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Article ; Online: Dynamics-Evolution Correspondence in Protein Structures.

Tang, Qian-Yuan / Kaneko, Kunihiko

2021 Volume 127, Issue 9, Page(s) 98103

Abstract: The genotype-phenotype mapping of proteins is a fundamental question in structural biology. In this Letter, with the analysis of a large dataset of proteins from hundreds of protein families, we quantitatively demonstrate the correlations between the ... ...

Abstract	The genotype-phenotype mapping of proteins is a fundamental question in structural biology. In this Letter, with the analysis of a large dataset of proteins from hundreds of protein families, we quantitatively demonstrate the correlations between the noise-induced protein dynamics and mutation-induced variations of native structures, indicating the dynamics-evolution correspondence of proteins. Based on the investigations of the linear responses of native proteins, the origin of such a correspondence is elucidated. It is essential that the noise- and mutation-induced deformations of the proteins are restricted on a common low-dimensional subspace, as confirmed from the data. These results suggest an evolutionary mechanism of the proteins gaining both dynamical flexibility and evolutionary structural variability.
Language	English
Publishing date	2021-09-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.127.098103
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Article ; Online: Long-range correlation in protein dynamics

Qian-Yuan Tang / Kunihiko Kaneko

PLoS Computational Biology, Vol 16, Iss 2, p e

Confirmation by structural data and normal mode analysis.

2020 Volume 1007670

Abstract: Proteins in cellular environments are highly susceptible. Local perturbations to any residue can be sensed by other spatially distal residues in the protein molecule, showing long-range correlations in the native dynamics of proteins. The long-range ... ...

Abstract	Proteins in cellular environments are highly susceptible. Local perturbations to any residue can be sensed by other spatially distal residues in the protein molecule, showing long-range correlations in the native dynamics of proteins. The long-range correlations of proteins contribute to many biological processes such as allostery, catalysis, and transportation. Revealing the structural origin of such long-range correlations is of great significance in understanding the design principle of biologically functional proteins. In this work, based on a large set of globular proteins determined by X-ray crystallography, by conducting normal mode analysis with the elastic network models, we demonstrate that such long-range correlations are encoded in the native topology of the proteins. To understand how native topology defines the structure and the dynamics of the proteins, we conduct scaling analysis on the size dependence of the slowest vibration mode, average path length, and modularity. Our results quantitatively describe how native proteins balance between order and disorder, showing both dense packing and fractal topology. It is suggested that the balance between stability and flexibility acts as an evolutionary constraint for proteins at different sizes. Overall, our result not only gives a new perspective bridging the protein structure and its dynamics but also reveals a universal principle in the evolution of proteins at all different sizes.
Keywords	Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2020-02-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Long-range correlation in protein dynamics: Confirmation by structural data and normal mode analysis.

Tang, Qian-Yuan / Kaneko, Kunihiko

PLoS computational biology

2020 Volume 16, Issue 2, Page(s) e1007670

Abstract	Proteins in cellular environments are highly susceptible. Local perturbations to any residue can be sensed by other spatially distal residues in the protein molecule, showing long-range correlations in the native dynamics of proteins. The long-range correlations of proteins contribute to many biological processes such as allostery, catalysis, and transportation. Revealing the structural origin of such long-range correlations is of great significance in understanding the design principle of biologically functional proteins. In this work, based on a large set of globular proteins determined by X-ray crystallography, by conducting normal mode analysis with the elastic network models, we demonstrate that such long-range correlations are encoded in the native topology of the proteins. To understand how native topology defines the structure and the dynamics of the proteins, we conduct scaling analysis on the size dependence of the slowest vibration mode, average path length, and modularity. Our results quantitatively describe how native proteins balance between order and disorder, showing both dense packing and fractal topology. It is suggested that the balance between stability and flexibility acts as an evolutionary constraint for proteins at different sizes. Overall, our result not only gives a new perspective bridging the protein structure and its dynamics but also reveals a universal principle in the evolution of proteins at all different sizes.
MeSH term(s)	Algorithms ; Allosteric Site ; Catalysis ; Computational Biology/methods ; Computer Simulation ; Crystallography, X-Ray ; Databases, Protein ; Elasticity ; Fractals ; HSP90 Heat-Shock Proteins/chemistry ; Humans ; Imaging, Three-Dimensional ; Ligands ; Magnetic Resonance Spectroscopy ; Models, Molecular ; Normal Distribution ; Protein Conformation ; Protein Interaction Mapping ; Proteins/chemistry ; Structure-Activity Relationship
Chemical Substances	HSP90 Heat-Shock Proteins ; Ligands ; Proteins
Language	English
Publishing date	2020-02-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1007670
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Article ; Online: The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database.

Tang, Qian-Yuan / Ren, Weitong / Wang, Jun / Kaneko, Kunihiko

Molecular biology and evolution

2022 Volume 39, Issue 10

Abstract: The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database ( ... ...

Abstract	The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.
MeSH term(s)	Artificial Intelligence ; Databases, Protein ; Phylogeny ; Proteome/genetics
Chemical Substances	Proteome
Language	English
Publishing date	2022-09-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	998579-7
ISSN	1537-1719 ; 0737-4038
ISSN (online)	1537-1719
ISSN	0737-4038
DOI	10.1093/molbev/msac197
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Article ; Online: Multi-scale molecular simulation of random peptide phase separation and its extended-to-compact structure transition driven by hydrophobic interactions.

Kang, Wen Bin / Bao, Lei / Zhang, Kai / Guo, Jia / Zhu, Ben Chao / Tang, Qian-Yuan / Ren, Wei Tong / Zhu, Gen

Soft matter

2023 Volume 19, Issue 41, Page(s) 7944–7954

Abstract: Intrinsically disordered proteins (IDPs) often undergo liquid-liquid phase separation (LLPS) and form membraneless organelles or protein condensates. One of the core problems is how do electrostatic repulsion and hydrophobic interactions in peptides ... ...

Abstract	Intrinsically disordered proteins (IDPs) often undergo liquid-liquid phase separation (LLPS) and form membraneless organelles or protein condensates. One of the core problems is how do electrostatic repulsion and hydrophobic interactions in peptides regulate the phase separation process? To answer this question, this study uses random peptides composed of positively charged arginine (Arg, R) and hydrophobic isoleucine (Ile, I) as the model systems, and conduct large-scale simulations using all atom and coarse-grained model multi-scale simulation methods. In this article, we investigate the phase separation of different sequences using a coarse-grained model. It is found that the stronger the electrostatic repulsion in the system, the more extended the single-chain structure, and the more likely the system forms a low-density homogeneous phase. In contrast, the stronger the hydrophobic effect of the system, the more compact the single-chain structure, the easier phase separation, and the higher the critical temperature of phase separation. Overall, by taking the random polypeptides composed of two types of amino acid residues as model systems, this study discusses the relationship between the protein sequence and phase behaviour, and provides theoretical insights into the interactions within or between proteins. It is expected to provide essential physical information for the sequence design of functional IDPs, as well as data to support the diagnosis and treatment of the LLPS-associated diseases.
MeSH term(s)	Intrinsically Disordered Proteins/chemistry ; Intrinsically Disordered Proteins/metabolism ; Peptides ; Computer Simulation ; Temperature ; Hydrophobic and Hydrophilic Interactions ; Phase Transition
Chemical Substances	Intrinsically Disordered Proteins ; Peptides
Language	English
Publishing date	2023-10-25
Publishing country	England
Document type	Journal Article
ZDB-ID	2191476-X
ISSN	1744-6848 ; 1744-683X
ISSN (online)	1744-6848
ISSN	1744-683X
DOI	10.1039/d3sm00633f
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Book ; Online: On the Overlooked Structure of Stochastic Gradients

Xie, Zeke / Tang, Qian-Yuan / Sun, Mingming / Li, Ping

2022

Abstract: Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs). Some works attempted to explain the success of stochastic optimization for deep learning by the arguably heavy-tail properties of gradient noise, ... ...

Abstract	Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs). Some works attempted to explain the success of stochastic optimization for deep learning by the arguably heavy-tail properties of gradient noise, while other works presented theoretical and empirical evidence against the heavy-tail hypothesis on gradient noise. Unfortunately, formal statistical tests for analyzing the structure and heavy tails of stochastic gradients in deep learning are still under-explored. In this paper, we mainly make two contributions. First, we conduct formal statistical tests on the distribution of stochastic gradients and gradient noise across both parameters and iterations. Our statistical tests reveal that dimension-wise gradients usually exhibit power-law heavy tails, while iteration-wise gradients and stochastic gradient noise caused by minibatch training usually do not exhibit power-law heavy tails. Second, we further discover that the covariance spectra of stochastic gradients have the power-law structures overlooked by previous studies and present its theoretical implications for training of DNNs. While previous studies believed that the anisotropic structure of stochastic gradients matters to deep learning, they did not expect the gradient covariance can have such an elegant mathematical structure. Our work challenges the existing belief and provides novel insights on the structure of stochastic gradients in deep learning. Comment: NeurIPS 2023. 20 pages, 16 figures, 17 Tables; Key Words: Deep Learning, Stochastic Gradient, Optimization. arXiv admin note: text overlap with arXiv:2201.13011
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	510
Publishing date	2022-12-05
Publishing country	us
Document type	Book ; Online
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Book ; Online: On the Power-Law Hessian Spectrums in Deep Learning

Xie, Zeke / Tang, Qian-Yuan / Cai, Yunfeng / Sun, Mingming / Li, Ping

2022

Abstract: It is well-known that the Hessian of deep loss landscape matters to optimization, generalization, and even robustness of deep learning. Recent works empirically discovered that the Hessian spectrum in deep learning has a two-component structure that ... ...

Abstract	It is well-known that the Hessian of deep loss landscape matters to optimization, generalization, and even robustness of deep learning. Recent works empirically discovered that the Hessian spectrum in deep learning has a two-component structure that consists of a small number of large eigenvalues and a large number of nearly-zero eigenvalues. However, the theoretical mechanism or the mathematical behind the Hessian spectrum is still largely under-explored. To the best of our knowledge, we are the first to demonstrate that the Hessian spectrums of well-trained deep neural networks exhibit simple power-law structures. Inspired by the statistical physical theories and the spectral analysis of natural proteins, we provide a maximum-entropy theoretical interpretation for explaining why the power-law structure exist and suggest a spectral parallel between protein evolution and training of deep neural networks. By conducing extensive experiments, we further use the power-law spectral framework as a useful tool to explore multiple novel behaviors of deep learning. Comment: 26 Pages, 21 Figures
Keywords	Computer Science - Machine Learning ; Physics - Biological Physics ; Quantitative Biology - Biomolecules
Subject code	612
Publishing date	2022-01-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Heart failure classification using deep learning to extract spatiotemporal features from ECG.

Zhang, Chang-Jiang / Yuan-Lu / Tang, Fu-Qin / Cai, Hai-Peng / Qian, Yin-Fen / Chao-Wang

BMC medical informatics and decision making

2024 Volume 24, Issue 1, Page(s) 17

Abstract: Background: Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal ... ...

Abstract	Background: Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal and spatial characteristics of electrocardiogram (ECG) signals from patients with heart failure. Methods: We developed a NYHA functional classification model for heart failure based on a deep learning method. We introduced an integrating attention mechanism based on the CNN-LSTM-SE model, segmenting the ECG signal into 2 to 20 s long segments. Ablation experiments showed that the 12 s ECG signal segments could be used with the proposed deep learning model for superior classification of heart failure. Results: The accuracy, positive predictive value, sensitivity, and specificity of the NYHA functional classification method were 99.09, 98.9855, 99.033, and 99.649%, respectively. Conclusions: The comprehensive performance of this model exceeds similar methods and can be used to assist in clinical medical diagnoses.
MeSH term(s)	Humans ; Deep Learning ; Arrhythmias, Cardiac/diagnosis ; Electrocardiography/methods ; Heart Failure/diagnosis ; Databases, Factual ; Algorithms
Language	English
Publishing date	2024-01-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-024-02415-4
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Article ; Online: Correction to: LILRB2-containing small extracellular vesicles from glioblastoma promote tumor progression by promoting the formation and expansion of myeloid-derived suppressor cells.

Wu, Peitao / Guo, Yuhang / Xiao, Li / Yuan, Jiaqi / Tang, Chao / Dong, Jun / Qian, Zhiyuan

Cancer immunology, immunotherapy : CII

2023 Volume 72, Issue 7, Page(s) 2195–2196

Language	English
Publishing date	2023-04-17
Publishing country	Germany
Document type	Published Erratum
ZDB-ID	195342-4
ISSN	1432-0851 ; 0340-7004
ISSN (online)	1432-0851
ISSN	0340-7004
DOI	10.1007/s00262-023-03426-2
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Article ; Online: Mapping trends and hotspots in research on global influenza vaccine hesitancy: A bibliometric analysis.

Zhang, Zhengyu / Tang, Songjia / Huang, Zhihui / Tan, Juntao / Wu, Xiaoxin / Hong, Qian / Yuan, Yuan

Health science reports

2024 Volume 7, Issue 2, Page(s) e1820

Abstract: Background and aims: Influenza is one of the most widespread respiratory infections and poses a huge burden on health care worldwide. Vaccination is key to preventing and controlling influenza. Influenza vaccine hesitancy is an important reason for the ... ...

Abstract	Background and aims: Influenza is one of the most widespread respiratory infections and poses a huge burden on health care worldwide. Vaccination is key to preventing and controlling influenza. Influenza vaccine hesitancy is an important reason for the low vaccination rate. In 2019, Vaccine hesitancy was identified as one of the top 10 threats to global health by the World Health Organization. However, there remains a glaring scarcity of bibliometric research in that regard. This study sought to identify research hotspots and future development trends on influenza vaccine hesitation and provide a new perspective and reference for future research. Methods: We retrieved publications on global influenza vaccine hesitancy from the Web of Science Core Collection database, Scopus, and PubMed databases from inception to 2022. This study used VOSviewer and CiteSpace for visualization analysis. Results: Influenza vaccine hesitancy-related publications increased rapidly from 2012 and peaked in 2022. One hundred and nine countries contributed to influenza vaccine hesitation research, and the United States ranked first with 541 articles and 7161 citations. Conclusions: The trend in the number of annual publications related to influenza vaccine hesitancy indicating the COVID-19 pandemic will prompt researchers to increase their attention to influenza vaccine hesitancy. With healthcare workers as the key, reducing vaccine hesitancy and improving vaccine acceptance in high-risk groups will be the research direction in the next few years.
Language	English
Publishing date	2024-02-06
Publishing country	United States
Document type	Journal Article
ISSN	2398-8835
ISSN (online)	2398-8835
DOI	10.1002/hsr2.1820
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