1
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Petersen BM, Kirby MB, Chrispens KM, Irvin OM, Strawn IK, Haas CM, Walker AM, Baumer ZT, Ulmer SA, Ayala E, Rhodes ER, Guthmiller JJ, Steiner PJ, Whitehead TA. An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries. Nat Commun 2024; 15:3974. [PMID: 38730230 PMCID: PMC11087541 DOI: 10.1038/s41467-024-48072-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.
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Affiliation(s)
- Brian M Petersen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Monica B Kirby
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Karson M Chrispens
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Olivia M Irvin
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Isabell K Strawn
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Cyrus M Haas
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Alexis M Walker
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Zachary T Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Sophia A Ulmer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Edgardo Ayala
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Emily R Rhodes
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Jenna J Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Paul J Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
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2
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Billerbeck S, Walker RSK, Pretorius IS. Killer yeasts: expanding frontiers in the age of synthetic biology. Trends Biotechnol 2024:S0167-7799(24)00067-2. [PMID: 38575438 DOI: 10.1016/j.tibtech.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/07/2024] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
Killer yeasts secrete protein toxins that are selectively lethal to other yeast and filamentous fungi. These exhibit exceptional genetic and functional diversity, and have several biotechnological applications. However, despite decades of research, several limitations hinder their widespread adoption. In this perspective we contend that technical advances in synthetic biology present an unprecedented opportunity to unlock the full potential of yeast killer systems across a spectrum of applications. By leveraging these new technologies, engineered killer toxins may emerge as a pivotal new tool to address antifungal resistance and food security. Finally, we speculate on the biotechnological potential of re-engineering host double-stranded (ds) RNA mycoviruses, from which many toxins derive, as a safe and noninfectious system to produce designer RNA.
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Affiliation(s)
- Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology institute, University of Groningen, Groningen 9747, AG, The Netherlands
| | - Roy S K Walker
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia; ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia.
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3
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Fannjiang C, Listgarten J. Is Novelty Predictable? Cold Spring Harb Perspect Biol 2024; 16:a041469. [PMID: 38052497 PMCID: PMC10835614 DOI: 10.1101/cshperspect.a041469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal applications ranging from drug development and plastic degradation to carbon sequestration. When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure. If one trusts learned models too much in extrapolation, one is likely to design rubbish. In contrast, if one does not extrapolate, one cannot find novelty. Herein, we ponder how one might strike a useful balance between these two extremes. We focus in particular on designing proteins with novel property values, although much of our discussion is relevant to machine learning-based design more broadly.
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Affiliation(s)
- Clara Fannjiang
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Jennifer Listgarten
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
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4
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Petersen BM, Kirby MB, Chrispens KM, Irvin OM, Strawn IK, Haas CM, Walker AM, Baumer ZT, Ulmer SA, Ayala E, Rhodes ER, Guthmiller JJ, Steiner PJ, Whitehead TA. An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.16.575852. [PMID: 38293170 PMCID: PMC10827193 DOI: 10.1101/2024.01.16.575852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.
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Affiliation(s)
- Brian M. Petersen
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Monica B. Kirby
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Karson M. Chrispens
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Olivia M. Irvin
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Isabell K. Strawn
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Cyrus M. Haas
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Alexis M. Walker
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Zachary T. Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Sophia A. Ulmer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Edgardo Ayala
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Emily R. Rhodes
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Jenna J. Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
| | - Paul J. Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
| | - Timothy A. Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80305, USA
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5
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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6
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Busia A, Listgarten J. MBE: model-based enrichment estimation and prediction for differential sequencing data. Genome Biol 2023; 24:218. [PMID: 37784130 PMCID: PMC10544408 DOI: 10.1186/s13059-023-03058-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023] Open
Abstract
Characterizing differences in sequences between two conditions, such as with and without drug exposure, using high-throughput sequencing data is a prevalent problem involving quantifying changes in sequence abundances, and predicting such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot use sequencing data effectively, nor be directly applied in many settings of interest. We introduce model-based enrichment (MBE) to overcome this shortcoming. We evaluate MBE using both simulated and real data. Overall, MBE improves accuracy compared to current differential analysis methods.
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Affiliation(s)
- Akosua Busia
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, 94720, CA, USA.
| | - Jennifer Listgarten
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, 94720, CA, USA.
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7
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Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. Bioinformatics 2023; 39:btad446. [PMID: 37478351 PMCID: PMC10477941 DOI: 10.1093/bioinformatics/btad446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/21/2023] [Accepted: 07/20/2023] [Indexed: 07/23/2023] Open
Abstract
MOTIVATION Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. RESULTS Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. AVAILABILITY AND IMPLEMENTATION All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.
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Affiliation(s)
- Matthew D Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Marshall A Case
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Emily K Makowski
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Peter M Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Protein Folding Disease Initiative, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Michigan Alzheimer’s Disease Center, University of Michigan, Ann Arbor, MI 48109-2200, United States
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8
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Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548448. [PMID: 37503142 PMCID: PMC10369870 DOI: 10.1101/2023.07.10.548448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Motivation Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. Results Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. Availability All deep sequencing datasets and code to do the analyses presented within are available via GitHub. Contact Peter Tessier, ptessier@umich.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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9
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Bonadio A, Wenig BL, Hockla A, Radisky ES, Shifman JM. Designed loop extension followed by combinatorial screening confers high specificity to a broad matrix metalloproteinaseinhibitor. J Mol Biol 2023; 435:168095. [PMID: 37068580 DOI: 10.1016/j.jmb.2023.168095] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/03/2023] [Accepted: 04/10/2023] [Indexed: 04/19/2023]
Abstract
Matrix metalloproteinases (MMPs) are key drivers of various diseases, including cancer. Development of selective probes and drugs capable of selectively inhibiting the individual members of the large MMP family remains a persistent challenge. The inhibitory N-terminal domain of tissue inhibitor of metalloproteinases-2 (N-TIMP2), a natural broad MMP inhibitor, can provide a scaffold for protein engineering to create more selective MMP inhibitors. Here, we pursued a unique approach harnessing both computational design and combinatorial screening to confer high binding specificity toward a target MMP in preference to an anti-target MMP. We designed a loop extension of N-TIMP2 to allow new interactions with the non-conserved MMP surface and generated an efficient focused library for yeast surface display, which was then screened for high binding to the target MMP-14 and low binding to anti-target MMP-3. Deep sequencing analysis identified the most promising variants, which were expressed, purified, and tested for selectivity of inhibition. Our best N-TIMP2 variant exhibited 29 pM binding affinity to MMP-14 and 2.4 µM affinity to MMP-3, revealing 7500-fold greater specificity than WT N-TIMP2. High-confidence structural models were obtained by including NGS data in the AlphaFold multiple sequence alignment. The modeling together with experimental mutagenesis validated our design predictions, demonstrating that the loop extension packs tightly against non-conserved residues on MMP-14 and clashes with MMP-3. This study demonstrates how introduction of loop extensions in a manner guided by target protein conservation data and loop design can offer an attractive strategy to achieve specificity in design of protein ligands.
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Affiliation(s)
- Alessandro Bonadio
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Israel
| | - Bernhard L Wenig
- Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida, USA; Paracelsus Medical University, Salzburg, Austria
| | - Alexandra Hockla
- Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida, USA
| | - Evette S Radisky
- Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida, USA.
| | - Julia M Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Israel.
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10
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Liu Y, Khusnutdinova A, Chen J, Crisante D, Batyrova K, Raj K, Feigis M, Shirzadi E, Wang X, Dorakhan R, Wang X, Stogios PJ, Yakunin AF, Sargent EH, Mahadevan R. Systems engineering of Escherichia coli for n-butane production. Metab Eng 2022; 74:98-107. [DOI: 10.1016/j.ymben.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/05/2022] [Accepted: 10/08/2022] [Indexed: 10/31/2022]
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11
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Shi Z, Liu P, Liao X, Mao Z, Zhang J, Wang Q, Sun J, Ma H, Ma Y. Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing. BIODESIGN RESEARCH 2022; 2022:9898461. [PMID: 37850146 PMCID: PMC10521697 DOI: 10.34133/2022/9898461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/26/2022] [Indexed: 10/19/2023] Open
Abstract
Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.
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Affiliation(s)
- Zhenkun Shi
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Pi Liu
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Xiaoping Liao
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Zhitao Mao
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Jianqi Zhang
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Qinhong Wang
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Jibin Sun
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Hongwu Ma
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Yanhe Ma
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
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12
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Horne J, Shukla D. Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering. Ind Eng Chem Res 2022; 61:6235-6245. [DOI: 10.1021/acs.iecr.1c04943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Jesse Horne
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
- Department of Bioengineering, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
- Department of Plant Biology, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
- Cancer Center at Illinois, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States
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13
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Ahmed S, Manjunath K, Chattopadhyay G, Varadarajan R. Identification of stabilizing point mutations through mutagenesis of destabilized protein libraries. J Biol Chem 2022; 298:101785. [PMID: 35247389 PMCID: PMC8971944 DOI: 10.1016/j.jbc.2022.101785] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 02/18/2022] [Accepted: 02/26/2022] [Indexed: 01/22/2023] Open
Abstract
Although there have been recent transformative advances in the area of protein structure prediction, prediction of point mutations that improve protein stability remains challenging. It is possible to construct and screen large mutant libraries for improved activity or ligand binding. However, reliable screens for mutants that improve protein stability do not yet exist, especially for proteins that are well folded and relatively stable. Here, we demonstrate that incorporation of a single, specific, destabilizing mutation termed parent inactivating mutation into each member of a single-site saturation mutagenesis library, followed by screening for suppressors, allows for robust and accurate identification of stabilizing mutations. We carried out fluorescence-activated cell sorting of such a yeast surface display, saturation suppressor library of the bacterial toxin CcdB, followed by deep sequencing of sorted populations. We found that multiple stabilizing mutations could be identified after a single round of sorting. In addition, multiple libraries with different parent inactivating mutations could be pooled and simultaneously screened to further enhance the accuracy of identification of stabilizing mutations. Finally, we show that individual stabilizing mutations could be combined to result in a multi-mutant that demonstrated an increase in thermal melting temperature of about 20 °C, and that displayed enhanced tolerance to high temperature exposure. We conclude that as this method is robust and employs small library sizes, it can be readily extended to other display and screening formats to rapidly isolate stabilized protein mutants.
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Affiliation(s)
- Shahbaz Ahmed
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Kavyashree Manjunath
- Centre for Chemical Biology and Therapeutics, Institute of Stem Cell Science and Regenerative Medicine, Bangalore, India
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14
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Raven SA, Payne B, Bruce M, Filipovska A, Rackham O. In silico evolution of nucleic acid-binding proteins from a nonfunctional scaffold. Nat Chem Biol 2022; 18:403-411. [PMID: 35210620 DOI: 10.1038/s41589-022-00967-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 01/04/2022] [Indexed: 11/09/2022]
Abstract
Directed evolution emulates the process of natural selection to produce proteins with improved or altered functions. These approaches have proven to be very powerful but are technically challenging and particularly time and resource intensive. To bypass these limitations, we constructed a system to perform the entire process of directed evolution in silico. We employed iterative computational cycles of mutation and evaluation to predict mutations that confer high-affinity binding activities for DNA and RNA to an initial de novo designed protein with no inherent function. Beneficial mutations revealed modes of nucleic acid recognition not previously observed in natural proteins, highlighting the ability of computational directed evolution to access new molecular functions. Furthermore, the process by which new functions were obtained closely resembles natural evolution and can provide insights into the contributions of mutation rate, population size and selective pressure on functionalization of macromolecules in nature.
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Affiliation(s)
- Samuel A Raven
- Harry Perkins Institute of Medical Research, Nedlands, Western Australia, Australia.,University of Western Australia Centre for Medical Research, Nedlands, Western Australia, Australia
| | - Blake Payne
- Harry Perkins Institute of Medical Research, Nedlands, Western Australia, Australia.,University of Western Australia Centre for Medical Research, Nedlands, Western Australia, Australia
| | - Mitchell Bruce
- Curtin Medical School, Curtin University, Bentley, Western Australia, Australia
| | - Aleksandra Filipovska
- Harry Perkins Institute of Medical Research, Nedlands, Western Australia, Australia.,University of Western Australia Centre for Medical Research, Nedlands, Western Australia, Australia.,School of Molecular Sciences, The University of Western Australia, Crawley, Western Australia, Australia.,Telethon Kids Institute, Northern Entrance, Perth Children's Hospital, Nedlands, Western Australia, Australia
| | - Oliver Rackham
- Harry Perkins Institute of Medical Research, Nedlands, Western Australia, Australia. .,Curtin Medical School, Curtin University, Bentley, Western Australia, Australia. .,Telethon Kids Institute, Northern Entrance, Perth Children's Hospital, Nedlands, Western Australia, Australia. .,Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, Australia.
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15
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Trivedi VD, Chappell TC, Krishna NB, Shetty A, Sigamani GG, Mohan K, Ramesh A, R PK, Nair NU. In-Depth Sequence–Function Characterization Reveals Multiple Pathways to Enhance Enzymatic Activity. ACS Catal 2022. [DOI: 10.1021/acscatal.1c05508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Vikas D. Trivedi
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Todd C. Chappell
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | | | - Anuj Shetty
- Kcat Enzymatic Private Limited, Bengaluru, Karnataka, India 560005
| | | | - Karishma Mohan
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Athreya Ramesh
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Pravin Kumar R
- Kcat Enzymatic Private Limited, Bengaluru, Karnataka, India 560005
| | - Nikhil U. Nair
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
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16
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Ogawa Y, Katsuyama Y, Ohnishi Y. Engineering of the Ligand Specificity of Transcriptional Regulator XylS by Deep Mutational Scanning. ACS Synth Biol 2022; 11:473-485. [PMID: 34964613 DOI: 10.1021/acssynbio.1c00564] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Deep mutational scanning is a method for protein engineering. Here, we applied it to alter the ligand specificity of the transcriptional regulator XylS from Pseudomonas putida to recognize p-toluic acid instead of the native ligand m-toluic acid. For this purpose, we used an antibiotic resistance gene-based dual screening system, which was constructed for the directed evolution of XylS toward the above-mentioned ligand specificity. We constructed a xylS mutant library in which each codon for the amino acid residue of the putative ligand-binding domain (residues 1-213, except 7th residue) was randomized to generate all possible single amino acid-substituted XylS variants and introduced it into Escherichia coli harboring the selection plasmid for the screening system. The cells were cultured in the presence of appropriate antibiotics and m-toluic acid or p-toluic acid, and the frequency of each mutation present in the library was examined using a next-generation sequencer before and after cultivation. Heatmaps showing the enrichment score of each XylS variant were obtained. By searching for a p-toluic-acid-specific heatmap pattern, we focused on G71 and H77. Analysis of the ligand specificities of G71- or H77-substituted XylS variants revealed that several G71-substituted XylS variants responded specifically to p-toluic acid. Thus, the 71st residue was found to be an unprecedented residue that is important for switching ligand specificity. Our study demonstrated the usefulness of deep mutational scanning in engineering the ligand specificity of a transcriptional regulator without structural information. We also discussed the advantages and disadvantages of deep mutational scanning compared with directed evolution.
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Affiliation(s)
- Yuki Ogawa
- Department of Biotechnology, The Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Yohei Katsuyama
- Department of Biotechnology, The Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Yasuo Ohnishi
- Department of Biotechnology, The Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
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17
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Mardikoraem M, Woldring D. Machine Learning-driven Protein Library Design: A Path Toward Smarter Libraries. Methods Mol Biol 2022; 2491:87-104. [PMID: 35482186 DOI: 10.1007/978-1-0716-2285-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Proteins are small yet valuable biomolecules that play a versatile role in therapeutics and diagnostics. The intricate sequence-structure-function paradigm in the realm of proteins opens the possibility for directly mapping amino acid sequence to function. However, the rugged nature of the protein fitness landscape and an astronomical number of possible mutations even for small proteins make navigating this system a daunting task. Moreover, the scarcity of functional proteins and the ease with which deleterious mutations are introduced, due to complex epistatic relationships, compound the existing challenges. This highlights the need for auxiliary tools in current techniques such as rational design and directed evolution. To that end, the state-of-the-art machine learning can offer time and cost efficiency in finding high fitness proteins, circumventing unnecessary wet-lab experiments. In the context of improving library design, machine learning provides valuable insights via its unique features such as high adaptation to complex systems, multi-tasking, and parallelism, and the ability to capture hidden trends in input data. Finally, both the advancements in computational resources and the rapidly increasing number of sequences in protein databases will allow more promising and detailed insights delivered from machine learning to protein library design. In this chapter, fundamental concepts and a method for machine learning-driven library design leveraging deep sequencing datasets will be discussed. We elaborate on (1) basic knowledge about machine learning algorithms, (2) the benefit of machine learning in library design, and (3) methodology for implementing machine learning in library design.
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Affiliation(s)
- Mehrsa Mardikoraem
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Daniel Woldring
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.
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18
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Raeeszadeh-Sarmazdeh M, Boder ET. Yeast Surface Display: New Opportunities for a Time-Tested Protein Engineering System. Methods Mol Biol 2022; 2491:3-25. [PMID: 35482182 DOI: 10.1007/978-1-0716-2285-8_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Yeast surface display has proven to be a powerful tool for the discovery of antibodies and other novel binding proteins and for engineering the affinity and selectivity of existing proteins for their targets. In the decades since the first demonstrations of the approach, the range of yeast display applications has greatly expanded to include many different protein targets and has grown to encompass methods for rapid protein characterization. Here, we briefly summarize the development of yeast display methodologies and highlight several selected examples of recent applications to timely and challenging protein engineering and characterization problems.
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Affiliation(s)
| | - Eric T Boder
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN, USA.
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19
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Gonzalez Somermeyer L, Fleiss A, Mishin AS, Bozhanova NG, Igolkina AA, Meiler J, Alaball Pujol ME, Putintseva EV, Sarkisyan KS, Kondrashov FA. Heterogeneity of the GFP fitness landscape and data-driven protein design. eLife 2022; 11:75842. [PMID: 35510622 PMCID: PMC9119679 DOI: 10.7554/elife.75842] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/25/2022] [Indexed: 11/24/2022] Open
Abstract
Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design - instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.
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Affiliation(s)
| | - Aubin Fleiss
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom
| | - Alexander S Mishin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of SciencesMoscowRussian Federation
| | - Nina G Bozhanova
- Department of Chemistry, Center for Structural Biology, Vanderbilt UniversityNashvilleUnited States
| | - Anna A Igolkina
- Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenterViennaAustria
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology, Vanderbilt UniversityNashvilleUnited States,Institute for Drug Discovery, Medical School, Leipzig UniversityLeipzigGermany
| | - Maria-Elisenda Alaball Pujol
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom
| | | | - Karen S Sarkisyan
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of SciencesMoscowRussian Federation
| | - Fyodor A Kondrashov
- Institute of Science and Technology AustriaKlosterneuburgAustria,Evolutionary and Synthetic Biology Unit, Okinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
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20
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Kuiper BP, Prins RC, Billerbeck S. Oligo Pools as an Affordable Source of Synthetic DNA for Cost-Effective Library Construction in Protein- and Metabolic Pathway Engineering. Chembiochem 2021; 23:e202100507. [PMID: 34817110 PMCID: PMC9300125 DOI: 10.1002/cbic.202100507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/23/2021] [Indexed: 11/11/2022]
Abstract
The construction of custom libraries is critical for rational protein engineering and directed evolution. Array‐synthesized oligo pools of thousands of user‐defined sequences (up to ∼350 bases in length) have emerged as a low‐cost commercially available source of DNA. These pools cost ≤10 % (depending on error rate and length) of other commercial sources of custom DNA, and this significant cost difference can determine whether an enzyme engineering project can be realized on a given research budget. However, while being cheap, oligo pools do suffer from a low concentration of individual oligos and relatively high error rates. Several powerful techniques that specifically make use of oligo pools have been developed and proven valuable or even essential for next‐generation protein and pathway engineering strategies, such as sequence‐function mapping, enzyme minimization, or de‐novo design. Here we consolidate the knowledge on these techniques and their applications to facilitate the use of oligo pools within the protein engineering community.
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Affiliation(s)
- Bastiaan P Kuiper
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
| | - Rianne C Prins
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
| | - Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
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21
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Heyne M, Shirian J, Cohen I, Peleg Y, Radisky ES, Papo N, Shifman JM. Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes. J Am Chem Soc 2021; 143:17261-17275. [PMID: 34609866 PMCID: PMC8532158 DOI: 10.1021/jacs.1c08707] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.
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Affiliation(s)
- Michael Heyne
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.,Avram and Stella Goldstein-Goren Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Jason Shirian
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Itay Cohen
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Yoav Peleg
- Life Sciences Core Facilities (LSCF) Structural Proteomics Unit (SPU), Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Evette S Radisky
- Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida 32224, United States
| | - Niv Papo
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Julia M Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
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22
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PacBio sequencing output increased through uniform and directional fivefold concatenation. Sci Rep 2021; 11:18065. [PMID: 34508117 PMCID: PMC8433307 DOI: 10.1038/s41598-021-96829-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 08/17/2021] [Indexed: 12/20/2022] Open
Abstract
Advances in sequencing technology have allowed researchers to sequence DNA with greater ease and at decreasing costs. Main developments have focused on either sequencing many short sequences or fewer large sequences. Methods for sequencing mid-sized sequences of 600-5,000 bp are currently less efficient. For example, the PacBio Sequel I system yields ~ 100,000-300,000 reads with an accuracy per base pair of 90-99%. We sought to sequence several DNA populations of ~ 870 bp in length with a sequencing accuracy of 99% and to the greatest depth possible. We optimised a simple, robust method to concatenate genes of ~ 870 bp five times and then sequenced the resulting DNA of ~ 5,000 bp by PacBioSMRT long-read sequencing. Our method improved upon previously published concatenation attempts, leading to a greater sequencing depth, high-quality reads and limited sample preparation at little expense. We applied this efficient concatenation protocol to sequence nine DNA populations from a protein engineering study. The improved method is accompanied by a simple and user-friendly analysis pipeline, DeCatCounter, to sequence medium-length sequences efficiently at one-fifth of the cost.
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23
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Burton TD, Eyre NS. Applications of Deep Mutational Scanning in Virology. Viruses 2021; 13:1020. [PMID: 34071591 PMCID: PMC8227372 DOI: 10.3390/v13061020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/26/2021] [Accepted: 05/26/2021] [Indexed: 12/20/2022] Open
Abstract
Several recently developed high-throughput techniques have changed the field of molecular virology. For example, proteomics studies reveal complete interactomes of a viral protein, genome-wide CRISPR knockout and activation screens probe the importance of every single human gene in aiding or fighting a virus, and ChIP-seq experiments reveal genome-wide epigenetic changes in response to infection. Deep mutational scanning is a relatively novel form of protein science which allows the in-depth functional analysis of every nucleotide within a viral gene or genome, revealing regions of importance, flexibility, and mutational potential. In this review, we discuss the application of this technique to RNA viruses including members of the Flaviviridae family, Influenza A Virus and Severe Acute Respiratory Syndrome Coronavirus 2. We also briefly discuss the reverse genetics systems which allow for analysis of viral replication cycles, next-generation sequencing technologies and the bioinformatics tools that facilitate this research.
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Affiliation(s)
| | - Nicholas S. Eyre
- College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia;
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24
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D'Amico RN, Bosken YK, O'Rourke KF, Murray AM, Admasu W, Chang CEA, Boehr DD. Substitution of a Surface-Exposed Residue Involved in an Allosteric Network Enhances Tryptophan Synthase Function in Cells. Front Mol Biosci 2021; 8:679915. [PMID: 34124159 PMCID: PMC8187860 DOI: 10.3389/fmolb.2021.679915] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 05/11/2021] [Indexed: 11/13/2022] Open
Abstract
Networks of noncovalent amino acid interactions propagate allosteric signals throughout proteins. Tryptophan synthase (TS) is an allosterically controlled bienzyme in which the indole product of the alpha subunit (αTS) is transferred through a 25 Å hydrophobic tunnel to the active site of the beta subunit (βTS). Previous nuclear magnetic resonance and molecular dynamics simulations identified allosteric networks in αTS important for its function. We show here that substitution of a distant, surface-exposed network residue in αTS enhances tryptophan production, not by activating αTS function, but through dynamically controlling the opening of the indole channel and stimulating βTS activity. While stimulation is modest, the substitution also enhances cell growth in a tryptophan-auxotrophic strain of Escherichia coli compared to complementation with wild-type αTS, emphasizing the biological importance of the network. Surface-exposed networks provide new opportunities in allosteric drug design and protein engineering, and hint at potential information conduits through which the functions of a metabolon or even larger proteome might be coordinated and regulated.
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Affiliation(s)
- Rebecca N D'Amico
- Department of Chemistry, The Pennsylvania State University, University Park, PA, United States
| | - Yuliana K Bosken
- Department of Chemistry, The University of California Riverside, Riverside, CA, United States
| | - Kathleen F O'Rourke
- Department of Chemistry, The Pennsylvania State University, University Park, PA, United States
| | - Alec M Murray
- Department of Chemistry, The Pennsylvania State University, University Park, PA, United States
| | - Woudasie Admasu
- Department of Chemistry, The Pennsylvania State University, University Park, PA, United States
| | - Chia-En A Chang
- Department of Chemistry, The University of California Riverside, Riverside, CA, United States
| | - David D Boehr
- Department of Chemistry, The Pennsylvania State University, University Park, PA, United States
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25
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Narayanan KK, Procko E. Deep Mutational Scanning of Viral Glycoproteins and Their Host Receptors. Front Mol Biosci 2021; 8:636660. [PMID: 33898517 PMCID: PMC8062978 DOI: 10.3389/fmolb.2021.636660] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/18/2021] [Indexed: 11/17/2022] Open
Abstract
Deep mutational scanning or deep mutagenesis is a powerful tool for understanding the sequence diversity available to viruses for adaptation in a laboratory setting. 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. Coupled with any of a number of selection strategies, deep mutagenesis can explore the mutational diversity available to viral glycoproteins, which mediate critical roles in cell entry and are exposed to the humoral arm of the host immune response. Mutational landscapes of viral glycoproteins for host cell attachment and membrane fusion reveal extensive epistasis and potential escape mutations to neutralizing antibodies or other therapeutics, as well as aiding in the design of optimized immunogens for eliciting broadly protective immunity. While less explored, deep mutational scans of host receptors further assist in understanding virus-host protein interactions. Critical residues on the host receptors for engaging with viral spikes are readily identified and may help with structural modeling. Furthermore, mutations may be found for engineering soluble decoy receptors as neutralizing agents that specifically bind viral targets with tight affinity and limited potential for viral escape. By untangling the complexities of how sequence contributes to viral glycoprotein and host receptor interactions, deep mutational scanning is impacting ideas and strategies at multiple levels for combatting circulating and emergent virus strains.
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Affiliation(s)
| | - Erik Procko
- Department of Biochemistry and Cancer Center at Illinois, University of Illinois, Urbana, IL, United States
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26
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Bonadio A, Shifman JM. Computational design and experimental optimization of protein binders with prospects for biomedical applications. Protein Eng Des Sel 2021; 34:gzab020. [PMID: 34436606 PMCID: PMC8388154 DOI: 10.1093/protein/gzab020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/11/2021] [Accepted: 07/11/2021] [Indexed: 11/12/2022] Open
Abstract
Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.
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Affiliation(s)
- Alessandro Bonadio
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Julia M Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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27
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Vinogradov AA, Nagai E, Chang JS, Narumi K, Onaka H, Goto Y, Suga H. Accurate Broadcasting of Substrate Fitness for Lactazole Biosynthetic Pathway from Reactivity-Profiling mRNA Display. J Am Chem Soc 2020; 142:20329-20334. [PMID: 33211968 DOI: 10.1021/jacs.0c10374] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We report a method for the high-throughput reactivity profiling of genetically encoded libraries as a tool to study substrate fitness landscapes for RiPP (ribosomally synthesized and post-translationally modified peptide) biosynthetic enzymes. This method allowed us to rapidly analyze the substrate preferences of the lactazole biosynthetic pathway using a saturation mutagenesis mRNA display library of lactazole precursor peptides. We demonstrate that the assay produces accurate and reproducible in vitro data, enabling the quantification of reaction yields with temporal resolution. Our results recapitulate the previously established knowledge on lactazole biosynthesis and expand it by identifying the extent of substrate promiscuity exhibited by the enzymes. This work lays a foundation for the construction and screening of mRNA display-based combinatorial thiopeptide libraries for the discovery of lactazole-inspired thiopeptides with de novo designed biological activities.
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Affiliation(s)
- Alexander A Vinogradov
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Emiko Nagai
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Jun Shi Chang
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Kakeru Narumi
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Hiroyasu Onaka
- Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Yuki Goto
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Hiroaki Suga
- Department of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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28
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Song H, Bremer BJ, Hinds EC, Raskutti G, Romero PA. Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning. Cell Syst 2020; 12:92-101.e8. [PMID: 33212013 DOI: 10.1016/j.cels.2020.10.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 08/13/2020] [Accepted: 10/22/2020] [Indexed: 10/22/2022]
Abstract
Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Notably, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function datasets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.
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Affiliation(s)
- Hyebin Song
- Department of Statistics, The Pennsylvania State University, State College, PA 16802, USA; Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Bennett J Bremer
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Emily C Hinds
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Garvesh Raskutti
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
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29
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Kureshi R, Zhu A, Shen J, Tzeng SY, Astrab LR, Sargunas PR, Green JJ, Campochiaro PA, Spangler JB. Structure-Guided Molecular Engineering of a Vascular Endothelial Growth Factor Antagonist to Treat Retinal Diseases. Cell Mol Bioeng 2020; 13:405-418. [PMID: 33184574 DOI: 10.1007/s12195-020-00641-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 07/22/2020] [Indexed: 12/11/2022] Open
Abstract
Background Ocular neovascularization is a hallmark of retinal diseases including neovascular age-related macular degeneration and diabetic retinopathy, two leading causes of blindness in adults. Neovascularization is driven by the interaction of soluble vascular endothelial growth factor (VEGF) ligands with transmembrane VEGF receptors (VEGFR), and inhibition of the VEGF pathway has shown tremendous clinical promise. However, anti-VEGF therapies require invasive intravitreal injections at frequent intervals and high doses, and many patients show incomplete responses to current drugs due to the lack of sustained VEGF signaling suppression. Methods We synthesized insights from structural biology with molecular engineering technologies to engineer an anti-VEGF antagonist protein. Starting from the clinically approved decoy receptor protein aflibercept, we strategically designed a yeast-displayed mutagenic library of variants and isolated clones with superior VEGF affinity compared to the clinical drug. Our lead engineered protein was expressed in the choroidal space of rat eyes via nonviral gene delivery. Results Using a structure-informed directed evolution approach, we identified multiple promising anti-VEGF antagonist proteins with improved target affinity. Improvements were primarily mediated through reduction in dissociation rate, and structurally significant convergent sequence mutations were identified. Nonviral gene transfer of our engineered antagonist protein demonstrated robust and durable expression in the choroid of treated rats one month post-injection. Conclusions We engineered a novel anti-VEGF protein as a new weapon against retinal diseases and demonstrated safe and noninvasive ocular delivery in rats. Furthermore, our structure-guided design approach presents a general strategy for discovery of targeted protein drugs for a vast array of applications.
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Affiliation(s)
- Rakeeb Kureshi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Angela Zhu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Jikui Shen
- Department of Ophthalmology, The Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Stephany Y Tzeng
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Insititute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD USA
| | - Leilani R Astrab
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Paul R Sargunas
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Jordan J Green
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD USA.,Department of Ophthalmology, The Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Insititute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD USA.,Department of Materials Science & Engineering, Johns Hopkins University, Baltimore, MD USA.,Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Peter A Campochiaro
- Department of Ophthalmology, The Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Jamie B Spangler
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD USA.,Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD USA
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30
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Xin S, Zhang W. Construction and analysis of the protein-protein interaction network for the olfactory system of the silkworm Bombyx mori. ARCHIVES OF INSECT BIOCHEMISTRY AND PHYSIOLOGY 2020; 105:e21737. [PMID: 32926465 DOI: 10.1002/arch.21737] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/21/2020] [Accepted: 08/24/2020] [Indexed: 06/11/2023]
Abstract
Olfaction plays an essential role in feeding and information exchange in insects. Previous studies on the olfaction of silkworms have provided a wealth of information about genes and proteins, yet, most studies have only focused on a single gene or protein related to the insect's olfaction. The aim of the current study is to determine key proteins in the olfactory system of the silkworm, and further understand protein-protein interactions (PPIs) in the olfactory system of Lepidoptera. To achieve this goal, we integrated Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses. Furthermore, we selected 585 olfactory-related proteins and constructed a (PPI) network for the olfactory system of the silkworm. Network analysis led to the identification of several key proteins, including GSTz1, LOC733095, BGIBMGA002169-TA, BGIBMGA010939-TA, GSTs2, GSTd2, Or-2, and BGIBMGA013255-TA. A comprehensive evaluation of the proteins showed that glutathione S-transferases (GSTs) had the highest ranking. GSTs also had the highest enrichment levels in GO and KEGG. In conclusion, our analysis showed that key nodes in the biological network had a significant impact on the network, and the key proteins identified via network analysis could serve as new research targets to determine their functions in olfaction.
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Affiliation(s)
- Shanghong Xin
- School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Wenjun Zhang
- School of Life Sciences, Sun Yat-sen University, Guangzhou, China
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31
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Tack DS, Romantseva EF, Tonner PD, Pressman A, Rammohan J, Strychalski EA. Measurements drive progress in directed evolution for precise engineering of biological systems. CURRENT OPINION IN SYSTEMS BIOLOGY 2020; 23:32-37. [PMID: 34611570 PMCID: PMC8489032 DOI: 10.1016/j.coisb.2020.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function. At the same time, the increasingly routine availability of large, high-quality data sets supports the integration of machine learning with directed evolution. Together, these advances herald striking capabilities for engineering biology.
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Affiliation(s)
- Drew S Tack
- National Institute of Standards and Technology, Gaithersburg, MD, 20898, USA
| | | | - Peter D Tonner
- National Institute of Standards and Technology, Gaithersburg, MD, 20898, USA
| | - Abe Pressman
- National Institute of Standards and Technology, Gaithersburg, MD, 20898, USA
| | - Jayan Rammohan
- National Institute of Standards and Technology, Gaithersburg, MD, 20898, USA
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32
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Mak WS, Wang X, Arenas R, Cui Y, Bertolani S, Deng WQ, Tagkopoulos I, Wilson DK, Siegel JB. Discovery, Design, and Structural Characterization of Alkane-Producing Enzymes across the Ferritin-like Superfamily. Biochemistry 2020; 59:3834-3843. [PMID: 32935984 DOI: 10.1021/acs.biochem.0c00665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
To complement established rational and evolutionary protein design approaches, significant efforts are being made to utilize computational modeling and the diversity of naturally occurring protein sequences. Here, we combine structural biology, genomic mining, and computational modeling to identify structural features critical to aldehyde deformylating oxygenases (ADOs), an enzyme family that has significant implications in synthetic biology and chemoenzymatic synthesis. Through these efforts, we discovered latent ADO-like function across the ferritin-like superfamily in various species of Bacteria and Archaea. We created a machine learning model that uses protein structural features to discriminate ADO-like activity. Computational enzyme design tools were then utilized to introduce ADO-like activity into the small subunit of Escherichia coli class I ribonucleotide reductase. The integrated approach of genomic mining, structural biology, molecular modeling, and machine learning has the potential to be utilized for rapid discovery and modulation of functions across enzyme families.
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Affiliation(s)
- Wai Shun Mak
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
| | - XiaoKang Wang
- Department of Biomedical Engineering, University of California, Davis, Davis, California 95616, United States
| | - Rigoberto Arenas
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States.,Chemistry Graduate Group, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
| | - Youtian Cui
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
| | - Steve Bertolani
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
| | - Wen Qiao Deng
- California College of Arts, 1111 Eighth Street, San Francisco, California 94107, United States
| | - Ilias Tagkopoulos
- Department of Biomedical Engineering, University of California, Davis, Davis, California 95616, United States.,Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, California 95616, United States.,Department of Computer Science, University of California, Davis, Davis, California 95616, United States
| | - David K Wilson
- Department of Molecular and Cellular Biology, University of California, Davis, Davis, California 95616, United States.,Chemistry Graduate Group, University of California, Davis, One Shields Avenue, Davis, California 95616, United States
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, One Shields Avenue, Davis, California 95616, United States.,Department of Biochemistry and Molecular Medicine, University of California, Davis, 2700 Stockton Boulevard, Suite 2102, Sacramento, California 95817, United States.,Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, California 95616, United States
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33
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Hilton SK, Huddleston J, Black A, North K, Dingens AS, Bedford T, Bloom JD. dms-view: Interactive visualization tool for deep mutational scanning data. JOURNAL OF OPEN SOURCE SOFTWARE 2020; 5:2353. [PMID: 34189395 PMCID: PMC8237788 DOI: 10.21105/joss.02353] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- Sarah K Hilton
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Molecular and Cell Biology, University of Washington, Seattle, WA, USA
| | - Allison Black
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Khrystyna North
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Adam S Dingens
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jesse D Bloom
- Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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34
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Weinstein J, Khersonsky O, Fleishman SJ. Practically useful protein-design methods combining phylogenetic and atomistic calculations. Curr Opin Struct Biol 2020; 63:58-64. [PMID: 32505941 DOI: 10.1016/j.sbi.2020.04.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 04/06/2020] [Indexed: 12/11/2022]
Abstract
Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscure these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.
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Affiliation(s)
- Jonathan Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Olga Khersonsky
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
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35
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Aharon L, Aharoni SL, Radisky ES, Papo N. Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method. Biochem J 2020; 477:1701-1719. [PMID: 32296833 PMCID: PMC7376575 DOI: 10.1042/bcj20200188] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/11/2020] [Accepted: 04/14/2020] [Indexed: 01/08/2023]
Abstract
To facilitate investigations of protein-protein interactions (PPIs), we developed a novel platform for quantitative mapping of protein binding specificity landscapes, which combines the multi-target screening of a mutagenesis library into high- and low-affinity populations with sophisticated next-generation sequencing analysis. Importantly, this method generates accurate models to predict affinity and specificity values for any mutation within a protein complex, and requires only a few experimental binding affinity measurements using purified proteins for calibration. We demonstrated the utility of the approach by mapping quantitative landscapes for interactions between the N-terminal domain of the tissue inhibitor of metalloproteinase 2 (N-TIMP2) and three matrix metalloproteinases (MMPs) having homologous structures but different affinities (MMP-1, MMP-3, and MMP-14). The binding landscapes for N-TIMP2/MMP-1 and N-TIMP2/MMP-3 showed the PPIs to be almost fully optimized, with most single mutations giving a loss of affinity. In contrast, the non-optimized PPI for N-TIMP2/MMP-14 was reflected in a wide range of binding affinities, where single mutations exhibited a far more attenuated effect on the PPI. Our new platform reliably and comprehensively identified not only hot- and cold-spot residues, but also specificity-switch mutations that shape target affinity and specificity. Thus, our approach provides a methodology giving an unprecedentedly rich quantitative analysis of the binding specificity landscape, which will broaden the understanding of the mechanisms and evolutionary origins of specific PPIs and facilitate the rational design of specific inhibitors for structurally similar target proteins.
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Affiliation(s)
- Lidan Aharon
- Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Shay-Lee Aharoni
- Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Evette S. Radisky
- Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville 32224, Florida, USA
| | - Niv Papo
- Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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36
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Reeb J, Wirth T, Rost B. Variant effect predictions capture some aspects of deep mutational scanning experiments. BMC Bioinformatics 2020; 21:107. [PMID: 32183714 PMCID: PMC7077003 DOI: 10.1186/s12859-020-3439-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/03/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND 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 to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs. RESULTS On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions. CONCLUSIONS DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.
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Affiliation(s)
- Jonas Reeb
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany.
| | - Theresa Wirth
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
- Department of Biochemistry and Molecular Biophysics, Columbia University, 701 West, 168th Street, New York, NY, 10032, USA
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37
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Mayorov A, Dal Peraro M, Abriata LA. Active Site-Induced Evolutionary Constraints Follow Fold Polarity Principles in Soluble Globular Enzymes. Mol Biol Evol 2020; 36:1728-1733. [PMID: 31004173 DOI: 10.1093/molbev/msz096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
A recent analysis of evolutionary rates in >500 globular soluble enzymes revealed pervasive conservation gradients toward catalytic residues. By looking at amino acid preference profiles rather than evolutionary rates in the same data set, we quantified the effects of active sites on site-specific constraints for physicochemical traits. We found that conservation gradients respond to constraints for polarity, hydrophobicity, flexibility, rigidity and structure in ways consistent with fold polarity principles; while sites far from active sites seem to experience no physicochemical constraint, rather being highly variable and favoring amino acids of low metabolic cost. Globally, our results highlight that amino acid variation contains finer information about protein structure than usually regarded in evolutionary models, and that this information is retrievable automatically with simple fits. We propose that analyses of the kind presented here incorporated into models of protein evolution should allow for better description of the physical chemistry that underlies molecular evolution.
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Affiliation(s)
- Alexander Mayorov
- Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Matteo Dal Peraro
- Laboratory for Biomolecular Modeling, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Luciano A Abriata
- Laboratory for Biomolecular Modeling, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Protein Production and Structure Core Facility, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland
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38
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Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization. Nat Commun 2020; 11:297. [PMID: 31941882 PMCID: PMC6962383 DOI: 10.1038/s41467-019-13895-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 11/28/2019] [Indexed: 11/09/2022] Open
Abstract
Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔGbind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔGbind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔGbind data points on purified proteins to generate ΔΔGbind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ΔΔGbind for this interaction could be quantified with high accuracy over the range of 12 kcal mol−1 displayed by various BPTI single mutants. Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.
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39
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Atkinson JT, Jones AM, Nanda V, Silberg JJ. Protein tolerance to random circular permutation correlates with thermostability and local energetics of residue-residue contacts. Protein Eng Des Sel 2019; 32:489-501. [PMID: 32626892 DOI: 10.1093/protein/gzaa012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/13/2020] [Accepted: 04/15/2020] [Indexed: 01/08/2023] Open
Abstract
Adenylate kinase (AK) orthologs with a range of thermostabilities were subjected to random circular permutation, and deep mutational scanning was used to evaluate where new protein termini were nondisruptive to activity. The fraction of circularly permuted variants that retained function in each library correlated with AK thermostability. In addition, analysis of the positional tolerance to new termini, which increase local conformational flexibility, showed that bonds were either functionally sensitive to cleavage across all homologs, differentially sensitive, or uniformly tolerant. The mobile AMP-binding domain, which displays the highest calculated contact energies, presented the greatest tolerance to new termini across all AKs. In contrast, retention of function in the lid and core domains was more dependent upon AK melting temperature. These results show that family permutation profiling identifies primary structure that has been selected by evolution for dynamics that are critical to activity within an enzyme family. These findings also illustrate how deep mutational scanning can be applied to protein homologs in parallel to differentiate how topology, stability, and local energetics govern mutational tolerance.
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Affiliation(s)
- Joshua T Atkinson
- Systems, Synthetic, and Physical Biology Graduate Program, Rice University, 6100 Main Street, MS-180, Houston, TX 77005, USA.,Department of BioSciences, Rice University, 6100 Main Street, MS-140, Houston, TX 77005, USA
| | - Alicia M Jones
- Biochemistry and Cell Biology Graduate Program, Rice University, 6100 Main Street, MS-140, Houston, TX 77005, USA
| | - Vikas Nanda
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jonathan J Silberg
- Department of BioSciences, Rice University, 6100 Main Street, MS-140, Houston, TX 77005, USA.,Department of Bioengineering, Rice University, 6100 Main Street, MS-142, Houston, TX 77005, USA.,Department of Chemical and Biomolecular Engineering, Rice University, 6100 Main Street, MS-362, Houston, TX 77005, USA
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40
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Affiliation(s)
- Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Centre for Clinical Research, St. Ann’s Hospital, 602 00 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Centre for Clinical Research, St. Ann’s Hospital, 602 00 Brno, Czech Republic
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41
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Sinha R, Shukla P. Current Trends in Protein Engineering: Updates and Progress. Curr Protein Pept Sci 2019; 20:398-407. [PMID: 30451109 DOI: 10.2174/1389203720666181119120120] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/09/2018] [Accepted: 11/12/2018] [Indexed: 12/15/2022]
Abstract
Proteins are one of the most important and resourceful biomolecules that find applications in health, industry, medicine, research, and biotechnology. Given its tremendous relevance, protein engineering has emerged as significant biotechnological intervention in this area. Strategic utilization of protein engineering methods and approaches has enabled better enzymatic properties, better stability, increased catalytic activity and most importantly, interesting and wide range applicability of proteins. In fact, the commercialization of engineered proteins have manifested in economically beneficial and viable solutions for industry and healthcare sector. Protein engineering has also evolved to become a powerful tool contributing significantly to the developments in both synthetic biology and metabolic engineering. The present review revisits the current trends in protein engineering approaches such as rational design, directed evolution, de novo design, computational approaches etc. and encompasses the recent progresses made in this field over the last few years. The review also throws light on advanced or futuristic protein engineering aspects, which are being explored for design and development of novel proteins with improved properties or advanced applications.
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Affiliation(s)
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak-124001, Haryana, India
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42
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Kuhlman B, Bradley P. Advances in protein structure prediction and design. Nat Rev Mol Cell Biol 2019; 20:681-697. [PMID: 31417196 PMCID: PMC7032036 DOI: 10.1038/s41580-019-0163-x] [Citation(s) in RCA: 365] [Impact Index Per Article: 73.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2019] [Indexed: 12/18/2022]
Abstract
The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.
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Affiliation(s)
- Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | - Philip Bradley
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
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43
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Wu B, Atkinson JT, Kahanda D, Bennett GN, Silberg JJ. Combinatorial design of chemical‐dependent protein switches for controlling intracellular electron transfer. AIChE J 2019. [DOI: 10.1002/aic.16796] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Bingyan Wu
- Biochemistry & Cell Biology Graduate Program Rice University Houston Texas
- Department of Biosciences Rice University Houston Texas
| | - Joshua T. Atkinson
- Department of Biosciences Rice University Houston Texas
- Systems, Synthetic, & Physical Biology Graduate Program Rice University Houston Texas
| | | | - George N. Bennett
- Department of Biosciences Rice University Houston Texas
- Department of Chemical & Biomolecular Engineering Rice University Houston Texas
| | - Jonathan J. Silberg
- Department of Biosciences Rice University Houston Texas
- Department of Chemical & Biomolecular Engineering Rice University Houston Texas
- Department of Bioengineering Rice University Houston Texas
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44
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Tools and systems for evolutionary engineering of biomolecules and microorganisms. ACTA ACUST UNITED AC 2019; 46:1313-1326. [DOI: 10.1007/s10295-019-02191-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 05/20/2019] [Indexed: 12/28/2022]
Abstract
Abstract
Evolutionary approaches have been providing solutions to various bioengineering challenges in an efficient manner. In addition to traditional adaptive laboratory evolution and directed evolution, recent advances in synthetic biology and fluidic systems have opened a new era of evolutionary engineering. Synthetic genetic circuits have been created to control mutagenesis and enable screening of various phenotypes, particularly metabolite production. Fluidic systems can be used for high-throughput screening and multiplexed continuous cultivation of microorganisms. Moreover, continuous directed evolution has been achieved by combining all the steps of evolutionary engineering. Overall, modern tools and systems for evolutionary engineering can be used to establish the artificial equivalent to natural evolution for various research applications.
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45
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Abstract
For nearly a century adaptive landscapes have provided overviews of the evolutionary process and yet they remain metaphors. We redefine adaptive landscapes in terms of biological processes rather than descriptive phenomenology. We focus on the underlying mechanisms that generate emergent properties such as epistasis, dominance, trade-offs and adaptive peaks. We illustrate the utility of landscapes in predicting the course of adaptation and the distribution of fitness effects. We abandon aged arguments concerning landscape ruggedness in favor of empirically determining landscape architecture. In so doing, we transform the landscape metaphor into a scientific framework within which causal hypotheses can be tested.
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Affiliation(s)
- Xiao Yi
- BioTechnology Institute, University of Minnesota, St. Paul, MN
| | - Antony M Dean
- BioTechnology Institute, University of Minnesota, St. Paul, MN.,Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN
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46
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Stein A, Fowler DM, Hartmann-Petersen R, Lindorff-Larsen K. Biophysical and Mechanistic Models for Disease-Causing Protein Variants. Trends Biochem Sci 2019; 44:575-588. [PMID: 30712981 PMCID: PMC6579676 DOI: 10.1016/j.tibs.2019.01.003] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 01/04/2019] [Accepted: 01/08/2019] [Indexed: 12/13/2022]
Abstract
The rapid decrease in DNA sequencing cost is revolutionizing medicine and science. In medicine, genome sequencing has revealed millions of missense variants that change protein sequences, yet we only understand the molecular and phenotypic consequences of a small fraction. Within protein science, high-throughput deep mutational scanning experiments enable us to probe thousands of variants in a single, multiplexed experiment. We review efforts that bring together these topics via experimental and computational approaches to determine the consequences of missense variants in proteins. We focus on the role of changes in protein stability as a driver for disease, and how experiments, biophysical models, and computation are providing a framework for understanding and predicting how changes in protein sequence affect cellular protein stability.
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Affiliation(s)
- Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Douglas M Fowler
- Departments of Genome Sciences and Bioengineering, University of Washington, Seattle, WA, USA
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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47
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Rotem A, Serohijos AWR, Chang CB, Wolfe JT, Fischer AE, Mehoke TS, Zhang H, Tao Y, Lloyd Ung W, Choi JM, Rodrigues JV, Kolawole AO, Koehler SA, Wu S, Thielen PM, Cui N, Demirev PA, Giacobbi NS, Julian TR, Schwab K, Lin JS, Smith TJ, Pipas JM, Wobus CE, Feldman AB, Weitz DA, Shakhnovich EI. Evolution on the Biophysical Fitness Landscape of an RNA Virus. Mol Biol Evol 2019; 35:2390-2400. [PMID: 29955873 PMCID: PMC6188569 DOI: 10.1093/molbev/msy131] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics that propagates millions of independent small viral subpopulations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction, an atypical case in evolution. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs between molecular traits of viral proteins shape viral evolution.
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Affiliation(s)
- Assaf Rotem
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Adrian W R Serohijos
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA.,Département de Biochimie et Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, Montréal, QC, Canada
| | - Connie B Chang
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.,Department of Chemical and Biological Engineering, Montana State University, Bozeman, MT
| | - Joshua T Wolfe
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Audrey E Fischer
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Thomas S Mehoke
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Huidan Zhang
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.,Key Laboratory of Cell Biology, Department of Cell Biology, Ministry of Public Health, China Medical University, Shenyang, China.,Key Laboratory of Medical Cell Biology, Ministry of Education, China Medical University, Shenyang, China
| | - Ye Tao
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - W Lloyd Ung
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Jeong-Mo Choi
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
| | - João V Rodrigues
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
| | - Abimbola O Kolawole
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
| | - Stephan A Koehler
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Susan Wu
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Peter M Thielen
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Naiwen Cui
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Plamen A Demirev
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | | | - Timothy R Julian
- Environmental Health Sciences and the Hopkins Water Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,Department of Environmental Microbiology, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland
| | - Kellogg Schwab
- Environmental Health Sciences and the Hopkins Water Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jeffrey S Lin
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD
| | - Thomas J Smith
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch at Galveston, Galveston, TX
| | - James M Pipas
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA
| | - Christiane E Wobus
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI
| | - Andrew B Feldman
- Department of Emergency Medicine, Johns Hopkins Medicine, Baltimore, MD
| | - David A Weitz
- Department of Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
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48
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Crnković A, Vargas-Rodriguez O, Söll D. Plasticity and Constraints of tRNA Aminoacylation Define Directed Evolution of Aminoacyl-tRNA Synthetases. Int J Mol Sci 2019; 20:ijms20092294. [PMID: 31075874 PMCID: PMC6540133 DOI: 10.3390/ijms20092294] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 04/29/2019] [Accepted: 05/07/2019] [Indexed: 02/07/2023] Open
Abstract
Genetic incorporation of noncanonical amino acids (ncAAs) has become a powerful tool to enhance existing functions or introduce new ones into proteins through expanded chemistry. This technology relies on the process of nonsense suppression, which is made possible by directing aminoacyl-tRNA synthetases (aaRSs) to attach an ncAA onto a cognate suppressor tRNA. However, different mechanisms govern aaRS specificity toward its natural amino acid (AA) substrate and hinder the engineering of aaRSs for applications beyond the incorporation of a single l-α-AA. Directed evolution of aaRSs therefore faces two interlinked challenges: the removal of the affinity for cognate AA and improvement of ncAA acylation. Here we review aspects of AA recognition that directly influence the feasibility and success of aaRS engineering toward d- and β-AAs incorporation into proteins in vivo. Emerging directed evolution methods are described and evaluated on the basis of aaRS active site plasticity and its inherent constraints.
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Affiliation(s)
- Ana Crnković
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
| | - Oscar Vargas-Rodriguez
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
| | - Dieter Söll
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
- Department of Chemistry, Yale University, New Haven, CT 06520, USA.
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49
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Lu H, Mazumder M, Jaikaran ASI, Kumar A, Leis EK, Xu X, Altmann M, Cochrane A, Woolley GA. A Yeast System for Discovering Optogenetic Inhibitors of Eukaryotic Translation Initiation. ACS Synth Biol 2019; 8:744-757. [PMID: 30901519 DOI: 10.1021/acssynbio.8b00386] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The precise spatiotemporal regulation of protein synthesis is essential for many complex biological processes such as memory formation, embryonic development, and tumor formation. Current methods used to study protein synthesis offer only a limited degree of spatiotemporal control. Optogenetic methods, in contrast, offer the prospect of controlling protein synthesis noninvasively within minutes and with a spatial scale as small as a single synapse. Here, we present a hybrid yeast system where growth depends on the activity of human eukaryotic initiation factor 4E (eIF4E) that is suitable for screening optogenetic designs for the down-regulation of protein synthesis. We used this system to screen a diverse initial panel of 15 constructs designed to couple a light switchable domain (PYP, RsLOV, AsLOV, Dronpa) to 4EBP2 (eukaryotic initiation factor 4E binding protein 2), a native inhibitor of translation initiation. We identified cLIPS1 (circularly permuted LOV inhibitor of protein synthesis 1), a fusion of a segment of 4EBP2 and a circularly permuted version of the LOV2 domain from Avena sativa, as a photoactivated inhibitor of translation. Adapting the screen for higher throughput, we tested small libraries of cLIPS1 variants and found cLIPS2, a construct with an improved degree of optical control. We show that these constructs can both inhibit translation in yeast harboring a human eIF4E in vivo, and bind human eIF4E in vitro in a light-dependent manner. This hybrid yeast system thus provides a convenient way for discovering optogenetic constructs that can regulate human eIF4E-dependent translation initiation in a mechanistically defined manner.
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Affiliation(s)
- Huixin Lu
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada
| | - Mostafizur Mazumder
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada
| | - Anna S. I. Jaikaran
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada
| | - Anil Kumar
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada
| | - Eric K. Leis
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada
| | - Xiuling Xu
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada
| | - Michael Altmann
- Institut für Biochemie und Molekulare Medizin, Universität Bern, Bühlstr. 28, CH-3012 Bern, Switzerland
| | - Alan Cochrane
- Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
| | - G. Andrew Woolley
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada
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50
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Wrenbeck EE, Bedewitz MA, Klesmith JR, Noshin S, Barry CS, Whitehead TA. An Automated Data-Driven Pipeline for Improving Heterologous Enzyme Expression. ACS Synth Biol 2019; 8:474-481. [PMID: 30721031 DOI: 10.1021/acssynbio.8b00486] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Enzymes are the ultimate entities responsible for chemical transformations in natural and engineered biosynthetic pathways. However, many natural enzymes suffer from suboptimal functional expression due to poor intrinsic protein stability. Further, stability enhancing mutations often come at the cost of impaired function. Here we demonstrate an automated protein engineering strategy for stabilizing enzymes while retaining catalytic function using deep mutational scanning coupled to multiple-filter based screening and combinatorial mutagenesis. We validated this strategy by improving the functional expression of a Type III polyketide synthase from the Atropa belladonna biosynthetic pathway for tropane alkaloids. The best variant had a total of 8 mutations with over 25-fold improved activity over wild-type in E. coli cell lysates, an improved melting temperature of 11.5 ± 0.6 °C, and only minimal reduction in catalytic efficiency. We show that the multiple-filter approach maintains acceptable sensitivity with homology modeling structures up to 4 Å RMS. Our results highlight an automated protein engineering tool for improving the stability and solubility of difficult to express enzymes, which has impact for biotechnological applications.
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Affiliation(s)
| | | | | | - Syeda Noshin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22903, United States
| | | | - Timothy A. Whitehead
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, Colorado 80305, United States
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