美國科學(xué)家最近開發(fā)出一種計(jì)算機(jī)建模方法，它能夠通過預(yù)測抗體的結(jié)構(gòu)變化來提升藥物的效力,。相關(guān)研究論文9月23日在線發(fā)表于《自然—生物技術(shù)》上,。
該研究成果源自美國麻省理工學(xué)院（MIT）Dane Wittrup和Bruce Tidor教授在實(shí)驗(yàn)和計(jì)算機(jī)模擬上的傾力合作,。研究人員分析了一種特殊抗體的氨基酸取代的多種可能性,，并計(jì)算出哪種取代方式所產(chǎn)生的結(jié)構(gòu)變化能夠?qū)е驴贵w與標(biāo)靶發(fā)生最緊密的作用,。
利用該模型，研究人員已經(jīng)對抗結(jié)腸癌藥物愛必妥（Erbitux）進(jìn)行了改造,，并使抗體與標(biāo)靶的親和力增加到原始蛋白分子的10倍。此外,，他們將該方法應(yīng)用于一種抗溶解酵素抗體D44.1,，提升了它的效力。論文第一作者Shaun Lippow表示,，“將抗體蛋白結(jié)構(gòu)和預(yù)測信息結(jié)合起來,，我們就能做出最合理的選擇，從而提升蛋白的功能,。”
美國國立常規(guī)醫(yī)學(xué)研究所（NIGMS）負(fù)責(zé)計(jì)算生物學(xué)項(xiàng)目的Janna Wehrle表示,，“該項(xiàng)研究是現(xiàn)代計(jì)算技術(shù)用于加速藥物開發(fā)的完美范例。”
傳統(tǒng)的抗體藥物開發(fā)方法主要通過自然選擇,，即先從小鼠體內(nèi)提取出抗體,，然后在實(shí)驗(yàn)室中進(jìn)行培養(yǎng)，使其進(jìn)化,，并檢測其功效,。該方法從時(shí)間上和研究人員可操控的層面上都不理想。相比之下,，MIT科學(xué)家的新方法能夠快速分析抗體大量的可能變異和構(gòu)造變化,，并預(yù)測出該蛋白分子和標(biāo)靶的親和力。
研究人員表示,，蛋白建模方法能夠減少抗體藥物開發(fā)的時(shí)間和成本,，并有助于科學(xué)家設(shè)計(jì)改造其他用途的蛋白,，比如將生物質(zhì)轉(zhuǎn)化為能源的酶。（科學(xué)網(wǎng) 任霄鵬/編譯）
原始出處:
Nature Biotechnology
Published online: 23 September 2007 | doi:10.1038/nbt1336
Computational design of antibody-affinity improvement beyond in vivo maturation
Shaun M Lippow1,4, K Dane Wittrup1,2 & Bruce Tidor2,3
Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages and increasing drug efficacy. However, antibody-affinity maturation in vivo often fails to produce antibody drugs of the targeted potency1, making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design procedure that focuses on electrostatic binding contributions and single mutants. By combining multiple designed mutations, a tenfold affinity improvement to 52 pM was engineered into the anti–epidermal growth factor receptor drug cetuximab (Erbitux), and a 140-fold improvement in affinity to 30 pM was obtained for the anti-lysozyme model antibody D44.1. The generality of the methods was further demonstrated through identification of known affinity-enhancing mutations in the therapeutic antibody bevacizumab (Avastin) and the model anti-fluorescein antibody 4-4-20. These results demonstrate computational capabilities for enhancing and accelerating the development of protein reagents and therapeutics.
Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Present address: Codon Devices, Inc., One Kendall Square, Building 300, Cambridge, Massachusetts 02139, USA.
Correspondence to: K Dane Wittrup1,2 e-mail: [email protected]
Correspondence to: Bruce Tidor2,3 e-mail: [email protected]
