About

We focus on investigating the relationship between enzyme sequences and their catalytic activities, a crucial aspect of rational enzyme engineering. We leverage a multi-disciplinary toolkit comprising AI, computational chemistry, and wet-lab. Our work thrives on the dynamic interplay among computational scientists, life scientists, and engineers, fostering an inclusive and collaborative research environment.

Research topics

• Organic chemistry
• Chemistry
• Biology
• Materials science
• Biophysics
• Geometry
• Condensed matter physics
• Cell biology
• Chemical physics
• Physics
• Biochemistry
• Computational chemistry
• Quantum mechanics
• Mathematics
• Genetics
• Molecular physics

Selected publications

• Author response for "Ultra-low concentration ether electrolytes with strong Coulomb interactions for high-voltage lithium metal batteries"

  2025-03-22

  peer-review
  PublisherDOI

• The vacuum characteristics of Ti-Zr-V and Ti film coatings on titanium alloy-lined thin-wall chambers at low temperatures

  Vacuum · 2025-12-04

  article
  PublisherDOI

• Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the S_N2 Step

  Journal of the American Chemical Society · 2025-01-10 · 11 citations

  articleOpen accessCorresponding

  Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the SN2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model. We then designed lower-order protein variants of haloalkane dehalogenase using the model. Kinetic measurements confirmed the successful design of protein variants that enhance catalytic activity, above that of the wild type, in the overall reaction and in particular in the SN2 step. On the simulation side, we provided molecular insights into these designs for the SN2 step using the empirical valence bond (EVB) and metadynamics simulations. The EVB calculations showed activation barriers consistent with experimental reaction rates, while examining the effect of amino acid replacements on the electrostatic effect on the activation barrier and the consequence of water penetration, as well as the extent of ground state destabilization/stabilization. Metadynamics simulations emphasize the importance of the substrate positioning in enzyme catalysis. Overall, our AI-guided approach successfully enabled the design of a variant with a faster rate for the SN2 step than the wild-type enzyme, despite haloalkane dehalogenase being extensively optimized through natural evolution.

  PublisherOA PDFDOI

• Do weaker solvation effects mean better performance of electrolytes for lithium metal batteries?

  Chemical Science · 2025-01-01 · 12 citations

  articleOpen access

  (NCM811) full cells at a 4.4 V cut-off voltage under practical conditions. This study offers critical insights for the design and high-throughput screening of next-generation high-performance WSEs for LMBs.

  PublisherDOI

• NRT1.1B acts as an abscisic acid receptor in integrating compound environmental cues for plants

  Cell · 2025-08-11 · 51 citations

  article
  PublisherDOI

• BGC-MAC and BGC-MAP: Attention-Based Models for Biosynthetic Gene Cluster Classification and Product Matching

  Journal of Chemical Information and Modeling · 2025-12-18

  articleOpen accessSenior authorCorresponding

  Natural products, synthesized via enzymes encoded by biosynthetic gene clusters (BGCs), represent a major source of therapeutic agents. Accurate BGC annotation is essential to unlocking the vast potential of natural product diversity. However, BGC annotation remains challenging due to our incomplete understanding of the enzymatic logic underlying biosynthesis. Here, we present two deep learning models trained on experimentally validated BGC-natural product pairs to advance BGC annotation. The BGC-multihead attention classifier (BGC-MAC) classifies BGCs by natural product class, outperforming antiSMASH and DeepBGC. The BGC-multihead attention product-matcher (BGC-MAP) associates BGCs with product structures, demonstrating potential to prioritize candidate BGCs given a natural product or to identify potential natural products from a given BGC. Importantly, the models' cross-attention mechanisms enable explainable AI, identifying key protein domains and revealing BGC-substructure relationships in the biosynthesis without requiring prior annotations. Together, BGC-MAC and BGC-MAP establish a data-driven, explainable AI framework that enhances BGC annotation, deepens biosynthetic insight, and accelerates the discovery of new natural products. The software is available at https://github.com/EvoCatalysis/BGC_annotation.

  PublisherOA PDFDOI

• BPS2025 - Leveraging generative AI and molecular modeling to unveil electrostatic catalysis in enzyme activity

  Biophysical Journal · 2025-02-01

  article1st authorCorresponding
  PublisherDOI

• The effect of eddy current in a titanium alloy-lined thin-wall chamber on vacuum characteristics and study of cooling methods

  Vacuum · 2025-11-26

  article
  PublisherDOI

• Bacterial Cytochrome P450 for Oxidative Halogenated Biaryl Coupling

  ACS Catalysis · 2025-12-19

  articleOpen access

  Biaryl motifs are fundamental structural elements in many pharmaceuticals, agrochemicals, and advanced materials. Traditional synthetic approaches for biaryl bond formation often require harsh conditions, costly catalysts, and prefunctionalized starting materials, which limit their efficiency, sustainability, and substrate scope. Enzymatic catalysis offers a more environmentally benign alternative. However, biocatalysts capable of directly coupling halogenated biaryl compounds remain largely underexplored. Here, we report the functional characterization of the marine-derived cytochrome P450 enzyme Bmp7, which catalyzes the formation of halogenated biaryls. We began by defining the product profile of recombinant Bmp7 using its native substrate 2,4-dibromophenol (1) and confirmed the dominant ortho-ortho C–C homocoupled product as MC21-A. Screening a halogenated aromatic substrate library revealed that Bmp7 binds and catalyzes the coupling of 17 halogenated phenols, as evidenced by spectral shift assays, LC-HRMS, HRMS/MS, and GC-MS analyses. Two homocoupled products were structurally confirmed by NMR analysis to possess ortho–ortho C–C linkages. In addition to efficient homocoupling, Bmp7 catalyzed heterocoupling reactions between substrate 1 and 16 other substrates, producing mixtures of homocoupled and heterocoupled halogenated biphenols. X-ray crystallography revealed the binding of two substrate 1 molecules within the active site, while DFT calculations supported a single-radical reaction mechanism, shedding light on the mechanistic basis of the coupling reaction. Together, these findings establish a foundation for future efforts in enzyme engineering and the development of biocatalytic strategies for synthetic applications.

  PublisherOA PDFDOI

• Author response for "Ultra-low concentration ether electrolytes with strong Coulomb interactions for high-voltage lithium metal batteries"

  2025-03-21

  peer-review
  PublisherDOI

Frequent coauthors

• Jun Meng

  65 shared

• Yi Qin Gao

  Peking University

  55 shared

• Zhen Chai

  53 shared

• Wensi Yang

  41 shared

• Cheng Luo

  Shanghai Institute of Materia Medica

  35 shared

• C. Luo

  Institute of Modern Physics

  32 shared

• G.D. Shen

  Boston University

  32 shared

• C.C. Li

  Chinese Academy of Sciences

  24 shared

Labs

• Xie LabPI

  Xie Lab @ UF

Education

• PhD, chemistry

  Peking University

  2017

• Visiting Student

  Max-Planck-Institut für Polymerforschung

  2016

• BS, Chemistry

  Peking University

  2012

Awards & honors

• Active Role: Principal Investigator Funding: NATL INST OF HL…
• Role: Principal Investigator Funding: US DEPT OF DEFENSE ADV…