127 research outputs found
Anisotropic coarse-grained statistical potentials improve the ability to identify native-like protein structures
We present a new method to extract distance and orientation dependent
potentials between amino acid side chains using a database of protein
structures and the standard Boltzmann device. The importance of orientation
dependent interactions is first established by computing orientational order
parameters for proteins with alpha-helical and beta-sheet architecture.
Extraction of the anisotropic interactions requires defining local reference
frames for each amino acid that uniquely determine the coordinates of the
neighboring residues. Using the local reference frames and histograms of the
radial and angular correlation functions for a standard set of non-homologue
protein structures, we construct the anisotropic pair potentials. The
performance of the orientation dependent potentials was studied using a large
database of decoy proteins. The results demonstrate that the new distance and
orientation dependent residue-residue potentials present a significantly
improved ability to recognize native folds from a set of native and decoy
protein structures.Comment: Submitted to "The Journal of Chemical Physics
Nanoscale Piezoelectric Properties of Self-Assembled Fmoc-FF Peptide Fibrous Networks
Fibrous peptide networks, such as the structural framework of self-assembled fluorenylmethyloxycarbonyl diphenylalanine (Fmoc-FF) nanofibrils, have mechanical properties that could successfully mimic natural tissues, making them promising materials for tissue engineering scaffolds. These nanomaterials have been determined to exhibit shear piezoelectricity using piezoresponse force microscopy, as previously reported for FF nanotubes. Structural analyses of Fmoc-FF nanofibrils suggest that the observed piezoelectric response may result from the noncentrosymmetric nature of an underlying β-sheet topology. The observed piezoelectricity of Fmoc-FF fibrous networks is advantageous for a range of biomedical applications where electrical or mechanical stimuli are required. © 2015 American Chemical Society
A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability
In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner.
Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and
sustainable innovation and its regulation.
This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We review modern multiscale physics-based models that generate advanced system-dependent (intrinsic) or timeand
environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considering distributions of structural advanced features rather than only averages. This review enables us to identify and highlight a number of key challenges associated with ENMs’ data generation, curation,
representation and use within machine learning or other advanced data-driven models to ultimately enhance toxicity assessment. Finally, the set up of dedicated databases as well as the development of grouping and read-across strategies based on the mode of action of ENMs using omics methods are identified as emerging methodologies for safety assessment and reduction of animal testing
A Condensation-Ordering Mechanism in Nanoparticle-Catalyzed Peptide Aggregation
Nanoparticles introduced in living cells are capable of strongly promoting
the aggregation of peptides and proteins. We use here molecular dynamics
simulations to characterise in detail the process by which nanoparticle
surfaces catalyse the self- assembly of peptides into fibrillar structures. The
simulation of a system of hundreds of peptides over the millisecond timescale
enables us to show that the mechanism of aggregation involves a first phase in
which small structurally disordered oligomers assemble onto the nanoparticle
and a second phase in which they evolve into highly ordered beta-sheets as
their size increases
Knowledge-based energy functions for computational studies of proteins
This chapter discusses theoretical framework and methods for developing
knowledge-based potential functions essential for protein structure prediction,
protein-protein interaction, and protein sequence design. We discuss in some
details about the Miyazawa-Jernigan contact statistical potential,
distance-dependent statistical potentials, as well as geometric statistical
potentials. We also describe a geometric model for developing both linear and
non-linear potential functions by optimization. Applications of knowledge-based
potential functions in protein-decoy discrimination, in protein-protein
interactions, and in protein design are then described. Several issues of
knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe
Variational Approach to Molecular Kinetics
The eigenvalues and eigenvectors of the molecular dynamics propagator (or transfer operator) contain the essential information about the molecular thermodynamics and kinetics. This includes the stationary distribution, the metastable states, and state-to-state transition rates. Here, we present a variational approach for computing these dominant eigenvalues and eigenvectors. This approach is analogous the variational approach used for computing stationary states in quantum mechanics. A corresponding method of linear variation is formulated. It is shown that the matrices needed for the linear variation method are correlation matrices that can be estimated from simple MD simulations for a given basis set. The method proposed here is thus to first define a basis set able to capture the relevant conformational transitions, then compute the respective correlation matrices, and then to compute their dominant eigenvalues and eigenvectors, thus obtaining the key ingredients of the slow kinetics
Evaluating the Effects of Cutoffs and Treatment of Long-range Electrostatics in Protein Folding Simulations
The use of molecular dynamics simulations to provide atomic-level descriptions of biological processes tends to be computationally demanding, and a number of approximations are thus commonly employed to improve computational efficiency. In the past, the effect of these approximations on macromolecular structure and stability has been evaluated mostly through quantitative studies of small-molecule systems or qualitative observations of short-timescale simulations of biological macromolecules. Here we present a quantitative evaluation of two commonly employed approximations, using a test system that has been the subject of a number of previous protein folding studies–the villin headpiece. In particular, we examined the effect of (i) the use of a cutoff-based force-shifting technique rather than an Ewald summation for the treatment of electrostatic interactions, and (ii) the length of the cutoff used to determine how many pairwise interactions are included in the calculation of both electrostatic and van der Waals forces. Our results show that the free energy of folding is relatively insensitive to the choice of cutoff beyond 9 Å, and to whether an Ewald method is used to account for long-range electrostatic interactions. In contrast, we find that the structural properties of the unfolded state depend more strongly on the two approximations examined here
Discrete Kinetic Models from Funneled Energy Landscape Simulations
A general method for facilitating the interpretation of computer simulations of protein folding with minimally frustrated energy landscapes is detailed and applied to a designed ankyrin repeat protein (4ANK). In the method, groups of residues are assigned to foldons and these foldons are used to map the conformational space of the protein onto a set of discrete macrobasins. The free energies of the individual macrobasins are then calculated, informing practical kinetic analysis. Two simple assumptions about the universality of the rate for downhill transitions between macrobasins and the natural local connectivity between macrobasins lead to a scheme for predicting overall folding and unfolding rates, generating chevron plots under varying thermodynamic conditions, and inferring dominant kinetic folding pathways. To illustrate the approach, free energies of macrobasins were calculated from biased simulations of a non-additive structure-based model using two structurally motivated foldon definitions at the full and half ankyrin repeat resolutions. The calculated chevrons have features consistent with those measured in stopped flow chemical denaturation experiments. The dominant inferred folding pathway has an “inside-out”, nucleation-propagation like character
The Free Energy Landscape of Small Molecule Unbinding
The spontaneous dissociation of six small ligands from the active site of FKBP
(the FK506 binding protein) is investigated by explicit water molecular dynamics
simulations and network analysis. The ligands have between four
(dimethylsulphoxide) and eleven (5-diethylamino-2-pentanone) non-hydrogen atoms,
and an affinity for FKBP ranging from 20 to 0.2 mM. The conformations of the
FKBP/ligand complex saved along multiple trajectories (50 runs at 310 K for each
ligand) are grouped according to a set of intermolecular distances into nodes of
a network, and the direct transitions between them are the links. The network
analysis reveals that the bound state consists of several subbasins, i.e.,
binding modes characterized by distinct intermolecular hydrogen bonds and
hydrophobic contacts. The dissociation kinetics show a simple (i.e.,
single-exponential) time dependence because the unbinding barrier is much higher
than the barriers between subbasins in the bound state. The unbinding transition
state is made up of heterogeneous positions and orientations of the ligand in
the FKBP active site, which correspond to multiple pathways of dissociation. For
the six small ligands of FKBP, the weaker the binding affinity the closer to the
bound state (along the intermolecular distance) are the transition state
structures, which is a new manifestation of Hammond behavior. Experimental
approaches to the study of fragment binding to proteins have limitations in
temporal and spatial resolution. Our network analysis of the unbinding
simulations of small inhibitors from an enzyme paints a clear picture of the
free energy landscape (both thermodynamics and kinetics) of ligand
unbinding
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