1,721,121 research outputs found
A Collective Variable for the Efficient Exploration of Protein Beta-Sheet Structures: Application to SH3 and GB1
No full textWe introduce a new class of collective variables which allow forming efficiently beta-sheet structures in all-atom explicit-solvent simulations of proteins. By this approach we are able to systematically fold a 16-residue beta hairpin using metadynamics on a single replica. Application to the 56-residue SH3 and GB1 proteins show that, starting from extended states, in ∼100 ns tens of structures containing more than 30% beta-sheet are obtained, including parts of the native fold. Using these variables may allow folding moderate size proteins with an accurate explicit solvent description. Moreover, it may allow investigating the presence of misfolded states that are relevant for diseases (e.g., prion and Alzheimer) and studying beta-aggregation (amyloid diseases)
Statistically unbiased free energy estimates from biased simulations
No full textEstimating the free energy in molecular simulation requires, implicitly or explicitly, counting how many times the system is observed in a finite region. If the simulation is biased by an external potential, the weight of the configurations within the region can vary significantly, and this can make the estimate numerically unstable. We introduce an approach to estimate the free energy as a simultaneous function of several collective variables starting from data generated in a statically biased simulation. The approach exploits the property of a free energy estimator recently introduced by us, which provides by construction of the estimate in a region of infinitely small size. We show that this property allows removing the effect of the external bias in a simple and rigorous manner. The approach is validated on model systems for which the free energy is known analytically and on a small peptide for which the ground truth free energy is estimated in an independent unbiased run. In both cases the free energy obtained with our approach is an unbiased estimator of the ground-truth free energy, with an error whose magnitude is also predicted by the model
A bias-exchange approach to protein folding
Full text linkBy suitably extending a recent approach [Bussi, G.; et al. J. Am. Chem. Soc. 2006, 128, 13435] we introduce
a powerful methodology that allows the parallel reconstruction of the free energy of a system in a virtually unlimited number of variables. Multiple metadynamics simulations of the same system at the same temperature are performed, biasing each replica with a time-dependent potential constructed in a different set of collective variables. Exchanges between the bias potentials in the different variables are periodically allowed according to a replica exchange scheme. Due to the efficaciously ultidimensional nature of the bias the method allows
exploring complex free energy landscapes with high efficiency. The usefulness of the method is demonstrated
by performing an atomistic simulation in explicit solvent of the folding of a Triptophane cage miniprotein.
It is shown that the folding free energy landscape can be fully characterized starting from an extended
conformation with use of only 40 ns of simulation on 8 replicas
A variational definition of electrostatic potential derived charges
No full textIn a recent work [Laio, A., VandeVondele, J., Rothlisberger, U. J. Phys. Chem. B2002106, 7300] a novel method has been proposed to define dynamical electrostatic potential derived (D-RESP) charges for systems described within a quantum mechanics/molecular mechanics (QM/MM) scheme. Here, we derive the analytic dependence of these charges on the quantum charge density and on the atomic positions. This variational property can be exploited for defining interaction potentials between the quantum and the classical subsystems that depend explicitly on the value of the D-RESP charges. Such potentials can be used for a multitude of different purposes, such as improving the computational efficiency of the electrostatic coupling between the QM and the MM subsystems and for defining a QM/MM analogue of the exclusion schemes commonly used in classical biomolecular force fields
Spontaneously Forming Dendritic Voids in Liquid Water Can Host Small Polymers
No full textSome liquids are characterized by the presence of large voids with dendritic shapes and for this reason are dubbed transiently porous. By using a battery of data analysis tools, we demonstrate that liquid water and methane are both characterized by transient porosity. We show that the thermodynamics of porosity is distinct from that associated with cavitation á la classical nucleation theory. The shapes of dendritic voids in both liquids with very different chemistries resemble those of small polymers. We further show, using free energy calculations, that the cost of solvating small hydrophobic polymers in water is consistent with the work associated with creating dendritic voids. The entropic and enthalpic contributions associated with hosting these polymers can thus be rationalized by the thermodynamics of fluctuations in bulk water
Unsupervised detection of semantic correlations in big data
Full text linkIn real-world data, information is stored in extremely large feature vectors. These variables are typically correlated due to complex interactions involving many features simultaneously. Such correlations qualitatively correspond to semantic roles and are naturally recognized by both the human brain and artificial neural networks. This recognition enables, for instance, the prediction of missing parts of an image or text based on their context. We present a method to detect these correlations in high-dimensional data represented as binary numbers. We estimate the binary intrinsic dimension of a dataset, which quantifies the minimum number of independent coordinates needed to describe the data, and is therefore a proxy of semantic complexity. The proposed algorithm is largely insensitive to the so-called curse of dimensionality, and can therefore be used in big data analysis. We test this approach identifying phase transitions in model magnetic systems and we then apply it to the detection of semantic correlations of images and text inside deep neural networks
Equilibrium free energies from non-equilibrium metadynamics
No full textIn this Letter we propose a new formalism to map history-dependent metadynamics in a Markovian process. We apply this formalism to model Langevin dynamics and determine the equilibrium distribution of a collection of simulations. We demonstrate that the reconstructed free energy is an unbiased estimate
of the underlying free energy and analytically derive an expression for the error. The present results can be
applied to other history-dependent stochastic processes, such as Wang-Landau sampling
Correlations among hydrogen bonds in liquid water
No full textBy performing computer simulations of water with the TIP5P potential we show that structures formed by two or more hydrogen bonds affect the dynamical and static properties of water, especially in the vicinity of freezing temperature. In particular, the short time correlation between two coupled hydrogen bonds cannot be predicted assuming the statistical independence of the single hydrogen bonds. This introduces an additional relaxation time of similar to9 ps close to the freezing point. We also find that the time persistence of structures formed by several hydrogen bonds (the first solvation shell) correlates with the local density, which is smaller around water molecules with a long-living environmen
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