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Variant effect predictions capture some aspects of deep mutational scanning experiments

Jonas Reeb, Theresa Wirth, Burkhard Rost
2019 biorxiv/medrxiv  
On a common subset of 32,981 SAVs, all methods capture some aspects of variant effects, albeit not the same.  ...  Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs) for particular  ...  On a common subset of 32,981 SAVs, all methods capture some 23 aspects of variant effects, albeit not the same.  ... 
doi:10.1101/859603 fatcat:wgxrurzysnamxn3vdhvqcll2e4

Variant effect predictions capture some aspects of deep mutational scanning experiments

Jonas Reeb, Theresa Wirth, Burkhard Rost
2020 BMC Bioinformatics  
On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same.  ...  Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred  ...  Acknowledgements The authors wish to thank all groups that work on DMS and readily provided their data, either as part of their manuscript or swiftly upon personal contact.  ... 
doi:10.1186/s12859-020-3439-4 pmid:32183714 fatcat:i2rmff4d2ncxpljahfneghanty

Neural networks to learn protein sequence–function relationships from deep mutational scanning data

Sam Gelman, Sarah A. Fahlberg, Pete Heinzelman, Philip A. Romero, Anthony Gitter
2021 Proceedings of the National Academy of Sciences of the United States of America  
We present a supervised deep learning framework to learn the sequence–function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants.  ...  Further analysis of the trained models reveals the networks' ability to learn biologically meaningful information about protein structure and mechanism.  ...  The number of reads per variant depends on the number of variants and the total number of reads in the deep mutational scanning experiment.  ... 
doi:10.1073/pnas.2104878118 pmid:34815338 pmcid:PMC8640744 fatcat:s3zkimuxo5axlho2pnarxrq6fq

Neural networks to learn protein sequence-function relationships from deep mutational scanning data [article]

Sam Gelman, Philip A Romero, Anthony Gitter
2020 bioRxiv   pre-print
We present a supervised deep learning framework to learn the sequence-function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants.  ...  Further analysis of the trained models reveals the networks' ability to learn biologically meaningful information about protein structure and mechanism.  ...  The number of reads per variant depends on the number of variants and the total number of reads in the deep mutational scanning experiment.  ... 
doi:10.1101/2020.10.25.353946 fatcat:4fc6l7utsvagtfcixfnjdcyzim

Rational Protein Engineering Guided by Deep Mutational Scanning

HyeonSeok Shin, Byung-Kwan Cho
2015 International Journal of Molecular Sciences  
Here, we discuss the current applications of deep mutational scanning and consider experimental design.  ...  A recently developed method termed deep mutational scanning explores the functional phenotype of thousands of mutants via massive sequencing.  ...  Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (MISP).  ... 
doi:10.3390/ijms160923094 pmid:26404267 pmcid:PMC4613353 fatcat:njh3tvetlncwpgiaort2xvdbni

News from the Protein Mutability Landscape

Maximilian Hecht, Yana Bromberg, Burkhard Rost
2013 Journal of Molecular Biology  
The explosion of deep sequencing and genotyping increasingly requires the distinction between effect and neutral variants.  ...  The simplest approach predicts all mutations of conserved residues to have an effect; however, this works poorly, at best.  ...  Outcome of alanine scans predicted Methods that predict functional effects have rarely been assessed in large-scale mutagenesis experiments. One reason is obviously the shortage of such experiments.  ... 
doi:10.1016/j.jmb.2013.07.028 pmid:23896297 fatcat:puwwuttpvff3vltdg3mqgmjc2i

Deep Mutational Scanning of Viral Glycoproteins and Their Host Receptors

Krishna K. Narayanan, Erik Procko
2021 Frontiers in Molecular Biosciences  
It generally involves tracking an in vitro selection of protein sequence variants with deep sequencing to map mutational effects based on changes in sequence abundance.  ...  While less explored, deep mutational scans of host receptors further assist in understanding virus-host protein interactions.  ...  the effects of thousands of mutations in a single experiment.  ... 
doi:10.3389/fmolb.2021.636660 pmid:33898517 pmcid:PMC8062978 fatcat:cyg7ot5j3fgxzi56cgxbog7yhy

Multiplexed assays reveal effects of missense variants in MSH2 and cancer predisposition

Sofie V Nielsen, Rasmus Hartmann-Petersen, Amelie Stein, Kresten Lindorff-Larsen
2021 PLoS Genetics  
In contrast, experiments based on multiplexed assays of variant effects (MAVEs; also sometimes known as deep mutational scanning) can be used to probe the effects of thousands of variants in a single experiment  ...  Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations. Mol Syst Biol. 2020; 16:e9380. https://doi.org/10.15252/msb.20199380 PMID: 32627955 35.  ... 
doi:10.1371/journal.pgen.1009496 pmid:33886538 pmcid:PMC8061925 fatcat:rjvpqfsd5zftjonkacnymrv2na

Exploring amino acid functions in a deep mutational landscape

Alistair S Dunham, Pedro Beltrao
2021 Molecular Systems Biology  
In this study, we gathered data from 28 deep mutational scanning studies, covering 6,291 positions in 30 proteins, and used the consequences of mutation at each position to define a mutational landscape  ...  Measuring large numbers of mutational consequences, which can elucidate the role an amino acid plays, was prohibitively time-consuming until recent developments in deep mutational scanning.  ...  to aid analysis of future deep mutational scans.  ... 
doi:10.15252/msb.202110305 pmid:34292650 pmcid:PMC8297461 fatcat:ucimjzbbcjatnexdeu6ceyqqbe

Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning [article]

Jonathan Frazer, Pascal Notin, Mafalda Dias, Aidan Gomez, Kelly Brock, Yarin Gal, Debora Marks
2020 bioRxiv   pre-print
By contrast, our approach leverages deep generative models to predict the clinical significance of protein variants without relying on labels.  ...  The natural distribution of protein sequences we observe across organisms is the result of billions of evolutionary experiments.  ...  Deep generative models of genetic variation capture the effects of mutations. Nat Methods 15, 816-822, doi:10.1038/s41592-018-0138-4 (2018). 11 Starita, L. M. et al.  ... 
doi:10.1101/2020.12.21.423785 fatcat:droprmppx5bqvaf3tqgk3oqomy

Protein design and variant prediction using autoregressive generative models

Jung-Eun Shin, Adam J Riesselman, Aaron W Kollasch, Conor McMahon, Elana Simon, Chris Sander, Aashish Manglik, Andrew C Kruse, Debora S Marks
2021 Nature Communications  
Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions.  ...  The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics.  ...  Acknowledgements We would like to thank John Ingraham, members of the Marks  ... 
doi:10.1038/s41467-021-22732-w pmid:33893299 fatcat:2rjnbm3uajaqfmfpqpop3gon2m

Deep generative models of genetic variation capture mutation effects [article]

Adam J. Riesselman, John B. Ingraham, Debora S. Marks
2017 arXiv   pre-print
We present a new probabilistic model for sequence families, DeepSequence, that can predict the effects of mutations across a variety of deep mutational scanning experiments significantly better than site  ...  this by simple additive effects.  ...  Acknowledgements We thank Chris Sander, Frank Poelwijk, David Duvenaud, Sam Sinai, Eric Kelsic and members of the Marks lab for helpful comments and discussions.  ... 
arXiv:1712.06527v1 fatcat:tnzxib67fnbzbcela3a6qqf2zu

Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins

Kamil Kamiński, Jan Ludwiczak, Maciej Jasiński, Adriana Bukala, Rafal Madaj, Krzysztof Szczepaniak, Stanisław Dunin-Horkawicz
2021 Briefings in Bioinformatics  
Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif.  ...  A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of  ...  Funding First TEAM program of the Foundation for Polish Science co-financed by the European Union under the Euro-  ... 
doi:10.1093/bib/bbab371 pmid:34571541 pmcid:PMC8769691 fatcat:4batgcj5bfhj3bp6immevjr74e

Deep generative models of genetic variation capture mutation effects [article]

Adam J Riesselman, John B Ingraham, Debora Susan Marks
2017 bioRxiv   pre-print
We present a new probabilistic model for sequence families, DeepSequence, that can predict the effects of mutations across a variety of deep mutational scanning experiments significantly better than site  ...  this by simple additive effects.  ...  A deep latent variable model captures the effects of mutations Deep mutational scanning (DMS) experiments provide a systematic survey of the mutational landscape of proteins and can be used to benchmark  ... 
doi:10.1101/235655 fatcat:p73wrfhcnzfchktg2llpryqmce

Predicting mean ribosome load for 5'UTR of any length using deep learning

Alexander Karollus, Žiga Avsec, Julien Gagneur, Predrag Radivojac
2021 PLoS Computational Biology  
Variant interpretation is demonstrated on a 5'UTR variant of the gene HBB associated with beta-thalassemia. Frame pooling could find applications in other bioinformatics predictive tasks.  ...  Here, we introduced frame pooling, a novel neural network operation that enabled the development of an MRL prediction model for 5'UTRs of any length.  ...  The heatmap shows the predicted impact (in terms of log 2 fold change) of mutating each position within the sequence. Strong effects are associated with uAUG-creating variants.  ... 
doi:10.1371/journal.pcbi.1008982 pmid:33970899 fatcat:gxua6oqpozhe7bxk6sihlmyvje
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