51 research outputs found
Politivoldsaken i Bergen - det endelige utfall
Kommentar til professor Kjell Inge Bjørvik i NTfK 1990, s. 13-29
Bayesian inference of protein structure from chemical shift data
Protein chemical shifts are routinely used to augment molecular mechanics force fields in protein structure simulations, with weights of the chemical shift restraints determined empirically. These weights, however, might not be an optimal descriptor of a given protein structure and predictive model, and a bias is introduced which might result in incorrect structures. In the inferential structure determination framework, both the unknown structure and the disagreement between experimental and back-calculated data are formulated as a joint probability distribution, thus utilizing the full information content of the data. Here, we present the formulation of such a probability distribution where the error in chemical shift prediction is described by either a Gaussian or Cauchy distribution. The methodology is demonstrated and compared to a set of empirically weighted potentials through Markov chain Monte Carlo simulations of three small proteins (ENHD, Protein G and the SMN Tudor Domain) using the PROFASI force field and the chemical shift predictor CamShift. Using a clustering-criterion for identifying the best structure, together with the addition of a solvent exposure scoring term, the simulations suggests that sampling both the structure and the uncertainties in chemical shift prediction leads more accurate structures compared to conventional methods using empirical determined weights. The Cauchy distribution, using either sampled uncertainties or predetermined weights, did, however, result in overall better convergence to the native fold, suggesting that both types of distribution might be useful in different aspects of the protein structure prediction
FCHL revisited:Faster and more accurate quantum machine learning
We introduce the FCHL19 representation for atomic environments in molecules
or condensed-phase systems. Machine learning models based on FCHL19 are able to
yield predictions of atomic forces and energies of query compounds with
chemical accuracy on the scale of milliseconds. FCHL19 is a revision of our
previous work [Faber et al. 2018] where the representation is discretized and
the individual features are rigorously optimized using Monte Carlo
optimization. Combined with a Gaussian kernel function that incorporates
elemental screening, chemical accuracy is reached for energy learning on the
QM7b and QM9 datasets after training for minutes and hours, respectively. The
model also shows good performance for non-bonded interactions in the condensed
phase for a set of water clusters with an MAE binding energy error of less than
0.1 kcal/mol/molecule after training on 3,200 samples. For force learning on
the MD17 dataset, our optimized model similarly displays state-of-the-art
accuracy with a regressor based on Gaussian process regression. When the
revised FCHL19 representation is combined with the operator quantum machine
learning regressor, forces and energies can be predicted in only a few
milliseconds per atom. The model presented herein is fast and lightweight
enough for use in general chemistry problems as well as molecular dynamics
simulations
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