ArticlePeerJ physical chemistry2022
Using genetic programming to predict and optimize protein function.
Article in PeerJ physical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine Learning-Based Development of Gadolinium Binding Peptides.NMR in biomedicine · 2026Article
- Machine learning-based development of Gadolinium binding peptides.bioRxiv : the preprint server for biology · 2025Article
- Engineered Peptide Scrambling for Enhanced Drug Delivery to Resistant Breast Cancer Cells via Small Extracellular Vesicles.ACS applied bio materials · 2025Article
- Computational design and evaluation of optimal bait sets for scalable proximity proteomics.Nature communications · 2025Article
- Computational peptide discovery with a genetic programming approach.Journal of computer-aided molecular design · 2024Article
- Development of a synthetic biosensor for chemical exchange MRI utilizing in silico optimized peptides.NMR in biomedicine · 2023Article
- Computational Peptide Discovery with a Genetic Programming Approach.Research square · 2023Article
- A Genetic Programming Approach to Engineering MRI Reporter Genes.ACS synthetic biology · 2023Article
- Development of a Synthetic Biosensor for Chemical Exchange MRI UtilizingbioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this article, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept, we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modeling tool like POET can help to find peptides with 400% better functionality than used before.
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Registered trials
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