ArticleJournal of chemical information and modeling2023
EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 19 citations in OpenAlex.
- MutexaGPT: An Intuition-to-Design Translator for Physics-based Enzyme Engineering.Research square · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures.Journal of chemical information and modeling · 2026Article
- Enzymatic degradation of biopolymers in amorphous and molten states: mechanisms and applications.FEBS open bio · 2026Review
- Biofilm-associated antibiotic tolerance in the era of multidrug resistance: quorum-sensing mechanisms and emerging therapeutic strategies.Frontiers in cellular and infection microbiology · 2026Review
- Finding the dark matter: Large language model-based enzyme kinetic data extractor and its validation.Protein science : a publication of the Protein Society · 2025Article
- Linker-mediated domain separation enhances cold adaptation in cellulases.Protein science : a publication of the Protein Society · 2025Article
- Enhancing Cold Adaptation of Bidomain Amylases by High-Throughput Computational Engineering.Angewandte Chemie (International ed. in English) · 2025Article
- SubTuner leverages physics-based modeling to complement AI in enzyme engineering toward non-native substrates.Chem catalysis · 2025Article
- Computational Studies of Enzymes for C-F Bond Degradation and Functionalization.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Review
- Physics-based modeling in the new era of enzyme engineering.Nature computational science · 2025Review
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
- A multifunctional flavoprotein monooxygenase HspB for hydroxylation and C-C cleavage of 6-hydroxy-3-succinoyl-pyridine.Applied and environmental microbiology · 2024Article
- Substrate Positioning Dynamics Involves a Non-Electrostatic Component to Mediate Catalysis.The journal of physical chemistry letters · 2023Article
- Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.Journal of chemical theory and computation · 2023Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.