99 research outputs found
A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction
<p>Abstract</p> <p>Background</p> <p>Current scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants.</p> <p>Results</p> <p>In our study, an automatic algorithm was developed for summarizing key protein-ligand interactions as a pharmacophore model and identifying the reference complex with a maximal similarity to the query complex. Our KGS strategy was evaluated in combination with two scoring functions (X-Score and PLP) on three test sets, containing 112 HIV protease complexes, 44 carbonic anhydrase complexes, and 73 trypsin complexes, respectively. Our results obtained on crystal structures as well as computer-generated docking poses indicated that application of the KGS strategy produced more accurate predictions especially when X-Score or PLP alone did not perform well.</p> <p>Conclusions</p> <p>Compared to other targeted scoring functions, our KGS strategy does not require any re-parameterization or modification on current scoring methods, and its application is not tied to certain systems. The effectiveness of our KGS strategy is in theory proportional to the ever-increasing knowledge of experimental protein-ligand binding data. Our KGS strategy may serve as a more practical remedy for current scoring functions to improve their accuracy in binding affinity prediction.</p
Quantum beats and metrology in a rapidly rotating Nitrogen-Vacancy center
In this paper, we study the dynamical behavior and quantum metrology in a
rotating Nitrogen-Vacancy(NV) center system which is subject to an external
magnetic field. Based on the recently realized rapid rotation of nano-rotor [J.
Ahn, et. al., Phys. Rev. Lett. 121, 033603 (2018) and R. Reimann, et. al.,
Phys. Rev. Lett. 121, 033602 (2018)], the frequency of the rotation is close to
that of the intrinsic frequency of the NV center system, we predict the quantum
beats phenomenon in the time domain and show that the quantum metrology can be
enhanced by the superposition effect in our system.Comment: 6 pages, 4 figures. Accepted by EPJ
Energy Minimization of Portable Video Communication Devices Based on Power-Rate-Distortion Optimization
Digital Object Identifier 10.1109/TCSVT.2008.918802Portable video communication devices operate on
batteries with limited energy supply. However, video compression is computationally intensive and energy-demanding. Therefore, one of the central challenging issues in portable video communication system design is to minimize the energy consumption of video encoding so as to prolong the operational lifetime of portable video devices. In this work, based on power-rate-distortion (P-R-D) optimization, we develop a new approach for energy minimization by exploring the energy tradeoff between video
encoding and wireless communication and exploiting the nonstationary characteristics of input video data. Both analytically and experimentally, we demonstrate that incorporating the third dimension of power consumption into conventional R-D analysis
gives us one extra dimension of flexibility in resource allocation and allows us to achieve significant energy saving. Within the P-R-D analysis framework, power is tightly coupled with rate, enabling us to trade bits for joules and perform energy minimization
through optimum bit allocation. Our experimental studies show that, for typical videos with nonstationary scene statistics, using the proposed P-R-D optimization
technology, the energy consumption of video encoding can be significantly reduced (by up to 50%), especially in delay-tolerant
portable video communication applications
Boundary effect and dressed states of a giant atom in a topological waveguide
The interaction between the quantum emitter and topological photonic system
makes the photon behave in exotic ways. We here study the properties of a giant
atom coupled to two sites of a one-dimensional topological waveguide, which is
described by the Su-Schrieffer-Heeger (SSH) chain. We find that the giant atom
can act as an effective boundary and induce the chiral zero modes, which are
similar to those in the SSH model with open boundary, for the waveguide under
the periodical boundary. Except for the boundary effect, we also find that the
giant atom can lift energy degeneracy inside the energy bands of the SSH chain
and adjust spatial symmetry of the photon distributions for the states of the
dressed giant atom and waveguide. That is, the giant atom can be used to change
the properties of the topological environment. Our work may stimulate more
studies on the interaction between matter and topological environment.Comment: 7 Pages, 4 Figure
Witness of topological phase transition and Weyl points in an open topological system
Recently, the tunable Weyl-semimetal bands and the associate topological
phase transition have been successfully simulated in superconducting quantum
circuits [X. Tan, \textit{et al.} Phys. Rev. Lett. {\bf 122}, 010501 (2019)].
Since the superconducting quantum circuits inevitably couple to the
environment, we here focus on the steady state and decoherence process by
taking the reservoir into consideration via quantum master equation. Our
results show that the purity of the steady state can be used to indicate the
topological phase transition and Weyl points. Furthermore, the coherence will
exponentially decay to zero at the Weyl points, and decay to a nonzero value
with oscillation at other points in the momentum space. Our work may have
significant impact on the study of quantum open topological system.Comment: 5 pages, 4 figure
Vitexin attenuates smoke inhalation induced acute lung injury in rats by inhibiting oxidative stress via PKC β/p66Shc signaling pathway
Purpose: To investigate the protective effect of vitexin on smoke inhalation-induced acute lung injury (SI-ALI), and the underlying mechanism of action.Methods: The ALI rat model was established by inhalation of smoke in a closed smoke chamber. Survival rate, arterial blood gas analysis, wet-to-dry weight ratio of lung tissues, bronchoalveolar lavage fluid protein concentration, lung tissue histology, and oxidative stress and inflammation level were evaluated. Expressions of protein kinase C β (PKC β), p66Shc, and phosphorylated p66Shc were determined by western blot or quantitative reverse transcription-polymerase chain reaction.Results: Compared with smoke inhalation group, vitexin alleviated the decline in arterial partial pressure of oxygen (p < 0.05), reduced lung tissue exudation and pathological lung tissue damage, inhibited the expression of PKC β/p66Shc signaling pathway proteins, downregulated the level of oxidative stress and inflammation, and ultimately improved the survival rate in SI-ALI rats (p < 0.05).Conclusion: Vitexin attenuates SI-ALI in rats by alleviating oxidative stress via inhibition of PKC β/p66Shc signaling pathway. Thus, this compound is a potential agent for the treatment of SI-ALI
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
Fully test-time adaptation aims to adapt the network model based on
sequential analysis of input samples during the inference stage to address the
cross-domain performance degradation problem of deep neural networks. We take
inspiration from the biological plausibility learning where the neuron
responses are tuned based on a local synapse-change procedure and activated by
competitive lateral inhibition rules. Based on these feed-forward learning
rules, we design a soft Hebbian learning process which provides an unsupervised
and effective mechanism for online adaptation. We observe that the performance
of this feed-forward Hebbian learning for fully test-time adaptation can be
significantly improved by incorporating a feedback neuro-modulation layer. It
is able to fine-tune the neuron responses based on the external feedback
generated by the error back-propagation from the top inference layers. This
leads to our proposed neuro-modulated Hebbian learning (NHL) method for fully
test-time adaptation. With the unsupervised feed-forward soft Hebbian learning
being combined with a learned neuro-modulator to capture feedback from external
responses, the source model can be effectively adapted during the testing
process. Experimental results on benchmark datasets demonstrate that our
proposed method can significantly improve the adaptation performance of network
models and outperforms existing state-of-the-art methods.Comment: CVPR2023 accepte
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