EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.
EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.
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EnzyHTP 具有自适应资源分配的计算定向进化。
DOI：
10.1021/acs.jcim.3c00618
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发表时间：
2023
影响因子：
5.6
通讯作者：
Yang,ZhongyueJ
中科院分区：
文献类型：
--
作者：
Shao,Qianzhen;Jiang,Yaoyukun;Yang,ZhongyueJ
定向进化通过迭代诱变促进酶工程。尽管高通量筛选应用广泛，但构建“智能库”以有效识别有益变异仍然是社区的主要挑战。在这里，我们开发了一种基于 EnzyHTP 的新计算定向进化协议，我们之前曾报道过该软件可以自动进行酶建模。为了提高吞吐量效率，我们实施了自适应资源分配策略，该策略根据工作流程中酶建模子任务的特定需求动态分配不同类型的计算资源（例如 GPU/CPU）。我们将该策略实现为 Python 库，并使用氟乙酸脱卤酶作为模型酶测试该库。结果表明，与整个工作流程中CPU和GPU都随叫随用的固定资源分配相比，应用自适应资源分配可以节省87%的CPU小时和14%的GPU小时。此外，我们在自适应资源分配的框架下构建了计算定向进化协议。在 Kemp 消除酶 (KE07) 的定向进化实验中，对工作流程进行了两轮突变筛选测试，总共有 184 个突变体。使用折叠稳定性和静电稳定能量作为计算读数，我们识别了所有四种实验观察到的目标变体。在工作流程的支持下，整个计算任务（即 18.4 μs MD 和 18,400 QM 单点计算）使用 ∼30 个 GPU 和 ∼1000 个 CPU 在 3 天的挂钟时间内完成。
Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building “smart libraries” to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.