第 79 篇 CNS！诺奖得主 David Baker 最新 Nature

笔者导读

在被问及作为科学家的终极目标是什么时，2024 年诺贝尔化学奖得主、华盛顿大学教授 David Baker 在近日的访谈中回答到：「没有一个特定的终极目标。我享受的是在实验室里与研究生和博士后一起发现新事物的过程。与其设定一个单一目标，我更喜欢不断攻克重要问题这个过程本身。」

David Baker 教授是「计算蛋白质设计」的先驱，致力于通过人工智能和计算方法进行蛋白质设计，并于 2003 年成功设计出一种自然界不存在的全新蛋白质，证明了从头设计蛋白质的可行性。他的工作还为设计新型药物、疫苗和纳米材料提供全新解决方案。截至目前，David Baker 以通讯作者在国际顶尖学术期刊Cell（5 篇）、Nature（36 篇）、Science（38 篇）发表论文 79 篇！（文章放在文末参考文献列表里供大家查阅，如有未统计到的研究，欢迎在评论区补充）

2003 年 11 月，团队在Science期刊发文报道了原子精度新型球状蛋白质折叠的设计。2005 年 9 月，团队在Science期刊发文报道了迈向小蛋白质的高分辨率从头结构预测，10 月在Science期刊发文总结了蛋白质结构与相互作用建模的进展。2006 年 6 月，团队在Nature期刊发文报道了核酸内切酶 DNA 结合与切割特异性的计算重设计。2007 年 2 月，团队在Cell期刊发文报道小的天然蛋白质的高度协同折叠很可能是自然选择的结果，10 月在Nature期刊发文讨论了高分辨率结构预测与晶体学相角问题。2008 年 3 月，团队在Nature期刊发文报道了通过计算酶设计实现 Kemp 消除反应的催化剂、在Science期刊发文报道了逆向醛缩酶的从头计算设计。2009 年 10 月，团队在Nature期刊发文报道了结合能在催化与设计中的利用。

2010 年 2 月，团队在Science期刊发文报道了使用仅主链数据对较大蛋白质进行 NMR 结构测定，7 月在 Science 期刊发文报道了用于立体选择性双分子 Diels-Alder 反应的酶催化剂的计算设计。2011 年 5 月，团队在Science期刊发文报道了靶向流感血凝素保守茎区蛋白质的计算设计、在Nature期刊发文报道了通过密度与能量引导的蛋白质结构优化改进分子置换。2012 年 6 月，团队在Science期刊发文报道了具有原子级精度的自组装蛋白质纳米材料的计算设计，11 在Nature期刊发文介绍了设计理想蛋白质结构的原则。2013 年 9 月，团队在Nature期刊发文报道了高亲和力与高选择性的配体结合蛋白的计算设计。2014 年 6 月，团队在Cell期刊发文，报道了计算策略设计的 EB 病毒 Bcl-2 蛋白抑制剂，其可诱导被感染细胞发生凋亡，10 月在Science期刊发文报道了参数化设计的螺旋束的高热力学稳定性。2015 年 5 月，团队在Nature期刊发文报道了共组装多组分蛋白质纳米材料的精确设计，7 月在Science期刊发文报道了通过非共价蛋白质-蛋白质界面介导的有序二维阵列设计，12 月在Nature期刊发文报道了通过计算蛋白质设计探索重复蛋白领域。

2016 年 5 月，团队在Science期刊发文报道了具有模块化氢键网络介导特异性的蛋白质同源寡聚体的从头设计， 7 月在Science期刊发文报道了兆道尔顿尺度双组分二十面体蛋白质复合物的精确设计，9 月在Nature期刊发文报道了超稳定约束肽的精确从头设计。2017 年 1 月，团队在Science期刊发文报道了设计具有由弯曲 β 折叠片层形成空腔的蛋白质的原则以及利用宏基因组序列数据进行蛋白质结构测定，7 月在Science期刊发文报道了使用大规模并行设计、合成和测试进行蛋白质折叠的整体分析，9 月在Nature期刊发文报道了用于靶向治疗的大规模并行从头蛋白质设计，12 月在Nature期刊发文报道了封装自身 RNA 基因组的蛋白质设计组装体的进化、在Science期刊发文报道了有序肽大环化合物的全面计算设计。2018 年 3 月，团队在Science期刊发文报道了多次跨膜蛋白的精确计算设计，9 月在Nature期刊发文报道了荧光激活 β-barrel 的从头设计，11 月在Science期刊发文报道了自组装螺旋蛋白丝的从头设计，12 月在Nature期刊发文报道了正交蛋白质异源二聚体的可编程设计。2019 年 1 月，团队在Nature期刊发文报道了 IL-2 和 IL-15 强效选择性模拟物的从头设计，5 月在Science期刊发文报道了可调控的、pH 驱动的构象变化的从头设计，7 月在Nature期刊发文报道了通过设计的蛋白质界面控制无机晶体上的蛋白质组装以及生物活性蛋白质开关的从头设计、在Science期刊发文报道了蛋白质组共进化揭示的蛋白质相互作用网络。

2020 年 4 月，团队在Science期刊发文报道了蛋白质逻辑门的从头设计，8 月在Science期刊发文报道了跨膜孔道的计算设计以及表明其设计的蛋白质逻辑门，可用于靶向具有精确表面抗原组合的细胞，9 月在Science期刊发文报道了皮摩尔级新冠病毒微型蛋白抑制剂的从头设计。2021 年 1 月，团队在Nature期刊发文报道了模块化与可调谐蛋白质生物传感器的从头设计，2 月在Science期刊发文报道了跨膜 β-barrel 的从头设计，4 月在Science期刊发文报道了新开发的一种新型的蛋白质设计方法，其可将抗体融合在规则的纳米结构之中，7 月在Science期刊发文报道了使用三轨道神经网络对蛋白质结构和相互作用进行精确预测，12 月在Nature期刊发文报道了通过深度网络「幻觉」（hallucination）进行从头蛋白质设计、在Science期刊发文报道了核心真核蛋白质复合物的计算结构。2022 年 1 月，团队在Science期刊发文报道了通过隐式负设计实现不对称蛋白质的快速、定向设计，3 月在Nature期刊发文报道了仅从靶标结构出发设计蛋白质结合蛋白，7 月在Science期刊发文报道了利用深度学习为蛋白质功能位点构建支架，8 月在Cell期刊发文报道了跨膜大环化合物的精确从头设计。2023 年 2 月，团队在Nature期刊发文报道了利用深度学习从头设计荧光素酶，4 月在Nature期刊发文报道了通过超螺旋匹配从头设计模块化肽结合蛋白、在Science期刊发文报道了基于强化学习的自上而下的蛋白质结构设计，7 月在Nature期刊发文报道了利用 RFdiffusion 进行蛋白质结构与功能的从头设计，8 月在Science期刊发文报道了响应刺激状态变换蛋白的设计，12 月在Nature期刊发文报道了生物活性螺旋肽高亲和力结合物的从头设计。

2024 年 3 月，团队在Nature期刊发文报道了使用标准化蛋白质模块设计可扩展的纳米材料，4 月在Science期刊发文报道了基于 RoseTTAFold 全原子的广义生物分子建模与设计、发现具有更强疗效和选择性的大环分子药物的新方向，6 月在Cell期刊发文，表明利用设计的寡聚体组装，能够调控 FGF 通路信号传导与血管分化，同月还发文报道了口服给药的 Th17 拮抗剂微型蛋白的临床前概念验证，7 月在Science期刊发文报道了利用形状互补的假环结合并感知多种小分子，8 月在Nature期刊发文报道了变构可切换蛋白质组装的从头设计，9 月在Nature期刊发文揭示了设计的诱导内吞蛋白质可降解靶标并放大信号；12 月在Nature期刊发文报道了通过程序化对称性破坏设计的四组分蛋白质纳米笼、在Science期刊发文报道了基于靶标条件扩散生成高效的 TNFR 超家族拮抗剂和激动剂。2025 年 1 月，团队在Nature期刊发文表明从头设计的蛋白质可中和致命的蛇毒毒素，4 月在Science期刊发文报道了丝氨酸水解酶的计算设计，7 月在Nature期刊发文表明可将蛋白质结合剂扩散至内在无序蛋白、在Science期刊发文报道了内在无序区域结合蛋白的设计以及针对肽-MHC-I 复合物的高特异性结合剂的设计，9 月在Nature期刊发文揭示了促进解离的设计使得细胞因子信号传导的时间调控成为可能，10 月在Nature期刊发文报道可从头设计具有精准几何结构的钙离子通道，11 月在Nature期刊发文报道了利用 RFdiffusion 实现抗体的原子精度从头设计，12 月在Nature期刊发文报道了金属水解酶的计算设计。

2026 年 4 月 29 日，David Baker 教授团队在Nature发表题为 The past, present and future of de novo protein design 的重磅综述，对从头蛋白质设计的发展历程、当前技术成熟度、以及未来十年有望解决的重大挑战等进行了系统综述。

图片来源：Nature

https://doi.org/10.1038/s41586-026-10328-7

研究团队提出，随着深度学习、计算科学的发展，从头蛋白质设计已经从传统的基于物理化学的力场方法（如 Rosetta）转向基于生成式人工智能的方法（如 RFdiffusion 和 ProteinMPNN）。同时，与以往寻找序列 —— 结构自由能最低点不同，新方法通过学习蛋白质数据库中的概率分布，生成高概率的序列 —— 结构对。RFdiffusion、ProteinMPNN 等开源设计方法以及蛋白质结构预测工具使生物化学家和分子生物学家能够广泛探索可能的应用。

在蛋白质组装领域，不同的研究团队已成功设计出多种一维纤维、二维阵列、三维纳米笼及晶体等，其中基于二十面体纳米颗粒的 SKYcovione 新冠疫苗已成为首个获得临床批准的从头设计药物。在蛋白质结合剂方面，RFdiffusion 等工具已针对超过 200 个蛋白质靶点设计出高亲和力结合剂，在抗病毒、中和毒素、受体拮抗以及靶向降解等方面展现出巨大潜力。此外，在小分子结合剂、生物传感器和酶设计上也取得了显著进展，例如利用 PLACER 过滤提高了丝氨酸水解酶和金属水解酶的催化效率等。

图片来源：Nature

https://doi.org/10.1038/s41586-026-10328-7

作者特别指出，当前，设计全新蛋白质结构、多蛋白组装体以及靶向蛋白质结合剂的长期挑战已接近解决，其核心问题不再是「如何设计」，而是「设计什么」以及如何去应用。

在谈到未来的挑战时，David Baker 教授团队指出，当前方法学发展的挑战包括设计高能垒反应的催化剂，以及更广泛地设计整合结合、构象变化和催化功能的开关和纳米机器，大规模生产和知识产权等问题。

最后，他们还预计，在未来五到十年内，将设计出更复杂的蛋白质纳米机器和材料，其功能范围远超自然演化所产生的功能，而是广泛应用于医学、技术和可持续发展领域。

结语

整体来看，从头蛋白质设计正在推动分子工程的范式转变。尽管在成功率和活性方面仍有改进空间，但设计新结构、组装体和结合蛋白的基本挑战已经基本解决，并且在催化方面也取得了显著进展。

目前，折叠、组装、结合和催化模式都已能被从头蛋白质设计所实现，下一个前沿领域涉及结合其中两种或更多种模式的更复杂功能。例如，设计由化学燃料驱动、在不同构象状态之间循环的纳米机器，以及不仅结合靶标还能对其进行修饰或降解、超越拮抗型抗体的疗法等。在未来，从头蛋白质设计应该会被用于应对人类面临的重大未解难题，并逐步成熟，作为医学、可持续性和生物技术领域的基础工具。

科学总是充满未知且迷人的，而醉心于科学的人也是幸福的，David Baker 教授正是这样的人。当提问者问他对人们有什么建议时，他回答：「我给大家的建议很简单，那就是在任何时候都去做让你最兴奋、最有热情的事情，不要太担心未来，也不要把计划排得太远，因为世界一直在变化。而这也正是我一直以来努力去做的事 —— 随时追随自己最感到兴奋的方向。这样往往会把你带到一些你从未预料到的地方，而世界也会以你意想不到的方式发生改变！」

也希望大家能够找到自己热爱的行业，并在自己热爱的行业里深耕、开花结果！最后，把《明朝那些事儿》的最后一句话送给大家：「成功只有一个 —— 按照自己的方式，去度过人生。」

参考文献（上下滑动查阅）

1. Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, Baker D. Design of a novel globular protein fold with atomic-level accuracy.Science. 2003 Nov 21;302(5649):1364-8. doi: 10.1126/science.1089427. PMID: 14631033.

2. Bradley P, Misura KM, Baker D. Toward high-resolution de novo structure prediction for small proteins.Science. 2005 Sep 16;309(5742):1868-71. doi: 10.1126/science.1113801. PMID: 16166519.

3. Schueler-Furman O, Wang C, Bradley P, Misura K, Baker D. Progress in modeling of protein structures and interactions.Science. 2005 Oct 28;310(5748):638-42. doi: 10.1126/science.1112160. PMID: 16254179.

4. Jiang L, Althoff EA, Clemente FR, Doyle L, Röthlisberger D, Zanghellini A, Gallaher JL, Betker JL, Tanaka F, Barbas CF 3rd, Hilvert D, Houk KN, Stoddard BL, Baker D. De novo computational design of retro-aldol enzymes.Science. 2008 Mar 7;319(5868):1387-91. doi: 10.1126/science.1152692. PMID: 18323453; PMCID: PMC3431203.

5. Raman S, Lange OF, Rossi P, Tyka M, Wang X, Aramini J, Liu G, Ramelot TA, Eletsky A, Szyperski T, Kennedy MA, Prestegard J, Montelione GT, Baker D. NMR structure determination for larger proteins using backbone-only data.Science. 2010 Feb 19;327(5968):1014-8. doi: 10.1126/science.1183649. Epub 2010 Feb 4. PMID: 20133520; PMCID: PMC2909653.

6. Siegel JB, Zanghellini A, Lovick HM, Kiss G, Lambert AR, St Clair JL, Gallaher JL, Hilvert D, Gelb MH, Stoddard BL, Houk KN, Michael FE, Baker D. Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction.Science. 2010 Jul 16;329(5989):309-13. doi: 10.1126/science.1190239. PMID: 20647463; PMCID: PMC3241958.

7. Fleishman SJ, Whitehead TA, Ekiert DC, Dreyfus C, Corn JE, Strauch EM, Wilson IA, Baker D. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin.Science. 2011 May 13;332(6031):816-21. doi: 10.1126/science.1202617. PMID: 21566186; PMCID: PMC3164876.

8. King NP, Sheffler W, Sawaya MR, Vollmar BS, Sumida JP, André I, Gonen T, Yeates TO, Baker D. Computational design of self-assembling protein nanomaterials with atomic level accuracy.Science. 2012 Jun 1;336(6085):1171-4. doi: 10.1126/science.1219364. PMID: 22654060; PMCID: PMC4138882.

9. 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 Oct 24;346(6208):481-485. doi: 10.1126/science.1257481. PMID: 25342806; PMCID: PMC4612401.

10. Gonen S, DiMaio F, Gonen T, Baker D. Design of ordered two-dimensional arrays mediated by noncovalent protein-protein interfaces.Science. 2015 Jun 19;348(6241):1365-8. doi: 10.1126/science.aaa9897. PMID: 26089516.

11. 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 May 6;352(6286):680-7. doi: 10.1126/science.aad8865. Erratum in:Science. 2016 May 20;352(6288):aag1318. doi: 10.1126/science.aag1318. PMID: 27151862; PMCID: PMC5497568.

12. Bale JB, Gonen S, Liu Y, Sheffler W, Ellis D, Thomas C, Cascio D, Yeates TO, Gonen T, King NP, Baker D. Accurate design of megadalton-scale two-component icosahedral protein complexes.Science. 2016 Jul 22;353(6297):389-94. doi: 10.1126/science.aaf8818. PMID: 27463675; PMCID: PMC5485857.

13. Marcos E, Basanta B, Chidyausiku TM, Tang Y, Oberdorfer G, Liu G, Swapna GV, Guan R, Silva DA, Dou J, Pereira JH, Xiao R, Sankaran B, Zwart PH, Montelione GT, Baker D. Principles for designing proteins with cavities formed by curved β sheets.Science. 2017 Jan 13;355(6321):201-206. doi: 10.1126/science.aah7389. PMID: 28082595; PMCID: PMC5588894.

14. Ovchinnikov S, Park H, Varghese N, Huang PS, Pavlopoulos GA, Kim DE, Kamisetty H, Kyrpides NC, Baker D. Protein structure determination using metagenome sequence data.Science. 2017 Jan 20;355(6322):294-298. doi: 10.1126/science.aah4043. PMID: 28104891; PMCID: PMC5493203.

15. Rocklin GJ, Chidyausiku TM, Goreshnik I, Ford A, Houliston S, Lemak A, Carter L, Ravichandran R, Mulligan VK, Chevalier A, Arrowsmith CH, Baker D. Global analysis of protein folding using massively parallel design, synthesis, and testing.Science. 2017 Jul 14;357(6347):168-175. doi: 10.1126/science.aan0693. PMID: 28706065; PMCID: PMC5568797.

16. Hosseinzadeh P, Bhardwaj G, Mulligan VK, Shortridge MD, Craven TW, Pardo-Avila F, Rettie SA, Kim DE, Silva DA, Ibrahim YM, Webb IK, Cort JR, Adkins JN, Varani G, Baker D. Comprehensive computational design of ordered peptide macrocycles.Science. 2017 Dec 15;358(6369):1461-1466. doi: 10.1126/science.aap7577. PMID: 29242347; PMCID: PMC5860875.

17. Lu P, Min D, DiMaio F, Wei KY, Vahey MD, Boyken SE, Chen Z, Fallas JA, Ueda G, Sheffler W, Mulligan VK, Xu W, Bowie JU, Baker D. Accurate computational design of multipass transmembrane proteins.Science. 2018 Mar 2;359(6379):1042-1046. doi: 10.1126/science.aaq1739. PMID: 29496880; PMCID: PMC7328376.

18. Shen H, Fallas JA, Lynch E, Sheffler W, Parry B, Jannetty N, Decarreau J, Wagenbach M, Vicente JJ, Chen J, Wang L, Dowling Q, Oberdorfer G, Stewart L, Wordeman L, De Yoreo J, Jacobs-Wagner C, Kollman J, Baker D. De novo design of self-assembling helical protein filaments.Science. 2018 Nov 9;362(6415):705-709. doi: 10.1126/science.aau3775. PMID: 30409885; PMCID: PMC6637945.

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