ArticleThe Journal of biological chemistry2018
A statistical model for improved membrane protein expression using sequence-derived features.
Article in The Journal of biological chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 23 citations in OpenAlex.
- Efficient experimental characterization of the GPCRome via deep receptor scanning.Nature communications · 2026Article
- Deep Receptor Scanning Reveals General Sequence Constraints on GPCR Biosynthesis.bioRxiv : the preprint server for biology · 2025Article
- Machine-learning-guided Directed Evolution for AAV Capsid Engineering.Current pharmaceutical design · 2024Review
- Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Machine Learning for Protein Engineering.ArXiv · 2023Article
- Towards generalizable predictions for G protein-coupled receptor variant expression.Biophysical journal · 2022Article
- Application of artificial intelligence to theCanada communicable disease report = Releve des maladies transmissibles au Canada · 2020Article
- Machine-learning-guided directed evolution for protein engineering.Nature methods · 2019Review
- Smoothing membrane protein structure determination by initial upstream stage improvements.Applied microbiology and biotechnology · 2019Review
- TMCrys: predict propensity of success for transmembrane protein crystallization.Bioinformatics (Oxford, England) · 2018Article
- A mighty stream of membrane proteins.Nature structural & molecular biology · 2018Article
- Learned protein embeddings for machine learning.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The heterologous expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino acid sequence for a given IMP can dramatically affect expression levels, yet these observations have not resulted in generalizable approaches to improve expression levels. Here, we develop a data-driven statistical predictor named IMProve that, using only sequence information, increases the likelihood of selecting an IMP that expresses in
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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.