Chunfu Xu, Ph.D.

Assistant Investigator, NIBS, Beijing

Email: xuchunfu@nibs.ac.cn

2013
Ph.D., Chemistry, Emory University, Atlanta, GA, USA

2008

B.S., Macromolecular Materials and Engineering, Fudan University, Shanghai, China.

2022-
Assistant Investigator, National Institute of Biological Sciences, Beijing, China

2017-2021
Research Associate, Howard Hughes Medical Institute, Chevy Chase, MD, USA/University of Washington, Seattle, WA, USA

2014-2017
Senior Fellow, University of Washington, Seattle, WA, USA

Proteins, directed by evolution, have elegantly solved a vast array of technical problems in nature. However, new challenges emerge in different aspects of our changing world, such as health, environment, and energy. Our laboratory aims to design new classes of functional proteins using computational algorithms to address these challenges. Computational protein design is one of the multidisciplinary research areas that has attracted substantial attention from pharmaceutical companies and technology giants in recent years. Deep learning algorithms have made breakthrough achievements in protein structure prediction, which bring our world to the verge of a protein design revolution. Computational protein design has the promise to transform biomedical research and the potential to tackle environmental and energy crises by integrating with synthetic biology methodologies, but it remains an emerging field of study in China. Our laboratory will develop new computational protein design methods and explore the applications of de novo designed proteins in numerous research areas. The primary research directions in our lab include 1. developing deep-learning-based protein design approaches; 2. designing functional protein devices for basic research and disease therapeutics and diagnosis; 3. designing novel enzymes to address environmental and energy crises.

Publications

12. Zhao YT, Fallas JA, Saini S, Ueda G, Somasundaram L, Zhou Z, Xavier Raj I, Xu C, Carter L, Wrenn S, Mathieu J, Sellers DL, Baker D, Ruohola-Baker H, F-domain valency determines outcome of signaling through the angiopoietin pathway. EMBO Rep. 2021;22; e53471.

11. Wang F, Gnewou O, Modlin C, Beltran LC, Xu C, Su Z, Juneja P, Grigoryan G, Egelman EH, Conticello VP. Structural analysis of cross α-helical nanotubes provides insight into the designability of filamentous peptide nanomaterials. Nature Communications. 2021;12(1):1-14.

10. Xu C*, Lu P*, Gamal El-Din TM, Pei XY, Johnson MC, Uyeda A, Bick MJ, Xu Q, Jiang D, Bai H, Reggiano, G, Hsia Y, Brunette TJ, Dou J, Ma D, Lynch E, Boyken SE, Huang P, Stewart L, Kollman JM, Luisi BF, Matsuura T, Catterall WA, Baker D. Computational Design of Transmembrane Pores. Nature, 2020;585(7823):129-134. (* contributed equally to this work)

9. Boyken SE, Chen Z, Groves B, Langan RA, Oberdorfer G, Ford A, Gilmore JM, Xu C, DiMaio F, Pereira JH, Sankaran B, Seelig G, Zwart PH, Baker D. De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity. Science. 2016;352(6286):680-7.

8. Hsia Y, Bale JB, Gonen S, Shi D, Sheffler W, Fong KK, Nattermann U, Xu C, Huang PS, Ravichandran R, Yi S, Davis TN, Gonen T, King NP, Baker D. Design of a hyperstable 60-subunit protein icosahedron. Nature. 2016;535(7610):136-9.

7. DiMaio F, Song YF, Li XM, Brunner MJ, Xu C, Conticello V, Egelman E, Marlovits TC, Cheng YF, Baker D. Atomic-accuracy models from 4.5-angstrom cryo-electron microscopy data with density-guided iterative local refinement. Nat Methods. 2015;12(4):361-5.

6. Egelman EH*, Xu C*, DiMaio F, Magnotti E, Modlin C, Yu X, Wright E, Baker D, Conticello VP. Structural Plasticity of Helical Nanotubes Based on Coiled-Coil Assemblies. Structure. 2015;23(2):280-9. (* contributed equally to this work)

5. Huang PS*, Oberdorfer G*, Xu C*, Pei XY, Nannenga BL, Rogers JM, DiMaio F, Gonen T, Luisi B, Baker D. High thermodynamic stability of parametrically designed helical bundles. Science. 2014;346(6208):481-5. (* contributed equally to this work)

4. Jiang T, Xu C, Zuo XB, Conticello VP. Structurally Homogeneous Nanosheets from Self-Assembly of a Collagen-Mimetic Peptide. Angew Chem Int Edit. 2014;53(32):8367-71.

3. Jiang T, Xu C, Liu Y, Liu Z, Wall JS, Zuo XB, Lian TQ, Salaita K, Ni CY, Pochan D, Conticello VP. Structurally Defined Nanoscale Sheets from Self-Assembly of Collagen-Mimetic Peptides. Journal of the American Chemical Society. 2014;136(11):4300-8.

2. Xu C, Liu R, Mehta AK, Guerrero-Ferreira RC, Wright ER, Dunin-Horkawicz S, Morris K, Serpell LC, Zuo XB, Wall JS, Conticello VP. Rational Design of Helical Nanotubes from Self-Assembly of Coiled-Coil Lock Washers. Journal of the American Chemical Society. 2013;135(41):15565-78.

1. Anzini P, Xu C, Hughes S, Magnotti E, Jiang T, Hemmingsen L, Demeler B, Conticello VP. Controlling Self-Assembly of a Peptide-Based Material via Metal-Ion Induced Registry Shift. Journal of the American Chemical Society.2013;135(28):10278-81.