LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning

Long, Yueming; Mora, Ariane; Li, Francesca-Zhoufan; Gürsoy, Emre; Johnston, Kadina E.; Arnold, Frances H.

doi:10.1021/acssynbio.4c00625

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning

Journal Article · Tue Dec 24 00:00:00 EST 2024 · ACS Synthetic Biology

DOI:https://doi.org/10.1021/acssynbio.4c00625· OSTI ID:2567050

^[1]; Mora, Ariane ^[1]; ^[1]; Gürsoy, Emre ^[2]; ^[3]; ^[1]

California Institute of Technology (CalTech), Pasadena, CA (United States)
California Institute of Technology (CalTech), Pasadena, CA (United States); ETH Zurich, Basel (Switzerland)
California Institute of Technology (CalTech), Pasadena, CA (United States); Merck & Co., Inc., South San Francisco, CA (United States)

Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization. The pipeline facilitates data-driven protein engineering by consolidating sequence-function data to inform directed evolution and provide the requisite data for machine learning-guided protein engineering (MLPE). LevSeq enables quality control of mutagenesis libraries prior to screening, which reduces time and resource costs. Simulation studies demonstrate LevSeq’s ability to accurately detect variants under various experimental conditions. Lastly, we show LevSeq’s utility in engineering protoglobins for new-to-nature chemistry. Widespread adoption of LevSeq and sharing of the data will enhance our understanding of protein sequence-function landscapes and empower data-driven directed evolution.

View Accepted Manuscript (DOE)

Research Organization:: California Institute of Technology (CalTech), Pasadena, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0022218

OSTI ID:: 2567050

Journal Information:: ACS Synthetic Biology, Journal Name: ACS Synthetic Biology Journal Issue: 1 Vol. 14; ISSN 2161-5063

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

References (52)

Directed Evolution: Bringing New Chemistry to Life Arnold, Frances H. Angewandte Chemie International Edition, Vol. 57, Issue 16 https://doi.org/10.1002/anie.201708408	journal	November 2017
Protoglobin‐Catalyzed Formation of cis‐Trifluoromethyl‐Substituted Cyclopropanes by Carbene Transfer Schaus, Lucas; Das, Anuvab; Knight, Anders M. Angewandte Chemie International Edition, Vol. 62, Issue 4 https://doi.org/10.1002/anie.202208936	journal	December 2022
Data‐Driven Protein Engineering for Improving Catalytic Activity and Selectivity Ao, Yu‐Fei; Dörr, Mark; Menke, Marian J. ChemBioChem, Vol. 25, Issue 3 https://doi.org/10.1002/cbic.202300754	journal	December 2023
Overview of Next-Generation Sequencing Technologies: Overview of Next-Generation Sequencing Slatko, Barton E.; Gardner, Andrew F.; Ausubel, Frederick M. Current Protocols in Molecular Biology, Vol. 122, Issue 1 https://doi.org/10.1002/cpmb.59	journal	April 2018
Epistasis in protein evolution: Epistasis in Protein Evolution Starr, Tyler N.; Thornton, Joseph W. Protein Science, Vol. 25, Issue 7 https://doi.org/10.1002/pro.2897	journal	February 2016
ProtaBank: A repository for protein design and engineering data: ProtaBank: A Protein Engineering Database Wang, Connie Y.; Chang, Paul M.; Ary, Marie L. Protein Science, Vol. 27, Issue 6 https://doi.org/10.1002/pro.3406	journal	April 2018
Machine Learning-driven Protein Library Design: A Path Toward Smarter Libraries Mardikoraem, Mehrsa; Woldring, Daniel Yeast Surface Display https://doi.org/10.1007/978-1-0716-2285-8_5	book	April 2022
Mathematical expressions useful in the construction, description and evaluation of protein libraries Bosley, Allen D.; Ostermeier, Marc Biomolecular Engineering, Vol. 22, Issue 1-3 https://doi.org/10.1016/j.bioeng.2004.11.002	journal	June 2005
Informed training set design enables efficient machine learning-assisted directed protein evolution Wittmann, Bruce J.; Yue, Yisong; Arnold, Frances H. Cell Systems https://doi.org/10.1016/j.cels.2021.07.008	journal	August 2021
Benchmarking of Nanopore R10.4 and R9.4.1 flow cells in single-cell whole-genome amplification and whole-genome shotgun sequencing Ni, Ying; Liu, Xudong; Simeneh, Zemenu Mengistie Computational and Structural Biotechnology Journal, Vol. 21 https://doi.org/10.1016/j.csbj.2023.03.038	journal	January 2023
Adaptive machine learning for protein engineering Hie, Brian L.; Yang, Kevin K. Current Opinion in Structural Biology, Vol. 72 https://doi.org/10.1016/j.sbi.2021.11.002	journal	February 2022
Miniaturisation of high-throughput plasmid DNA library preparation for next-generation sequencing using multifactorial optimisation Suckling, Lorna; McFarlane, Ciaran; Sawyer, Chelsea Synthetic and Systems Biotechnology, Vol. 4, Issue 1 https://doi.org/10.1016/j.synbio.2019.01.002	journal	March 2019
Directed Evolution: Methodologies and Applications Wang, Yajie; Xue, Pu; Cao, Mingfeng Chemical Reviews, Vol. 121, Issue 20 https://doi.org/10.1021/acs.chemrev.1c00260	journal	July 2021
Machine Learning-Guided Protein Engineering Kouba, Petr; Kohout, Pavel; Haddadi, Faraneh ACS Catalysis, Vol. 13, Issue 21 https://doi.org/10.1021/acscatal.3c02743	journal	October 2023
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering Yang, Jason; Li, Francesca-Zhoufan; Arnold, Frances H. ACS Central Science, Vol. 10, Issue 2 https://doi.org/10.1021/acscentsci.3c01275	journal	February 2024
Diverse Engineered Heme Proteins Enable Stereodivergent Cyclopropanation of Unactivated Alkenes Knight, Anders M.; Kan, S. B. Jennifer; Lewis, Russell D. ACS Central Science, Vol. 4, Issue 3 https://doi.org/10.1021/acscentsci.7b00548	journal	February 2018
uPIC–M: Efficient and Scalable Preparation of Clonal Single Mutant Libraries for High-Throughput Protein Biochemistry Appel, Mason J.; Longwell, Scott A.; Morri, Maurizio ACS Omega, Vol. 6, Issue 45 https://doi.org/10.1021/acsomega.1c04180	journal	November 2021
Engineering Sensor Proteins Merkx, Maarten; Smith, Bradley; Jewett, Michael ACS Sensors, Vol. 4, Issue 12 https://doi.org/10.1021/acssensors.9b02459	journal	December 2019
evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library Wittmann, Bruce J.; Johnston, Kadina E.; Almhjell, Patrick J. ACS Synthetic Biology, Vol. 11, Issue 3 https://doi.org/10.1021/acssynbio.1c00592	journal	February 2022
DeCOIL: Optimization of Degenerate Codon Libraries for Machine Learning-Assisted Protein Engineering Yang, Jason; Ducharme, Julie; Johnston, Kadina E. ACS Synthetic Biology, Vol. 12, Issue 8 https://doi.org/10.1021/acssynbio.3c00301	journal	July 2023
DuBA.flow─A Low-Cost, Long-Read Amplicon Sequencing Workflow for the Validation of Synthetic DNA Constructs Ramírez Rojas, Adán A.; Brinkmann, Cedric K.; Köbel, Tania S. ACS Synthetic Biology, Vol. 13, Issue 2 https://doi.org/10.1021/acssynbio.3c00522	journal	January 2024
Biocatalytic Carbene Transfer Using Diazirines Porter, Nicholas J.; Danelius, Emma; Gonen, Tamir Journal of the American Chemical Society, Vol. 144, Issue 20 https://doi.org/10.1021/jacs.2c02723	journal	May 2022
Enzymatic Nitrogen Incorporation Using Hydroxylamine Gao, Shilong; Das, Anuvab; Alfonzo, Edwin Journal of the American Chemical Society, Vol. 145, Issue 37 https://doi.org/10.1021/jacs.3c08053	journal	September 2023
Low-Cost, High-Throughput Sequencing of DNA Assemblies Using a Highly Multiplexed Nextera Process Shapland, Elaine B.; Holmes, Victor; Reeves, Christopher D. ACS Synthetic Biology, Vol. 4, Issue 7 https://doi.org/10.1021/sb500362n	journal	April 2015
Natural Selection and the Concept of a Protein Space Maynard Smith, John Nature, Vol. 225, Issue 5232 https://doi.org/10.1038/225563a0	journal	February 1970
Molecular evolution by staggered extension process (StEP) in vitro recombination Zhao, Huimin; Giver, Lori; Shao, Zhixin Nature Biotechnology, Vol. 16, Issue 3 https://doi.org/10.1038/nbt0398-258	journal	March 1998
Deep mutational scanning: a new style of protein science Fowler, Douglas M.; Fields, Stanley Nature Methods, Vol. 11, Issue 8 https://doi.org/10.1038/nmeth.3027	journal	July 2014
Sequencing depth and coverage: key considerations in genomic analyses Sims, David; Sudbery, Ian; Ilott, Nicholas E. Nature Reviews Genetics, Vol. 15, Issue 2 https://doi.org/10.1038/nrg3642	journal	January 2014
Exploring protein fitness landscapes by directed evolution Romero, Philip A.; Arnold, Frances H. Nature Reviews Molecular Cell Biology, Vol. 10, Issue 12 https://doi.org/10.1038/nrm2805	journal	December 2009
Learning the pattern of epistasis linking genotype and phenotype in a protein Poelwijk, Frank J.; Socolich, Michael; Ranganathan, Rama Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12130-8	journal	September 2019
UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution Zurek, Paul Jannis; Knyphausen, Philipp; Neufeld, Katharina Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19687-9	journal	November 2020
Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis Sahlin, Kristoffer; Medvedev, Paul Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-020-20340-8	journal	January 2021
Nanopore sequencing technology, bioinformatics and applications Wang, Yunhao; Zhao, Yue; Bollas, Audrey Nature Biotechnology, Vol. 39, Issue 11 https://doi.org/10.1038/s41587-021-01108-x	journal	November 2021
Machine-learning-guided directed evolution for protein engineering Yang, Kevin K.; Wu, Zachary; Arnold, Frances H. Nature Methods, Vol. 16, Issue 8 https://doi.org/10.1038/s41592-019-0496-6	journal	July 2019
Cluster learning-assisted directed evolution Qiu, Yuchi; Hu, Jian; Wei, Guo-Wei Nature Computational Science, Vol. 1, Issue 12 https://doi.org/10.1038/s43588-021-00168-y	journal	December 2021
A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice Wierbowski, Shayne D.; Vo, Tommy V.; Falter-Braun, Pascal Proceedings of the National Academy of Sciences, Vol. 117, Issue 21 https://doi.org/10.1073/pnas.1918068117	journal	May 2020
A combinatorially complete epistatic fitness landscape in an enzyme active site Johnston, Kadina E.; Almhjell, Patrick J.; Watkins-Dulaney, Ella J. Proceedings of the National Academy of Sciences, Vol. 121, Issue 32 https://doi.org/10.1073/pnas.2400439121	journal	July 2024
Accurate gene consensus at low nanopore coverage Espada, Rocío; Zarevski, Nikola; Dramé-Maigné, Adèle GigaScience, Vol. 11 https://doi.org/10.1093/gigascience/giac102	journal	November 2022
Rhea, the reaction knowledgebase in 2022 Bansal, Parit; Morgat, Anne; Axelsen, Kristian B. Nucleic Acids Research, Vol. 50, Issue D1 https://doi.org/10.1093/nar/gkab1016	journal	November 2021
UniProt: the Universal Protein Knowledgebase in 2023 No authors listed Nucleic Acids Research, Vol. 51, Issue D1 https://doi.org/10.1093/nar/gkac1052	journal	November 2022
Arrayed in vivo barcoding for multiplexed sequence verification of plasmid DNA and demultiplexing of pooled libraries Li, Weiyi; Miller, Darach; Liu, Xianan Nucleic Acids Research, Vol. 52, Issue 10 https://doi.org/10.1093/nar/gkae332	journal	May 2024
Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples Smith, A. M.; Heisler, L. E.; St. Onge, R. P. Nucleic Acids Research, Vol. 38, Issue 13 https://doi.org/10.1093/nar/gkq368	journal	May 2010
BRENDA in 2019: a European ELIXIR core data resource Jeske, Lisa; Placzek, Sandra; Schomburg, Ida Nucleic Acids Research, Vol. 47, Issue D1 https://doi.org/10.1093/nar/gky1048	journal	November 2018
Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries Currin, Andrew; Swainston, Neil; Dunstan, Mark S. Synthetic Biology, Vol. 4, Issue 1 https://doi.org/10.1093/synbio/ysz025	journal	January 2019
Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing Campbell, Nathan R.; Harmon, Stephanie A.; Narum, Shawn R. Molecular Ecology Resources, Vol. 15, Issue 4 https://doi.org/10.1111/1755-0998.12357	journal	December 2014
Directed evolution of enzymatic silicon-carbon bond cleavage in siloxanes Sarai, Nicholas S.; Fulton, Tyler J.; O’Meara, Ryen L. Science, Vol. 383, Issue 6681 https://doi.org/10.1126/science.adi5554	journal	January 2024
ONTbarcoder and MinION barcodes aid biodiversity discovery and identification by everyone, for everyone Srivathsan, Amrita; Lee, Leshon; Katoh, Kazutaka BMC Biology, Vol. 19, Issue 1 https://doi.org/10.1186/s12915-021-01141-x	journal	September 2021
Sequencing DNA with nanopores: Troubles and biases Delahaye, Clara; Nicolas, Jacques PLOS ONE, Vol. 16, Issue 10 https://doi.org/10.1371/journal.pone.0257521	journal	October 2021
MAECI: A pipeline for generating consensus sequence with nanopore sequencing long-read assembly and error correction Lang, Jidong PLOS ONE, Vol. 17, Issue 5 https://doi.org/10.1371/journal.pone.0267066	journal	May 2022
Machine Learning for Protein Engineering Johnston, Kadina E.; Fannjiang, Clara; Wittmann, Bruce J. arXiv https://doi.org/10.48550/arXiv.2305.16634	preprint	January 2023
Adaptation in protein fitness landscapes is facilitated by indirect paths Wu, Nicholas C.; Dai, Lei; Olson, C. Anders eLife, Vol. 5 https://doi.org/10.7554/eLife.16965	journal	July 2016
Expanding the Scope of Metalloprotein Families and Substrate Classes in New-to-Nature Reactions Knight, Anders Matthew California Institute of Technology https://doi.org/10.7907/7qh5-5130	text	January 2021

Similar Records

evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library

Journal Article · Wed Feb 16 19:00:00 EST 2022 · ACS Synthetic Biology · OSTI ID:1853986

Enzyme Engineering Database (EnzEngDB): a platform for sharing and interpreting sequence–function relationships across protein engineering campaigns

Journal Article · Sun Dec 07 19:00:00 EST 2025 · Nucleic Acids Research · OSTI ID:3014245

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Directed Evolution
Genetics
Machine Learning
Mathematical Methods
Mutagenesis Libraries
Nanopore Sequencing
Nanopores
Protein Engineering
Sequence Function Data
Software

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning

Citation Formats

References (52)

Similar Records

Related Subjects