4,162 research outputs found
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
Statistical Geometry of Packing Defects of Lattice Chain Polymer from Enumeration and Sequential Monte Carlo Method
Voids exist in proteins as packing defects and are often associated with
protein functions. We study the statistical geometry of voids in
two-dimensional lattice chain polymers. We define voids as topological features
and develop a simple algorithm for their detection. For short chains, void
geometry is examined by enumerating all conformations. For long chains, the
space of void geometry is explored using sequential Monte Carlo importance
sampling and resampling techniques. We characterize the relationship of
geometric properties of voids with chain length, including probability of void
formation, expected number of voids, void size, and wall size of voids. We
formalize the concept of packing density for lattice polymers, and further
study the relationship between packing density and compactness, two parameters
frequently used to describe protein packing. We find that both fully extended
and maximally compact polymers have the highest packing density, but polymers
with intermediate compactness have low packing density. To study the
conformational entropic effects of void formation, we characterize the
conformation reduction factor of void formation and found that there are strong
end-effect. Voids are more likely to form at the chain end. The critical
exponent of end-effect is twice as large as that of self-contacting loop
formation when existence of voids is not required. We also briefly discuss the
sequential Monte Carlo sampling and resampling techniques used in this study.Comment: 29 pages, including 12 figure
The Injury and Therapy of Reactive Oxygen Species in Intracerebral Hemorrhage Looking at Mitochondria
Intracerebral hemorrhage is an emerging major health problem often resulting in death or disability. Reactive oxygen species (ROS) have been identified as one of the major damaging factors in ischemic stroke. However, there is less discussion about ROS in hemorrhage stroke. Metabolic products of hemoglobin, excitatory amino acids, and inflammatory cells are all sources of ROS, and ROS harm the central nervous system through cell death and structural damage, especially disruption of the blood-brain barrier. We have considered the antioxidant system of the CNS itself and the drugs aiming to decrease ROS after ICH, and we find that mitochondria are key players in all of these aspects. Moreover, when the mitochondrial permeability transition pore opens, ROS-induced ROS release, which leads to extensive liberation of ROS and mitochondrial failure, occurs. Therefore, the mitochondrion may be a significant target for elucidating the problem of ROS in ICH; however, additional experimental support is required
Approximation for Dominating Set Problem with Measure Functions
In this paper, we study the Dominating Set problem with measure functions, which is extended from the general Dominating Set problem. We study the correspondnig problems on complexity, approximation and inapproximability for Dominating Set problem with measure functions. In addition, we extend our results to the weighted graphs
Origin of Scaling Behavior of Protein Packing Density: A Sequential Monte Carlo Study of Compact Long Chain Polymers
Single domain proteins are thought to be tightly packed. The introduction of
voids by mutations is often regarded as destabilizing. In this study we show
that packing density for single domain proteins decreases with chain length. We
find that the radius of gyration provides poor description of protein packing
but the alpha contact number we introduce here characterize proteins well. We
further demonstrate that protein-like scaling relationship between packing
density and chain length is observed in off-lattice self-avoiding walks. A key
problem in studying compact chain polymer is the attrition problem: It is
difficult to generate independent samples of compact long self-avoiding walks.
We develop an algorithm based on the framework of sequential Monte Carlo and
succeed in generating populations of compact long chain off-lattice polymers up
to length . Results based on analysis of these chain polymers suggest
that maintaining high packing density is only characteristic of short chain
proteins. We found that the scaling behavior of packing density with chain
length of proteins is a generic feature of random polymers satisfying loose
constraint in compactness. We conclude that proteins are not optimized by
evolution to eliminate packing voids.Comment: 9 pages, 10 figures. Accepted by J. Chem. Phy
Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning
Self-supervised pre-training methods based on contrastive learning or
regression tasks can utilize more unlabeled data to improve the performance of
automatic speech recognition (ASR). However, the robustness impact of combining
the two pre-training tasks and constructing different negative samples for
contrastive learning still remains unclear. In this paper, we propose a
noise-robust data2vec for self-supervised speech representation learning by
jointly optimizing the contrastive learning and regression tasks in the
pre-training stage. Furthermore, we present two improved methods to facilitate
contrastive learning. More specifically, we first propose to construct
patch-based non-semantic negative samples to boost the noise robustness of the
pre-training model, which is achieved by dividing the features into patches at
different sizes (i.e., so-called negative samples). Second, by analyzing the
distribution of positive and negative samples, we propose to remove the easily
distinguishable negative samples to improve the discriminative capacity for
pre-training models. Experimental results on the CHiME-4 dataset show that our
method is able to improve the performance of the pre-trained model in noisy
scenarios. We find that joint training of the contrastive learning and
regression tasks can avoid the model collapse to some extent compared to only
training the regression task.Comment: Submitted to ICASSP 202
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