412 research outputs found
Simple solvation potential for coarse-grained models of proteins
We formulate a simple solvation potential based on a coarsed-grain
representation of amino acids with two spheres modeling the atom and
an effective side-chain centroid. The potential relies on a new method for
estimating the buried area of residues, based on counting the effective number
of burying neighbours in a suitable way. This latter quantity shows a good
correlation with the buried area of residues computed from all atom
crystallographic structures. We check the discriminatory power of the solvation
potential alone to identify the native fold of a protein from a set of decoys
and show the potential to be considerably selective.Comment: 18 pages, 8 tables, 3 figure
Efficient, sparse representation of manifold distance matrices for classical scaling
Geodesic distance matrices can reveal shape properties that are largely
invariant to non-rigid deformations, and thus are often used to analyze and
represent 3-D shapes. However, these matrices grow quadratically with the
number of points. Thus for large point sets it is common to use a low-rank
approximation to the distance matrix, which fits in memory and can be
efficiently analyzed using methods such as multidimensional scaling (MDS). In
this paper we present a novel sparse method for efficiently representing
geodesic distance matrices using biharmonic interpolation. This method exploits
knowledge of the data manifold to learn a sparse interpolation operator that
approximates distances using a subset of points. We show that our method is 2x
faster and uses 20x less memory than current leading methods for solving MDS on
large point sets, with similar quality. This enables analyses of large point
sets that were previously infeasible.Comment: Conference CVPR 201
Empirical Potential Function for Simplified Protein Models: Combining Contact and Local Sequence-Structure Descriptors
An effective potential function is critical for protein structure prediction
and folding simulation. Simplified protein models such as those requiring only
or backbone atoms are attractive because they enable efficient
search of the conformational space. We show residue specific reduced discrete
state models can represent the backbone conformations of proteins with small
RMSD values. However, no potential functions exist that are designed for such
simplified protein models. In this study, we develop optimal potential
functions by combining contact interaction descriptors and local
sequence-structure descriptors. The form of the potential function is a
weighted linear sum of all descriptors, and the optimal weight coefficients are
obtained through optimization using both native and decoy structures. The
performance of the potential function in test of discriminating native protein
structures from decoys is evaluated using several benchmark decoy sets. Our
potential function requiring only backbone atoms or atoms have
comparable or better performance than several residue-based potential functions
that require additional coordinates of side chain centers or coordinates of all
side chain atoms. By reducing the residue alphabets down to size 5 for local
structure-sequence relationship, the performance of the potential function can
be further improved. Our results also suggest that local sequence-structure
correlation may play important role in reducing the entropic cost of protein
folding.Comment: 20 pages, 5 figures, 4 tables. In press, Protein
Online context recognition in multisensor systems using Dynamic Time Warping
In this paper, we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA\u27s benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy.<br /
Potential function of simplified protein models for discriminating native proteins from decoys: Combining contact interaction and local sequence-dependent geometry
An effective potential function is critical for protein structure prediction
and folding simulation. For simplified models of proteins where coordinates of
only atoms need to be specified, an accurate potential function is
important. Such a simplified model is essential for efficient search of
conformational space. In this work, we present a formulation of potential
function for simplified representations of protein structures. It is based on
the combination of descriptors derived from residue-residue contact and
sequence-dependent local geometry. The optimal weight coefficients for contact
and local geometry is obtained through optimization by maximizing margins among
native and decoy structures. The latter are generated by chain growth and by
gapless threading. The performance of the potential function in blind test of
discriminating native protein structures from decoys is evaluated using several
benchmark decoy sets. This potential function have comparable or better
performance than several residue-based potential functions that require in
addition coordinates of side chain centers or coordinates of all side chain
atoms.Comment: 4 pages, 2 figures, Accepted by 26th IEEE-EMBS Conference, San
Francisc
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