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Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification

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arXiv 2024年

作者： Yang, Ziduo Zhao, Yi-Ming Wang, Xian Liu, Xiaoqing Zhang, Xiuying Li, Yifan Lv, Qiujie Chen, Calvin Yu-Chian Shen, Lei Department of Mechanical Engineering National University of Singapore Singapore117575 Singapore Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Department of Physics National University of Singapore Singapore117551 Singapore -Preferred Program School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China State Key Laboratory of Chemical Oncogenomics School of Chemical Biology and Biotechnology Peking University Shenzhen Graduate School Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong L-Med Biotechnology Co. Ltd. Guangdong Meizhou514699 China Research Institute Chongqing401123 China

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Code for DeepRelax is available at https://***/Shen-Group/DeepRelax. Copyright © 2024, The Authors. All rights reserved.

关键词： Point defects

Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Ecological landscapes guide the assembly of optimal microbial communities

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arXiv 2021年

作者： George, Ashish B. Korolev, Kirill S. Department of Physics Boston University BostonMA02215 United States Biological Design Center Boston University BostonMA02215 United States Carl R. Woese Institute for Genomic Biology Department of Plant Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Graduate Program in Bioinformatics Boston University BostonMA02215 United States

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia. © 2021, CC BY-NC-ND.

关键词： Microorganisms

Single-molecule localization microscopy reveals the ultrastructural constitution of distal appendages in expanded mammalian centrioles

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Nature communications 2023年第1期14卷 1688页

作者： Ting-Jui Ben Chang Jimmy Ching-Cheng Hsu T Tony Yang Department of Physics National Taiwan University Taipei Taiwan. Department of Electrical Engineering National Taiwan University Taipei Taiwan. Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Taipei Taiwan. Department of Electrical Engineering National Taiwan University Taipei Taiwan. tonyyang@ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. tonyyang@ntu.edu.tw.

Distal appendages (DAPs) are vital in cilia formation, mediating vesicular and ciliary docking to the plasma membrane during early ciliogenesis. Although numerous DAP proteins arranging a nine-fold symmetry have been studied using superresolution microscopy analyses, the extensive ultrastructural understanding of the DAP structure developing from the centriole wall remains elusive owing to insufficient resolution. Here, we proposed a pragmatic imaging strategy for two-color single-molecule localization microscopy of expanded mammalian DAP. Importantly, our imaging workflow enables us to push the resolution limit of a light microscope well close to a molecular level, thus achieving an unprecedented mapping resolution inside intact cells. Upon this workflow, we unravel the ultra-resolved higher-order protein complexes of the DAP and its associated proteins. Intriguingly, our images show that C2CD3, microtubule triplet, MNR, CEP90, OFD1, and ODF2 jointly constitute a unique molecular configuration at the DAP base. Moreover, our finding suggests that ODF2 plays an auxiliary role in coordinating and maintaining DAP nine-fold symmetry. Together, we develop an organelle-based drift correction protocol and a two-color solution with minimum crosstalk, allowing a robust localization microscopy imaging of expanded DAP structures deep into the gel-specimen composites.

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Synthesis and Evaluation of Metal-Impregnated Carbon Cloth Supplementation to Improve Anaerobic Digestion Performance from Municipal Sludge

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SSRN

SSRN 2023年

作者： Kumar, Vikas Devi, Parmila Patil, Abhishek Santana, Juan Camilo Bobowski, Jake S. Hallam, Steven J. Eskicioglu, Cigdem UBC Bioreactor Technology Group School of Engineering University of British Columbia Okanagan Campus KelownaBCV1V 1V7 Canada Department of Microbiology and Immunology University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics University of British Columbia Okanagan Campus KelownaBCV1V 1V7 Canada Graduate Program in Bioinformatics University of British Columbia VancouverBCV6T 1Z4 Canada Genome Science and Technology Program University of British Columbia 2329 West Mall VancouverBCV6T 1Z4 Canada Life Sciences Institute University of British Columbia VancouverBCV6T 1Z3 Canada Ecoscope Training Program University of British Columbia VancouverBCV6T 1Z3 Canada

This study investigated the effect of metal-impregnated carbon cloth (CC) supplementation on thermophilic anaerobic digestion (AD) improvement utilizing municipal sludge as a biomass input. Four types of metal-impregnated CC nanocomposites (Fe/CC, Ni/CC, Ni/Fe/CC, NiPcS/Fe/CC) were synthesized from activated CC (A-CC) using hydrothermal synthesis and compared with untreated CC (U-CC) to evaluate their functional significance for enhanced AD. The main factors inspected were electrical conductivity (EC) and Brunauer–Emmett–Teller (BET) surface area. A-CC had the highest surface area of 386.15 m2/g, followed by Fe/CC, NiPcS/Fe/CC, Ni/CC and Ni/Fe/CC with 140.96, 52.27, 43.52 and 36.07 m2/g, respectively. The A-CC and Fe/CC reactors produced 3.45 and 2.70 times more methane than Control (without CC) in the first 9 days. In contrast, the methane production for U-CC, Ni/CC, Ni/Fe/CC and NiPcS/Fe/CC was only 0.87, 1.26, 1.01 and 1.29 times, respectively, compared to Control. At a higher BET surface (>350 m2/g), the surface area becomes a more dominant factor, and the increase of EC value (>2.9*104 1/Ω.m) does not have an impact on methane enhancement. Microbial community analysis revealed that A-CC had the highest abundances of Syntrophomonas, Coprothermobacter and methanogenic populations, including Methanothermobacter and Methanosarcina, within 3 days compared to Control and metal-impregnated CC. There were minimal changes in microbial community composition in metal-impregnated CC compared to the U-CC. The result implies that the efficacy of CC supplementation for AD upgrading largely depends on the higher surface area compared to metal impregnation or conductivity. © 2023, The Authors. All rights reserved.

关键词： Anaerobic digestion

Traveling fronts in self-replicating persistent random walks with multiple internal states

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arXiv 2020年

作者： Ishihara, Keisuke George, Ashish B. Cornelius, Ryan Korolev, Kirill S. Max Planck Institute for the Physics of Complex Systems Nöthnitzer Strasse 38 Dresden01187 Germany Max Planck Institute of Molecular Cell Biology and Genetics Pfotenhauer Strasse 108 Dresden01307 Germany Department of Physics Boston University BostonMA02215 United States Graduate Program in Bioinformatics Boston University BostonMA02215 United States

Self-activation coupled to a transport mechanism results in traveling waves that describe polymerization reactions, forest fires, tumor growth, and even the spread of epidemics. Diffusion is a simple and commonly used model of particle transport. Many physical and biological systems are, however, better described by persistent random walks that switch between multiple states of ballistic motion. So far, traveling fronts in persistent random walk models have only been analyzed in special, simplified cases. Here, we formulate the general model of reaction-transport processes in such systems and show how to compute the expansion velocity for arbitrary number of states. For the two-state model, we obtain a closed-form expression for the velocity and report how it is affected by different transport and replication parameters. We also show that nonzero death rates result in a discontinuous transition from quiescence to propagation. We compare our results to a recent observation of a discontinuous onset of propagation in mi-crotubule asters and comment on the universal nature of the underlying mechanism. Copyright © 2020, The Authors. All rights reserved.

关键词： Deforestation

Contact network changes in ordered and disordered disk packings

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arXiv 2020年

作者： Tuckman, Philip J. VanderWerf, Kyle Yuan, Ye Zhang, Shiyun Zhang, Jerry Shattuck, Mark D. O’Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of New York New YorkNY10031 United States Department of Physics Yale University Department of Applied New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

We investigate the mechanical response of packings of purely repulsive, frictionless disks to quasistatic deformations. The deformations include simple shear strain at constant packing fraction and at constant pressure, "polydispersity" strain (in which we change the particle size distribution) at constant packing fraction and at constant pressure, and isotropic compression. For each deformation, we show that there are two classes of changes in the interparticle contact networks: jump changes and point changes. Jump changes occur when a contact network becomes mechanically unstable, particles "rearrange", and the potential energy (when the strain is applied at constant packing fraction) or enthalpy (when the strain is applied at constant pressure) and all derivatives are discontinuous. During point changes, a single contact is either added to or removed from the contact network. For repulsive linear spring interactions, second- and higher-order derivatives of the potential energy/enthalpy are discontinuous at a point change, while for Hertzian interactions, third- and higher-order derivatives of the potential energy/enthalpy are discontinuous. We illustrate the importance of point changes by studying the transition from a hexagonal crystal to a disordered crystal induced by applying polydispersity strain. During this transition, the system only undergoes point changes, with no jump changes. We emphasize that one must understand point changes, as well as jump changes, to predict the mechanical properties of jammed packings. Copyright © 2020, The Authors. All rights reserved.

关键词： Potential energy

Genetic drift in range expansions is very sensitive to density feedback in dispersal and growth

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arXiv 2019年

作者： Birzu, Gabriel Matin, Sakib Hallatschek, Oskar Korolev, Kirill S. Department of Physics Boston University BostonMA02215 United States Departments of Physics and Integrative Biology University of California BerkeleyCA94720 United States Department of Physics and Graduate Program in Bioinformatics Boston University BostonMA02215 United States

Theory predicts rapid genetic drift in expanding populations due to the serial founder effect at the expansion front. Yet, many natural populations maintain high genetic diversity in the newly colonized regions. Here, we investigate whether density-dependent dispersal could provide a resolution of this paradox. We find that genetic drift is dramatically suppressed when dispersal rates increase with the population density because many more migrants from the diverse, high-density regions arrive at the expansion edge. When density-dependence is weak or negative, the effective population size of the front scales only logarithmically with the carrying capacity. The dependence, however, switches to a sublinear power law and then to a linear increase as the density-dependence becomes strongly positive. To understand these results, we introduce a unified framework that predicts how the strength of genetic drift depends on the density-dependence in both dispersal and growth. This theory reveals that the transitions between different regimes of diversity loss are controlled by a single, universal parameter: the ratio of the expansion velocity to the geometric mean of dispersal and growth rates at expansion edge. Importantly, our results suggest that positive density-dependence could dramatically alter evolution in expanding populations even when its contributions to the expansion velocity is small. Copyright © 2019, The Authors. All rights reserved.

关键词： Expansion

Using physical features of protein core packing to distinguish real proteins from decoys

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arXiv 2020年

作者： Grigas, Alex T. Mei, Zhe Treado, John D. Levine, Zachary A. Regan, Lynne 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 Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute signifficantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (speciffically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins