ReviewJournal of chemical theory and computation2023
Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.
Review in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 18 citations in OpenAlex.
- Sustainable production of glutaric acid in microbial cell factories: Current advances and future prospects.Synthetic and systems biotechnology · 2026Review
- Iterative acylation on mature lasso peptides by widespread acetyltransferases.Nature chemical biology · 2026Article
- Sequence redesign of glycosyltransferases for enhanced heterologous expression and glycosylation efficiency in Escherichia coli.Nature communications · 2026Article
- QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures.Journal of chemical information and modeling · 2026Article
- Spirocyclic β-lactone secondary metabolites modulate spliceosome function.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- 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
- Digging out the Molecular Connections between the Catalytic Mechanism of Human Lysosomal α-Mannosidase and Its Pathophysiology.Journal of chemical information and modeling · 2025Article
- 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
- EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution.Chemical science · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.
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What OpenQuestion holds
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.