Algorithmes adaptatifs pour la simulation moléculaire

Les simulations moléculaires sont devenues un outil essentiel en biologie, chimie et physique. Malheureusement, elles restent très couteuses. Dans cette thèse, nous proposons des algorithmes qui accélèrent les simulations moléculaires en regroupant des particules en plusieurs objets rigides. Nous étudions d'abord plusieurs algorithmes de recherche de voisins dans le cas des grands objets rigides, et démontrons que les algorithmes hiérarchiques permettent d'obtenir des accélérations importantes. En conséquence, nous proposons une technique pour construire une représentation hiérarchique d'un graphe moléculaire arbitraire. Nous démontrons l'usage de cette technique pour la mécanique adaptative en angles de torsion, une méthode de simulation qui décrit les molécules comme des objets rigides articulés. Enfin, nous introduisons ARPS - Adaptively Restrained Particle Simulations ("Simulations de particules restreintes de façon adaptative") - une méthode mathématiquement fondée capable d'activer et de désactiver les degrés de liberté en position. Nous proposons deux stratégies d'adaptation, et illustrons les avantages de ARPS sur plusieurs exemples. En particulier, nous démontrons comment ARPS permet de choisir finement le compromis entre précision et vitesse, ainsi que d'obtenir rapidement des statistiques non biaisées sur les systèmes moléculaires.Molecular simulations have become an essential tool in biology, chemistry and physics. Unfortunately, they still remain computationally challenging. In this dissertation, we propose algorithms that accelerate molecular simulations by clustering particles into rigid bodies. We first study several neighbor-search algorithms for large rigid bodies, and show that hierarchy-based algorithms may provide significant speedups. Accordingly, we propose a technique to build a hierarchical representation of an arbitrary molecular graph. We show how this technique can be used in adaptive torsion-angle mechanics, a simulation method that describes molecules as articulated rigid bodies. Finally, we introduce ARPS - Adaptively Restrained Particle Simulations - a mathematically-grounded method able to switch positional degrees of freedom on and off. We propose two switching strategies, and illustrate the advantages of ARPS on various examples. In particular, we show how ARPS allow us to smoothly trade between precision and speed, and efficiently collect unbiased statistics on molecular systems.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Adaptive free energy sampling in multidimensional collective variable space using boxed molecular dynamics

The past decade has seen the development of a new class of rare event methods in which molecular configuration space is divided into a set of boundaries/interfaces, and then short trajectories are run between boundaries. For all these methods, an important concern is how to generate boundaries. In this paper, we outline an algorithm for adaptively generating boundaries along a free energy surface in multi-dimensional collective variable (CV) space, building on the boxed molecular dynamics (BXD) rare event algorithm. BXD is a simple technique for accelerating the simulation of rare events and free energy sampling which has proven useful for calculating kinetics and free energy profiles in reactive and non-reactive molecular dynamics (MD) simulations across a range of systems, in both NVT and NVE ensembles. Two key developments outlined in this paper make it possible to automate BXD, and to adaptively map free energy and kinetics in complex systems. First, we have generalized BXD to multidimensional CV space. Using strategies from rigid-body dynamics, we have derived a simple and general velocity-reflection procedure that conserves energy for arbitrary collective variable definitions in multiple dimensions, and show that it is straightforward to apply BXD to sampling in multidimensional CV space so long as the Cartesian gradients ∇CV are available. Second, we have modified BXD to undertake on-the-fly statistical analysis during a trajectory, harnessing the information content latent in the dynamics to automatically determine boundary locations. Such automation not only makes BXD considerably easier to use; it also guarantees optimal boundaries, speeding up convergence. We have tested the multidimensional adaptive BXD procedure by calculating the potential of mean force for a chemical reaction recently investigated using both experimental and computational approaches - i.e., F + CD3CN → DF + D2CN in both the gas phase and a strongly coupled explicit CD3CN solvent. The results obtained using multidimensional adaptive BXD agree well with previously published experimental and computational results, providing good evidence for its reliability

A local resampling trick for focused molecular dynamics

We describe a method that focuses sampling effort on a user-defined selection of a large system, which can lead to substantial decreases in computational effort by speeding up the calculation of nonbonded interactions. A naive approach can lead to incorrect sampling if the selection depends on the configuration in a way that is not accounted for. We avoid this pitfall by introducing appropriate auxiliary variables. This results in an implementation that is closely related to configurational freezing and elastic barrier dynamical freezing. We implement the method and validate that it can be used to supplement conventional molecular dynamics in free energy calculations (absolute hydration and relative binding)

Optimal friction matrix for underdamped Langevin sampling

A systematic procedure for optimising the friction coefficient in underdamped Langevin dynamics as a sampling tool is given by taking the gradient of the associated asymptotic variance with respect to friction. We give an expression for this gradient in terms of the solution to an appropriate Poisson equation and show that it can be approximated by short simulations of the associated first variation/tangent process under concavity assumptions on the log density. Our algorithm is applied to the estimation of posterior means in Bayesian inference problems and reduced variance is demonstrated when compared to the original underdamped and overdamped Langevin dynamics in both full and stochastic gradient cases

Enhanced sampling and applications in protein folding

We show that a single-copy tempering method is useful in protein-folding simulations of large scale and high accuracy (explicit solvent, atomic representation, and physics-based potential). The method uses a runtime estimate of the average potential energy from an integral identity to guide a random walk in the continuous temperature space. It was used for folding three mini-proteins, trpzip2 (PDB ID: 1LE1), trp-cage (1L2Y), and villin headpiece (1VII) within atomic accuracy. Further, using a modification of the method with a dihedral bias potential added on the roof temperature, we were able to fold four larger helical proteins: α3D (2A3D), α3W (1LQ7), Fap1-NRα (2KUB) and S-836 (2JUA). We also discuss how to optimally use simulation data through an integral identity. With the help of a general mean force formula, the identity makes better use of data collected in a molecular dynamics simulation and is more accurate and precise than the common histogram approach