Wenjun Xie

Wenjun Xie, Ph.D.

Assistant Professor

Department: Medicinal Chemistry
Business Phone: (352) 273-8846
Business Email: wenjunxie@ufl.edu

About Wenjun Xie

I am a computational chemist deeply passionate about unraveling the principles of enzyme evolution and applying this knowledge to rational enzyme engineering. My focus centers on using physics-based and data-driven approaches to generate hypotheses and test them through experiments. My previous work has forged a connection between evolutionary information and the physicochemical attributes of enzymes. By discerning distinct biophysical constraints in different enzyme regions, I’ve successfully predicted the effects of mutations on enzyme activity and stability. This strategic insight has empowered the targeted enhancement of natural enzymes, yielding remarkable success rates in experimental outcomes. My lab will focus on developing new strategies for enzyme engineering and covalent inhibitor design for kinases. We are enthusiastic about our potential contributions to the fields of enzyme evolution and catalysis, as well as our role in advancing the development of novel therapeutics.

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Research Profile

The Xie Lab is dedicated to unraveling the methods nature uses to forge efficient catalysts and applying these discoveries to revolutionize enzyme engineering, with the ultimate goal of devising cutting-edge therapeutic solutions. We leverage a multi-disciplinary toolkit comprising artificial intelligence, computational chemistry, and biochemistry assays. Our focus lies in elucidating the intricate relationships between evolution and catalysis in enzymes, from both theoretical and practical perspectives. Our work thrives on the dynamic interplay among computational scientists, life scientists, and engineers, fostering a rich, collaborative research environment.

Open Researcher and Contributor ID (ORCID)



Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences
Proceedings of the National Academy of Sciences. 120(48) [DOI] 10.1073/pnas.2312848120. [PMID] 37983512.
Enhancing computational enzyme design by a maximum entropy strategy
Proceedings of the National Academy of Sciences. 119(7) [DOI] 10.1073/pnas.2122355119. [PMID] 35135886.
Exploring the Role of Chemical Reactions in the Selectivity of Tyrosine Kinase Inhibitors
Journal of the American Chemical Society. 144(36):16638-16646 [DOI] 10.1021/jacs.2c07307. [PMID] 36044733.
Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes
Proceedings of the National Academy of Sciences. 119(31) [DOI] 10.1073/pnas.2207904119. [PMID] 35901204.
pH regulates potassium conductance and drives a constitutive proton current in human TMEM175
Science Advances. 8(12) [DOI] 10.1126/sciadv.abm1568. [PMID] 35333573.
Characterizing chromatin folding coordinate and landscape with deep learning
PLOS Computational Biology. 16(9) [DOI] 10.1371/journal.pcbi.1008262. [PMID] 32986691.
Single-molecule and in silico dissection of the interaction between Polycomb repressive complex 2 and chromatin
Proceedings of the National Academy of Sciences. 117(48):30465-30475 [DOI] 10.1073/pnas.2003395117. [PMID] 33208532.
Molecular mechanism for NLRP6 inflammasome assembly and activation
Proceedings of the National Academy of Sciences. 116(6):2052-2057 [DOI] 10.1073/pnas.1817221116. [PMID] 30674671.


Postdoctoral Associate
2020-2023 · University of Southern California
Postdoctoral Associate
2017-2020 · Massachusetts Institute of Technology
Ph.D. in Physical Chemistry
2017 · Peking University
B.S. in Chemistry and Statistics
2012 · Peking University

Contact Details

(352) 273-8846
Business Mailing:
PO Box 100485
Business Street:
MSB P6-29
1345 Center Drive