ArticleScientific reports2018
A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Multidimensional maturation of antibody variable domains with machine-learning assistance.mAbs · 2026Article
- Engineering peroxygenases for pharmaceutical applications: recent advances and future perspectives.Chemical science · 2026Review
- Best Practices for Machine Learning-Assisted Protein Engineering.Journal of chemical information and modeling · 2025Review
- A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-DependentACS synthetic biology · 2025Article
- AI-enhanced bioprocess technologies: machine learning implementations from upstream to downstream operations.World journal of microbiology & biotechnology · 2025Review
- Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.Nature communications · 2025Article
- A protein fitness predictive framework based on feature combination and intelligent searching.Protein science : a publication of the Protein Society · 2024Article
- Evolutionary insights into the stereoselectivity of imine reductases based on ancestral sequence reconstruction.Nature communications · 2024Article
- Enhanced stereodivergent evolution of carboxylesterase for efficient kinetic resolution of near-symmetric esters through machine learning.Nature communications · 2024Article
- Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity.Nature communications · 2024Article
- Unsupervised evolution of protein and antibody complexes with a structure-informed language model.Science (New York, N.Y.) · 2024Article
- Computational peptide discovery with a genetic programming approach.Journal of computer-aided molecular design · 2024Article
- Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development.ACS omega · 2024Review
- Guiding discovery of protein sequence-structure-function modeling.Bioinformatics (Oxford, England) · 2024Article
- ACIDES: on-line monitoring of forward genetic screens for protein engineering.Nature communications · 2023Article
- Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution.bioRxiv : the preprint server for biology · 2023Article
- Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning.Molecules (Basel, Switzerland) · 2023Article
- Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.Journal of chemical theory and computation · 2023Review
- EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution.Chemical science · 2023Article
- Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design.ACS catalysis · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov'SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2
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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.