Structural bioinformatics software for epitope selection and antibody engineering

描述(申请人提供)：人类健康极大地受益于单抗的治疗应用，治疗痛苦和破坏性的疾病，如类风湿性关节炎和癌症等。然而，单抗的研制是一个费时费力的过程。更快的mAb开发对健康的好处是显而易见的，这就产生了对工具的巨大需求，以指导科学家发现最有希望的抗原靶标--特别是关于B细胞表位(抗体识别的抗原的一部分)。在这一领域取得进展的关键障碍是，在缺乏用于预测线性B细胞表位的已知结构的情况下，无法推断蛋白质序列的构象特征。线性B细胞表位是已知的最大、最多样化和具有药学价值的一类表位。对现有预测方法的普遍批评是，它们不准确，没有解决B细胞表位的构象性质。DNAStar建议创建一个软件管道来指导B细胞表位的预测，对单抗和其实验识别的抗原之间的动态结构界面进行建模，并在电子定点突变中进行筛选，以设计出更强大的抗体，提高结合亲和力。第一阶段的目标是从目标蛋白序列和实验或预测的结构中改进抗原肽的预测。为了实现这一目标，DNAStar与专家们在单抗生产、3D结构预测以及蛋白质结构和动力学方面建立了合作关系，包括获取他们的实验方法、数据和软件工具。我们的预测模型将受益于三个关键创新：1)对单抗生产的卓越数据集和专业洞察力；2)引入最先进的3D结构预测以培训我们的表位预测者；以及3)首次将基于结构的蛋白质动力学用于B细胞表位预测。在第一阶段结束时，我们将提供一个增强的纯序列B细胞表位预测模型，与目前的TOP预测方法(目标1)相比，以及使用三维结构预测和蛋白质动力学的更好的基于序列和结构的表位预测模型(目标2)。在创建这些模型时，我们将考虑蛋白质序列的化学和物理性质以及调节蛋白质-蛋白质相互作用的生物物理，包括溶剂可及性、氢键、残基灵活性、结合核和分子表面的几何轮廓。拟议的软件流水线将建立在我们新的分子结构和模拟查看器Protean 3D的基础上，并将提高广泛的实验科学家从未知结构的蛋白质中估计关键抗原结构特性的技术能力-所有这些都在他们的台式计算机上进行。一旦实现了这些目标，科学家们将认识到，仅用氨基酸频率或倾向性标度来描述B细胞表位已不再够用。 公共卫生相关性：单抗是诊断和治疗人类疾病的宝贵工具。不幸的是，今天用来识别最有希望的免疫原靶的实验方法既耗时又不完全有效。通过在我们的桌面计算机软件产品中结合来自高质量3D结构预测的蛋白质序列信息和结构特征的新方法，我们建议提高广泛的生命科学家正确预测适用于其感兴趣区域的B细胞表位(抗体识别的抗原的一部分)的能力。这将加速发现新的单抗药物，从而改善人类对许多疾病的健康。

DESCRIPTION (provided by applicant): Human health has benefited tremendously from the therapeutic application of monoclonal antibodies (mAb), treating painful and devastating diseases such as rheumatoid arthritis and cancer, among others. However, mAb development is a laborious and time consuming process. The health benefits gained from faster mAb development are clear, creating a great need for tools to guide scientists toward discovering the most promising antigenic targets-particularly with regard to B-cell epitopes (the part of an antigen recognized by an antibody). The critical barrier to progress in this domain is the inability to deduce the conformational characteristics of protein sequence in the absence of known structure for predicting linear B-cell epitopes-the largest, most diverse, and pharmaceutically valuable class of known epitopes. The general criticism of existing prediction methods is that they are inaccurate and do not address the conformational nature of B-cell epitopes. DNASTAR proposes to create a software pipeline that guides the prediction of B-cell epitopes, models the dynamic structural interface between a monoclonal antibody and its experimentally identified antigen, and screens in silico site-directed mutations to engineer more potent antibodies with enhanced binding affinity. The Phase I goal is to improve the prediction of antigenic peptides from target protein sequences and experimental or predicted structures. Toward this goal, DNASTAR has established collaborations with experts in monoclonal antibody production, 3D structure prediction, and protein structure and dynamics, including access to their experimental methods, data, and software tools. Our predictive models will benefit from three key innovations: 1) a superior data set and professional insights into monoclonal antibody production, 2) the introduction of state of the art 3D structure prediction for training our epitope predictors, and 3) the first use of structure-based protein dynamics in B-cell epitope prediction. At the conclusion of Phase I, we will deliver an enhanced sequence-only B-cell epitope prediction model when compared to current top prediction methods (Aim 1) and a superior sequence and structure-based epitope prediction model using 3D structure prediction and protein dynamics (Aim 2). In creating these models, we will account for the chemical and physical properties of a protein sequence and the biophysics that mediate protein-protein interactions, including solvent accessibility, hydrogen bonding, residue flexibility, binding nuclei, and geometric contours of the molecular surface. The proposed software pipeline will be built upon Protean 3D, our new molecular structure and simulation viewer, and will elevate the technical capability of a broad range of experimental scientists to estimate key antigenic structural properties from proteins without known structure-all on their desktop computer. Upon achieving these aims, scientists will recognize that it is no longer adequate to describe B-cell epitopes using amino acid frequencies or propensity scales alone. PUBLIC HEALTH RELEVANCE: Monoclonal antibodies are invaluable tools for diagnosing and treating human diseases. Unfortunately, the experimental methods used today to identify the most promising immunogenic targets are time consuming and less than totally effective. By taking the novel approach of incorporating both protein sequence information and structural features derived from high quality 3D structure predictions within our desktop computer software product, we propose to advance the ability of a broad range of life scientists to properly predict B-cell epitopes (the part of an antigen recognized by an antibody) applicable to their area of interest. This will accelerate the discovery of new monoclonal antibody pharmaceuticals, leading to improved human health across many diseases.