Engineering protein developability

项目摘要 工程蛋白质驱动生物技术和生物学作为治疗，诊断和试剂。而 工程的主要功能-例如结合-已经变得相对强大，识别蛋白质，满足 临床和实际应用的严格性仍然是很成问题的。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-这最终限制了实用性。蛋白质序列 空间是巨大的，序列-功能关系是复杂的。因此，需要更有效的方法 绘制序列可开发性景观图，减少识别可开发性景观的实际负担， 序列的对景观的可靠、定量的了解将[1]使图书馆的设计受到限制， [2]使突变体的设计能够拯救具有引人注目的主要功能的先导分子 但发展的责任，[3]提高基本的洞察力的因素，决定蛋白质 鲁棒性高效的技术还可以[4]实现集成的上游库规模选择， 可展性序列模型对选择指标具有适度的预测性，但不能稳健地量化 整体景观。目前的实验方法效率低下。因此，创建和实施一个 用于蛋白质可开发性的库规模评估平台将是变革性的， 简化蛋白质发现和工程管道。我们将通过三个具体目标来实现这一目标。 目标1：设计一个用于蛋白质可开发性的文库规模评估的平台。我们将开发一套 结合[i]基因型-表型连锁，[ii]通过流式细胞术分选的表型分层的细胞测定 或增长竞争，以及[iii]深度测序，以有效地量化数百万 蛋白质变体，从而相对于目前的可开发性表征提高了几个数量级， 方法.目标2：阐明粘合剂支架的序列/可开发性景观。我们将从数量上 阐明三种配体支架的序列可开发性景观，以[i]使突变体设计能够拯救 具有引人注目的主要功能但具有可开发性责任的领先分子，以及[ii]推进基本的 理解决定蛋白质稳健性的物理化学原理。目标3：设计受限 这些库产生了显著更可开发的绑定器。我们将利用这种洞察力来设计和测试 限制性组合文库产生比不限制性文库显著更多的可显影结合物。 我们将测试三个假设：[i]嵌套采样使序列/可开发性的有效遍历成为可能 景观，以确定一个有效的约束图书馆设计; [ii]可开发的空间比天真的进化 空间（前提是图书馆规模多样性得以维持）;以及[iii]可发展性和可进化性的交叉点 通过这些方法可以有效地识别。

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.