6,384 research outputs found
Complex Networks from Classical to Quantum
Recent progress in applying complex network theory to problems in quantum
information has resulted in a beneficial crossover. Complex network methods
have successfully been applied to transport and entanglement models while
information physics is setting the stage for a theory of complex systems with
quantum information-inspired methods. Novel quantum induced effects have been
predicted in random graphs---where edges represent entangled links---and
quantum computer algorithms have been proposed to offer enhancement for several
network problems. Here we review the results at the cutting edge, pinpointing
the similarities and the differences found at the intersection of these two
fields.Comment: 12 pages, 4 figures, REVTeX 4-1, accepted versio
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Bond-Order Time Series Analysis for Detecting Reaction Events in Ab Initio Molecular Dynamics Simulations.
Ab initio molecular dynamics is able to predict novel reaction mechanisms by directly observing the individual reaction events that occur in simulation trajectories. In this article, we describe an approach for detecting reaction events from simulation trajectories using a physically motivated model based on time series analysis of ab initio bond orders. We found that applying a threshold to the bond order was insufficient for accurate detection, whereas peak finding on the first time derivative resulted in significantly improved accuracy. The model is trained on a reference set of reaction events representing the ideal result given unlimited computing resources. Our study includes two model systems: a heptanylium carbocation that undergoes hydride shifts and an unsaturated iron carbonyl cluster that features CO ligand migration and bridging behavior. The results indicate a high level of promise for this analysis approach to be used in mechanistic analysis of reactive AIMD simulations more generally
Atomic-accuracy prediction of protein loop structures through an RNA-inspired ansatz
Consistently predicting biopolymer structure at atomic resolution from
sequence alone remains a difficult problem, even for small sub-segments of
large proteins. Such loop prediction challenges, which arise frequently in
comparative modeling and protein design, can become intractable as loop lengths
exceed 10 residues and if surrounding side-chain conformations are erased. This
article introduces a modeling strategy based on a 'stepwise ansatz', recently
developed for RNA modeling, which posits that any realistic all-atom molecular
conformation can be built up by residue-by-residue stepwise enumeration. When
harnessed to a dynamic-programming-like recursion in the Rosetta framework, the
resulting stepwise assembly (SWA) protocol enables enumerative sampling of a 12
residue loop at a significant but achievable cost of thousands of CPU-hours. In
a previously established benchmark, SWA recovers crystallographic conformations
with sub-Angstrom accuracy for 19 of 20 loops, compared to 14 of 20 by KIC
modeling with a comparable expenditure of computational power. Furthermore, SWA
gives high accuracy results on an additional set of 15 loops highlighted in the
biological literature for their irregularity or unusual length. Successes
include cis-Pro touch turns, loops that pass through tunnels of other
side-chains, and loops of lengths up to 24 residues. Remaining problem cases
are traced to inaccuracies in the Rosetta all-atom energy function. In five
additional blind tests, SWA achieves sub-Angstrom accuracy models, including
the first such success in a protein/RNA binding interface, the YbxF/kink-turn
interaction in the fourth RNA-puzzle competition. These results establish
all-atom enumeration as a systematic approach to protein structure that can
leverage high performance computing and physically realistic energy functions
to more consistently achieve atomic resolution.Comment: Identity of four-loop blind test protein and parts of figures 5 have
been omitted in this preprint to ensure confidentiality of the protein
structure prior to its public releas
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