An Integrated Approach for Computationally Designing and Experimentally Characterizing Fully-Human Antibodies

1133040/1132511 Maranas/Wood抗体是Y形的多功能蛋白质结构，能够以高亲和力和特异性识别生物体可能遇到的全部抗原（从小分子到蛋白质）。 这种特性已被用于实验性结合测定，如ELISA和ELISPOT以及许多挽救生命的药物（即Avastin，Rituximab，Herceptin等）。传统的抗体设计方法依赖于文库构建、诱变和筛选的连续步骤，具有有限的计算衍生输入。然而，抗体是用于计算设计的极好的靶标，这是由于它们的功能通常限于结合，而不是催化，以及将它们的一级序列与结构连接的良好建立的规则的可用性。该团队已经开发、出版并免费提供了OptCDR方法，用于从头设计由针对任何特定抗原的互补决定区（CDR）组成的抗体结合口袋。使用OptCDR作为起点，将追求从头设计全人抗体的整个可变区以结合任何特定抗原的所需表位的工作流程。开发的方法将通过设计抗体库结合CD 20进行实验验证，CD 20是一种肽抗原，是B细胞淋巴瘤和白血病的治疗相关靶点。该项目的实验部分将使拟议的计算工作流程的微调和设计预测的有效性的定量评估。 根据目标1，将构建人类基因组中编码的所有种系抗体可变结构域结构的组合数据库。使用该种系结构的组合数据库，将在目标2下追求从头设计针对任何特定抗原的全人抗体所需的计算工具的开发和传播。设想的计算设计方法OptMAVEn（抗体可变区工程的最佳方法）将把OptCDR中开创的概念扩展到整个可变结构域的设计，而不仅仅是CDR。随后将设计、构建和筛选5个抗CD 20抗体库，以测试计算驱动抗体设计的有效性（目标3）。这五个文库具有相同的大约5*108个抗体的大小，将逐步探索更大胆的计算衍生的修饰。他们将跨越随机诱变、六个合理选择的位置的饱和诱变、十二个位置的靶向计算重新设计、使用OptCDR选择全新的CDR以及使用OptMAVEn从头设计整个可变结构域。除了方法上的进步，全面的实验表征将为公平评估抗体设计中的计算性能提供一个标准，可能揭示建模和模拟中的关键优势和不足之处。在这项研究中获得的经验教训将广泛适用于其他蛋白质设计的努力。在教育方面，将本科生引入科学研究。所有研究成果和方法将通过期刊出版物，会议演示，课程工作和通过网络提供所有开发的软件程序，数据库，实验结果和协议广泛传播。

1133040/1132511 Maranas/WoodAntibodies are Y-shaped, versatile protein structures able to recognize with high affinity and specificity the full range of antigens (from small molecules to proteins) that an organism may encounter. This property has been leveraged in experimental binding assays such as ELISA and ELISPOT and in many life-saving medications (i.e. Avastin, Rituximab, Herceptin, etc.). Traditional antibody design methods rely on successive steps of library construction, mutagenesis and screening with limited computationally derived input. However, antibodies are an excellent target for computational design due to their function that is typically limited to binding, not catalysis, and the availability of well-established rules linking their primary sequence to structure. The team has already developed, published, and made freely available the OptCDR method for the de novo design of antibody binding pockets composed by the Complementarity Determining Regions (CDRs) against any specified antigen. Using OptCDR as a starting point, a workflow for the de novo design of the entire variable regions of fully-human antibodies to bind the desired epitope of any specified antigen will be pursued. The developed methods will be experimentally validated by designing antibody libraries to bind CD20, a peptide antigen that is a therapeutically relevant target in B-cell lymphomas and leukemias. The experimental component of this project will enable the fine-tuning of the proposed computational workflow and the quantitative assessment of the efficacy of the design predictions. Under Aim 1, a combinatorial database of all germline antibody variable domain structures encoded in the human genome will be constructed. Using this combinatorial database of germline structures, the development and dissemination of the computational tools necessary for the de novo design of fully human antibodies against any specified antigen will be pursued under Aim 2. The envisioned computational design method OptMAVEn, (Optimal Method for Antibody Variable region Engineering) will expand the concepts pioneered in OptCDR to the design of the entire variable domains instead of only the CDRs. This will be followed by the design, construction and screening of five anti-CD20 antibody libraries to test the effectiveness of computations to drive antibody design (Aim 3). The five libraries, with the same approximate size of 5*108 antibodies, will progressively explore bolder computational-derived modifications. They will span random mutagenesis, saturation mutagenesis of six rationally chosen positions, targeted computational redesign of twelve positions, selection of entirely new CDRs with OptCDR, and de novo design of the entire variable domains with OptMAVEn. Beyond the methodological advances, the comprehensive experimental characterization will provide a standard for fairly evaluating the performance of computations in antibody design, potentially revealing key benefits and inadequacies in modeling and simulation. The lessons learned in this study would be broadly applicable in other protein design endeavors. On the educational front, undergraduate students will be introduced to scientific research. All research results and methods will be broadly disseminated through journal publications, conference presentations, course-work and by making available through the web all developed software programs, databases, experimental results and protocols.