5,448 research outputs found
A self-learning algorithm for biased molecular dynamics
A new self-learning algorithm for accelerated dynamics, reconnaissance
metadynamics, is proposed that is able to work with a very large number of
collective coordinates. Acceleration of the dynamics is achieved by
constructing a bias potential in terms of a patchwork of one-dimensional,
locally valid collective coordinates. These collective coordinates are obtained
from trajectory analyses so that they adapt to any new features encountered
during the simulation. We show how this methodology can be used to enhance
sampling in real chemical systems citing examples both from the physics of
clusters and from the biological sciences.Comment: 6 pages, 5 figures + 9 pages of supplementary informatio
Recurrent oligomers in proteins - an optimal scheme reconciling accurate and concise backbone representations in automated folding and design studies
A novel scheme is introduced to capture the spatial correlations of
consecutive amino acids in naturally occurring proteins. This knowledge-based
strategy is able to carry out optimally automated subdivisions of protein
fragments into classes of similarity. The goal is to provide the minimal set of
protein oligomers (termed ``oligons'' for brevity) that is able to represent
any other fragment. At variance with previous studies where recurrent local
motifs were classified, our concern is to provide simplified protein
representations that have been optimised for use in automated folding and/or
design attempts. In such contexts it is paramount to limit the number of
degrees of freedom per amino acid without incurring in loss of accuracy of
structural representations. The suggested method finds, by construction, the
optimal compromise between these needs. Several possible oligon lengths are
considered. It is shown that meaningful classifications cannot be done for
lengths greater than 6 or smaller than 4. Different contexts are considered
were oligons of length 5 or 6 are recommendable. With only a few dozen of
oligons of such length, virtually any protein can be reproduced within typical
experimental uncertainties. Structural data for the oligons is made publicly
available.Comment: 19 pages, 13 postscript figure
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing
continues to decline and small sequencing devices become available. Enormous
community databases store and share this data with the research community, but
some of these genomic data analysis problems require large scale computational
platforms to meet both the memory and computational requirements. These
applications differ from scientific simulations that dominate the workload on
high end parallel systems today and place different requirements on programming
support, software libraries, and parallel architectural design. For example,
they involve irregular communication patterns such as asynchronous updates to
shared data structures. We consider several problems in high performance
genomics analysis, including alignment, profiling, clustering, and assembly for
both single genomes and metagenomes. We identify some of the common
computational patterns or motifs that help inform parallelization strategies
and compare our motifs to some of the established lists, arguing that at least
two key patterns, sorting and hashing, are missing
A scale-free network hidden in the collapsing polymer
We show that the collapsed globular phase of a polymer accommodates a
scale-free incompatibility graph of its contacts. The degree distribution of
this network is found to decay with the exponent up to a
cut-off degree , where is the loop exponent for dense
polymers ( in two dimensions) and is the length of the polymer. Our
results exemplify how a scale-free network (SFN) can emerge from standard
criticality.Comment: 4 pages, 3 figures, address correcte
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