Promiscuous post-translational modification (PTM) enzymes often display nonobvious substrate preferences by acting on diverse yet well-defined sets of peptides and/or proteins. Understanding of substrate fitness landscapes for PTM enzymes is important in many areas of contemporary science, including natural product biosynthesis, molecular biology, and biotechnology. Here, we report an integrated platform for accurate profiling of substrate preferences for PTM enzymes. The platform features (i) a combination of mRNA display with next-generation sequencing as an ultrahigh throughput technique for data acquisition and (ii) deep learning for data analysis. The high accuracy (>0.99 in each of two studies) of the resulting deep learning models enables comprehensive analysis of enzymatic substrate preferences. The models can quantify fitness across sequence space, map modification sites, and identify important amino acids in the substrate. To benchmark the platform, we performed profiling of a Ser dehydratase (LazBF) and a Cys/Ser cyclodehydratase (LazDEF), two enzymes from the lactazole biosynthesis pathway. In both studies, our results point to complex enzymatic preferences, which, particularly for LazBF, cannot be reduced to a set of simple rules. The ability of the constructed models to dissect such complexity suggests that the developed platform can facilitate a wider study of PTM enzymes.

中文翻译：

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结合 mRNA 展示和深度学习的翻译后修饰酶底物偏好的准确模型

混杂的翻译后修饰 (PTM) 酶通常通过作用于不同但定义明确的肽和/或蛋白质组来表现出不明显的底物偏好。了解 PTM 酶的底物适应性景观在当代科学的许多领域都很重要，包括天然产物生物合成、分子生物学和生物技术。在这里，我们报告了一个集成平台，用于准确分析 PTM 酶的底物偏好。该平台具有 (i) 将 mRNA 展示与下一代测序相结合，作为用于数据采集的超高通量技术和 (ii) 用于数据分析的深度学习。由此产生的深度学习模型的高精度（两项研究中的每一项均>0.99）能够对酶底物偏好进行全面分析。这些模型可以量化跨序列空间的适应度、映射修改位点并识别底物中的重要氨基酸。为了对该平台进行基准测试，我们对来自乳唑生物合成途径的两种酶 Ser 脱水酶 (LazBF) 和 Cys/Ser 环化脱水酶 (LazDEF) 进行了分析。在这两项研究中，我们的结果都指向复杂的酶偏好，特别是对于 LazBF，不能简化为一组简单的规则。构建模型剖析这种复杂性的能力表明，开发的平台可以促进对 PTM 酶的更广泛研究。在这两项研究中，我们的结果都指向复杂的酶偏好，特别是对于 LazBF，不能简化为一组简单的规则。构建模型剖析这种复杂性的能力表明，开发的平台可以促进对 PTM 酶的更广泛研究。在这两项研究中，我们的结果都指向复杂的酶偏好，特别是对于 LazBF，不能简化为一组简单的规则。构建模型剖析这种复杂性的能力表明，开发的平台可以促进对 PTM 酶的更广泛研究。