A5000680377- Protein Structure and Dynamics
- Material Dynamics and Properties
- RNA and protein synthesis mechanisms
- Spectroscopy and Quantum Chemical Studies
- Enzyme Structure and Function
- Quantum, superfluid, helium dynamics
- Machine Learning in Bioinformatics
- Ship Hydrodynamics and Maneuverability
- Fluid Dynamics Simulations and Interactions
- Petri Nets in System Modeling
- Glass properties and applications
- RNA modifications and cancer
- Nanopore and Nanochannel Transport Studies
- Wave and Wind Energy Systems
- Formal Methods in Verification
- Genomics and Phylogenetic Studies
- Advanced NMR Techniques and Applications
- Orbital Angular Momentum in Optics
- Mathematical Biology Tumor Growth
- Flexible and Reconfigurable Manufacturing Systems
- Mass Spectrometry Techniques and Applications
- Advanced Materials and Semiconductor Technologies
- Advanced Energy Technologies and Civil Engineering Innovations
- Computational Drug Discovery Methods
- Microfluidic and Bio-sensing Technologies
Shanghai Jiao Tong University
2016-2025
Shanghai Artificial Intelligence Laboratory
2022-2025
Shanghai Advanced Research Institute
2023-2025
Beijing Academy of Artificial Intelligence
2022-2025
Institute of Natural Science
2018-2025
University of Electronic Science and Technology of China
2025
Applied Mathematics (United States)
2025
Intel (United States)
2025
Oak Ridge National Laboratory
2011-2024
Chinese University of Hong Kong
2006-2024
Abstract Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, post-transcriptional regulations. These all among the core problems in field. With rapid growth of sequencing technology, we have accumulated a massive amount unannotated sequences. On other hand, expensive experimental observatory results only limited numbers annotated data 3D structures. Hence, it is still challenging design computational...
Accurately modeling the protein fitness landscapes holds great importance for engineering. Pre-trained language models have achieved state-of-the-art performance in predicting without wet-lab experimental data, but their accuracy and interpretability remain limited. On other hand, traditional supervised deep learning require abundant labeled training examples improvements, posing a practical barrier. In this work, we introduce FSFP, strategy that can effectively optimize under extreme data...
Interactions of water with cellulose are both fundamental and technological importance. Here, we characterize the properties associated using deuterium labeling, neutron scattering molecular dynamics simulation. Quasi-elastic provided quantitative details about dynamical relaxation processes that occur was supported by structural characterization small-angle X-ray diffraction. We can unambiguously detect two populations cellulose. The first is "non-freezing bound" gradually becomes mobile...
Amorphous anion-rich materials would offer a previously unexplored solution for high-capacity Al-ion storage.
The electrocaloric effect demands the maximized degree of freedom (DOF) polar domains and lowest energy barrier to facilitate transition polarization. However, optimization DOF barrier-including domain size, crystallinity, multiconformation coexistence, correlation, other factors in bulk ferroelectrics-has reached a limit. We used organic crystal dimethylhexynediol (DMHD) as three-dimensional sacrificial master assemble conformations at heterogeneous interface poly(vinylidene fluoride)-based...
Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model predicting functional fitness high-order mutants. Here, we develop SESNet, a supervised deep-learning predict mutants leveraging both sequence and structure information, exploiting attention mechanism. Our integrates local evolutionary context from homologous sequences, global encoding rich semantic universal space information accounting...
Accurate prediction of RNA three-dimensional (3D) structures remains an unsolved challenge. Determining 3D is crucial for understanding their functions and informing RNA-targeting drug development synthetic biology design. The structural flexibility RNA, which leads to the scarcity experimentally determined data, complicates computational efforts. Here we present RhoFold+, language model-based deep learning method that accurately predicts single-chain RNAs from sequences. By integrating...
Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes lightweight graph neural network scheme for structures, which efficiently analyzes the microenvironment amino acids wild-type proteins and reconstructs distribution acid sequences that are more likely pass natural selection. This serves as...
Designing protein mutants with both high stability and activity is a critical yet challenging task in engineering. Here, we introduce PRIME, deep learning model, which can suggest improved without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on public dataset across 283 assays. Furthermore, validated PRIME’s predictions five...
We report on cell damage of single cells confined in continuous-wave (cw), near-infrared (NIR) multimode optical traps as a result multiphoton absorption phenomena. Trapping beams at NIR wavelengths less than 800 nm are capable damaging through two-photon process. Cell is more pronounced cw compared with single-frequency true because transient power enhancement by longitudinal mode beating. Partial locking tunable Ti:sapphire lasers used trapping beam sources can produce unstable...
A simplified description of the 295 K dynamics a globular protein over wide frequency range (1--1000 GHz) is obtained by combining neutron scattering lysozyme with molecular simulation. The simulation agrees quantitatively experiment for both and hydration water shows that, whereas molecules subdiffuse, atoms undergo confined motion decomposable into three distinct classes: localized diffusion, methyl group rotations, jumps. Each classes gives rise to characteristic susceptibility signal.
Dynamics of hydration water is essential for the function biomacromolecules. Previous studies have demonstrated that molecules exhibit subdiffusion on surface biomacromolecules; yet microscopic mechanism remains vague. Here, by performing neutron scattering, molecular dynamics simulations, and analytic modeling hydrated perdeuterated protein powders, we found jump randomly between trapping sites surfaces, whose waiting times obey a broad distribution, resulting in subdiffusion. Moreover,...
The scattering of neutrons can be used to provide information on the structure and dynamics biological systems multiple length time scales. Pursuant a National Science Foundation-funded workshop in February 2018, recent developments this field are reviewed here, as well future prospects that expected given advances sources, instrumentation computational power methods. Crystallography, solution scattering, dynamics, membranes, labeling imaging examined. For extraction maximum information,...
Proton conductors, particularly hydrated solid membranes, have various applications in sensors, fuel cells, and cellular biological systems. Unraveling the intrinsic proton transfer mechanism is critical for establishing foundation of conduction. Two scenarios on electrical conduction, Grotthuss vehicle mechanisms, been reported by experiments simulations. But separating quantifying contributions these two components from difficult. Here, we present conductive behavior a two-dimensional...
Prokaryotic Argonaute proteins (pAgos) widely participate in hosts to defend against the invasion of nucleic acids. Compared with CRISPR-Cas system, which requires a specific motif on target and can only use RNA as guide, pAgos exhibit precise endonuclease activity any arbitrary sequence both DNA thus rendering great potential for genome editing applications. Hitherto, most in-depth studies structure-function relationship were conducted thermophilic ones, functioning at ∼60 100°C, whose...
The identification of protein homologs in large databases using conventional methods, such as sequence comparison, often misses remote homologs. Here, we offer an ultrafast, highly sensitive method, dense homolog retriever (DHR), for detecting on the basis a language model and retrieval techniques. Its dual-encoder architecture generates different embeddings same easily locates by comparing these representations. alignment-free nature improves speed incorporates rich evolutionary structural...
The nature of the low-frequency vibrations, so-called boson peak, in spectra glass-forming systems remains a subject active discussions. It appears that densification glasses leads to significant change peak vibrations and opens additional possibility verify different model predictions. We present light (Raman Brillouin) scattering studies influence pressure (up 1.5 GPa) on elastic properties five polymers. demonstrate pressure-induced shift frequency all cases is significantly stronger than...