徐纯福 博士

北京生命科学研究所研究员

Chunfu Xu, Ph.D.

Assistant Investigator, NIBS, Beijing

Email: xuchunfu@nibs.ac.cn

2008年  复旦大学高分子材料与工程学士

天然蛋白质经过亿万年的进化精巧地解决了自然界中各种复杂的问题，但是当今世界在健康、环境和能源等不同领域仍面临迫切需要解决的新的挑战。我们实验室旨在利用计算方法设计具有不同功能的全新的人工蛋白质来应对这些挑战。蛋白质计算设计是目前国际上生物医药领域的跨学科热门研究方向之一，成为各大制药公司的重点投资对象，同时也吸引了传统信息技术公司的浓厚注意力。深度学习算法在蛋白质结构预测中取得的革命性突破更将会推动蛋白质计算设计新一轮的变革式发展。蛋白质计算设计不仅在生命健康领域有着广阔前景，它同时也可以和合成生物学相结合在环境与能源方面具有巨大应用的潜力，但是它在国内尚属亟待开发的新兴学科。

我们实验室将着眼于开发新的蛋白质计算设计方法并探索全新的功能性蛋白在不同研究领域中的应用，主要研究方向包括：

1. 开发基于深度学习算法的新的蛋白设计方法；

2. 设计可用于基础研究和疾病诊疗的蛋白质器件；

3. 设计新型蛋白质酶以应对环境与能源危机。

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.

发表文章

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.