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Analysis of protein determinants of genotype-specific properties of group a rotaviruses using machine learning

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Computers in Biology and Medicine 2025年 191卷 110143-110143页

作者： Cho, Myeongji Been, Nara Son, Hyeon S. Laboratory of Computational Virology & Viroinformatics of Public Health Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Public Health AI Lab of Public Health Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Institute of Health and Environment Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

Group A rotaviruses (RVAs) are the leading cause of viral diarrhoea across various host species, including mammals and birds. The VP7 and VP4 proteins of these viruses play critical roles in determining genotype specificity, influencing viral infectivity and host adaptation. This study employed machine-learning techniques to classify RVA genotypes based on the molecular and physicochemical properties of these proteins. A dataset of 94 VP7 and 68 VP4 protein sequences was collected from various host species. Seven machine-learning algorithms—Naïve Bayes (NB), logistic regression (LR), decision tree (DT), random forest (RF), k-nearest neighbour (kNN), support vector machine (SVM), and artificial neural network (ANN)—were used for genotype classification. Feature subsets were configured using ranking-based attribute selection, and classification performance was evaluated using accuracy (ACC), precision, recall, Matthews’ correlation coefficient (MCC), and the area under the curve (AUC). kNN demonstrated the highest classification accuracy for both VP7 (ACC = 97.87 %) and VP4 (ACC = 100 %), outperforming NB, LR, DT, RF, SVM, and ANN. For VP7 sequences, key properties influencing genotype classification included hydrophobicity, normalised van der Waals volume, and leucine composition. For VP4, polarity, normalised van der Waals volume, and polarizability were the most significant factors. In summary, the genotype-specific molecular features of VP7 and VP4 proteins served as reliable markers for RVA classification. Our findings highlight the potential of machine-learning approaches to predict RVA genotypes based on the physicochemical properties of amino acids, providing valuable insights into the molecular mechanisms that drive viral evolution, host specificity, and immune evasion. © 2025 Elsevier Ltd

关键词： Adversarial machine learning

Glucose metabolism controls humanγδ-cell-mediated tumor immunosurveillance in diabetes

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Cellular & Molecular Immunology 2022年第8期19卷 944-956页

作者： Xiaofeng Mu Zheng Xiang Yan Xu Jing He Jianwen Lu Yuyuan Chen Xiwei Wang Chloe Ran Tu Yanme Wenyue Zhang Zhinan Yin Wing-hang Leung Yu-Lung Lau Yinping Liu Wenwei Tu Department of Pediatrics and Adolescent Medicine Li Ka Shing Faculty of MedicineThe University of Hong KongHong Kong SARPR China The First Affliated Hospital Faculty of Medical ScienceJinan UniversityGuangzhou 510632 GuangdongPR China Zhuhai Precision Medical Center Zhuhai People's Hospital(Zhuhai Hospital Affliated with Jinan University)Jinan UniversityZhuhai 519000 GuangdongPR China The Biomedical Translational Research Institute Jinan UniversityGuangzhou 510632 GuangdongPR China GuangDong 999 Brain Hospital Guangzhou CityGuangdong ProvincePR China Department of Endocrinology Guangzhou First People's HospitalSchool of MedicineSouth China University of TechnologyGuangzhou 510180PR China Computational and Systems Biology Interdepartmental Program University of CaliforniaLos AngelesLos AngelesCA 90095USA

Patients with type 2 diabetes mellitus(T2DM)have an increased risk of *** effect of glucose metabolism onγδT cells and their impact on tumor surveillance remain ***,we showed that high glucose induced Warburg effect type of bioenergetic profle in Vy9vδ2 T cells,leading to excessive lactate accumulation,which further inhibited lytic granule secretion by impairing the traffcking of cytolytic machinery to the Vy9vδ2 T-cell-tumor synapse by suppressing AMPK activation and resulted in the loss of antitumor activity in vitro,in vivo and in ***,activating the AMPK pathway through glucose control or metformin treatment reversed the metabolic abnormalities and restored the antitumor activity of Vy9vδ2 T *** results suggest that the impaired antitumor activity of Vy9vδ2 T cells induced by dysregulated glucose metabolism may contribute to the increased cancer risk in T2DM patients and that metabolic reprogramming by targeting the AMPK pathway with metformin may improve tumor immunosurveillance.

关键词： γδT cells Glucose metabolism Tumor surveillance Lactate AMPK T2DM

Ogden Material Calibration via Magnetic Resonance Cartography, Parameter Sensitivity, and Variational System Identification

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arXiv 2022年

作者： Nikolov, Denislav P. Srivastava, Siddhartha Abeid, Bachir A. Scheven, Ulrich M. Arruda, Ellen M. Garikipati, Krishna Estrada, Jonathan B. Department of Mechanical Engineering University of Michigan United States Department of Biomedical Engineering University of Michigan United States Program in Macromolecular Science and Engineering University of Michigan United States Department of Mathematics University of Michigan United States Michigan Institute for Computational Discovery and Eng. University of Michigan United States

Contemporary material characterisation techniques that leverage deformation fields and the weak form of the equilibrium equations face challenges in the numerical solution procedure of the inverse characterisation problem. As material models and descriptions differ, so too must the approaches for identifying parameters and their corresponding mechanisms. The widely-used Ogden material model can be comprised of a chosen number of terms of the same mathematical form, which presents challenges of parsimonious representation, interpretability, and stability. Robust techniques for system identification of any material model are important to assess and improve experimental design, in addition to their centrality to forward computations. Using fully 3D displacement fields acquired in silicone elastomers with our recently-developed magnetic resonance cartography (MR-u) technique on the order of > 20, 000 points per sample, we leverage PDE-constrained optimisation as the basis of variational system identification of our material parameters. We incorporate the statistical F-test to maintain parsimony of representation. Using a new, local deformation decomposition locally into mixtures of biaxial and uniaxial tensile states, we evaluate experiments based on an analytical sensitivity metric, and discuss the implications for experimental design. © 2022, CC BY-NC-ND.

关键词： Inverse problems

Deep learning inter-atomic potential for thermal and phonon behaviour of silicon carbide with quantum accuracy

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arXiv 2021年

作者： Fu, Baoqin Sun, Yandong Zhang, Linfeng Wang, Han Xu, Ben Key Laboratory of Radiation Physics and Technology of the Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu610064 China Graduate School China Academy of Engineering Physics Beijing100193 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China

Silicon carbide (SiC) is an essential material for next generation semiconductors and components for nuclear plants. It’s applications are strongly dependent on its thermal conductivity, which is highly sensitive to microstructures. Molecular dynamics (MD) simulation is the most used methods to address thermal transportation mechanisms in devices or microstructures of nano-meters. However, the implementation of MD is limited in SiC because of lacking accurate inter-atomic potentials. In this work, using the Deep Potential (DP) methodology, we developed two inter-atomic potentials (DP-*Corresponding author. IAPs) for SiC based on two adaptively generated datasets within the density functional approximations at the local density and the generalized gradient levels. These two DP-IAPs manifest their speed with quantum accuracy in lattice dynamics simulations as well as scattering rate analysis of phonon transportation. Combining with molecular dynamics simulations, the thermal transport and mechanical properties were systematically investigated. The presented methodology and the inter-atomic potentials pave the way for a systematic approach to model heat transport in SiC related devices using multiscale modelling. Copyright © 2021, The Authors. All rights reserved.

关键词： Silicon carbide

The Role of Heterogeneity in Autonomous Perimeter Defense Problems

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arXiv 2022年

作者： Adler, Aviv Mickelin, Oscar Ramachandran, K. Sukhatme, Gaurav S. Karaman, Sertac Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Program in Applied and Computational Mathematics Princeton University Princeton NJ08544 United States Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States

When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

Forecasting high-frequency spatio-temporal wind power with dimensionally reduced echo state networks

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arXiv 2021年

作者： Huang, Huang Castruccio, Stefano Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46556 United States

Fast and accurate hourly forecasts of wind speed and power are crucial in quan-tifying and planning the energy budget in the electric grid. Modeling wind at a high resolution brings forth considerable challenges given its turbulent and highly nonlin-ear dynamics. In developing countries, where wind farms over a large domain are currently under construction or consideration, this is even more challenging given the necessity of modeling wind over space as well. In this work, we propose a machine learning approach to model the nonlinear hourly wind dynamics in Saudi Arabia with a domain-specific choice of knots to reduce the spatial dimensionality. Our re-sults show that for locations highlighted as wind abundant by a previous work, our approach results in an 11% improvement in the two-hour-ahead forecasted power against operational standards in the wind energy sector, yielding a saving of nearly one million US dollars over a year under current market prices in Saudi Arabia. © 2021, CC BY.

关键词： Wind power

Static dipole polarizabilities of polyacenes using self-interaction-corrected density functional approximations

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arXiv 2021年

作者： Akter, Sharmin Yamamoto, Yoh Zope, Rajendra R. Baruah, Tunna Computational Science Program University of Texas at El Paso El PasoTX79968 United States Department of Physics University of Texas at El Paso El PasoTX79968 United States

Density functional approximations are known to significantly overestimate the polarizabilities of long chain-like molecules. We study the static electric dipole polarizabilities and the vertical ionization potentials of polyacenes from benzene to pentacene using the Fermi-Löwdin orbital based self-interaction corrected (FLOSIC) density functional method. The orbital-by-orbital self-interaction correction corrects for the overestimation tendency of density functional approximations. The polarizabilities calculated with FLOSIC-DFA are however overly corrected. We also tested the recently developed locally-scaled self-interaction correction (LSIC) method on the polyacenes. The local-scaling method applies full SIC in the one-electron regions and restores the proper behavior of the SIC exchange-correlation functionals in the uniform density limit. The results show that LSIC removes the overcorrection tendency of the FLOSIC-DFA and produces results that are in excellent agreement with reference CCSD values. The vertical ionization potentials with LSIC also show good agreement with available experimental values. Copyright © 2021, The Authors. All rights reserved.

关键词： Silicon compounds

Generating large disordered stealthy hyperuniform systems with ultra-high accuracy to determine their physical properties

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arXiv 2023年

作者： Morse, Peter K. Kim, Jaeuk Steinhardt, Paul J. Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Institute of Materials Princeton University PrincetonNJ08544 United States Princeton Center for Theoretical Science Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Hyperuniform many-particle systems are characterized by a structure factor S(k) that is precisely zero as |k| → 0;and stealthy hyperuniform systems have S(k) = 0 for the finite range 0 30 for systems sizes more than an order of magnitude larger. We further show that this ultra-high accuracy is required to draw conclusions about their corresponding characteristics, such as the nature of the associated energy landscape and the presence or absence of Anderson localization, which might be masked, even when deviations are relatively small. © 2023, CC BY.

关键词： Physical properties

Protein folding as a jamming transition

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arXiv 2024年

作者： Grigas, Alex T. Liu, Zhuoyi Logan, Jack A. Shattuck, Mark D. O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Proteins fold to a specific functional conformation with a densely packed hydrophobic core that controls their stability. We develop a geometric, yet all-atom model for proteins that explains the universal core packing fraction of φc = 0.55 found in experimental measurements. We show that as the hydrophobic interactions increase relative to the temperature, a novel jamming transition occurs when the core packing fraction exceeds φc. The model also recapitulates the global structure of proteins since it can accurately refold to native-like structures from partially unfolded states. © 2024, CC BY.

关键词： Proteins