Proteins and DNA are large, complex molecules that carry out biological functions essential to all life. Their successful operation relies on adopting specific structures, stabilized by intra-molecular interactions between atoms. The spatial and temporal resolution required to study the mechanics of these molecules in full detail can only be obtained using computer simulations of molecular models. In a molecular dynamics simulation, a trajectory of the system is generated, which allows mapping out the states and dynamics of the molecule. However, the time and length scales characteristic of biological events are many orders of magnitude larger than the resolution needed to accurately describe the microscopic processes of the atoms. To overcome this problem, sampling methods have been developed that enhance the occurrence of rare but important events, which improves the statistics of simulation data. This thesis summarizes my work on developing the AWH method, an algorithm that adaptively optimizes sampling toward a target function and simultaneously finds and assigns probabilities to states of the simulated system. I have adapted AWH for use in molecular dynamics simulations. In doing so, I investigated the convergence of the method as a function of its input parameters and improved the robustness of the method. I have also worked on a generally applicable approach for calculating the target function in an automatic and non-arbitrary way. Traditionally, the target is set in an ad hoc way, while now sampling can be improved by 50% or more without extra effort. I have also used AWH to improve sampling in two biologically relevant applications. In one paper, we study the opening of a DNA base pair, which due to the stability of the DNA double helix only very rarely occurs spontaneously. We show that the probability of opening depends on both nearest-neighbor and longer-range sequence effect and furthermore structurally characterize the open states. In the second application the permeability and ammonia selectivity of the membrane protein aquaporin is investigated and we show that these functions are sensitive to specific mutations. / <p>QC 20171117</p>

Numerical simulations of large complex systems such as biomolecules often suffer from the full description of the system having too many dimensions for direct numerical calculations and Monte Carlo methods having trouble overcoming energy barriers. It is therefore desirable to formulate a description in lower dimension which captures the system’s macroscopic behaviour. Recently, Lindahl et al [1] proposed a metric, g(λ), on the extended space Λ based on the dynamics of the system to optimize Monte Carlo sampling within extended ensemble formalism. In this thesis, we formulate a low-dimensional effective coarse-grained dynamic on Λ as a diffusion process and ask if it is possible to use this metric to calculate thelocal effective diffusion matrix as D(λ) = g−1(λ). By testing various scenarios we conclude that computing D(λ) in this manner indeed gives a correct effective dynamic in most cases, where the scale of coarse-graining can be tuned. However, an incorrect dynamic is received for example when the scale of coarse-graining is comparable to the size of oscillations in the energy landscape. / Numeriska simuleringar av stora komplexa system såsom biomolekyler lider ofta av att den fulla beskrivningen av systemet har för många dimensioner för direkta numeriska beräkningar samt att Monte Carlo-metoder har svårt att komma över energibarriärer. Det är därför önskvärt att formulera en beskrivning i lägre dimension som fångar systemets makroskopiska beteende. Nyligen föreslog Lindahl et al [1] en metrik g(λ) på det utvidgade rummet Λ baserad på dynamiken av systemet för att optimera Monte Carlo-sampling inom formalismen av utvidgade ensembler. I den här uppsatsen formulerar vi en lågdimensionell effektiv grov dynamik på Λ som en diffusionsprocess och frågar om det är möjligt att använda den här metriken för att beräkna den lokala effektiva diffusionsmatrisen som D(λ) = g(λ)−1. Genom testning av flera scenarier drar vi slutsatsen att beräkna D(λ) på det här sättet ger en korrekt effektiv dynamik i de flesta fall, där skalan på förgrovningen kan ställas in. Däremot fås en inkorrekt dynamik till exempel när skalan på förgrovningen ärjämförbar med storleken på oscillationer i energilandskapet.