786 research outputs found
Protein Structure Determination Using Chemical Shifts
In this PhD thesis, a novel method to determine protein structures using
chemical shifts is presented.Comment: Univ Copenhagen PhD thesis (2014) in Biochemistr
Hybrid RHF/MP2 geometry optimizations with the Effective Fragment Molecular Orbital Method
The frozen domain effective fragment molecular orbital method is extended to
allow for the treatment of a single fragment at the MP2 level of theory. The
approach is applied to the conversion of chorismate to prephenate by chorismate
mutase, where the substrate is treated at the MP2 level of theory while the
rest of the system is treated at the RHF level. MP2 geometry optimization is
found to lower the barrier by up to 3.5 kcal/mol compared to RHF optimzations
and ONIOM energy refinement and leads to a smoother convergence with respect to
the basis set for the reaction profile. For double zeta basis sets the increase
in CPU time relative to RHF is roughly a factor of two.Comment: 11 pages, 3 figure
Alchemical and structural distribution based representation for improved QML
We introduce a representation of any atom in any chemical environment for the
generation of efficient quantum machine learning (QML) models of common
electronic ground-state properties. The representation is based on scaled
distribution functions explicitly accounting for elemental and structural
degrees of freedom. Resulting QML models afford very favorable learning curves
for properties of out-of-sample systems including organic molecules,
non-covalently bonded protein side-chains, (HO)-clusters, as well as
diverse crystals. The elemental components help to lower the learning curves,
and, through interpolation across the periodic table, even enable "alchemical
extrapolation" to covalent bonding between elements not part of training, as
evinced for single, double, and triple bonds among main-group elements
Interface of the polarizable continuum model of solvation with semi-empirical methods in the GAMESS program
An interface between semi-empirical methods and the polarized continuum model
(PCM) of solvation successfully implemented into GAMESS following the approach
by Chudinov et al (Chem. Phys. 1992, 160, 41). The interface includes energy
gradients and is parallelized. For large molecules such as ubiquitin a
reasonable speedup (up to a factor of six) is observed for up to 16 cores. The
SCF convergence is greatly improved by PCM for proteins compared to the gas
phase
Operator quantum machine learning: Navigating the chemical space of response properties
The identification and use of structure property relationships lies at the
heart of the chemical sciences. Quantum mechanics forms the basis for the
unbiased virtual exploration of chemical compound space (CCS), imposing
substantial compute needs if chemical accuracy is to be reached. In order to
accelerate predictions of quantum properties without compromising accuracy, our
lab has been developing quantum machine learning (QML) based models which can
be applied throughout CCS. Here, we briefly explain, review, and discuss the
recently introduced operator formalism which substantially improves the data
efficiency for QML models of common response properties
Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics
We present the ProCS method for the rapid and accurate prediction of protein
backbone amide proton chemical shifts - sensitive probes of the geometry of key
hydrogen bonds that determine protein structure. ProCS is parameterized against
quantum mechanical (QM) calculations and reproduces high level QM results
obtained for a small protein with an RMSD of 0.25 ppm (r = 0.94). ProCS is
interfaced with the PHAISTOS protein simulation program and is used to infer
statistical protein ensembles that reflect experimentally measured amide proton
chemical shift values. Such chemical shift-based structural refinements,
starting from high-resolution X-ray structures of Protein G, ubiquitin, and SMN
Tudor Domain, result in average chemical shifts, hydrogen bond geometries, and
trans-hydrogen bond (h3JNC') spin-spin coupling constants that are in excellent
agreement with experiment. We show that the structural sensitivity of the
QM-based amide proton chemical shift predictions is needed to refine protein
structures to this agreement. The ProCS method thus offers a powerful new tool
for refining the structures of hydrogen bonding networks to high accuracy with
many potential applications such as protein flexibility in ligand binding.Comment: PLOS ONE accepted, Nov 201
A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
We address the degree to which machine learning (ML) can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based machine learning (MOB-ML) method is applied to several test systems. Strikingly, for the second-order Møller-Plessett perturbation theory, coupled cluster with singles and doubles (CCSD), and CCSD with perturbative triples levels of theory, it is shown that the thermally accessible (350 K) potential energy surface for a single water molecule can be described to within 1 mhartree using a model that is trained from only a single reference calculation at a randomized geometry. To explore the breadth of chemical diversity that can be described, MOB-ML is also applied to a new dataset of thermalized (350 K) geometries of 7211 organic models with up to seven heavy atoms. In comparison with the previously reported Δ-ML method, MOB-ML is shown to reach chemical accuracy with threefold fewer training geometries. Finally, a transferability test in which models trained for seven-heavy-atom systems are used to predict energies for thirteen-heavy-atom systems reveals that MOB-ML reaches chemical accuracy with 36-fold fewer training calculations than Δ-ML (140 vs 5000 training calculations)
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