not explicitly consider electrons, so chemical reactions and excited states cannot be investigated; however, they constitute the ideal tool for those systems that are mainly governed by non-covalent interactions, like electrostatic and Van der Waals forces. MD also allows environmental conditions to be included through the addition of explicit solvent molecules, ions, and other solute molecules into the system.

The main outputs from an MD simulation are molecular trajectories, the post-processing of which can provide structural information binding poses, protein conformation as well as energetic information such as interaction energies.

The characteristic time and length scales of MD simulations are in the tens to hundreds of nanoseconds up to ns and tens of nanometers up to 20 nm , respectively.

However, many phenomena of interest e. A way to overcome this issue is to use enhanced sampling methods, which allow enhancement of the transitions between different metastable states separated by energy barriers higher than the thermal energy k B T , which would not be crossed in a standard simulation at temperature T where k B is the Boltzmann constant and T is absolute temperature. As recently reviewed Camilloni and Pietrucci, , there are three different suitable approaches: i increasing the temperature T ; ii changing the potential U r , and iii adding an external bias potential V r.

Each approach has its own methods, the discussion of which along with their theoretical basis is well beyond the purpose of this review; the interested reader is referred to ad hoc reviews Miao and Mccammon, ; Camilloni and Pietrucci, In particular, WTM and its variant forms allow the free energy of the system under investigation to be recovered by adding an external bias on a selected number of degrees of freedom, commonly referred to as collective variables CVs.

Collective variables must be chosen so that they can discriminate between metastable states and can be representative of the transition mechanism. Typical applications of WTM and WTM-based methods are the study of protein conformations also in the presence of denaturants Owczarz et al.

Some phenomena, such as protein folding, require a relevant number of collective variables to perform meaningful simulations. Although conceptually feasible, running a WTM simulation with many CVs introduces some issues such as a drop in computational efficiency and a non—trivial analysis of the results obtained.

The discussion of the theoretical basis of these methods is beyond the purpose of this review; the interested reader is referred to the corresponding papers. Trajectories can be computed by integrating Newton's equation of motion and also adding other components to the force such as friction due to the solvent if implicit solvent methods are used vide infra. It is worth mentioning that the coarse-graining procedure can be performed to different extents, since a bead can enclose a group of atoms 3—4 heavy atoms , a group of monomers or amino acids , an entire protein or an entire microparticle, according to the aim of the simulation.

A common drawback of CG models is that parameterization is strictly tailored for the system under investigation and in principle should be repeated for every new system; in other words, parameters are not transferable.

Beads which include groups of 3—4 heavy atoms still interact with each other through a simple potential energy function, as described for MD vide supra. MARTINI offers a library of parameterized beads, mainly divided into four categories: polar, non-polar, apolar, and charged; in addition, each group includes subgroups representative of polarity and hydrogen bond capability.

Parameters for bonded interactions bonds, angle, dihedrals must be determined from detailed MD simulations, while non-bonded interactions are tuned in order to reproduce thermodynamic properties like free energy of hydration, free energy of vaporization, and partitioning between water and different solvents.

Figure 1. Standard water bead embedding four water molecules A. Polarizable water bead with embedded charges B. DMPC lipid C.

Polysaccharide fragment D. Peptide E. DNA fragment F. Polystyrene fragment G. Fullerene H. Published by the Royal Society of Chemistry.

Bead parameterization can be further refined by the user in order to improve agreement with full atomistic simulations. Even with simulations based on the MARTINI force field, some phenomena of interest can be still characterized at a time scale that is not accessible. In this framework, enhanced sampling methods like Metadynamics can be employed to alleviate this issue, as already shown in the literature Lelimousin et al.

Bead trajectories are still obtained by means of Newton's equation of motion, assuming that each i-th particle is subjected to three pair-additive forces that arise from the interactions with the other j-th particles: a conservative force, a dissipative force, and a random force Liu et al.

The conservative force F c is due to the interaction potential of particles and accounts for both bonded and long-range interactions through an elastic force and a soft repulsion force, respectively.

F d is a dissipative force that damps the relative motion between particles, and F r is a random force directed along the line that connects beads centers. Dissipative and random forces are momentum-conserving and represent the minimal model that takes into account viscous forces and thermal noise between particles.

In this framework, full atomistic models provide the highest level of detail, since all atoms are explicitly included. On the other side, the inclusion of explicit solvent molecules, ions, and other solute molecules allows environmental effects to be taken into account; the impact of pH is accounted for by appropriately changing protonation states.

Focusing on proteins, by means of molecular dynamics simulations and their resolution at atomic scale it is possible to highlight the most relevant amino acids that drive the interactions at the nano—bio interface and protein structural changes at the single amino acid level, achieving a level of detail that is usually out of reach from an experimental point of view. On top of this, the reliability of the simulation results can be assessed by comparing theoretical quantities such as circular dichroism spectra with the corresponding experimental outcomes.

The importance of this aspect cannot be underestimated since it strengthens the connection between experiments and simulations. Molecular dynamics simulations are still limited by the characteristic time and length scales accessible by the method: microseconds and nanometers, respectively.

In this regard, switching to coarse-grained models is a forced but attractive choice due to the longer accessible time and length scales tens of microseconds and tens of nanometers, respectively. The drawback is the loss of the atomic detail, which implies that some interactions strong electrostatic interactions, hydrogen bonds, solvation effects are accounted for only in a roughly qualitative way.

CG simulations can also provide some input guess structures for, e. On top of that, enhanced sampling methods in particular, Well-Tempered Metadynamics have proved to be useful for simulations at CG scale when the time scale is still not accessible.

All these aspects are discussed in detail, along with selected examples, in the following paragraphs. Molecular modeling is essentially employed for two purposes in the framework of nanomaterial—biology interactions. On the one side, it can shed light on the early events leading to the protein corona, highlighting the main mechanisms behind protein adsorption on the nanomaterial surface hydrophobic effects, hydrogen bonds, electrostatic interactions, et cetera , the most important amino acids involved in the binding and the attainment of conformational changes.

On the other side, simulations at the molecular scale allow the evaluation in a trend-wise manner of the impact of environmental effects, nanoparticle material, and surface functionalization on cellular uptake; some preliminary theoretical insights can also be obtained concerning the effect of protein corona formation. Molecular modeling, thanks to its resolution at the atomic scale, represents the natural choice for the study of early events that lead to protein corona formation.

Knowledge of the structural changes experienced by the protein after adsorption is essential for understanding system behavior, as discussed in the introduction vide supra. Molecular modeling can offer an exhaustive overview of the structural transitions thanks to the resolution at a molecular level, highlighting the portion of proteins subjected to structural changes along with the most important amino acids that cause this and the main driving forces electrostatic interactions, hydrophobic effects, et cetera.

This allows information to be obtained that is challenging or impossible to achieve experimentally, and this is why molecular modeling has emerged as the natural and ideal complement to experiments. A typical application is constituted by detailed MD simulations of the interactions between a protein and a particle and the resulting changes in protein structure. The particle is usually modeled as a flat surface. On the one hand, there is no need to account for the entire sphere, since the interactions occur only at the interface.

On the other hand, if the size of the protein is much smaller than the particle size, surface curvature effects can be safely neglected; this approximation is not valid for nanoparticles, whose size is comparable to those of proteins, and particle curvature must be accounted for by building the molecular model of the NP surface properly.

The specific structural changes of the protein can be directly correlated with experimental data, circular dichroism results, or NMR spectra.

Molecular modelling

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Schlick Published Computer Science. From the Publisher: The basic goal of this new text is to introduce students to molecular modeling and simulation and to the wide range of biomolecular problems being attacked by computational techniques. The premise of the author is that the dazzling modeling and simulation software now available often leaves practitioners unaware of the fundamental problems and the complex algorithmic approaches to them that still form the heart of ongoing research. View via Publisher.

Molecular Modelling and Simulation

The presentations are in English. Lattice models. A number of phenomena are shown using a two-dimensional molecular model of matter: condensation of gas and crystallization of liquid on cooling, melting and evaporation on heating up, crystal defects, capillary action, atmosphere in a gravitational field, nucleation, diffusion After downloading the respective zip-file, unpack it to a suitable folder and run simolant.

Student Resources

Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. The simplest calculations can be performed by hand, but inevitably computers are required to perform molecular modelling of any reasonably sized system. The common feature of molecular modelling methods is the atomistic level description of the molecular systems.

Molecular modelling

A common consequence is the formation of the protein corona, that is, a network of adsorbed proteins that can strongly alter the surface properties of the nanoparticle. The protein corona and the resulting structural changes experienced by adsorbed proteins can lead to substantial deviations from the expected cellular uptake as well as biological responses such as NP aggregation and NP-induced protein fibrillation, NP interference with enzymatic activity, or the exposure of new antigenic epitopes. Achieving a detailed understanding of the nano—bio interface is still challenging due to the synergistic effects of several influencing factors like pH, ionic strength, and hydrophobic effects, to name just a few. Because of the multiscale complexity of the system, modeling approaches at a molecular level represent the ideal choice for a detailed understanding of the driving forces and, in particular, the early events at the nano—bio interface. This review aims at exploring and discussing the opportunities and perspectives offered by molecular modeling in this field through selected examples from literature.

A rigorous and up-to-date treatment of the foundations, enlivened by engaging anecdotes and historical notes. I am also often approached by my colleagues in computational biology to recommend a solid textbook for a graduate course in the area. Tamar Schlick has written the book that I will be recommending to both groups. Tamar has done an amazing job in writing a book that is both suitably accessible for beginners, and suitably rigorous for experts.

A rigorous and up-to-date treatment of the foundations, enlivened by engaging anecdotes and historical notes. I am also often approached by my colleagues in computational biology to recommend a solid textbook for a graduate course in the area. Tamar Schlick has written the book that I will be recommending to both groups. Tamar has done an amazing job in writing a book that is both suitably accessible for beginners, and suitably rigorous for experts. The 14 chapters offer keys to understand the broader context of this field and the impact it can have on our everyday life for example through medical applications…The main achievement of the book is that even the most sophisticated problems are part of a gradual approach…certainly efficient…The book will obviously be of great interest to students and teachers but it should also be very valuable for research scientists, especially newcomers to the field of molecular modeling, as a reference book and a point of entry in the more specialized literature. The text emphasises that the field is changing very rapidly and that it is full of exciting discoveries.


Molecular dynamics (MD) simulations represent the computer approach to statistical mechanics. As a counterpart to experiment, MD simulations.


Student Resources

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