1
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Guclu TF, Atilgan AR, Atilgan C. Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis. J Phys Chem B 2024; 128:7987-7996. [PMID: 39115184 DOI: 10.1021/acs.jpcb.4c04916] [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: 08/23/2024]
Abstract
Mutational changes that affect the binding of the C2 fragment of Streptococcal protein G (GB1) to the Fc domain of human IgG (IgG-Fc) have been extensively studied using deep mutational scanning (DMS), and the binding affinity of all single mutations has been measured experimentally in the literature. To investigate the underlying molecular basis, we perform in silico mutational scanning for all possible single mutations, along with 2 μs-long molecular dynamics (WT-MD) of the wild-type (WT) GB1 in both unbound and IgG-Fc bound forms. We compute the hydrogen bonds between GB1 and IgG-Fc in WT-MD to identify the dominant hydrogen bonds for binding, which we then assess in conformations produced by Mutation and Minimization (MuMi) to explain the fitness landscape of GB1 and IgG-Fc binding. Furthermore, we analyze MuMi and WT-MD to investigate the dynamics of binding, focusing on the relative solvent accessibility of residues and the probability of residues being located at the binding interface. With these analyses, we explain the interactions between GB1 and IgG-Fc and display the structural features of binding. In sum, our findings highlight the potential of MuMi as a reliable and computationally efficient tool for predicting protein fitness landscapes, offering significant advantages over traditional methods. The methodologies and results presented in this study pave the way for improved predictive accuracy in protein stability and interaction studies, which are crucial for advancements in drug design and synthetic biology.
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Affiliation(s)
- Tandac F Guclu
- Faculty of Natural Sciences and Engineering, Sabanci University, Tuzla, Istanbul 34956, Turkey
| | - Ali Rana Atilgan
- Faculty of Natural Sciences and Engineering, Sabanci University, Tuzla, Istanbul 34956, Turkey
| | - Canan Atilgan
- Faculty of Natural Sciences and Engineering, Sabanci University, Tuzla, Istanbul 34956, Turkey
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2
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Lipsh-Sokolik R, Fleishman SJ. Addressing epistasis in the design of protein function. Proc Natl Acad Sci U S A 2024; 121:e2314999121. [PMID: 39133844 PMCID: PMC11348311 DOI: 10.1073/pnas.2314999121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024] Open
Abstract
Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity.
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Affiliation(s)
- Rosalie Lipsh-Sokolik
- 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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3
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McGee RS, Kinsler G, Petrov D, Tikhonov M. Improving the Accuracy of Bulk Fitness Assays by Correcting Barcode Processing Biases. Mol Biol Evol 2024; 41:msae152. [PMID: 39041198 PMCID: PMC11316221 DOI: 10.1093/molbev/msae152] [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: 11/02/2023] [Revised: 06/28/2024] [Accepted: 07/11/2024] [Indexed: 07/24/2024] Open
Abstract
Measuring the fitnesses of genetic variants is a fundamental objective in evolutionary biology. A standard approach for measuring microbial fitnesses in bulk involves labeling a library of genetic variants with unique sequence barcodes, competing the labeled strains in batch culture, and using deep sequencing to track changes in the barcode abundances over time. However, idiosyncratic properties of barcodes can induce nonuniform amplification or uneven sequencing coverage that causes some barcodes to be over- or under-represented in samples. This systematic bias can result in erroneous read count trajectories and misestimates of fitness. Here, we develop a computational method, named REBAR (Removing the Effects of Bias through Analysis of Residuals), for inferring the effects of barcode processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to two independent data sets, and demonstrate that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. REBAR mitigates bias and improves fitness estimates in high-throughput assays without introducing additional complexity to the experimental protocols, with potential applications in a range of experimental evolution and mutation screening contexts.
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Affiliation(s)
| | - Grant Kinsler
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Dmitri Petrov
- Department of Biology, Stanford University, Palo Alto, CA, USA
| | - Mikhail Tikhonov
- Department of Physics, Washington University, St. Louis, MO, USA
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4
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Wirnsberger G, Pritišanac I, Oberdorfer G, Gruber K. Flattening the curve-How to get better results with small deep-mutational-scanning datasets. Proteins 2024; 92:886-902. [PMID: 38501649 DOI: 10.1002/prot.26686] [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: 11/08/2023] [Revised: 02/24/2024] [Accepted: 03/07/2024] [Indexed: 03/20/2024]
Abstract
Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches use some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pretraining, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.
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Affiliation(s)
| | - Iva Pritišanac
- Institute of Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Gustav Oberdorfer
- BioTechMed-Graz, Graz, Austria
- Institute of Biochemistry, Graz University of Technology, Graz, Austria
| | - Karl Gruber
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Field of Excellence BioHealth, University of Graz, Graz, Austria
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5
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Bromberg Y, Prabakaran R, Kabir A, Shehu A. Variant Effect Prediction in the Age of Machine Learning. Cold Spring Harb Perspect Biol 2024; 16:a041467. [PMID: 38621825 PMCID: PMC11216171 DOI: 10.1101/cshperspect.a041467] [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: 04/17/2024]
Abstract
Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein "sentences"? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.
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Affiliation(s)
- Yana Bromberg
- Department of Biology, Emory University, Atlanta 30322, Georgia, USA
- Department of Computer Science, Emory University, Atlanta 30322, Georgia, USA
| | - R Prabakaran
- Department of Biology, Emory University, Atlanta 30322, Georgia, USA
| | - Anowarul Kabir
- Department of Computer Science, George Mason University, Fairfax 22030, Virginia, USA
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax 22030, Virginia, USA
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6
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Maso L, Rajak E, Bang I, Koide A, Hattori T, Neel BG, Koide S. Molecular basis for antibody recognition of multiple drug-peptide/MHC complexes. Proc Natl Acad Sci U S A 2024; 121:e2319029121. [PMID: 38781214 PMCID: PMC11145297 DOI: 10.1073/pnas.2319029121] [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: 10/30/2023] [Accepted: 02/14/2024] [Indexed: 05/25/2024] Open
Abstract
The HapImmuneTM platform exploits covalent inhibitors as haptens for creating major histocompatibility complex (MHC)-presented tumor-specific neoantigens by design, combining targeted therapies with immunotherapy for the treatment of drug-resistant cancers. A HapImmune antibody, R023, recognizes multiple sotorasib-conjugated KRAS(G12C) peptides presented by different human leukocyte antigens (HLAs). This high specificity to sotorasib, coupled with broad HLA-binding capability, enables such antibodies, when reformatted as T cell engagers, to potently and selectively kill sotorasib-resistant KRAS(G12C) cancer cells expressing different HLAs upon sotorasib treatment. The loosening of HLA restriction could increase the patient population that can benefit from this therapeutic approach. To understand the molecular basis for its unconventional binding capability, we used single-particle cryogenic electron microscopy to determine the structures of R023 bound to multiple sotorasib-peptide conjugates presented by different HLAs. R023 forms a pocket for sotorasib between the VH and VL domains, binds HLAs in an unconventional, angled way, with VL making most contacts with them, and makes few contacts with the peptide moieties. This binding mode enables the antibody to accommodate different hapten-peptide conjugates and to adjust its conformation to different HLAs presenting hapten-peptides. Deep mutational scanning validated the structures and revealed distinct levels of mutation tolerance by sotorasib- and HLA-binding residues. Together, our structural information and sequence landscape analysis reveal key features for achieving MHC-restricted recognition of multiple hapten-peptide antigens, which will inform the development of next-generation therapeutic antibodies.
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Affiliation(s)
- Lorenzo Maso
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY10016
| | - Epsa Rajak
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY10016
| | - Injin Bang
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY10016
| | - Akiko Koide
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY10016
- Department of Medicine, New York University School of Medicine, New York, NY10016
| | - Takamitsu Hattori
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY10016
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY10016
| | - Benjamin G. Neel
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY10016
- Department of Medicine, New York University School of Medicine, New York, NY10016
| | - Shohei Koide
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY10016
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY10016
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7
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Rao J, Xin R, Macdonald C, Howard MK, Estevam GO, Yee SW, Wang M, Fraser JS, Coyote-Maestas W, Pimentel H. Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage. Genome Biol 2024; 25:138. [PMID: 38789982 PMCID: PMC11127319 DOI: 10.1186/s13059-024-03279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.
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Affiliation(s)
- Jingyou Rao
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Ruiqi Xin
- Computational and Systems Biology Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Christian Macdonald
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
| | - Matthew K Howard
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Tetrad Graduate Program, UCSF, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, UCSF, San Francisco, CA, USA
| | - Gabriella O Estevam
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Tetrad Graduate Program, UCSF, San Francisco, CA, USA
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
| | - Mingsen Wang
- Department of Mathematics, Baruch College, CUNY, New York, NY, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Quantitative Biosciences Institute, UCSF, San Francisco, CA, USA
| | - Willow Coyote-Maestas
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA.
- Quantitative Biosciences Institute, UCSF, San Francisco, CA, USA.
| | - Harold Pimentel
- Department of Computer Science, UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
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8
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Ryu J, Barkal S, Yu T, Jankowiak M, Zhou Y, Francoeur M, Phan QV, Li Z, Tognon M, Brown L, Love MI, Bhat V, Lettre G, Ascher DB, Cassa CA, Sherwood RI, Pinello L. Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification. Nat Genet 2024; 56:925-937. [PMID: 38658794 DOI: 10.1038/s41588-024-01726-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/21/2024] [Indexed: 04/26/2024]
Abstract
CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.
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Affiliation(s)
- Jayoung Ryu
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sam Barkal
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tian Yu
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Martin Jankowiak
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yunzhuo Zhou
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Matthew Francoeur
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Quang Vinh Phan
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhijian Li
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Manuel Tognon
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Computer Science Department, University of Verona, Verona, Italy
| | - Lara Brown
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael I Love
- Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Vineel Bhat
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, Quebec, Canada
- Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Christopher A Cassa
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Richard I Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
- Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Pathology, Harvard Medical School, Boston, MA, USA.
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9
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Schmutzer M, Dasmeh P, Wagner A. Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation. J Mol Evol 2024; 92:104-120. [PMID: 38470504 PMCID: PMC10978624 DOI: 10.1007/s00239-024-10161-4] [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: 10/07/2023] [Accepted: 02/16/2024] [Indexed: 03/14/2024]
Abstract
Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.
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Affiliation(s)
- Michael Schmutzer
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pouria Dasmeh
- Center for Human Genetics, Philipps University of Marburg, Marburg, Germany
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Santa Fe Institute, Santa Fe, NM, USA.
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10
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Blake KS, Kumar H, Loganathan A, Williford EE, Diorio-Toth L, Xue YP, Tang WK, Campbell TP, Chong DD, Angtuaco S, Wencewicz TA, Tolia NH, Dantas G. Sequence-structure-function characterization of the emerging tetracycline destructase family of antibiotic resistance enzymes. Commun Biol 2024; 7:336. [PMID: 38493211 PMCID: PMC10944477 DOI: 10.1038/s42003-024-06023-w] [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: 10/10/2023] [Accepted: 03/07/2024] [Indexed: 03/18/2024] Open
Abstract
Tetracycline destructases (TDases) are flavin monooxygenases which can confer resistance to all generations of tetracycline antibiotics. The recent increase in the number and diversity of reported TDase sequences enables a deep investigation of the TDase sequence-structure-function landscape. Here, we evaluate the sequence determinants of TDase function through two complementary approaches: (1) constructing profile hidden Markov models to predict new TDases, and (2) using multiple sequence alignments to identify conserved positions important to protein function. Using the HMM-based approach we screened 50 high-scoring candidate sequences in Escherichia coli, leading to the discovery of 13 new TDases. The X-ray crystal structures of two new enzymes from Legionella species were determined, and the ability of anhydrotetracycline to inhibit their tetracycline-inactivating activity was confirmed. Using the MSA-based approach we identified 31 amino acid positions 100% conserved across all known TDase sequences. The roles of these positions were analyzed by alanine-scanning mutagenesis in two TDases, to study the impact on cell and in vitro activity, structure, and stability. These results expand the diversity of TDase sequences and provide valuable insights into the roles of important residues in TDases, and flavin monooxygenases more broadly.
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Affiliation(s)
- Kevin S Blake
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hirdesh Kumar
- Host-Pathogen Interactions and Structural Vaccinology section (HPISV), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Anisha Loganathan
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Emily E Williford
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
| | - Luke Diorio-Toth
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yao-Peng Xue
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Wai Kwan Tang
- Host-Pathogen Interactions and Structural Vaccinology section (HPISV), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Tayte P Campbell
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - David D Chong
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven Angtuaco
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy A Wencewicz
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA.
| | - Niraj H Tolia
- Host-Pathogen Interactions and Structural Vaccinology section (HPISV), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA.
| | - Gautam Dantas
- The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA.
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11
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Vanella R, Küng C, Schoepfer AA, Doffini V, Ren J, Nash MA. Understanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing. Nat Commun 2024; 15:1807. [PMID: 38418512 PMCID: PMC10902396 DOI: 10.1038/s41467-024-45630-3] [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: 03/24/2023] [Accepted: 01/26/2024] [Indexed: 03/01/2024] Open
Abstract
Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we present the development of enzyme proximity sequencing, a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We use enzyme proximity sequencing to analyze how 6399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase from Rhodotorula gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. Enzyme proximity sequencing can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.
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Affiliation(s)
- Rosario Vanella
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland.
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
| | - Christoph Küng
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland
| | - Alexandre A Schoepfer
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland
- Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
- National Center for Competence in Research (NCCR), Catalysis, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Vanni Doffini
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland
| | - Jin Ren
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland
| | - Michael A Nash
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland.
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
- National Center for Competence in Research (NCCR), Molecular Systems Engineering, 4058, Basel, Switzerland.
- Swiss Nanoscience Institute, 4056, Basel, Switzerland.
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12
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Deng J, Yuan Y, Cui Q. Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR. J Am Chem Soc 2024; 146:2757-2768. [PMID: 38231868 PMCID: PMC10843641 DOI: 10.1021/jacs.3c12494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of cooperativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semiquantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different rescuabilities observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same noninducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscores the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions and therefore provides quantitative guidance to allostery modulation for therapeutic and engineering applications.
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Affiliation(s)
- Jiahua Deng
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Yuchen Yuan
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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13
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Sun R, Qian MG, Zhang X. T and B cell epitope analysis for the immunogenicity evaluation and mitigation of antibody-based therapeutics. MAbs 2024; 16:2324836. [PMID: 38512798 PMCID: PMC10962608 DOI: 10.1080/19420862.2024.2324836] [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] [Received: 10/06/2023] [Accepted: 02/26/2024] [Indexed: 03/23/2024] Open
Abstract
The surge in the clinical use of therapeutic antibodies has reshaped the landscape of pharmaceutical therapy for many diseases, including rare and challenging conditions. However, the administration of exogenous biologics could potentially trigger unwanted immune responses such as generation of anti-drug antibodies (ADAs). Real-world experiences have illuminated the clear correlation between the ADA occurrence and unsatisfactory therapeutic outcomes as well as immune-related adverse events. By retrospectively examining research involving immunogenicity analysis, we noticed the growing emphasis on elucidating the immunogenic epitope profiles of antibody-based therapeutics aiming for mechanistic understanding the immunogenicity generation and, ideally, mitigating the risks. As such, we have comprehensively summarized here the progress in both experimental and computational methodologies for the characterization of T and B cell epitopes of therapeutics. Furthermore, the successful practice of epitope-driven deimmunization of biotherapeutics is exceptionally highlighted in this article.
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Affiliation(s)
- Ruoxuan Sun
- Global Drug Metabolism, Pharmacokinetics & Modeling, Preclinical & Translational Sciences, Takeda Development Center Americas, Inc. (TDCA), Cambridge, MA, USA
| | - Mark G. Qian
- Global Drug Metabolism, Pharmacokinetics & Modeling, Preclinical & Translational Sciences, Takeda Development Center Americas, Inc. (TDCA), Cambridge, MA, USA
| | - Xiaobin Zhang
- Global Drug Metabolism, Pharmacokinetics & Modeling, Preclinical & Translational Sciences, Takeda Development Center Americas, Inc. (TDCA), Cambridge, MA, USA
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14
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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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15
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Deng J, Yuan Y, Cui Q. Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.29.555381. [PMID: 37905112 PMCID: PMC10614727 DOI: 10.1101/2023.08.29.555381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of co-operativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semi-quantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different degrees of rescuability observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same non-inducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscore the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions, and therefore provide quantitative guidance to allostery modulation for therapeutic and engineering applications.
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Affiliation(s)
- Jiahua Deng
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Yuchen Yuan
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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16
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Yang ZJ, Shao Q, Jiang Y, Jurich C, Ran X, Juarez RJ, Yan B, Stull SL, Gollu A, Ding N. Mutexa: A Computational Ecosystem for Intelligent Protein Engineering. J Chem Theory Comput 2023; 19:7459-7477. [PMID: 37828731 PMCID: PMC10653112 DOI: 10.1021/acs.jctc.3c00602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Indexed: 10/14/2023]
Abstract
Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.
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Affiliation(s)
- Zhongyue J. Yang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute of Chemical Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235, United States
- Data
Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Qianzhen Shao
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yaoyukun Jiang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Christopher Jurich
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute of Chemical Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
| | - Xinchun Ran
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Reecan J. Juarez
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37235, United States
| | - Bailu Yan
- Department
of Biostatistics, Vanderbilt University, Nashville, Tennessee 37205, United States
| | - Sebastian L. Stull
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Anvita Gollu
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Ning Ding
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
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17
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Wang Y, Zhao Y, Li Y, Zhang K, Fan Y, Li B, Su W, Li S. piggyBac-mediated genomic integration of linear dsDNA-based library for deep mutational scanning in mammalian cells. Cell Mol Life Sci 2023; 80:321. [PMID: 37815732 PMCID: PMC11071730 DOI: 10.1007/s00018-023-04976-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/15/2023] [Accepted: 09/20/2023] [Indexed: 10/11/2023]
Abstract
Deep mutational scanning (DMS) makes it possible to perform massively parallel quantification of the relationship between genetic variants and phenotypes of interest. However, the difficulties in introducing large variant libraries into mammalian cells greatly hinder DMS under physiological states. Here, we developed two novel strategies for DMS library construction in mammalian cells, namely 'piggyBac-in vitro ligation' and 'piggyBac-in vitro ligation-PCR'. For the first strategy, we took the 'in vitro ligation' approach to prepare high-diversity linear dsDNAs, and integrate them into the mammalian genome with a piggyBac transposon system. For the second strategy, we further added a PCR step using the in vitro ligation dsDNAs as templates, for the construction of high-content genome-integrated libraries via large-scale transfection. Both strategies could successfully establish genome-integrated EGFP-chromophore-randomized libraries in HEK293T cells and enrich the green fluorescence-chromophore amino-acid sequences. And we further identified a novel transcriptional activator peptide with the 'piggyBac-in vitro ligation-PCR' strategy. Our novel strategies greatly facilitate the construction of large variant DMS library in mammalian cells, and may have great application potential in the future.
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Affiliation(s)
- Yi Wang
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Yanjie Zhao
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Yifan Li
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Kaili Zhang
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Yan Fan
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Bo Li
- Beijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Life Science, Beijing Institute of Technology, Beijing, China
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Weijun Su
- School of Medicine, Nankai University, Tianjin, 300071, China.
| | - Shuai Li
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
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18
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Hu D, Irving AT. Massively-multiplexed epitope mapping techniques for viral antigen discovery. Front Immunol 2023; 14:1192385. [PMID: 37818363 PMCID: PMC10561112 DOI: 10.3389/fimmu.2023.1192385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 09/04/2023] [Indexed: 10/12/2023] Open
Abstract
Following viral infection, viral antigens bind specifically to receptors on the surface of lymphocytes thereby activating adaptive immunity in the host. An epitope, the smallest structural and functional unit of an antigen, binds specifically to an antibody or antigen receptor, to serve as key sites for the activation of adaptive immunity. The complexity and diverse range of epitopes are essential to study and map for the diagnosis of disease, the design of vaccines and for immunotherapy. Mapping the location of these specific epitopes has become a hot topic in immunology and immune therapy. Recently, epitope mapping techniques have evolved to become multiplexed, with the advent of high-throughput sequencing and techniques such as bacteriophage-display libraries and deep mutational scanning. Here, we briefly introduce the principles, advantages, and disadvantages of the latest epitope mapping techniques with examples for viral antigen discovery.
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Affiliation(s)
- Diya Hu
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, China
| | - Aaron T. Irving
- Department of Clinical Laboratory Studies, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Centre for Infection, Immunity & Cancer, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, China
- Biomedical and Health Translational Research Centre of Zhejiang Province (BIMET), Haining, China
- College of Medicine & Veterinary Medicine, The University of Edinburgh, Edinburgh, United Kingdom
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19
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Ryu J, Barkal S, Yu T, Jankowiak M, Zhou Y, Francoeur M, Phan QV, Li Z, Tognon M, Brown L, Love MI, Lettre G, Ascher DB, Cassa CA, Sherwood RI, Pinello L. Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.08.23295253. [PMID: 37732177 PMCID: PMC10508837 DOI: 10.1101/2023.09.08.23295253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR, we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization.
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Affiliation(s)
- Jayoung Ryu
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sam Barkal
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Tian Yu
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Yunzhuo Zhou
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Matthew Francoeur
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Quang Vinh Phan
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhijian Li
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Manuel Tognon
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Computer Science Department, University of Verona, Verona, Italy
| | - Lara Brown
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael I. Love
- Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, QC H1T 1C8, Canada
- Faculté de Médecine, Université de Montréal, Montréal, QC H3T 1J4, Canada
| | - David B. Ascher
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Christopher A. Cassa
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Richard I. Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
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20
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Chen J, Woldring DR, Huang F, Huang X, Wei GW. Topological deep learning based deep mutational scanning. Comput Biol Med 2023; 164:107258. [PMID: 37506452 PMCID: PMC10528359 DOI: 10.1016/j.compbiomed.2023.107258] [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: 05/09/2023] [Revised: 06/28/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023]
Abstract
High-throughput deep mutational scanning (DMS) experiments have significantly impacted protein engineering, drug discovery, immunology, cancer biology, and evolutionary biology by enabling the systematic understanding of protein functions. However, the mutational space associated with proteins is astronomically large, making it overwhelming for current experimental capabilities. Therefore, alternative methods for DMS are imperative. We propose a topological deep learning (TDL) paradigm to facilitate in silico DMS. We utilize a new topological data analysis (TDA) technique based on the persistent spectral theory, also known as persistent Laplacian, to capture both topological invariants and the homotopic shape evolution of data. To validate our TDL-DMS model, we use SARS-CoV-2 datasets and show excellent accuracy and reliability for binding interface mutations. This finding is significant for SARS-CoV-2 variant forecasting and designing effective antibodies and vaccines. Our proposed model is expected to have a significant impact on drug discovery, vaccine design, precision medicine, and protein engineering.
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Affiliation(s)
- Jiahui Chen
- Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Daniel R Woldring
- Department of Chemical Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Faqing Huang
- Department of Chemistry and Biochemistry, University of Southern Mississippi, Hattiesburg, MS 39406, USA
| | - Xuefei Huang
- Department of Chemistry, Michigan State University, MI 48824, USA; Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA; The Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
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21
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McConnell A, Hackel BJ. Protein engineering via sequence-performance mapping. Cell Syst 2023; 14:656-666. [PMID: 37494931 PMCID: PMC10527434 DOI: 10.1016/j.cels.2023.06.009] [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: 02/27/2023] [Revised: 05/10/2023] [Accepted: 06/21/2023] [Indexed: 07/28/2023]
Abstract
Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.
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Affiliation(s)
- Adam McConnell
- Department of Biomedical Engineering, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA
| | - Benjamin J Hackel
- Department of Biomedical Engineering, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA; Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN 55455, USA.
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22
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Wang Y, Zhang K, Zhao Y, Li Y, Su W, Li S. Construction and Applications of Mammalian Cell-Based DNA-Encoded Peptide/Protein Libraries. ACS Synth Biol 2023; 12:1874-1888. [PMID: 37315219 DOI: 10.1021/acssynbio.3c00043] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
DNA-encoded peptide/protein libraries are the starting point for protein evolutionary modification and functional peptide/antibody selection. Different display technologies, protein directed evolution, and deep mutational scanning (DMS) experiments employ DNA-encoded libraries to provide sequence variations for downstream affinity- or function-based selections. Mammalian cells promise the inherent post-translational modification and near-to-natural conformation of exogenously expressed mammalian proteins and thus are the best platform for studying transmembrane proteins or human disease-related proteins. However, due to the current technical bottlenecks of constructing mammalian cell-based large size DNA-encoded libraries, the advantages of mammalian cells as screening platforms have not been fully exploited. In this review, we summarize the current efforts in constructing DNA-encoded libraries in mammalian cells and the existing applications of these libraries in different fields.
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Affiliation(s)
- Yi Wang
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Kaili Zhang
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Yanjie Zhao
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Yifan Li
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Weijun Su
- School of Medicine, Nankai University, Tianjin 300071, China
| | - Shuai Li
- Department of Breast Cancer Pathology and Research Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
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23
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Ghose DA, Przydzial KE, Mahoney EM, Keating AE, Laub MT. Marginal specificity in protein interactions constrains evolution of a paralogous family. Proc Natl Acad Sci U S A 2023; 120:e2221163120. [PMID: 37098061 PMCID: PMC10160972 DOI: 10.1073/pnas.2221163120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/24/2023] [Indexed: 04/26/2023] Open
Abstract
The evolution of novel functions in biology relies heavily on gene duplication and divergence, creating large paralogous protein families. Selective pressure to avoid detrimental cross-talk often results in paralogs that exhibit exquisite specificity for their interaction partners. But how robust or sensitive is this specificity to mutation? Here, using deep mutational scanning, we demonstrate that a paralogous family of bacterial signaling proteins exhibits marginal specificity, such that many individual substitutions give rise to substantial cross-talk between normally insulated pathways. Our results indicate that sequence space is locally crowded despite overall sparseness, and we provide evidence that this crowding has constrained the evolution of bacterial signaling proteins. These findings underscore how evolution selects for "good enough" rather than optimized phenotypes, leading to restrictions on the subsequent evolution of paralogs.
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Affiliation(s)
- Dia A. Ghose
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Kaitlyn E. Przydzial
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Emily M. Mahoney
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Amy E. Keating
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
- Koch Center for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Michael T. Laub
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA02139
- HHMI, Massachusetts Institute of Technology, Cambridge, MA02139
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24
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Moulana A, Dupic T, Phillips AM, Desai MM. Genotype-phenotype landscapes for immune-pathogen coevolution. Trends Immunol 2023; 44:384-396. [PMID: 37024340 PMCID: PMC10147585 DOI: 10.1016/j.it.2023.03.006] [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: 02/03/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 04/07/2023]
Abstract
Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.
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Affiliation(s)
- Alief Moulana
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Angela M Phillips
- Department of Microbiology and Immunology, University of California at San Francisco, San Francisco, CA 94143, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Department of Physics, Harvard University, Cambridge, MA 02138, USA; NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA 02138, USA; Quantitative Biology Initiative, Harvard University, Cambridge, MA 02138, USA.
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25
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Diaz DJ, Kulikova AV, Ellington AD, Wilke CO. Using machine learning to predict the effects and consequences of mutations in proteins. Curr Opin Struct Biol 2023; 78:102518. [PMID: 36603229 PMCID: PMC9908841 DOI: 10.1016/j.sbi.2022.102518] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/07/2022] [Accepted: 11/20/2022] [Indexed: 01/05/2023]
Abstract
Machine and deep learning approaches can leverage the increasingly available massive datasets of protein sequences, structures, and mutational effects to predict variants with improved fitness. Many different approaches are being developed, but systematic benchmarking studies indicate that even though the specifics of the machine learning algorithms matter, the more important constraint comes from the data availability and quality utilized during training. In cases where little experimental data are available, unsupervised and self-supervised pre-training with generic protein datasets can still perform well after subsequent refinement via hybrid or transfer learning approaches. Overall, recent progress in this field has been staggering, and machine learning approaches will likely play a major role in future breakthroughs in protein biochemistry and engineering.
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Affiliation(s)
- Daniel J Diaz
- Department of Chemistry, The University of Texas at Austin, 105 E 24TH St., Austin, 78712, Texas, USA; Department of Molecular Biosciences, The University of Texas at Austin, 100 East 24th St., Stop A5000, Austin, 78712, Texas, USA. https://twitter.com/aiproteins
| | - Anastasiya V Kulikova
- Department of Integrative Biology, The University of Texas at Austin, 2415 Speedway, Stop C0930, Austin, 78712, Texas, USA
| | - Andrew D Ellington
- Department of Molecular Biosciences, The University of Texas at Austin, 100 East 24th St., Stop A5000, Austin, 78712, Texas, USA. https://twitter.com/CSSBatUT
| | - Claus O Wilke
- Department of Integrative Biology, The University of Texas at Austin, 2415 Speedway, Stop C0930, Austin, 78712, Texas, USA.
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26
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Jiang Y, Ran X, Yang ZJ. Data-driven enzyme engineering to identify function-enhancing enzymes. Protein Eng Des Sel 2023; 36:gzac009. [PMID: 36214500 PMCID: PMC10365845 DOI: 10.1093/protein/gzac009] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/08/2022] [Accepted: 09/28/2022] [Indexed: 01/22/2023] Open
Abstract
Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.
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Affiliation(s)
- Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Xinchun Ran
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Zhongyue J Yang
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37235, USA
- Data Science Institute, Vanderbilt University, Nashville, TN 37235, USA
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235, USA
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27
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Miton CM, Tokuriki N. Insertions and Deletions (Indels): A Missing Piece of the Protein Engineering Jigsaw. Biochemistry 2023; 62:148-157. [PMID: 35830609 DOI: 10.1021/acs.biochem.2c00188] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Over the years, protein engineers have studied nature and borrowed its tricks to accelerate protein evolution in the test tube. While there have been considerable advances, our ability to generate new proteins in the laboratory is seemingly limited. One explanation for these shortcomings may be that insertions and deletions (indels), which frequently arise in nature, are largely overlooked during protein engineering campaigns. The profound effect of indels on protein structures, by way of drastic backbone alterations, could be perceived as "saltation" events that bring about significant phenotypic changes in a single mutational step. Should we leverage these effects to accelerate protein engineering and gain access to unexplored regions of adaptive landscapes? In this Perspective, we describe the role played by indels in the functional diversification of proteins in nature and discuss their untapped potential for protein engineering, despite their often-destabilizing nature. We hope to spark a renewed interest in indels, emphasizing that their wider study and use may prove insightful and shape the future of protein engineering by unlocking unique functional changes that substitutions alone could never achieve.
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Affiliation(s)
- Charlotte M Miton
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 BC, Canada
| | - Nobuhiko Tokuriki
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 BC, Canada
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28
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Hattori T, Maso L, Araki KY, Koide A, Hayman J, Akkapeddi P, Bang I, Neel BG, Koide S. Creating MHC-Restricted Neoantigens with Covalent Inhibitors That Can Be Targeted by Immune Therapy. Cancer Discov 2023; 13:132-145. [PMID: 36250888 PMCID: PMC9827112 DOI: 10.1158/2159-8290.cd-22-1074] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 01/16/2023]
Abstract
Intracellular oncoproteins can be inhibited with targeted therapy, but responses are not durable. Immune therapies can be curative, but most oncogene-driven tumors are unresponsive to these agents. Fragments of intracellular oncoproteins can act as neoantigens presented by the major histocompatibility complex (MHC), but recognizing minimal differences between oncoproteins and their normal counterparts is challenging. We have established a platform technology that exploits hapten-peptide conjugates generated by covalent inhibitors to create distinct neoantigens that selectively mark cancer cells. Using the FDA-approved covalent inhibitors sotorasib and osimertinib, we developed "HapImmune" antibodies that bind to drug-peptide conjugate/MHC complexes but not to the free drugs. A HapImmune-based bispecific T-cell engager selectively and potently kills sotorasib-resistant lung cancer cells upon sotorasib treatment. Notably, it is effective against KRASG12C-mutant cells with different HLA supertypes, HLA-A*02 and A*03/11, suggesting loosening of MHC restriction. Our strategy creates targetable neoantigens by design, unifying targeted and immune therapies. SIGNIFICANCE Targeted therapies against oncoproteins often have dramatic initial efficacy but lack durability. Immunotherapies can be curative, yet most tumors fail to respond. We developed a generalizable technology platform that exploits hapten-peptides generated by covalent inhibitors as neoantigens presented on MHC to enable engineered antibodies to selectively kill drug-resistant cancer cells. See related commentary by Cox et al., p. 19. This article is highlighted in the In This Issue feature, p. 1.
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Affiliation(s)
- Takamitsu Hattori
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York.,Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, New York
| | - Lorenzo Maso
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York
| | - Kiyomi Y. Araki
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York
| | - Akiko Koide
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York.,Division of Hematology Oncology, Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - James Hayman
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York
| | - Padma Akkapeddi
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York
| | - Injin Bang
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York
| | - Benjamin G. Neel
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York.,Division of Hematology Oncology, Department of Medicine, New York University Grossman School of Medicine, New York, New York.,Corresponding Authors: Shohei Koide, Smilow Research Center, Room 1105, 522 First Avenue, New York, NY 10016. Phone: 646-501-4601; E-mail: ; and Benjamin G. Neel, Smilow Research Center, Suite 1201, 522 First Avenue, New York, NY 10016. Phone: 212-263-3019; E-mail:
| | - Shohei Koide
- Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, New York.,Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, New York.,Corresponding Authors: Shohei Koide, Smilow Research Center, Room 1105, 522 First Avenue, New York, NY 10016. Phone: 646-501-4601; E-mail: ; and Benjamin G. Neel, Smilow Research Center, Suite 1201, 522 First Avenue, New York, NY 10016. Phone: 212-263-3019; E-mail:
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29
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An L, Wang Y, Wu G, Wang Z, Shi Z, Liu C, Wang C, Yi M, Niu C, Duan S, Li X, Tang W, Wu K, Chen S, Xu H. Defining the sensitivity landscape of EGFR variants to tyrosine kinase inhibitors. Transl Res 2022; 255:14-25. [PMID: 36347492 DOI: 10.1016/j.trsl.2022.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/06/2022] [Accepted: 11/01/2022] [Indexed: 11/08/2022]
Abstract
Tyrosine kinase inhibitor (TKI) is a standard treatment for patients with NSCLC harboring constitutively active epidermal growth factor receptor (EGFR) mutations. However, most rare EGFR mutations lack treatment regimens except for the well-studied ones. We constructed two EGFR variant libraries containing substitutions, deletions, or insertions using the saturation mutagenesis method. All the variants were located in the EGFR mutation hotspot (exons 18-21). The sensitivity of these variants to afatinib, erlotinib, gefitinib, icotinib, and osimertinib was systematically studied by determining their enrichment in massively parallel cytotoxicity assays using an endogenous EGFR-depleted cell line. A total of 3914 and 70,475 variants were detected in the constructed EGFR Substitution-Deletion (Sub-Del) and exon 20 Insertion (Ins) libraries. Of the 3914 Sub-Del variants, 221 proliferated fast in the control assay and were sensitive to EGFR-TKIs. For the 70,475 Ins variants, insertions at amino acid positions 770-774 were highly enriched in all 5 TKI cytotoxicity assays. Moreover, the top 5% of the enriched insertion variants included a glycine or serine insertion at high frequency. We present a comprehensive reference for the sensitivity of EGFR variants to five commonly used TKIs. The approach used here should be applicable to other genes and targeted drugs. BACKGROUND: Tyrosine kinase inhibitors (TKIs) therapy is a standard treatment for patients with advanced non-small-cell lung carcinoma (NSCLC) when activating epidermal growth factor receptor (EGFR) mutations are detected. However, except for the well-studied EGFR mutations, most EGFR mutations lack treatment regimens. TRANSLATIONAL SIGNIFICANCE: The results demonstrated that patients with rare EGFR mutations were most likely to benefit from osimertinib therapy compared to afatinib, erlotinib, gefitinib, or icotinib therapy. This study provides a case of deep mutational scanning that simultaneously assayed substitution, deletion, and insertion variants. This approach is applicable for other oncogenes and targeted drugs.
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Affiliation(s)
- Lei An
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | | | - Guangyao Wu
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | - Zhenxing Wang
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | - Zeyuan Shi
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | - Chang Liu
- School of Pharmacy, Henan University, Kaifeng 475000, China
| | - Chunli Wang
- School of Pharmacy, Henan University, Kaifeng 475000, China
| | - Ming Yi
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chenguang Niu
- Key Laboratory of Clinical Resources Translation, The First Affiliated Hospital of Henan University, Kaifeng 475000, China
| | - Shaofeng Duan
- School of Pharmacy, Henan University, Kaifeng 475000, China
| | - Xiaodong Li
- Translational Medicine Center, Huaihe Hospital of Henan University, Henan University, Kaifeng 475000, China
| | - Wenxue Tang
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450000, China; The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Kongming Wu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shuqing Chen
- Shenzhen Typhoon HealthCare, Shenzhen 518000, China.
| | - Hongen Xu
- Precision Medicine Center, Academy of Medical Science, Zhengzhou University, Zhengzhou 450000, China; The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China.
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30
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Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
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Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
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31
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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. [PMID: 36051311 PMCID: PMC9432854 DOI: 10.1021/acs.iecr.1c04943] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Proteins are Nature's molecular machinery and comprise diverse roles while consisting of chemically similar building blocks. In recent years, protein engineering and design have become important research areas, with many applications in the pharmaceutical, energy, and biocatalysis fields, among others-where the aim is to ultimately create a protein given desired structural and functional properties. It is often critical to model the relationship between a protein's sequence, folded structure, and biological function to assist in such protein engineering pursuits. However, significant challenges remain in concretely mapping an amino acid sequence to specific protein properties and biological activities. Mutations may enhance or diminish molecular protein function, and the epistatic interactions between mutations result in an inherently complex mapping between genetic modifications and protein function. Therefore, estimating the quantitative effects of mutations on protein function(s) remains a grand challenge of biology, bioinformatics, and many related fields and would rapidly accelerate protein engineering tasks when successful. Such estimation is often known as variant effect prediction (VEP). However, progress has been demonstrated in recent years with the development of machine learning (ML) methods in modeling the relationship between mutations and protein function. In this Review, recent advances in variant effect prediction (VEP) are discussed as tools for protein engineering, focusing on techniques incorporating gains from the broader ML community and challenges in estimating biomolecular functional differences. Primary developments highlighted include convolutional neural networks, graph neural networks, and natural language embeddings for protein sequences.
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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 and Department of Bioengineering, University of Illinois Urbana-Champaign, Champaign, Illinois 61801, United States; Department of Plant Biology, Cancer Center at Illinois, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Champaign, Illinois 61801, United States
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32
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Linking protein structural and functional change to mutation using amino acid networks. PLoS One 2022; 17:e0261829. [PMID: 35061689 PMCID: PMC8782487 DOI: 10.1371/journal.pone.0261829] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 12/11/2021] [Indexed: 11/30/2022] Open
Abstract
The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.
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33
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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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34
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Katrekar D, Xiang Y, Palmer N, Saha A, Meluzzi D, Mali P. Comprehensive interrogation of the ADAR2 deaminase domain for engineering enhanced RNA editing activity and specificity. eLife 2022; 11:e75555. [PMID: 35044296 PMCID: PMC8809894 DOI: 10.7554/elife.75555] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 01/18/2022] [Indexed: 11/17/2022] Open
Abstract
Adenosine deaminases acting on RNA (ADARs) can be repurposed to enable programmable RNA editing, however their exogenous delivery leads to transcriptome-wide off-targeting, and additionally, enzymatic activity on certain RNA motifs, especially those flanked by a 5' guanosine is very low thus limiting their utility as a transcriptome engineering toolset. Towards addressing these issues, we first performed a novel deep mutational scan of the ADAR2 deaminase domain, directly measuring the impact of every amino acid substitution across 261 residues, on RNA editing. This enabled us to create a domain-wide mutagenesis map while also revealing a novel hyperactive variant with improved enzymatic activity at 5'-GAN-3' motifs. As overexpression of ADAR enzymes, especially hyperactive variants, can lead to significant transcriptome-wide off-targeting, we next engineered a split-ADAR2 deaminase which resulted in >100-fold more specific RNA editing as compared to full-length deaminase overexpression. Taken together, we anticipate this systematic engineering of the ADAR2 deaminase domain will enable broader utility of the ADAR toolset for RNA biotechnology applications.
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Affiliation(s)
- Dhruva Katrekar
- Department of Bioengineering, University of California San DiegoSan DiegoUnited States
| | - Yichen Xiang
- Department of Bioengineering, University of California San DiegoSan DiegoUnited States
| | - Nathan Palmer
- Division of Biological Sciences, University of California San DiegoSan DiegoUnited States
| | - Anushka Saha
- Department of Bioengineering, University of California San DiegoSan DiegoUnited States
| | - Dario Meluzzi
- Department of Bioengineering, University of California San DiegoSan DiegoUnited States
| | - Prashant Mali
- Department of Bioengineering, University of California San DiegoSan DiegoUnited States
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35
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Sharma P, Procko E, Kranz DM. Engineering Proteins by Combining Deep Mutational Scanning and Yeast Display. Methods Mol Biol 2022; 2491:117-142. [PMID: 35482188 DOI: 10.1007/978-1-0716-2285-8_7] [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] [Indexed: 06/14/2023]
Abstract
Protein engineering using display platforms such as yeast display and phage display has allowed discovery of proteins with therapeutic and industrial applications. Antibodies and T cell receptors developed for therapeutic applications are often engineered by constructing libraries of mutations in loops of five to ten residues called complementarity determining regions that are in proximity to the antigen. In the past decade, deep mutational scanning has become a powerful tool in a protein engineer's toolbox, as it allows one to compare the impact of all 20 amino acids at each position, across the length of the protein. Thus, a single experiment can provide a sequence-activity landscape with information about hotspots or suboptimal binding sites in the original proteins. These residues or regions may be overlooked by engineering methods that are driven solely by structures or directed evolution of error-prone PCR libraries. Here, we describe experimental methods to engineer proteins by combining yeast display and deep mutational scanning mutagenesis, using T cell receptors as an example.
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Affiliation(s)
- Preeti Sharma
- Department of Biochemistry, University of Illinois, Urbana, IL, USA
- Cancer Center at Illinois, University of Illinois, Urbana, IL, USA
| | - Erik Procko
- Department of Biochemistry, University of Illinois, Urbana, IL, USA
- Cancer Center at Illinois, University of Illinois, Urbana, IL, USA
| | - David M Kranz
- Department of Biochemistry, University of Illinois, Urbana, IL, USA.
- Cancer Center at Illinois, University of Illinois, Urbana, IL, USA.
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36
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Intelligent host engineering for metabolic flux optimisation in biotechnology. Biochem J 2021; 478:3685-3721. [PMID: 34673920 PMCID: PMC8589332 DOI: 10.1042/bcj20210535] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.
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37
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Saito A, Yamashita M. HIV-1 capsid variability: viral exploitation and evasion of capsid-binding molecules. Retrovirology 2021; 18:32. [PMID: 34702294 PMCID: PMC8549334 DOI: 10.1186/s12977-021-00577-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/13/2021] [Indexed: 11/17/2022] Open
Abstract
The HIV-1 capsid, a conical shell encasing viral nucleoprotein complexes, is involved in multiple post-entry processes during viral replication. Many host factors can directly bind to the HIV-1 capsid protein (CA) and either promote or prevent HIV-1 infection. The viral capsid is currently being explored as a novel target for therapeutic interventions. In the past few decades, significant progress has been made in our understanding of the capsid–host interactions and mechanisms of action of capsid-targeting antivirals. At the same time, a large number of different viral capsids, which derive from many HIV-1 mutants, naturally occurring variants, or diverse lentiviruses, have been characterized for their interactions with capsid-binding molecules in great detail utilizing various experimental techniques. This review provides an overview of how sequence variation in CA influences phenotypic properties of HIV-1. We will focus on sequence differences that alter capsid–host interactions and give a brief account of drug resistant mutations in CA and their mutational effects on viral phenotypes. Increased knowledge of the sequence-function relationship of CA helps us deepen our understanding of the adaptive potential of the viral capsid.
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Affiliation(s)
- Akatsuki Saito
- Department of Veterinary Medicine, Faculty of Agriculture, University of Miyazaki, Miyazaki, Miyazaki, Japan.,Center for Animal Disease Control, University of Miyazaki, Miyazaki, Miyazaki, Japan
| | - Masahiro Yamashita
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
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38
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Shams A, Higgins SA, Fellmann C, Laughlin TG, Oakes BL, Lew R, Kim S, Lukarska M, Arnold M, Staahl BT, Doudna JA, Savage DF. Comprehensive deletion landscape of CRISPR-Cas9 identifies minimal RNA-guided DNA-binding modules. Nat Commun 2021; 12:5664. [PMID: 34580310 PMCID: PMC8476515 DOI: 10.1038/s41467-021-25992-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 09/10/2021] [Indexed: 11/28/2022] Open
Abstract
Proteins evolve through the modular rearrangement of elements known as domains. Extant, multidomain proteins are hypothesized to be the result of domain accretion, but there has been limited experimental validation of this idea. Here, we introduce a technique for genetic minimization by iterative size-exclusion and recombination (MISER) for comprehensively making all possible deletions of a protein. Using MISER, we generate a deletion landscape for the CRISPR protein Cas9. We find that the catalytically-dead Streptococcus pyogenes Cas9 can tolerate large single deletions in the REC2, REC3, HNH, and RuvC domains, while still functioning in vitro and in vivo, and that these deletions can be stacked together to engineer minimal, DNA-binding effector proteins. In total, our results demonstrate that extant proteins retain significant modularity from the accretion process and, as genetic size is a major limitation for viral delivery systems, establish a general technique to improve genome editing and gene therapy-based therapeutics.
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Affiliation(s)
- Arik Shams
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Sean A Higgins
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720, USA
- Scribe Therapeutics, Alameda, CA, 94501, USA
| | - Christof Fellmann
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
- Gladstone Institutes, San Francisco, CA, 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Thomas G Laughlin
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
- Division of Biological Sciences, University of California, San Diego, San Diego, CA, 92093, USA
| | - Benjamin L Oakes
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720, USA
- Scribe Therapeutics, Alameda, CA, 94501, USA
| | - Rachel Lew
- Gladstone Institutes, San Francisco, CA, 94158, USA
| | - Shin Kim
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Maria Lukarska
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Madeline Arnold
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Brett T Staahl
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720, USA
- Scribe Therapeutics, Alameda, CA, 94501, USA
| | - Jennifer A Doudna
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720, USA
- Gladstone Institutes, San Francisco, CA, 94158, USA
- Graduate Group in Biophysics, University of California, Berkeley, Berkeley, CA, 94720, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, 94720, USA
- Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, CA, 94720, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - David F Savage
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, 94720, USA.
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720, USA.
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39
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Baumer ZT, Whitehead TA. The inner workings of an enzyme. Science 2021; 373:391-392. [PMID: 34437105 DOI: 10.1126/science.abj8346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Zachary T Baumer
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO 80305, USA
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO 80305, USA.
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40
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Markin CJ, Mokhtari DA, Sunden F, Appel MJ, Akiva E, Longwell SA, Sabatti C, Herschlag D, Fordyce PM. Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics. Science 2021; 373:373/6553/eabf8761. [PMID: 34437092 DOI: 10.1126/science.abf8761] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 05/24/2021] [Indexed: 12/21/2022]
Abstract
Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK (High-Throughput Microfluidic Enzyme Kinetics), a microfluidic platform for high-throughput expression, purification, and characterization of more than 1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA (phosphate-irrepressible alkaline phosphatase of Flavobacterium), we performed more than 670,000 reactions and determined more than 5000 kinetic and physical constants for multiple substrates and inhibitors. We uncovered extensive kinetic partitioning to a misfolded state and isolated catalytic effects, revealing spatially contiguous regions of residues linked to particular aspects of function. Regions included active-site proximal residues but extended to the enzyme surface, providing a map of underlying architecture not possible to derive from existing approaches. HT-MEK has applications that range from understanding molecular mechanisms to medicine, engineering, and design.
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Affiliation(s)
- C J Markin
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - D A Mokhtari
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - F Sunden
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - M J Appel
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - E Akiva
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
| | - S A Longwell
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - C Sabatti
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.,Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - D Herschlag
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA. .,Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.,ChEM-H Institute, Stanford University, Stanford, CA 94305, USA
| | - P M Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. .,ChEM-H Institute, Stanford University, Stanford, CA 94305, USA.,Department of Genetics, Stanford University, Stanford, CA 94305, USA.,Chan Zuckerberg Biohub; San Francisco, CA 94110, USA
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41
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Medina E, Yik EJ, Herdewijn P, Chaput JC. Functional Comparison of Laboratory-Evolved XNA Polymerases for Synthetic Biology. ACS Synth Biol 2021; 10:1429-1437. [PMID: 34029459 DOI: 10.1021/acssynbio.1c00048] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Artificial genetic polymers (XNAs) have enormous potential as new materials for synthetic biology, biotechnology, and molecular medicine; yet, very little is known about the biochemical properties of XNA polymerases that have been developed to synthesize and reverse-transcribe XNA polymers. Here, we compare the substrate specificity, thermal stability, reverse transcriptase activity, and fidelity of laboratory-evolved polymerases that were established to synthesize RNA, 2'-fluoroarabino nucleic acid (FANA), arabino nucleic acid (ANA), hexitol nucleic acid (HNA), threose nucleic acid (TNA), and phosphonomethylthreosyl nucleic acid (PMT). We find that the mutations acquired to facilitate XNA synthesis increase the tolerance of the enzymes for sugar-modified substrates with some sacrifice to protein-folding stability. Bst DNA polymerase was found to have weak reverse transcriptase activity on ANA and uncontrolled reverse transcriptase activity on HNA, differing from its known recognition of FANA and TNA templates. These data benchmark the activity of current XNA polymerases and provide opportunities for generating new polymerase variants that function with greater activity and substrate specificity.
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Affiliation(s)
| | | | - Piet Herdewijn
- KU Leuven, Rega Institute for Medical Research, Herestraat 49-bus 1041, 3000 Leuven, Belgium
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42
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Kutlu Y, Ben-Tal N, Haliloglu T. Global Dynamics Renders Protein Sites with High Functional Response. J Phys Chem B 2021; 125:4734-4745. [PMID: 33914546 DOI: 10.1021/acs.jpcb.1c02511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Deep mutational scanning enables examination of the effects of many mutations at each amino acid position in a query protein, readily disclosing positions that are particularly sensitive. Mutations in these positions alter protein function the most. Here, on the premise that dynamics underlie function, we explore to what extent the measured sensitivity to mutations could be linked to-perhaps be explained by-the structural dynamics of the protein. We employ a minimalist perturbation-response approach based on the Gaussian Network Model (GNM) on a data set of seven proteins with deep mutational scanning data. The analysis shows that the mutation-sensitive positions are often of capacity to modulate the global dynamics and to intermediate allosteric interactions in the structure. With that, upon strain perturbation, these positions decrease residue fluctuations the most, affecting function via entropy changes. This is particularly relevant for positions that are distant from binding sites or other functional regions of the protein and are sensitive to mutations, nevertheless. Our results indicate that mutations in these positions allosterically manipulate protein function.
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Affiliation(s)
- Yiǧit Kutlu
- Department of Chemical Engineering and Polymer Research Center, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Turkan Haliloglu
- Department of Chemical Engineering and Polymer Research Center, Bogazici University, Bebek, Istanbul 34342, Turkey
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43
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Yang G, Miton CM, Tokuriki N. A mechanistic view of enzyme evolution. Protein Sci 2020; 29:1724-1747. [PMID: 32557882 PMCID: PMC7380680 DOI: 10.1002/pro.3901] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/14/2020] [Accepted: 06/16/2020] [Indexed: 12/15/2022]
Abstract
New enzyme functions often evolve through the recruitment and optimization of latent promiscuous activities. How do mutations alter the molecular architecture of enzymes to enhance their activities? Can we infer general mechanisms that are common to most enzymes, or does each enzyme require a unique optimization process? The ability to predict the location and type of mutations necessary to enhance an enzyme's activity is critical to protein engineering and rational design. In this review, via the detailed examination of recent studies that have shed new light on the molecular changes underlying the optimization of enzyme function, we provide a mechanistic perspective of enzyme evolution. We first present a global survey of the prevalence of activity-enhancing mutations and their distribution within protein structures. We then delve into the molecular solutions that mediate functional optimization, specifically highlighting several common mechanisms that have been observed across multiple examples. As distinct protein sequences encounter different evolutionary bottlenecks, different mechanisms are likely to emerge along evolutionary trajectories toward improved function. Identifying the specific mechanism(s) that need to be improved upon, and tailoring our engineering efforts to each sequence, may considerably improve our chances to succeed in generating highly efficient catalysts in the future.
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Affiliation(s)
- Gloria Yang
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Charlotte M. Miton
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Nobuhiko Tokuriki
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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44
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Abstract
DNA polymerases play a central role in biology by transferring genetic information from one generation to the next during cell division. Harnessing the power of these enzymes in the laboratory has fueled an increase in biomedical applications that involve the synthesis, amplification, and sequencing of DNA. However, the high substrate specificity exhibited by most naturally occurring DNA polymerases often precludes their use in practical applications that require modified substrates. Moving beyond natural genetic polymers requires sophisticated enzyme-engineering technologies that can be used to direct the evolution of engineered polymerases that function with tailor-made activities. Such efforts are expected to uniquely drive emerging applications in synthetic biology by enabling the synthesis, replication, and evolution of synthetic genetic polymers with new physicochemical properties.
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45
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Nikoomanzar A, Vallejo D, Yik EJ, Chaput JC. Programmed Allelic Mutagenesis of a DNA Polymerase with Single Amino Acid Resolution. ACS Synth Biol 2020; 9:1873-1881. [PMID: 32531152 DOI: 10.1021/acssynbio.0c00236] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Most DNA polymerase libraries sample unknown portions of mutational space and are constrained by the limitations of random mutagenesis. Here we describe a programmed allelic mutagenesis (PAM) strategy to comprehensively evaluate all possible single-point mutations in the entire catalytic domain of a replicative DNA polymerase. By applying the PAM strategy with ultrafast high-throughput screening, we show how DNA polymerases can be mapped for allelic mutations that exhibit enhanced activity for unnatural nucleic acid substrates. We suggest that comprehensive missense mutational scans may aid the discovery of specificity determining residues that are necessary for reprogramming the biological functions of natural DNA polymerases.
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Affiliation(s)
- Ali Nikoomanzar
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697-3958, United States
| | - Derek Vallejo
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697-3958, United States
| | - Eric J. Yik
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697-3958, United States
| | - John C. Chaput
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697-3958, United States
- Department of Chemistry, University of California, Irvine, California 92697-3958, United States
- Department of Molecular Biology and Biochemistry, University of California, Irvine, California 92697-3958, United States
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46
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Saad-Roy CM, Arinaminpathy N, Wingreen NS, Levin SA, Akey JM, Grenfell BT. Implications of localized charge for human influenza A H1N1 hemagglutinin evolution: Insights from deep mutational scans. PLoS Comput Biol 2020; 16:e1007892. [PMID: 32584807 PMCID: PMC7316228 DOI: 10.1371/journal.pcbi.1007892] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/21/2020] [Indexed: 01/01/2023] Open
Abstract
Seasonal influenza A viruses of humans evolve rapidly due to strong selection pressures from host immune responses, principally on the hemagglutinin (HA) viral surface protein. Based on mouse transmission experiments, a proposed mechanism for immune evasion consists of increased avidity to host cellular receptors, mediated by electrostatic charge interactions with negatively charged cell surfaces. In support of this, the HA charge of the globally circulating H3N2 has increased over time since its pandemic. However, the same trend was not seen in H1N1 HA sequences. This is counter-intuitive, since immune escape due to increased avidity (due itself to an increase in charge) was determined experimentally. Here, we explore whether patterns of local charge of H1N1 HA can explain this discrepancy and thus further associate electrostatic charge with immune escape and viral evolutionary dynamics. Measures of site-wise functional selection and expected charge computed from deep mutational scan data on an early H1N1 HA yield a striking division of residues into three groups, separated by charge. We then explored evolutionary dynamics of these groups from 1918 to 2008. In particular, one group increases in net charge over time and consists of sites that are evolving the fastest, that are closest to the receptor binding site (RBS), and that are exposed to solvent (i.e., on the surface). By contrast, another group decreases in net charge and consists of sites that are further away from the RBS and evolving slower, but also exposed to solvent. The last group consists of those sites in the HA core, with no change in net charge and that evolve very slowly. Thus, there is a group of residues that follows the same trend as seen for the entire H3N2 HA. It is possible that the H1N1 HA is under other biophysical constraints that result in compensatory decreases in charge elsewhere on the protein. Our results implicate localized charge in HA interactions with host cells, and highlight how deep mutational scan data can inform evolutionary hypotheses. The hemagglutinin (HA) surface protein of influenza A viruses evolves rapidly to evade host immunity, leading to sizable yearly epidemics in human populations. Previous transmission experiments with H1N1 in mice have tied immune escape to an increase in HA avidity for cellular receptors, mediated by electrostatic charge. Furthermore, retrospective sequence analyses from a previous study confirmed that the HA of circulating global H3N2 has increased in net charge, yet surprisingly, that of H1N1 has not varied significantly. How is a stable net charge related to local patterns of H1N1 HA charge in response to selection? To elucidate the role of local electrostatic charge in host-virus interactions, we investigate characteristics of local charge on the H1N1 HA using functional data from deep mutational scan experiments. Combining measures of functional selection and expected charge at each site on DMS data from a 1933 H1N1 HA yields a striking visual pattern that identifies three groups of sites that have different biophysical properties and that prove to have distinct evolutionary patterns in natural human sequences. Essentially, we find evidence for an increase in charge near the receptor binding site and for compensatory changes elsewhere on the protein. Thus, our findings may reconcile disparate results from transmission experiments and natural sequence analyses, and highlight the importance of local properties of the HA protein. Overall, our findings further support the hypothesis of immune escape due to increased HA avidity to cellular receptors mediated by electrostatic charge. More generally, our work illustrates a novel method of leveraging deep mutational scans in conjunction with natural sequences to shed light on virus-host interactions.
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Affiliation(s)
- Chadi M. Saad-Roy
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- * E-mail: (CMSR); (BTG)
| | - Nimalan Arinaminpathy
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Ned S. Wingreen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Joshua M. Akey
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (CMSR); (BTG)
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47
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Medina-Cucurella AV, Bammert GF, Dunkle W, Javens C, Zhu Y, Mutchler VT, Teel JT, Stein CA, Dunham SA, Whitehead TA. Feline Interleukin-31 Shares Overlapping Epitopes with the Oncostatin M Receptor and IL-31RA. Biochemistry 2020; 59:2171-2181. [PMID: 32459958 DOI: 10.1021/acs.biochem.0c00176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Interleukin-31 (IL-31) is a major protein involved in severe inflammatory skin disorders. Its signaling pathway is mediated through two type I cytokine receptors, IL-31RA (also known as the gp130-like receptor) and the oncostatin M receptor (OSMR). Understanding molecular details in these interactions would be helpful for developing antagonist anti-IL-31 monoclonal antibodies (mAbs) as potential therapies. Previous studies suggest that human IL-31 binds to IL-31RA and then recruits OSMR to form a ternary complex. In this model, OSMR cannot interact with IL-31 in the absence of IL-31RA. In this work, we show that feline IL-31 (fIL-31) binds independently with feline OSMR using surface plasmon resonance, an enzyme-linked immunosorbent assay, and yeast surface display. Moreover, competition experiments suggest that OSMR shares a partially overlapping epitope with IL-31RA. We then used deep mutational scanning to map the binding sites of both receptors on fIL-31. In agreement with previous studies of the human homologue, the binding site for IL31-RA contains fIL-31 positions E20 and K82, while the binding site for OSMR comprises the "PADNFERK" motif (P103-K110) and position G38. However, our results also revealed a new overlapping site, composed of positions R69, R72, P73, D76, D81, and E97, between both receptors that we called the "shared site". The conformational epitope of an anti-feline IL-31 mAb that inhibits both OSMR and IL-31RA also mapped to this shared site. Combined, our results show that fIL-31 binds IL-31RA and OSMR independently through a partially shared epitope. These results suggest reexamination of the putative canonical mechanisms for IL-31 signaling in higher animals.
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Affiliation(s)
- Angelica V Medina-Cucurella
- Department of Chemical Engineering and Materials Science, Michigan State University, Engineering Building, 428 South Shaw Lane, Room 2100, East Lansing, Michigan 48824, United States
| | - Gary F Bammert
- Veterinary Medicine Research and Development, Zoetis Global Therapeutic Research, 333 Portage Street, Kalamazoo, Michigan 49007, United States
| | - William Dunkle
- Veterinary Medicine Research and Development, Zoetis Global Therapeutic Research, 333 Portage Street, Kalamazoo, Michigan 49007, United States
| | - Christopher Javens
- Veterinary Medicine Research and Development, Zoetis Global Therapeutic Research, 333 Portage Street, Kalamazoo, Michigan 49007, United States
| | - Yaqi Zhu
- Veterinary Medicine Research and Development, Zoetis Global Therapeutic Research, 333 Portage Street, Kalamazoo, Michigan 49007, United States
| | - Veronica T Mutchler
- Veterinary Medicine Research and Development, Zoetis Global Therapeutic Research, 333 Portage Street, Kalamazoo, Michigan 49007, United States
| | - Janet T Teel
- Veterinary Medicine Research and Development, Zoetis Global Therapeutic Research, 333 Portage Street, Kalamazoo, Michigan 49007, United States
| | - Caitlin A Stein
- Department of Chemical Engineering and Materials Science, Michigan State University, Engineering Building, 428 South Shaw Lane, Room 2100, East Lansing, Michigan 48824, United States
| | - Steve A Dunham
- Veterinary Medicine Research and Development, Zoetis Global Therapeutic Research, 333 Portage Street, Kalamazoo, Michigan 49007, United States
| | - Timothy A Whitehead
- Department of Chemical Engineering and Materials Science, Michigan State University, Engineering Building, 428 South Shaw Lane, Room 2100, East Lansing, Michigan 48824, United States.,Department of Chemical and Biological Engineering, University of Colorado, Boulder, Colorado 80303, United States
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Vallejo D, Nikoomanzar A, Chaput JC. Directed evolution of custom polymerases using droplet microfluidics. Methods Enzymol 2020; 644:227-253. [PMID: 32943147 DOI: 10.1016/bs.mie.2020.04.056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
DNA polymerases are critical tools for a large number of emerging applications in biotechnology, but oftentimes polymerases with desired functions are not readily available. Directed evolution provides a possible solution to this problem by enabling the creation of engineered polymerases that are better equipped to recognize a given unnatural substrate. Here we report a microfluidic-based method for evolving new polymerase functions that involves ultrahigh throughput sorting of fluorescent water-in-oil (w/o) microdroplets. The workflow entails the expression of a diverse population of polymerase variants in E. coli, production of microfluidic droplets containing one or less E. coli, bacteria lysis to release the polymerase and encoding plasmid into the surrounding droplet, a fluorescence-based activity assay to identify variants with a desired activity, isolation of fluorescent droplets using a fluorescence activated droplet sorting (FADS) device, and plasmid recovery with DNA sequencing to determine the identity of the functional variants. This technique is amenable to any type of unnatural nucleic acid and/or polymerase function, including DNA-templated synthesis, reverse transcription, and replication.
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Affiliation(s)
- Derek Vallejo
- Departments of Pharmaceutical Sciences, Chemistry, Molecular Biology, and Biochemistry, University of California, Irvine, CA, United States
| | - Ali Nikoomanzar
- Departments of Pharmaceutical Sciences, Chemistry, Molecular Biology, and Biochemistry, University of California, Irvine, CA, United States
| | - John C Chaput
- Departments of Pharmaceutical Sciences, Chemistry, Molecular Biology, and Biochemistry, University of California, Irvine, CA, United States.
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Miller M, Vitale D, Kahn PC, Rost B, Bromberg Y. funtrp: identifying protein positions for variation driven functional tuning. Nucleic Acids Res 2020; 47:e142. [PMID: 31584091 PMCID: PMC6868392 DOI: 10.1093/nar/gkz818] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 09/05/2019] [Accepted: 09/12/2019] [Indexed: 12/12/2022] Open
Abstract
Evaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. An in-depth understanding of sequence changes is also fundamental for synthetic protein design and stability assessments. However, the variant effect predictor performance gain observed in recent years has not kept up with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene and protein features for modeling variant effects, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the variation observable in vivo is arguably weaker in its impact, thus requiring evaluation at a higher level of resolution. Here, we describe functionNeutral/Toggle/Rheostatpredictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types can improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.
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Affiliation(s)
- Maximilian Miller
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ 08901, USA
| | - Daniel Vitale
- Columbian College of Arts and Sciences Data Science Program Corcoran Hall, 725 21st Street NW, Washington, DC 20052, USA
| | - Peter C Kahn
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ 08901, USA
| | - Burkhard Rost
- Department for Bioinformatics and Computational Biology, Technische Universität München, Boltzmannstr. 3, 85748 Garching/Munich, Germany.,Institute for Advanced Study at Technische Universität München (TUM-IAS), Lichtenbergstraße 2a 85748 Garching/Munich, Germany
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ 08901, USA.,Institute for Advanced Study at Technische Universität München (TUM-IAS), Lichtenbergstraße 2a 85748 Garching/Munich, Germany.,Department of Genetics, Rutgers University, Human Genetics Institute, Life Sciences Building, 145 Bevier Road, Piscataway, NJ 08854, USA
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50
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Cerchione D, Loveluck K, Tillotson EL, Harbinski F, DaSilva J, Kelley CP, Keston-Smith E, Fernandez CA, Myer VE, Jayaram H, Steinberg BE. SMOOT libraries and phage-induced directed evolution of Cas9 to engineer reduced off-target activity. PLoS One 2020; 15:e0231716. [PMID: 32298334 PMCID: PMC7161989 DOI: 10.1371/journal.pone.0231716] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 03/30/2020] [Indexed: 12/26/2022] Open
Abstract
RNA-guided endonucleases such as Cas9 provide efficient on-target genome editing in cells but may also cleave at off-target loci throughout the genome. Engineered variants of Streptococcus pyogenes Cas9 (SpCas9) have been developed to globally reduce off-target activity, but individual off-targets may remain, or on-target activity may be compromised. In order to evolve against activity at specific off-targets while maintaining strong on-target editing, we developed a novel M13 bacteriophage-mediated selection method. Using this method, sequential rounds of positive and negative selection are used to identify mutations to Cas9 that enhance or diminish editing activity at particular genomic sequences. We also introduce scanning mutagenesis of oligo-directed targets (SMOOT), a comprehensive mutagenesis method to create highly diverse libraries of Cas9 variants that can be challenged with phage-based selection. Our platform identifies novel SpCas9 mutants which mitigate cleavage against off-targets both in biochemical assays and in T-cells while maintaining higher on-target activity than previously described variants. We describe an evolved variant, S. pyogenes Adapted to Reduce Target Ambiguity Cas9 (SpartaCas), composed of the most enriched mutations, each of unknown function. This evolved Cas9 mutant reduces off-target cleavage while preserving efficient editing at multiple therapeutically relevant targets. Directed evolution of Cas9 using our system demonstrates an improved structure-independent methodology to effectively engineer nuclease activity.
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Affiliation(s)
- Derek Cerchione
- Editas Medicine, Cambridge, Massachusetts, United States of America
| | | | | | - Fred Harbinski
- Editas Medicine, Cambridge, Massachusetts, United States of America
| | - Jen DaSilva
- Editas Medicine, Cambridge, Massachusetts, United States of America
| | - Chase P. Kelley
- Editas Medicine, Cambridge, Massachusetts, United States of America
| | | | | | - Vic E. Myer
- Editas Medicine, Cambridge, Massachusetts, United States of America
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