ArticlePLoS computational biology2017
Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed, 132 citations in OpenAlex.
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- RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.Journal of chemical information and modeling · 2024Article
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- One-shot design elevates functional expression levels of a voltage-gated potassium channel.Protein science : a publication of the Protein Society · 2024Article
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- Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution.bioRxiv : the preprint server for biology · 2023Article
- Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models.Briefings in bioinformatics · 2023Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
There is growing interest in studying and engineering integral membrane proteins (MPs) that play key roles in sensing and regulating cellular response to diverse external signals. A MP must be expressed, correctly inserted and folded in a lipid bilayer, and trafficked to the proper cellular location in order to function. The sequence and structural determinants of these processes are complex and highly constrained. Here we describe a predictive, machine-learning approach that captures this complexity to facilitate successful MP engineering and design. Machine learning on carefully-chosen training sequences made by structure-guided SCHEMA recombination has enabled us to accurately predict the rare sequences in a diverse library of channelrhodopsins (ChRs) that express and localize to the plasma membrane of mammalian cells. These light-gated channel proteins of microbial origin are of interest for neuroscience applications, where expression and localization to the plasma membrane is a prerequisite for function. We trained Gaussian process (GP) classification and regression models with expression and localization data from 218 ChR chimeras chosen from a 118,098-variant library designed by SCHEMA recombination of three parent ChRs. We use these GP models to identify ChRs that express and localize well and show that our models can elucidate sequence and structure elements important for these processes. We also used the predictive models to convert a naturally occurring ChR incapable of mammalian localization into one that localizes well.
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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.