Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme–ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles. Scientific contributions The pipeline introduced in this work allows for the detailed analysis of a large set of protein–ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git . The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/ .

中文翻译：

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同源酶-配体复合物中生化相关口袋和隧道的大规模注释


具有埋藏活性位点的酶中的隧道是允许底物进入和释放产物的关键结构特征，从而有助于提高催化效率。针对蛋白质隧道的瓶颈也是一种强大的蛋白质工程策略。然而，识别多种蛋白质结构中的功能隧道是一项非同艰的任务，只能通过计算来解决。我们提出了一个管道，该管道将自动结构分析与内部机器学习预测器集成在一起，用于注释蛋白质口袋，然后通过生化相关隧道计算配体运输的能量学。使用八种不同的分子系统进行的全面验证表明，CaverDock 对配体解/结合的分析与耗时的分子动力学模拟相当，但速度要快得多。优化和验证的流程用于注释超过 17,000 个同源酶-配体复合物。配体 un/binding energetics 的分析表明，在 75% 的情况下，最高优先级隧道具有最有利的能量。此外，同源配体的能量分布表明，简单的几何分析只能在 50% 的情况下正确识别隧道瓶颈。我们的研究为解释机械酶学和蛋白质工程中的隧道计算和能量分析结果提供了必要的信息。我们制定了几个简单的规则，允许根据结合口袋、隧道几何形状和配体转运能量曲线来识别生化相关的隧道。科学贡献这项工作中引入的管道允许对大量蛋白质-配体复合物进行详细分析，重点是运输途径。 我们正在引入一种新的预测器，用于确定结合口袋与隧道计算的相关性。在该领域，我们首次提出了配体结合和解结合的高通量能量分析，表明与纯几何方法相比，这些模拟的近似方法可以识别酶中的其他诱变热点。预测变量包含在补充材料中，也可以在 https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git 中访问 。本研究中计算的隧道数据已作为 ChannelsDB 2.0 数据库的一部分公开提供，可在 https://channelsdb2.biodata.ceitec.cz/ 上访问。