17,341 research outputs found
GreMuTRRR: A Novel Genetic Algorithm to Solve Distance Geometry Problem for Protein Structures
Nuclear Magnetic Resonance (NMR) Spectroscopy is a widely used technique to
predict the native structure of proteins. However, NMR machines are only able
to report approximate and partial distances between pair of atoms. To build the
protein structure one has to solve the Euclidean distance geometry problem
given the incomplete interval distance data produced by NMR machines. In this
paper, we propose a new genetic algorithm for solving the Euclidean distance
geometry problem for protein structure prediction given sparse NMR data. Our
genetic algorithm uses a greedy mutation operator to intensify the search, a
twin removal technique for diversification in the population and a random
restart method to recover stagnation. On a standard set of benchmark dataset,
our algorithm significantly outperforms standard genetic algorithms.Comment: Accepted for publication in the 8th International Conference on
Electrical and Computer Engineering (ICECE 2014
Eigenvector Synchronization, Graph Rigidity and the Molecule Problem
The graph realization problem has received a great deal of attention in
recent years, due to its importance in applications such as wireless sensor
networks and structural biology. In this paper, we extend on previous work and
propose the 3D-ASAP algorithm, for the graph realization problem in
, given a sparse and noisy set of distance measurements. 3D-ASAP
is a divide and conquer, non-incremental and non-iterative algorithm, which
integrates local distance information into a global structure determination.
Our approach starts with identifying, for every node, a subgraph of its 1-hop
neighborhood graph, which can be accurately embedded in its own coordinate
system. In the noise-free case, the computed coordinates of the sensors in each
patch must agree with their global positioning up to some unknown rigid motion,
that is, up to translation, rotation and possibly reflection. In other words,
to every patch there corresponds an element of the Euclidean group Euc(3) of
rigid transformations in , and the goal is to estimate the group
elements that will properly align all the patches in a globally consistent way.
Furthermore, 3D-ASAP successfully incorporates information specific to the
molecule problem in structural biology, in particular information on known
substructures and their orientation. In addition, we also propose 3D-SP-ASAP, a
faster version of 3D-ASAP, which uses a spectral partitioning algorithm as a
preprocessing step for dividing the initial graph into smaller subgraphs. Our
extensive numerical simulations show that 3D-ASAP and 3D-SP-ASAP are very
robust to high levels of noise in the measured distances and to sparse
connectivity in the measurement graph, and compare favorably to similar
state-of-the art localization algorithms.Comment: 49 pages, 8 figure
Efficient model chemistries for peptides. I. Split-valence Gaussian basis sets and the heterolevel approximation in RHF and MP2
We present an exhaustive study of more than 250 ab initio potential energy
surfaces (PESs) of the model dipeptide HCO-L-Ala-NH2. The model chemistries
(MCs) used are constructed as homo- and heterolevels involving possibly
different RHF and MP2 calculations for the geometry and the energy. The basis
sets used belong to a sample of 39 selected representants from Pople's
split-valence families, ranging from the small 3-21G to the large
6-311++G(2df,2pd). The reference PES to which the rest are compared is the
MP2/6-311++G(2df,2pd) homolevel, which, as far as we are aware, is the more
accurate PES of a dipeptide in the literature. The aim of the study presented
is twofold: On the one hand, the evaluation of the influence of polarization
and diffuse functions in the basis set, distinguishing between those placed at
1st-row atoms and those placed at hydrogens, as well as the effect of different
contraction and valence splitting schemes. On the other hand, the investigation
of the heterolevel assumption, which is defined here to be that which states
that heterolevel MCs are more efficient than homolevel MCs. The heterolevel
approximation is very commonly used in the literature, but it is seldom
checked. As far as we know, the only tests for peptides or related systems,
have been performed using a small number of conformers, and this is the first
time that this potentially very economical approximation is tested in full
PESs. In order to achieve these goals, all data sets have been compared and
analyzed in a way which captures the nearness concept in the space of MCs.Comment: 54 pages, 16 figures, LaTeX, AMSTeX, Submitted to J. Comp. Che
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Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix.
The refinement of biomolecular crystallographic models relies on geometric restraints to help to address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here, an integration of the full all-atom Amber molecular-dynamics force field into Phenix crystallographic refinement is presented, which enables more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion-angle potentials, an extensive and flexible set of atom types, Lennard-Jones treatment of nonbonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over 22 000 protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better, clashscores and MolProbity scores are significantly improved, and the modeling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined using traditional geometry restraints. In general it is found that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum-mechanical representation of active sites and improved geometric restraints for simulated annealing
Machine Learning, Quantum Mechanics, and Chemical Compound Space
We review recent studies dealing with the generation of machine learning
models of molecular and solid properties. The models are trained and validated
using standard quantum chemistry results obtained for organic molecules and
materials selected from chemical space at random
Finite-Difference Calculations for Atoms and Diatomic Molecules in Strong Magnetic and Static Electric Fields
Fully numerical mesh solutions of 2D quantum equations of Schroedinger and
Hartree-Fock type allow us to work with wavefunctions which possess a very
flexible geometry. This flexibility is especially important for calculations of
atoms and molecules in strong external fields where neither the external field
nor the internal interactions can be considered as a perturbation. The
applications of the present approach include calculations of atoms and diatomic
molecules in strong static electric and magnetic fields. For the latter we have
carried out Hartree-Fock calculations for He, Li, C and several other atoms.
This yields in particular the first comprehensive investigation of the ground
state configurations of the Li and C atoms in the whole range of magnetic
fields (0<B<10000 a.u.) and a study of the ground state electronic
configurations of all the atoms with 1<Z<11 and their ions A^+ in the
high-field fully spin-polarised regime. The results in a case of a strong
electric field relate to single-electron systems including the correct solution
of the Schroedinger equation for the H_2^+ ion (energies and decay rates) and
the hydrogen atom in strong parallel electric and magnetic fields.Comment: 20 pages, 7 figure
A general hybrid radiation transport scheme for star formation simulations on an adaptive grid
Radiation feedback plays a crucial role in the process of star formation. In
order to simulate the thermodynamic evolution of disks, filaments, and the
molecular gas surrounding clusters of young stars, we require an efficient and
accurate method for solving the radiation transfer problem. We describe the
implementation of a hybrid radiation transport scheme in the adaptive
grid-based FLASH general magnetohydrodynamics code. The hybrid scheme splits
the radiative transport problem into a raytracing step and a diffusion step.
The raytracer captures the first absorption event, as stars irradiate their
environments, while the evolution of the diffuse component of the radiation
field is handled by a flux-limited diffusion (FLD) solver. We demonstrate the
accuracy of our method through a variety of benchmark tests including the
irradiation of a static disk, subcritical and supercritical radiative shocks,
and thermal energy equilibration. We also demonstrate the capability of our
method for casting shadows and calculating gas and dust temperatures in the
presence of multiple stellar sources. Our method enables radiation-hydrodynamic
studies of young stellar objects, protostellar disks, and clustered star
formation in magnetized, filamentary environments.Comment: 16 pages, 15 figures, accepted to Ap
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