Engineering protein developability

项目摘要 工程蛋白作为治疗学、诊断学和试剂推动生物技术和生物学。而当 设计主要功能--例如结合--已经变得相对强大，识别符合 临床和实际应用的严谨性仍然是非常有问题的。许多蛋白质的可开发性很差- 不稳定性、不溶性、低表达和非特异性结合--这些最终限制了实用性。蛋白质序列 空间是巨大的，序列-函数关系复杂。因此，需要更有效的方法。 绘制序列可开发性图景，减少识别可开发性的实际负担 序列。稳健的、定量的景观知识将[1]增强受限图书馆的设计能力 对于可开发的空间，[2]使突变体的设计能够拯救具有令人信服的主要功能的铅分子 但发展性的责任，和[3]加强了对决定蛋白质的因素的基本洞察力 健壮性。高效的技术还可以[4]实现集成的、上游图书馆规模的选择 可发展性。序列模型对选定的指标具有适度的预测性，但不能很好地量化 整体景观。目前的实验方法效率低下。因此，创建和实现 图书馆规模的蛋白质可开发性评估平台将具有变革性，以加速和 简化蛋白质发现和工程流水线。我们将通过三个具体目标来实现这一目标。 目的1：设计一个图书馆规模的蛋白质可开发性评估平台。我们将开发一套 通过流式细胞仪分类结合[I]基因-表型连锁、[II]表型分层的细胞分析 或增长竞争，以及[III]深度排序，以有效量化数百万人的可发展性指标 蛋白质变体从而提高了相对于电流数量级的可展开性特征 方法：研究方法。目的2：阐明粘结剂支架的序列/发展前景。我们将在数量上 阐明三种配体支架的序列可展开性景观[I]使突变设计能够拯救 具有令人信服的主要功能但具有可开发性的铅分子和[II]推进基本原理 理解决定蛋白质稳定性的物理化学原理。目标3：设计受限 产生更多可开发绑定器的库。我们将利用这一洞察力来设计和测试 受约束的组合库产生比不受约束的库显著更多的可开发绑定器。 我们将测试三个假设：[i]嵌套采样允许高效地遍历序列/可开发性 确定有效的受限图书馆设计的景观；[ii]可开发空间比天真更具进化性 空间(前提是保持图书馆规模的多样性)；以及[3]发展性和进化性的交集 可以通过这些方法有效地识别。

Project Summary Engineered proteins drive biotechnology and biology as therapeutics, diagnostics, and reagents. While engineering the primary function – e.g. binding – has become relatively robust, identifying proteins that meet the rigors of clinical and practical use remains highly problematic. Many proteins suffer from poor developability – instability, insolubility, low expression, and non-specific binding – that ultimately limits utility. Protein sequence space is immense, and sequence-function relationships are complex. Thus, more efficient methods are needed to map the sequence-developability landscape and reduce the practical burden of identifying developable sequences. Robust, quantitative knowledge of the landscape would [1] empower design of libraries constrained to developable space, [2] enable design of mutants to rescue lead molecules with compelling primary function but developability liabilities, and [3] enhance fundamental insight of factors that dictate protein robustness. Efficient techniques could also [4] enable integrated, upstream library-scale selection for developability. Sequence models are moderately predictive of select metrics but do not robustly quantify the overall landscape. Current experimental approaches are inefficient. Thus, creation and implementation of a platform for library-scale evaluation of protein developability would be transformative to accelerate and streamline the protein discovery and engineering pipeline. We will pursue this objective via three specific aims. Aim 1: Engineer a platform for library-scale evaluation of protein developability. We will develop a set of cellular assays that couple [i] genotype-phenotype linkage, [ii] phenotypic stratification via flow cytometric sorting or growth competition, and [iii] deep sequencing to efficiently quantify metrics of developability for millions of protein variants thereby elevating developability characterization by orders of magnitude relative to current methods. Aim 2: Elucidate sequence/developability landscapes for binder scaffolds. We will quantitatively elucidate sequence-developability landscapes for three ligand scaffolds to [i] empower mutant design to rescue lead molecules with compelling primary function but developability liabilities and [ii] to advance fundamental understanding of the physicochemical principles that dictate protein robustness. Aim 3: Design constrained libraries that yield significantly more developable binders. We will use this insight to design and test constrained combinatorial libraries to yield significantly more developable binders than an unconstrained library. We will test three hypotheses: [i] nested sampling enables the efficient traversal of the sequence/developability landscape to identify an effective constrained library design; [ii] developable space is more evolvable than naïve space (provided library scale diversity is maintained); and [iii] the intersection of developability and evolvability can be effectively identified via these methods.