徐純福博士
- 基本信息
- 教育經(jīng)歷
- 工作經(jīng)歷
- 研究概述
- 發(fā)表文章
徐純福 博士北京生命科學(xué)研究所研究員Chunfu Xu, Ph.D.Assistant Investigator, NIBS, BeijingEmail: xuchunfu@nibs.ac.cn
Ph.D., Chemistry, Emory University, Atlanta, GA, USA
2008年? 復(fù)旦大學(xué)高分子材料與工程學(xué)士
B.S., Macromolecular Materials and Engineering, Fudan University, Shanghai, China.?Assistant Investigator, National Institute of Biological Sciences, Beijing, China
2017-2021 霍華德休斯醫(yī)學(xué)研究所/華盛頓大學(xué)副研究員
Research Associate, Howard Hughes Medical Institute, Chevy Chase, MD, USA/University of Washington, Seattle, WA, USA
2014-2017 華盛頓大學(xué)博士后研究員
Senior Fellow, University of Washington, Seattle, WA, USA
天然蛋白質(zhì)經(jīng)過億萬年的進(jìn)化精巧地解決了自然界中各種復(fù)雜的問題，但是當(dāng)今世界在健康、環(huán)境和能源等不同領(lǐng)域仍面臨迫切需要解決的新的挑戰(zhàn)。我們實(shí)驗(yàn)室旨在利用計(jì)算方法設(shè)計(jì)具有不同功能的全新的人工蛋白質(zhì)來應(yīng)對(duì)這些挑戰(zhàn)。蛋白質(zhì)計(jì)算設(shè)計(jì)是目前國際上生物醫(yī)藥領(lǐng)域的跨學(xué)科熱門研究方向之一，成為各大制藥公司的重點(diǎn)投資對(duì)象，同時(shí)也吸引了傳統(tǒng)信息技術(shù)公司的濃厚注意力。深度學(xué)習(xí)算法在蛋白質(zhì)結(jié)構(gòu)預(yù)測(cè)中取得的革命性突破更將會(huì)推動(dòng)蛋白質(zhì)計(jì)算設(shè)計(jì)新一輪的變革式發(fā)展。蛋白質(zhì)計(jì)算設(shè)計(jì)不僅在生命健康領(lǐng)域有著廣闊前景，它同時(shí)也可以和合成生物學(xué)相結(jié)合在環(huán)境與能源方面具有巨大應(yīng)用的潛力，但是它在國內(nèi)尚屬亟待開發(fā)的新興學(xué)科。
我們實(shí)驗(yàn)室將著眼于開發(fā)新的蛋白質(zhì)計(jì)算設(shè)計(jì)方法并探索全新的功能性蛋白在不同研究領(lǐng)域中的應(yīng)用，主要研究方向包括：
1. 開發(fā)基于深度學(xué)習(xí)算法的新的蛋白設(shè)計(jì)方法；
2. 設(shè)計(jì)可用于基礎(chǔ)研究和疾病診療的蛋白質(zhì)器件；
3. 設(shè)計(jì)新型蛋白質(zhì)酶以應(yīng)對(duì)環(huán)境與能源危機(jī)。
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.
發(fā)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.
