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SSRN 2024年

作者： Ni, Eric Knight, Elizabeth Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University 100 College Street New HavenCT06511 United States Department of Molecular Biophysics & Biochemistry Yale University 266 Whitney Avenue New HavenCT06511 United States Yale School of Medicine 333 Cedar Street New HavenCT06510 United States

Blockchain technology is gaining increased traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in the handling of health data. However, the large volume of biomedical datasets has slowed its adoption due to poor scalability. This challenge is especially relevant for applications that rely on blockchain’s strong immutability by storing data directly on-chain. Here, we demonstrate the potential of blockchain to create a secure and trustless environment for managing large on-chain records. Specifically, we detail an efficient, index-based approach for storing data on the Ethereum blockchain. We show that insertion and retrieval speeds remain nearly constant relative to database size, scaling linearly with the amount of data processed. Additionally, we achieve substantial efficiency gains through low-level assembly optimizations on the Ethereum Virtual Machine, underscoring the limitations of the Solidity compiler. Finally, we illustrate this approach through a practical case study, designing a smart contract for storing and querying training certificates on the Ethereum blockchain. Our solution achieves ~2x faster data insertion, ~500x faster retrieval, ~60% lower gas costs, and 50% lower storage usage compared to baseline methods. It won first place for track 1 of the 2022 iDASH secure genome analysis competition. We also demonstrate that this solution readily adapts to other data types, enabling efficient on-chain storage and retrieval of text, RNA-seq, or biomedical image data. © 2024, The Authors. All rights reserved.

关键词： Chains

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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Alternatively spliced MAP4 isoforms have key roles in maintaining microtubule organization and skeletal muscle function

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iScience 2024年第11期27卷 111104页

作者： Lucas, Lathan Nitschke, Larissa Nguyen, Brandon Loehr, James A. Rodney, George G. Cooper, Thomas A. Chemical Physical Structural Biology Graduate Program Baylor College of Medicine Houston TX United States Department of Pathology & Immunology Baylor College of Medicine Houston TX United States Department of Molecular Physiology and Biophysics Baylor College of Medicine Houston TX United States Department of Molecular and Cellular Biology Baylor College of Medicine Houston TX United States University of Otago Te Tari Matai Whaiariaro Department of Physiology Dunedin New Zealand

Skeletal muscle cells (myofibers) are elongated non-mitotic, multinucleated syncytia that have adapted a microtubule lattice. Microtubule-associated proteins (MAPs) play roles in regulating microtubule architecture. The most abundant MAP in skeletal muscle is MAP4. MAP4 consists of a ubiquitous MAP4 isoform (uMAP4), expressed in most tissues, and a striated-muscle-specific alternatively spliced isoform (mMAP4) that includes a 3,180-nucleotide exon (exon 8). To determine the role of mMAP4 in skeletal muscle, we generated mice that lack mMAP4 and express only uMAP4 due to genomic deletion of exon 8. We demonstrate that loss of mMAP4 leads to disorganized microtubule architecture and intrinsic loss of force generation. We show that mMAP4 exhibits enhanced association with microtubules compared to uMAP4 and that both the loss of mMAP4 and the concomitant gain of uMAP4 cause loss of muscle function. These results demonstrate the critical role for balanced expression of mMAP4 and uMAP4 for skeletal muscle homeostasis. © 2024 The Author(s)

关键词： Cellular physiology Functional aspects of cell biology molecular physiology molecular Structure

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EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment

arXiv

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arXiv 2023年

作者： Wang, Yuanqing Pulido, Iván Takaba, Kenichiro Kaminow, Benjamin Scheen, Jenke Wang, Lily Chodera, John D. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program Weill Cornell Medical College Cornell University New YorkNY10065 United States Pharmaceutical Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medical College Cornell University New YorkNY10065 United States Open Molecular Sciences Foundation DavisCA95618 United States

Atomic partial charges are crucial parameters in molecular dynamics (MD) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC, and is expensive for large systems or large numbers of molecules. We propose a hybrid physical / graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserves total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling, for the first time, the use of fully consistent charge models for small molecules and biopolymers for the construction of nextgeneration self-consistent biomolecular force fields. Implemented in the free and open source package espaloma_charge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://***/choderalab/espaloma_charge. © 2023, CC BY.

关键词： Molecules

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qlty: handling large tensors in scientific imaging deep-learning workflows

arXiv

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arXiv 2024年

作者： Zwart, Petrus H. Center for Advanced Mathematics in Energy Research Applications Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Berkeley Synchrotron Infrared Structural Biology program Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States

In scientific imaging, deep learning has become a pivotal tool for image analytics. However, handling large volumetric datasets, which often exceed the memory capacity of standard GPUs, require special attention when subjected to deep learning efforts. This paper introduces qlty, a toolkit designed to address these challenges through tensor management techniques. qlty offers robust methods for subsampling, cleaning, and stitching of large-scale spatial data, enabling effective training and inference even in resource-limited environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Visualizing cortical blood perfusion after photothrombotic stroke in vivo by needle-shaped beam optical coherence tomography angiography

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PhotoniX 2024年第1期5卷 605-621页

作者： Xiangyu Guo Jingjing Zhao Liqun Sun Varun Gupta Lin Du Komal Sharma Aidan Van Vleck Kaitlyn Liang Liangcai Cao Lingjie Kong Yuanmu Yang Yong Huang Adam de la Zerda Guofan Jin State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijing 100084China Department of Structural Biology Stanford University School of MedicineStanford UniversityStanfordCA 94305USA Department of Neurosurgery Stanford University School of MedicineStanford UniversityStanfordCA 94305USA Department of Radiology Stanford University School of MedicineStanford UniversityStanfordCA 94305USA Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeley 94720USA School of Optics and Photonics Beijing Institute of TechnologyNo.5 South Zhongguancun StreetHaidianBeijing 100081China Biophysics Program Molecular Imaging Programand Bio-X Program at Stanford UniversityStanfordCA 94305USA Chan Zuckerberg Biohub San FranciscoCA 94110USA Institute of Medical Equipment Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China

Optical imaging techniques provide low-cost,non-radiative images with high spatiotemporal resolution,making them advantageous for long-term dynamic observation of blood perfusion in stroke research and other brain studies compared to non-optical ***,high-resolution imaging in optical microscopy fundamentally requires a tight optical focus,and thus a limited depth of field(DOF).Consequently,large-scale,non-stitched,high-resolution images of curved surfaces,like brains,are difficult to acquire without z-axis *** overcome this limitation,we developed a needle-shaped beam optical coherence tomography angiography(NB-OCTA)system,and for the first time,achieved a volumetric resolution of less than 8μm in a non-stitched volume space of 6.4 mm×4 mm×620μm in *** system captures the distribution of blood vessels at 3.4-times larger depths than normal OCTA equipped with a Gaussian beam(GB-OCTA).We then employed NB-OCTA to perform long-term observation of cortical blood perfusion after stroke in vivo,and quantitatively analyzed the vessel area density(VAD)and the diameters of representative vessels in different regions over 10 days,revealing different spatiotemporal dynamics in the acute,sub-acute and chronic phase of post-ischemic *** from our NB-OCTA,we revealed that the recovery process is not only the result of spontaneous reperfusion,but also the formation of new *** study provides visual and mechanistic insights into strokes and helps to deepen our understanding of the spontaneous response of brain after stroke.

关键词： Optical coherence tomography angiography Diffractive optical elements Needle-shaped beam Brain imaging Stroke Cortical blood perfusion

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Development of label-free triboelectric nanosensors as screening platforms for anti-tumor drugs

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Nano Energy 2024年 125卷

作者： Cheng, Yu-Ying Ganguly, Anindita Cheng, Yi-Yun Ortiz, Christopher Llynard D. Pal, Arnab Shah, Pramod Kaswan, Kuldeep Yang, Lee-Wei Lin, Zong-Hong Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Institute of Biomedical Engineering National Tsing Hua University Hsinchu30013 Taiwan Internet of Senses Research Center National Tsing Hua University Hsinchu30013 Taiwan Praexisio Taiwan Inc. New Taipei221425 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu300044 Taiwan Chemical Biology and Molecular Biophysics Program Taiwan International Graduate Program Institute of Biological Chemistry Academia Sinica Taipei115 Taiwan Department of Chemistry National Tsing Hua University Hsinchu300044 Taiwan International Intercollegiate PhD Program National Tsing Hua University Hsinchu300044 Taiwan Department of Power Mechanical Engineering National Tsing Hua University Hsinchu300044 Taiwan Biomedical Artificial Intelligence PhD Program National Tsing Hua University Hsinchu300044 Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan

This study introduces a general label-free drug screening platform with potential to support high-throughput drug screening for any protein target. Its innovation lies in quantifying the affinity of molecule-molecule interactions via voltage output variation, which achieves unprecedented selectivity and sensitivity. It combines computational analysis complemented by experimental substantiation with a solid-liquid triboelectric nanosensor. Owing to its high binding affinity, FKBP-rapamycin is used as a model system to demonstrate the platform's sensitivity. Subsequently, the research is extended to study drug interactions with an oncogenic protein ATG4B. The findings unveil the binding of S130 and Tioconazole to ATG4B, a critical cysteine protease involved in autophagy, while revealing that Dexamethasone does not bind ATG4B. This binding exerts a specific inhibitory effect on autophagic flux and triggers cancer cell apoptosis. The results support the previously verified inhibitory effects of these drugs and the effectiveness of a newly developed self-powered drug screening platform, leveraging the principles of solid-liquid contact electrification. This approach adeptly confronts enduring challenges in traditional drug development methods where biochemical assays need to be designed for each individual protein target or only time-consuming and concentration-demanding molecular interaction measurements were made available. © 2024 Elsevier Ltd

关键词： Nanosensors

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Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad

arXiv

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arXiv 2024年

作者： Warrell, Jonathan Alesiani, Francesco Smith, Cameron Mösch, Anja Min, Martin Renqiang NEC Laboratories America Machine Learning Department PrincetonNJ08540 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States NEC Laboratories Europe Kurfuerstenanlage 36 HeidelbergD-69115 Germany Molecular Pathology Unit Center for Cancer Research Massachusetts General Hospital Research Institute Department of Pathology Harvard Medical School BostonMA United States Broad Institute of MIT and Harvard CambridgeMA United States

Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms over arbitrarily many levels based on concepts from category theory and population genetics. We formulate a multilevel version of the Wright-Fisher process using this approach, and we show that this model can be analyzed to clarify key features of multilevel selection. Particularly, we derive an extended multilevel probabilistic version of Price's Equation via the Kantorovich Monad, and we use this to characterize regimes of parameter space within which selection acts antagonistically or cooperatively across levels. Finally, we show how our framework can provide a unified setting for learning genetic algorithms (GAs), and we show how we can use a Variational Optimization and a multi-level analogue of coalescent analysis to fit multilevel GAs to simulated data. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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BPS2025 - Sticker strength modulates intrachain vs. interchain tug-of-war in multivalent disordered regions

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Biophysical Journal 2025年第3期124卷 557a-557a页

作者： David Moses Garrett M. Ginell Alex S. Holehouse Shahar Sukenik Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA USA Department of Biochemistry and Molecular Biophysics Washington University in St. Louis St. Louis MO USA Department of Chemistry Syracuse University Syracuse NY USA

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structural Basis of RNA-Induced Autoregulation of the DExH-type RNA Helicase Maleless

SSRN

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SSRN 2022年

作者： Ankush Jagtap, Pravin Kumar Müller, Marisa Kiss, Anna E. Thomae, Andreas W. Lapouge, Karine Beck, Martin Becker, Peter B. Hennig, Janosch Structural and Computational Biology Unit EMBL Heidelberg Meyerhofstraße 1 Heidelberg69117 Germany Chair of Biochemistry IV Biophysical Chemistry University of Bayreuth Bayreuth Germany Molecular Biology Division Biomedical Center LMU Munich Planegg-Martinsried 82152 Germany Core Facility Bioimaging The Biomedical Center LMU Munich Planegg-Martinsried 82152 Germany Protein Expression and Purification Core Facility EMBL Heidelberg Heidelberg69117 Germany Department of Molecular Sociology Max Planck Institute of Biophysics Frankfurt am Main60438 Germany

Unwinding RNA secondary structures by RNA helicases is essential for RNA metabolism. How the basic unwinding reaction of DExH-type helicases is regulated by their accessory domains is unresolved. Here, we combine structural and functional analyses to address this challenge for the prototypic DExH RNA helicase maleless (MLE) from *** captured the helicase cycle of MLE with multiple structural snapshots. We discovered that initially, dsRBD2 flexibly samples substrate dsRNA and aligns it with the open helicase tunnel. Subsequently, dsRBD2 releases RNA and associates with the helicase core, leading to closure of the tunnel around ssRNA. Structure-based MLE mutations confirm the functional relevance of the structural model in cells. We propose a molecular model in which the dsRBD2 domain of MLE orchestrates large structural transitions that depend on substrate RNA but are independent of *** findings reveal the fundamental mechanics of dsRNA unwinding by DExH helicases with high general relevance for dosage compensation and specific implications for MLE’s human orthologue DHX9/RHA mechanisms in disease. © 2022, The Authors. All rights reserved.

关键词： RNA

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