1
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Gao CY, Yang GY, Ding XW, Xu JH, Cheng X, Zheng GW, Chen Q. Engineering of Halide Methyltransferase BxHMT through Dynamic Cross-Correlation Network Analysis. Angew Chem Int Ed Engl 2024; 63:e202401235. [PMID: 38623716 DOI: 10.1002/anie.202401235] [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: 01/18/2024] [Revised: 03/18/2024] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Halide methyltransferases (HMTs) provide an effective way to regenerate S-adenosyl methionine (SAM) from S-adenosyl homocysteine and reactive electrophiles, such as methyl iodide (MeI) and methyl toluene sulfonate (MeOTs). As compared with MeI, the cost-effective unnatural substrate MeOTs can be accessed directly from cheap and abundant alcohols, but shows only limited reactivity in SAM production. In this study, we developed a dynamic cross-correlation network analysis (DCCNA) strategy for quickly identifying hot spots influencing the catalytic efficiency of the enzyme, and applied it to the evolution of HMT from Paraburkholderia xenovorans. Finally, the optimal mutant, M4 (V55T/C125S/L127T/L129P), exhibited remarkable improvement, with a specific activity of 4.08 U/mg towards MeOTs, representing an 82-fold increase as compared to the wild-type (WT) enzyme. Notably, M4 also demonstrated a positive impact on the catalytic ability with other methyl donors. The structural mechanism behind the enhanced enzyme activity was uncovered by molecular dynamics simulations. Our work not only contributes a promising biocatalyst for the regeneration of SAM, but also offers a strategy for efficient enzyme engineering.
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Affiliation(s)
- Chun-Yu Gao
- State Key Laboratory of Bioreactor Engineering and Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, 200237, China
| | - Gui-Ying Yang
- State Key Laboratory of Bioreactor Engineering and Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, 200237, China
| | - Xu-Wei Ding
- State Key Laboratory of Bioreactor Engineering and Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, 200237, China
| | - Jian-He Xu
- State Key Laboratory of Bioreactor Engineering and Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, 200237, China
| | - Xiaolin Cheng
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, OH 43210, United States
| | - Gao-Wei Zheng
- State Key Laboratory of Bioreactor Engineering and Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, 200237, China
| | - Qi Chen
- State Key Laboratory of Bioreactor Engineering and Shanghai Collaborative Innovation Center for Biomanufacturing, East China University of Science and Technology, Shanghai, 200237, China
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2
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Billerbeck S, Walker RSK, Pretorius IS. Killer yeasts: expanding frontiers in the age of synthetic biology. Trends Biotechnol 2024:S0167-7799(24)00067-2. [PMID: 38575438 DOI: 10.1016/j.tibtech.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/07/2024] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
Killer yeasts secrete protein toxins that are selectively lethal to other yeast and filamentous fungi. These exhibit exceptional genetic and functional diversity, and have several biotechnological applications. However, despite decades of research, several limitations hinder their widespread adoption. In this perspective we contend that technical advances in synthetic biology present an unprecedented opportunity to unlock the full potential of yeast killer systems across a spectrum of applications. By leveraging these new technologies, engineered killer toxins may emerge as a pivotal new tool to address antifungal resistance and food security. Finally, we speculate on the biotechnological potential of re-engineering host double-stranded (ds) RNA mycoviruses, from which many toxins derive, as a safe and noninfectious system to produce designer RNA.
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Affiliation(s)
- Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology institute, University of Groningen, Groningen 9747, AG, The Netherlands
| | - Roy S K Walker
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia; ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia.
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3
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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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4
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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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5
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Yang J, Li FZ, Arnold FH. Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering. ACS CENTRAL SCIENCE 2024; 10:226-241. [PMID: 38435522 PMCID: PMC10906252 DOI: 10.1021/acscentsci.3c01275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/26/2023] [Accepted: 01/16/2024] [Indexed: 03/05/2024]
Abstract
Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.
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Affiliation(s)
- Jason Yang
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Francesca-Zhoufan Li
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Frances H. Arnold
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
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6
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Couce A, Limdi A, Magnan M, Owen SV, Herren CM, Lenski RE, Tenaillon O, Baym M. Changing fitness effects of mutations through long-term bacterial evolution. Science 2024; 383:eadd1417. [PMID: 38271521 DOI: 10.1126/science.add1417] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/12/2023] [Indexed: 01/27/2024]
Abstract
The distribution of fitness effects of new mutations shapes evolution, but it is challenging to observe how it changes as organisms adapt. Using Escherichia coli lineages spanning 50,000 generations of evolution, we quantify the fitness effects of insertion mutations in every gene. Macroscopically, the fraction of deleterious mutations changed little over time whereas the beneficial tail declined sharply, approaching an exponential distribution. Microscopically, changes in individual gene essentiality and deleterious effects often occurred in parallel; altered essentiality is only partly explained by structural variation. The identity and effect sizes of beneficial mutations changed rapidly over time, but many targets of selection remained predictable because of the importance of loss-of-function mutations. Taken together, these results reveal the dynamic-but statistically predictable-nature of mutational fitness effects.
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Affiliation(s)
- Alejandro Couce
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
- Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain
| | - Anurag Limdi
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Melanie Magnan
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
| | - Siân V Owen
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Cristina M Herren
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
- Department of Marine and Environmental Sciences, Northeastern University, Boston, MA 02115, USA
| | - Richard E Lenski
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
- Program in Ecology, Evolution, and Behavior, Michigan State University, East Lansing, MI 48824, USA
| | - Olivier Tenaillon
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
- Université Paris Cité, Inserm, Institut Cochin, F-75014 Paris, France
| | - Michael Baym
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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7
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Stock M, Gorochowski TE. Open-endedness in synthetic biology: A route to continual innovation for biological design. SCIENCE ADVANCES 2024; 10:eadi3621. [PMID: 38241375 DOI: 10.1126/sciadv.adi3621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024]
Abstract
Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.
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Affiliation(s)
- Michiel Stock
- KERMIT & Biobix, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK
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8
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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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9
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Notin P, Kollasch AW, Ritter D, van Niekerk L, Paul S, Spinner H, Rollins N, Shaw A, Weitzman R, Frazer J, Dias M, Franceschi D, Orenbuch R, Gal Y, Marks DS. ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570727. [PMID: 38106144 PMCID: PMC10723403 DOI: 10.1101/2023.12.07.570727] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.
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Affiliation(s)
| | | | | | | | | | | | | | - Ada Shaw
- Applied Mathematics, Harvard University
| | | | | | - Mafalda Dias
- Centre for Genomic Regulation, Universitat Pompeu Fabra
| | | | | | - Yarin Gal
- Computer Science, University of Oxford
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10
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Qu Y, Niu Z, Ding Q, Zhao T, Kong T, Bai B, Ma J, Zhao Y, Zheng J. Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction. Int J Mol Sci 2023; 24:16496. [PMID: 38003686 PMCID: PMC10671426 DOI: 10.3390/ijms242216496] [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: 10/13/2023] [Revised: 11/11/2023] [Accepted: 11/17/2023] [Indexed: 11/26/2023] Open
Abstract
Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction. There remains considerable untapped potential in these developed models, such as whether the prediction performance can be further improved by integrating different models to further improve the accuracy of prediction. Furthermore, how to utilize large-scale models for prediction methods of mutational effects on quantifiable properties of proteins due to the nonlinear relationship between protein fitness and the quantification of specific functionalities has yet to be explored thoroughly. In this study, we propose an ensemble learning approach for predicting mutational effects of proteins integrating protein sequence features extracted from multiple large protein language models, as well as evolutionarily coupled features extracted in homologous sequences, while comparing the differences between linear regression and deep learning models in mapping these features to quantifiable functional changes. We tested our approach on a dataset of 17 protein deep mutation scans and indicated that the integrated approach together with linear regression enables the models to have higher prediction accuracy and generalization. Moreover, we further illustrated the reliability of the integrated approach by exploring the differences in the predictive performance of the models across species and protein sequence lengths, as well as by visualizing clustering of ensemble and non-ensemble features.
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Affiliation(s)
- Yang Qu
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Zitong Niu
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Qiaojiao Ding
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Taowa Zhao
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Tong Kong
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Bing Bai
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Jianwei Ma
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Yitian Zhao
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
| | - Jianping Zheng
- Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo 315300, China; (Y.Q.); (Z.N.); (Q.D.); (T.Z.)
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315300, China; (T.K.); (B.B.); (J.M.)
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11
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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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12
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Deng J, Cui Q. Second-Shell Residues Contribute to Catalysis by Predominately Preorganizing the Apo State in PafA. J Am Chem Soc 2023; 145:11333-11347. [PMID: 37172218 PMCID: PMC10810092 DOI: 10.1021/jacs.3c02423] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Residues beyond the first coordination shell are often observed to make considerable cumulative contributions in enzymes. Due to typically indirect perturbations of multiple physicochemical properties of the active site, however, their individual and specific roles in enzyme catalysis and disease-causing mutations remain difficult to predict and understand at the molecular level. Here we analyze the contributions of several second-shell residues in phosphate-irrepressible alkaline phosphatase of flavobacterium (PafA), a representative system as one of the most efficient enzymes. By adopting a multifaceted approach that integrates quantum-mechanical/molecular-mechanical free energy computations, molecular-mechanical molecular dynamics simulations, and density functional theory cluster model calculations, we probe the rate-limiting phosphoryl transfer step and structural properties of all relevant enzyme states. In combination with available experimental data, our computational results show that mutations of the studied second-shell residues impact catalytic efficiency mainly by perturbation of the apo state and therefore substrate binding, while they do not affect the ground state or alter the nature of phosphoryl transfer transition state significantly. Several second-shell mutations also modulate the active site hydration level, which in turn influences the energetics of phosphoryl transfer. These mechanistic insights also help inform strategies that may improve the efficiency of enzyme design and engineering by going beyond the current focus on the first coordination shell.
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Affiliation(s)
- Jiahua Deng
- 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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Chen Y, Hu R, Li K, Zhang Y, Fu L, Zhang J, Si T. Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects. ACS Synth Biol 2023; 12:1461-1473. [PMID: 37066862 PMCID: PMC10204710 DOI: 10.1021/acssynbio.2c00662] [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: 12/08/2022] [Indexed: 04/18/2023]
Abstract
Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from Chlamydomonas reinhardtii, we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.
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Affiliation(s)
- Yongcan Chen
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruyun Hu
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Keyi Li
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yating Zhang
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lihao Fu
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianzhi Zhang
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tong Si
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- BGI-Shenzhen, Shenzhen 518083, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
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14
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Gantz M, Neun S, Medcalf EJ, van Vliet LD, Hollfelder F. Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments. Chem Rev 2023; 123:5571-5611. [PMID: 37126602 PMCID: PMC10176489 DOI: 10.1021/acs.chemrev.2c00910] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >107-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >107 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.
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Affiliation(s)
- Maximilian Gantz
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
| | - Stefanie Neun
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
| | - Elliot J Medcalf
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
| | - Liisa D van Vliet
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
| | - Florian Hollfelder
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
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15
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Sugiki S, Niide T, Toya Y, Shimizu H. Logistic Regression-Guided Identification of Cofactor Specificity-Contributing Residues in Enzyme with Sequence Datasets Partitioned by Catalytic Properties. ACS Synth Biol 2022; 11:3973-3985. [PMID: 36321539 PMCID: PMC9764414 DOI: 10.1021/acssynbio.2c00315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Changing the substrate/cofactor specificity of an enzyme requires multiple mutations at spatially adjacent positions around the substrate pocket. However, this is challenging when solely based on crystal structure information because enzymes undergo dynamic conformational changes during the reaction process. Herein, we proposed a method for estimating the contribution of each amino acid residue to substrate specificity by deploying a phylogenetic analysis with logistic regression. Since this method can estimate the candidate amino acids for mutation by ranking, it is readable and can be used in protein engineering. We demonstrated our concept using redox cofactor conversion of the Escherichia coli malic enzyme as a model, which still lacks crystal structure elucidation. The use of logistic regression with amino acid sequences classified by cofactor specificity showed that the NADP+-dependent malic enzyme completely switched cofactor specificity to NAD+ dependence without the need for a practical screening step. The model showed that surrounding residues made a greater contribution to cofactor specificity than those in the interior of the substrate pocket. These residues might be difficult to identify from crystal structure observations. We show that a highly accurate and inferential machine learning model was obtained using amino acid sequences of structurally homologous and functionally distinct enzymes as input data.
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16
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Yan B, Ran X, Gollu A, Cheng Z, Zhou X, Chen Y, Yang ZJ. IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database. J Chem Inf Model 2022; 62:5841-5848. [PMID: 36286319 DOI: 10.1021/acs.jcim.2c01139] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here we describe IntEnzyDB as an integrated structure-kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational database architecture with a flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1050 enzyme structure-kinetics pairs, we investigated the efficiency-perturbing propensities of mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded and that deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we describe a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution.
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Affiliation(s)
- Bailu Yan
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37205, United States
| | - Xinchun Ran
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Anvita Gollu
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zihao Cheng
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Xiang Zhou
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yiwen Chen
- Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - 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.,Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States.,Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37205, United States
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17
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Després PC, Cisneros AF, Alexander EMM, Sonigara R, Gagné-Thivierge C, Dubé AK, Landry CR. Asymmetrical dose responses shape the evolutionary trade-off between antifungal resistance and nutrient use. Nat Ecol Evol 2022; 6:1501-1515. [PMID: 36050399 DOI: 10.1038/s41559-022-01846-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 07/07/2022] [Indexed: 12/22/2022]
Abstract
Antimicrobial resistance is an emerging threat for public health. The success of resistance mutations depends on the trade-off between the benefits and costs they incur. This trade-off is largely unknown and uncharacterized for antifungals. Here, we systematically measure the effect of all amino acid substitutions in the yeast cytosine deaminase Fcy1, the target of the antifungal 5-fluorocytosine (5-FC, flucytosine). We identify over 900 missense mutations granting resistance to 5-FC, a large fraction of which appear to act through destabilization of the protein. The relationship between 5-FC resistance and growth sustained by cytosine deamination is characterized by a sharp trade-off, such that small gains in resistance universally lead to large losses in canonical enzyme function. We show that this steep relationship can be explained by differences in the dose-response functions of 5-FC and cytosine. Finally, we observe the same trade-off shape for the orthologue of FCY1 in Cryptoccocus neoformans, a human pathogen. Our results provide a powerful resource and platform for interpreting drug target variants in fungal pathogens as well as unprecedented insights into resistance-function trade-offs.
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Affiliation(s)
- Philippe C Després
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec, Canada.
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Canada.
- PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, Canada.
- Centre de Recherche sur les Données Massives, Université Laval, Québec, Canada.
| | - Angel F Cisneros
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives, Université Laval, Québec, Canada
| | - Emilie M M Alexander
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives, Université Laval, Québec, Canada
| | - Ria Sonigara
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives, Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Cynthia Gagné-Thivierge
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives, Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Alexandre K Dubé
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, Canada
- Centre de Recherche sur les Données Massives, Université Laval, Québec, Canada
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada
| | - Christian R Landry
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec, Canada.
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Canada.
- PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec, Canada.
- Centre de Recherche sur les Données Massives, Université Laval, Québec, Canada.
- Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec, Canada.
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18
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Substrate multiplexed protein engineering facilitates promiscuous biocatalytic synthesis. Nat Commun 2022; 13:5242. [PMID: 36068220 PMCID: PMC9448781 DOI: 10.1038/s41467-022-32789-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/17/2022] [Indexed: 11/19/2022] Open
Abstract
Enzymes with high activity are readily produced through protein engineering, but intentionally and efficiently engineering enzymes for an expanded substrate scope is a contemporary challenge. One approach to address this challenge is Substrate Multiplexed Screening (SUMS), where enzyme activity is measured on competing substrates. SUMS has long been used to rigorously quantitate native enzyme specificity, primarily for in vivo settings. SUMS has more recently found sporadic use as a protein engineering approach but has not been widely adopted by the field, despite its potential utility. Here, we develop principles of how to design and interpret SUMS assays to guide protein engineering. This rich information enables improving activity with multiple substrates simultaneously, identifies enzyme variants with altered scope, and indicates potential mutational hot-spots as sites for further engineering. These advances leverage common laboratory equipment and represent a highly accessible and customizable method for enzyme engineering. Efficient engineering of enzymes for expanded substrate scope is currently challenging. Here, the authors develop simple principles of how to design and interpret Substrate Multiplexed Screening assays to guide protein engineering to enable activity improvements with simultaneously with multiple substrates.
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19
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Yang T, Ye Z, Lynch MD. "Multiagent" Screening Improves Directed Enzyme Evolution by Identifying Epistatic Mutations. ACS Synth Biol 2022; 11:1971-1983. [PMID: 35507897 DOI: 10.1021/acssynbio.2c00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Enzyme evolution has enabled numerous advances in biotechnology and synthetic biology, yet still requires many iterative rounds of screening to identify optimal mutant sequences. This is due to the sparsity of the fitness landscape, which is caused by epistatic mutations that only offer improvements when combined with other mutations. We report an approach that incorporates diverse substrate analogues in the screening process, where multiple substrates act like multiple agents navigating the fitness landscape, identifying epistatic mutant residues without a need for testing the entire combinatorial search space. We initially validate this approach by engineering a malonyl-CoA synthetase and identify numerous epistatic mutations improving activity for several diverse substrates. The majority of these mutations would have been missed upon screening for a single substrate alone. We expect that this approach can accelerate a wide array of enzyme engineering programs.
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Affiliation(s)
- Tian Yang
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27701, United States
| | - Zhixia Ye
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27701, United States
| | - Michael D. Lynch
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27701, United States
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20
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Lin WC, Tang HC, Wang HY, Kao CY, Chang YC, Li AH, Yang SB, Mou KY. Fragment-Directed Random Mutagenesis by the Reverse Kunkel Method. ACS Synth Biol 2022; 11:1658-1668. [PMID: 35324156 DOI: 10.1021/acssynbio.2c00086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Two fundamentally different approaches are routinely used for protein engineering: user-defined mutagenesis and random mutagenesis, each with its own strengths and weaknesses. Here, we invent a unique mutagenesis protocol, which combines the advantages of user-defined mutagenesis and random mutagenesis. The new method, termed the reverse Kunkel method, allows the user to create random mutations at multiple specified regions in a one-pot reaction. We demonstrated the reverse Kunkel method by mimicking the somatic hypermutation in antibodies that introduces random mutations concentrated in complementarity-determining regions. Coupling with the phage display and yeast display selections, we successfully generated dramatically improved antibodies against a model protein and a neurotransmitter peptide in terms of affinity and immunostaining performance. The reverse Kunkel method is especially suitable for engineering proteins whose activities are determined by multiple variable regions, such as antibodies and adeno-associated virus capsids, or whose functional domains are composed of several discontinuous sequences, such as Cas9 and Cas12a.
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Affiliation(s)
- Wen-Ching Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
| | - Hao-Cheng Tang
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
| | - Han Ying Wang
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
| | - Chia-Yi Kao
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
- Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei 11529, Taiwan
| | - You-Chiun Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
- Taiwan International Graduate Program in Chemical Biology and Molecular Biophysics, National Taiwan University and Academia Sinica, Taipei 11529, Taiwan
| | - Athena Hsu Li
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
- Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Yang Ming Chiao Tung University and Academia Sinica, Taipei 11529, Taiwan
| | - Shi-Bing Yang
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
| | - Kurt Yun Mou
- Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan
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21
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Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape. Nat Ecol Evol 2022; 6:427-438. [PMID: 35210579 DOI: 10.1038/s41559-022-01675-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 12/14/2021] [Indexed: 11/08/2022]
Abstract
Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC50) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.
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22
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Jiang Y, Yan B, Chen Y, Juarez RJ, Yang ZJ. Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates. J Phys Chem B 2022; 126:2486-2495. [PMID: 35324218 DOI: 10.1021/acs.jpcb.2c00142] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme-substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.
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Affiliation(s)
- Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Bailu Yan
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yu Chen
- School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Reecan J Juarez
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - 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.,Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
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23
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Vanella R, Kovacevic G, Doffini V, Fernández de Santaella J, Nash MA. High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering. Chem Commun (Camb) 2022; 58:2455-2467. [PMID: 35107442 PMCID: PMC8851469 DOI: 10.1039/d1cc04635g] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/23/2022] [Indexed: 12/29/2022]
Abstract
Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and in vitro evolution campaigns include improved folding stability, catalytic activity, and/or substrate specificity. Despite significant progress in recent years in the areas of high-throughput screening and DNA sequencing, our ability to explore the vast space of functional enzyme sequences remains severely limited. Here, we review the currently available suite of modern methods for enzyme engineering, with a focus on novel readout systems based on enzyme cascades, and new approaches to reaction compartmentalization including single-cell hydrogel encapsulation techniques to achieve a genotype-phenotype link. We further summarize systematic scanning mutagenesis approaches and their merger with deep mutational scanning and massively parallel next-generation DNA sequencing technologies to generate mutability landscapes. Finally, we discuss the implementation of machine learning models for computational prediction of enzyme phenotypic fitness from sequence. This broad overview of current state-of-the-art approaches for enzyme engineering and evolution will aid newcomers and experienced researchers alike in identifying the important challenges that should be addressed to move the field forward.
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Affiliation(s)
- Rosario Vanella
- Department of Chemistry, University of Basel, 4058 Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
| | - Gordana Kovacevic
- Department of Chemistry, University of Basel, 4058 Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
| | - Vanni Doffini
- Department of Chemistry, University of Basel, 4058 Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
| | - Jaime Fernández de Santaella
- Department of Chemistry, University of Basel, 4058 Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
| | - Michael A Nash
- Department of Chemistry, University of Basel, 4058 Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
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24
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Trivedi VD, Chappell TC, Krishna NB, Shetty A, Sigamani GG, Mohan K, Ramesh A, R PK, Nair NU. In-Depth Sequence–Function Characterization Reveals Multiple Pathways to Enhance Enzymatic Activity. ACS Catal 2022. [DOI: 10.1021/acscatal.1c05508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Vikas D. Trivedi
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Todd C. Chappell
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | | | - Anuj Shetty
- Kcat Enzymatic Private Limited, Bengaluru, Karnataka, India 560005
| | | | - Karishma Mohan
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Athreya Ramesh
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Pravin Kumar R
- Kcat Enzymatic Private Limited, Bengaluru, Karnataka, India 560005
| | - Nikhil U. Nair
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, United States
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25
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Shao Q, Jiang Y, Yang ZJ. EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling. J Chem Inf Model 2022; 62:647-655. [DOI: 10.1021/acs.jcim.1c01424] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Qianzhen Shao
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - 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
- Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
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26
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Mokhtari DA, Appel MJ, Fordyce PM, Herschlag D. High throughput and quantitative enzymology in the genomic era. Curr Opin Struct Biol 2021; 71:259-273. [PMID: 34592682 PMCID: PMC8648990 DOI: 10.1016/j.sbi.2021.07.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 07/23/2021] [Indexed: 12/28/2022]
Abstract
Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.
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Affiliation(s)
- D A Mokhtari
- Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA
| | - M J Appel
- Department of Biochemistry, 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.
| | - 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.
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27
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Kuiper BP, Prins RC, Billerbeck S. Oligo Pools as an Affordable Source of Synthetic DNA for Cost-Effective Library Construction in Protein- and Metabolic Pathway Engineering. Chembiochem 2021; 23:e202100507. [PMID: 34817110 PMCID: PMC9300125 DOI: 10.1002/cbic.202100507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/23/2021] [Indexed: 11/11/2022]
Abstract
The construction of custom libraries is critical for rational protein engineering and directed evolution. Array‐synthesized oligo pools of thousands of user‐defined sequences (up to ∼350 bases in length) have emerged as a low‐cost commercially available source of DNA. These pools cost ≤10 % (depending on error rate and length) of other commercial sources of custom DNA, and this significant cost difference can determine whether an enzyme engineering project can be realized on a given research budget. However, while being cheap, oligo pools do suffer from a low concentration of individual oligos and relatively high error rates. Several powerful techniques that specifically make use of oligo pools have been developed and proven valuable or even essential for next‐generation protein and pathway engineering strategies, such as sequence‐function mapping, enzyme minimization, or de‐novo design. Here we consolidate the knowledge on these techniques and their applications to facilitate the use of oligo pools within the protein engineering community.
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Affiliation(s)
- Bastiaan P Kuiper
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
| | - Rianne C Prins
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
| | - Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
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28
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Heyne M, Shirian J, Cohen I, Peleg Y, Radisky ES, Papo N, Shifman JM. Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes. J Am Chem Soc 2021; 143:17261-17275. [PMID: 34609866 PMCID: PMC8532158 DOI: 10.1021/jacs.1c08707] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Indexed: 12/18/2022]
Abstract
Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.
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Affiliation(s)
- Michael Heyne
- Department
of Biological Chemistry, The Alexander Silberman Institute of Life
Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
- Avram
and Stella Goldstein-Goren Department of Biotechnology Engineering
and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Jason Shirian
- Department
of Biological Chemistry, The Alexander Silberman Institute of Life
Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Itay Cohen
- Avram
and Stella Goldstein-Goren Department of Biotechnology Engineering
and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Yoav Peleg
- Life
Sciences Core Facilities (LSCF) Structural Proteomics Unit (SPU), Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Evette S. Radisky
- Department
of Cancer Biology, Mayo Clinic Comprehensive
Cancer Center, Jacksonville, Florida 32224, United States
| | - Niv Papo
- Avram
and Stella Goldstein-Goren Department of Biotechnology Engineering
and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel
| | - Julia M. Shifman
- Department
of Biological Chemistry, The Alexander Silberman Institute of Life
Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
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29
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Yan B, Ran X, Jiang Y, Torrence SK, Yuan L, Shao Q, Yang ZJ. Rate-Perturbing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling. J Phys Chem B 2021; 125:10682-10691. [PMID: 34524819 DOI: 10.1021/acs.jpcb.1c05901] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Hydrolases are a critical component for modern chemical, pharmaceutical, and environmental sciences. Identifying mutations that enhance catalytic efficiency presents a roadblock to design and to discover new hydrolases for broad academic and industrial uses. Here, we report the statistical profiling for rate-perturbing mutant hydrolases with a single amino acid substitution. We constructed an integrated structure-kinetics database for hydrolases, IntEnzyDB, which contains 3907 kcats, 4175 KMs, and 2715 Protein Data Bank IDs. IntEnzyDB adopts a relational architecture with a flattened data structure, enabling facile and efficient access to clean and tabulated data for machine learning uses. We conducted statistical analyses on how single amino acids mutations influence the turnover number (i.e., kcat) and efficiency (i.e., kcat/KM), with a particular emphasis on profiling the features for rate-enhancing mutations. The results show that mutation to bulky nonpolar residues with a hydrocarbon chain involves a higher likelihood for rate acceleration than to other types of residues. Linear regression models reveal geometric descriptors of substrate and mutation residues that mediate rate-perturbing outcomes for hydrolases with bulky nonpolar mutations. On the basis of the analyses of the structure-kinetics relationship, we observe that the propensity for rate enhancement is independent of protein sizes. In addition, we observe that distal mutations (i.e., >10 Å from the active site) in hydrolases are significantly more prone to induce efficiency neutrality and avoid efficiency deletion but involve similar propensity for rate enhancement. The studies reveal the statistical features for identifying rate-enhancing mutations in hydrolases, which will potentially guide hydrolase discovery in biocatalysis.
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Affiliation(s)
- Bailu Yan
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37203, United States
| | - Xinchun Ran
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Sarah K Torrence
- Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Li Yuan
- Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Qianzhen Shao
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - 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.,Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
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30
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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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31
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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: 73] [Impact Index Per Article: 24.3] [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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32
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Zheng J, Guo N, Wagner A. Mistranslation reduces mutation load in evolving proteins through negative epistasis with DNA mutations. Mol Biol Evol 2021; 38:4792-4804. [PMID: 34255074 PMCID: PMC8557407 DOI: 10.1093/molbev/msab206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Translational errors during protein synthesis cause phenotypic mutations that are several orders of magnitude more frequent than DNA mutations. Such phenotypic mutations may affect adaptive evolution through their interactions with DNA mutations. To study how mistranslation may affect the adaptive evolution of evolving proteins, we evolved populations of green fluorescent protein (GFP) in either high-mistranslation or low-mistranslation Escherichia coli hosts. In both hosts, we first evolved GFP under purifying selection for the ancestral phenotype green fluorescence, and then under directional selection toward the new phenotype yellow fluorescence. High-mistranslation populations evolved modestly higher yellow fluorescence during each generation of evolution than low-mistranslation populations. We demonstrate by high-throughput sequencing that elevated mistranslation reduced the accumulation of deleterious DNA mutations under both purifying and directional selection. It did so by amplifying the fitness effects of deleterious DNA mutations through negative epistasis with phenotypic mutations. In contrast, mistranslation did not affect the incidence of beneficial mutations. Our findings show that phenotypic mutations interact epistatically with DNA mutations. By reducing a population’s mutation load, mistranslation can affect an important determinant of evolvability.
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Affiliation(s)
- Jia Zheng
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, Lausanne, Switzerland
| | - Ning Guo
- Zwirnereistrasse 11, Wallisellen, Zurich, Switzerland
| | - Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, Lausanne, Switzerland.,The Santa Fe Institute, Santa Fe, New Mexico, USA
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33
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Bepler T, Berger B. Learning the protein language: Evolution, structure, and function. Cell Syst 2021; 12:654-669.e3. [PMID: 34139171 PMCID: PMC8238390 DOI: 10.1016/j.cels.2021.05.017] [Citation(s) in RCA: 144] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 05/20/2021] [Accepted: 05/20/2021] [Indexed: 02/06/2023]
Abstract
Language models have recently emerged as a powerful machine-learning approach for distilling information from massive protein sequence databases. From readily available sequence data alone, these models discover evolutionary, structural, and functional organization across protein space. Using language models, we can encode amino-acid sequences into distributed vector representations that capture their structural and functional properties, as well as evaluate the evolutionary fitness of sequence variants. We discuss recent advances in protein language modeling and their applications to downstream protein property prediction problems. We then consider how these models can be enriched with prior biological knowledge and introduce an approach for encoding protein structural knowledge into the learned representations. The knowledge distilled by these models allows us to improve downstream function prediction through transfer learning. Deep protein language models are revolutionizing protein biology. They suggest new ways to approach protein and therapeutic design. However, further developments are needed to encode strong biological priors into protein language models and to increase their accessibility to the broader community.
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Affiliation(s)
- Tristan Bepler
- Simons Machine Learning Center, New York Structural Biology Center, New York, NY, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.
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34
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Development of a versatile and efficient C–N lyase platform for asymmetric hydroamination via computational enzyme redesign. Nat Catal 2021. [DOI: 10.1038/s41929-021-00604-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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35
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Barlukova A, Rouzine IM. The evolutionary origin of the universal distribution of mutation fitness effect. PLoS Comput Biol 2021; 17:e1008822. [PMID: 33684109 PMCID: PMC7971868 DOI: 10.1371/journal.pcbi.1008822] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 03/18/2021] [Accepted: 02/19/2021] [Indexed: 01/27/2023] Open
Abstract
An intriguing fact long defying explanation is the observation of a universal exponential distribution of beneficial mutations in fitness effect for different microorganisms. To explain this effect, we use a population model including mutation, directional selection, linkage, and genetic drift. The multiple-mutation regime of adaptation at large population sizes (traveling wave regime) is considered. We demonstrate analytically and by simulation that, regardless of the inherent distribution of mutation fitness effect across genomic sites, an exponential distribution of fitness effects emerges in the long term. This result follows from the exponential statistics of the frequency of the less-fit alleles, f, that we predict to evolve, in the long term, for both polymorphic and monomorphic sites. We map the logarithmic slope of the distribution onto the previously derived fixation probability and demonstrate that it increases linearly in time. Our results demonstrate a striking difference between the distribution of fitness effects observed experimentally for naturally occurring mutations, and the "inherent" distribution obtained in a directed-mutagenesis experiment, which can have any shape depending on the organism. Based on these results, we develop a new method to measure the fitness effect of mutations for each variable residue using DNA sequences sampled from adapting populations. This new method is not sensitive to linkage effects and does not require the one-site model assumptions.
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Affiliation(s)
- Ayuna Barlukova
- Sorbonne Université, Institute de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Igor M. Rouzine
- Sorbonne Université, Institute de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
- * E-mail: ,
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36
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Abstract
QM/MM simulations have become an indispensable tool in many chemical and biochemical investigations. Considering the tremendous degree of success, including recognition by a 2013 Nobel Prize in Chemistry, are there still "burning challenges" in QM/MM methods, especially for biomolecular systems? In this short Perspective, we discuss several issues that we believe greatly impact the robustness and quantitative applicability of QM/MM simulations to many, if not all, biomolecules. We highlight these issues with observations and relevant advances from recent studies in our group and others in the field. Despite such limited scope, we hope the discussions are of general interest and will stimulate additional developments that help push the field forward in meaningful directions.
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Affiliation(s)
- Qiang Cui
- Departments of Chemistry, Physics, and Biomedical Engineering, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Tanmoy Pal
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Luke Xie
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
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37
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Copley SD. Evolution of new enzymes by gene duplication and divergence. FEBS J 2021; 287:1262-1283. [PMID: 32250558 DOI: 10.1111/febs.15299] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 03/13/2020] [Accepted: 03/17/2020] [Indexed: 12/22/2022]
Abstract
Thousands of new metabolic and regulatory enzymes have evolved by gene duplication and divergence since the dawn of life. New enzyme activities often originate from promiscuous secondary activities that have become important for fitness due to a change in the environment or a mutation. Mutations that make a promiscuous activity physiologically relevant can occur in the gene encoding the promiscuous enzyme itself, but can also occur elsewhere, resulting in increased expression of the enzyme or decreased competition between the native and novel substrates for the active site. If a newly useful activity is inefficient, gene duplication/amplification will set the stage for divergence of a new enzyme. Even a few mutations can increase the efficiency of a new activity by orders of magnitude. As efficiency increases, amplified gene arrays will shrink to provide two alleles, one encoding the original enzyme and one encoding the new enzyme. Ultimately, genomic rearrangements eliminate co-amplified genes and move newly evolved paralogs to a distant region of the genome.
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Affiliation(s)
- Shelley D Copley
- Department of Molecular, Cellular and Developmental Biology and the Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, CO, USA
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38
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Goris T, Pérez‐Valero Á, Martínez I, Yi D, Fernández‐Calleja L, San León D, Bornscheuer UT, Magadán‐Corpas P, Lombó F, Nogales J. Repositioning microbial biotechnology against COVID-19: the case of microbial production of flavonoids. Microb Biotechnol 2021; 14:94-110. [PMID: 33047877 PMCID: PMC7675739 DOI: 10.1111/1751-7915.13675] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/19/2022] Open
Abstract
Coronavirus-related disease 2019 (COVID-19) became a pandemic in February 2020, and worldwide researchers try to tackle the disease with approved drugs of all kinds, or to develop novel compounds inhibiting viral spreading. Flavonoids, already investigated as antivirals in general, also might bear activities specific for the viral agent causing COVID-19, SARS-CoV-2. Microbial biotechnology and especially synthetic biology may help to produce flavonoids, which are exclusive plant secondary metabolites, at a larger scale or indeed to find novel pharmaceutically active flavonoids. Here, we review the state of the art in (i) antiviral activity of flavonoids specific for coronaviruses and (ii) results derived from computational studies, mostly docking studies mainly inhibiting specific coronaviral proteins such as the 3CL (main) protease, the spike protein or the RNA-dependent RNA polymerase. In the end, we strive towards a synthetic biology pipeline making the fast and tailored production of valuable antiviral flavonoids possible by applying the last concepts of division of labour through co-cultivation/microbial community approaches to the DBTL (Design, Build, Test, Learn) principle.
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Affiliation(s)
- Tobias Goris
- Department of Molecular Toxicology, Research Group Intestinal MicrobiologyGerman Institute of Human Nutrition Potsdam‐RehbrueckeArthur‐Scheunert‐Allee 114‐116NuthetalBrandenburg14558Germany
| | - Álvaro Pérez‐Valero
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Igor Martínez
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
| | - Dong Yi
- Department of Biotechnology & Enzyme CatalysisInstitute of BiochemistryUniversity GreifswaldFelix‐Hausdorff‐Str. 4GreifswaldD‐17487Germany
| | - Luis Fernández‐Calleja
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - David San León
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
| | - Uwe T. Bornscheuer
- Department of Biotechnology & Enzyme CatalysisInstitute of BiochemistryUniversity GreifswaldFelix‐Hausdorff‐Str. 4GreifswaldD‐17487Germany
| | - Patricia Magadán‐Corpas
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Felipe Lombó
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Juan Nogales
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish National Research Council (SusPlast‐CSIC)MadridSpain
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39
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Munro D, Singh M. DeMaSk: a deep mutational scanning substitution matrix and its use for variant impact prediction. Bioinformatics 2020; 36:5322-5329. [PMID: 33325500 PMCID: PMC8016454 DOI: 10.1093/bioinformatics/btaa1030] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 10/16/2020] [Accepted: 11/30/2020] [Indexed: 01/27/2023] Open
Abstract
Motivation Accurately predicting the quantitative impact of a substitution on a protein’s molecular function would be a great aid in understanding the effects of observed genetic variants across populations. While this remains a challenging task, new approaches can leverage data from the increasing numbers of comprehensive deep mutational scanning (DMS) studies that systematically mutate proteins and measure fitness. Results We introduce DeMaSk, an intuitive and interpretable method based only upon DMS datasets and sequence homologs that predicts the impact of missense mutations within any protein. DeMaSk first infers a directional amino acid substitution matrix from DMS datasets and then fits a linear model that combines these substitution scores with measures of per-position evolutionary conservation and variant frequency across homologs. Despite its simplicity, DeMaSk has state-of-the-art performance in predicting the impact of amino acid substitutions, and can easily and rapidly be applied to any protein sequence. Availability and implementation https://demask.princeton.edu generates fitness impact predictions and visualizations for any user-submitted protein sequence. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel Munro
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, 08544, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, 08544, USA.,Department of Computer Science, Princeton University, Princeton, 08544, USA
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40
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Zurek PJ, Knyphausen P, Neufeld K, Pushpanath A, Hollfelder F. UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution. Nat Commun 2020; 11:6023. [PMID: 33243970 PMCID: PMC7691348 DOI: 10.1038/s41467-020-19687-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 10/12/2020] [Indexed: 11/09/2022] Open
Abstract
The success of protein evolution campaigns is strongly dependent on the sequence context in which mutations are introduced, stemming from pervasive non-additive interactions between a protein's amino acids ('intra-gene epistasis'). Our limited understanding of such epistasis hinders the correct prediction of the functional contributions and adaptive potential of mutations. Here we present a straightforward unique molecular identifier (UMI)-linked consensus sequencing workflow (UMIC-seq) that simplifies mapping of evolutionary trajectories based on full-length sequences. Attaching UMIs to gene variants allows accurate consensus generation for closely related genes with nanopore sequencing. We exemplify the utility of this approach by reconstructing the artificial phylogeny emerging in three rounds of directed evolution of an amine dehydrogenase biocatalyst via ultrahigh throughput droplet screening. Uniquely, we are able to identify lineages and their founding variant, as well as non-additive interactions between mutations within a full gene showing sign epistasis. Access to deep and accurate long reads will facilitate prediction of key beneficial mutations and adaptive potential based on in silico analysis of large sequence datasets.
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Affiliation(s)
- Paul Jannis Zurek
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
- Johnson Matthey Plc, Cambridge, CB4 0WE, UK
| | - Philipp Knyphausen
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - Katharina Neufeld
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
- Johnson Matthey Plc, Cambridge, CB4 0WE, UK
| | | | - Florian Hollfelder
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK.
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41
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Rix G, Watkins-Dulaney EJ, Almhjell PJ, Boville CE, Arnold FH, Liu CC. Scalable continuous evolution for the generation of diverse enzyme variants encompassing promiscuous activities. Nat Commun 2020; 11:5644. [PMID: 33159067 PMCID: PMC7648111 DOI: 10.1038/s41467-020-19539-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 10/19/2020] [Indexed: 01/11/2023] Open
Abstract
Enzyme orthologs sharing identical primary functions can have different promiscuous activities. While it is possible to mine this natural diversity to obtain useful biocatalysts, generating comparably rich ortholog diversity is difficult, as it is the product of deep evolutionary processes occurring in a multitude of separate species and populations. Here, we take a first step in recapitulating the depth and scale of natural ortholog evolution on laboratory timescales. Using a continuous directed evolution platform called OrthoRep, we rapidly evolve the Thermotoga maritima tryptophan synthase β-subunit (TmTrpB) through multi-mutation pathways in many independent replicates, selecting only on TmTrpB's primary activity of synthesizing L-tryptophan from indole and L-serine. We find that the resulting sequence-diverse TmTrpB variants span a range of substrate profiles useful in industrial biocatalysis and suggest that the depth and scale of evolution that OrthoRep affords will be generally valuable in enzyme engineering and the evolution of biomolecular functions.
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Affiliation(s)
- Gordon Rix
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, USA
| | - Ella J Watkins-Dulaney
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Patrick J Almhjell
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Christina E Boville
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Aralez Bio, Emeryville, CA, USA
| | - Frances H Arnold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Chang C Liu
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, USA.
- Department of Biomedical Engineering, University of California, Irvine, CA, USA.
- Department of Chemistry, University of California, Irvine, CA, USA.
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42
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Rouzine IM. An Evolutionary Model of Progression to AIDS. Microorganisms 2020; 8:microorganisms8111714. [PMID: 33142907 PMCID: PMC7692852 DOI: 10.3390/microorganisms8111714] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 10/30/2020] [Accepted: 10/30/2020] [Indexed: 11/16/2022] Open
Abstract
The time to the onset of AIDS symptoms in an HIV infected individual is known to correlate inversely with viremia and the level of immune activation. The correlation exists against the background of strong individual fluctuations demonstrating the existence of hidden variables depending on patient and virus parameters. At the moment, prognosis of the time to AIDS based on patient parameters is not possible. In addition, it is of paramount importance to understand the reason of progression to AIDS in untreated patients to be able to learn to control it by means other than anti-retroviral therapy. Here we develop a mechanistic mathematical model to predict the speed of progression to AIDS in individual untreated patients and patients treated with suboptimal therapy, based on a single-time measurement of several virological and immunological parameters. We show that the gradual increase in virus fitness during a chronic infection causes slow gradual depletion of CD4 T cells. Using the existing evolution models of HIV, we obtain general expressions predicting the time to the onset of AIDS symptoms in terms of the patient parameters, for low-viremia and high-viremia patients separately. We show that the evolution model of AIDS fits the existing data on virus-time correlations better than the alternative model of the deregulation of homeostatic response.
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Affiliation(s)
- Igor M Rouzine
- Laboratory of Computational and Quantitative Biology, 7238 CNRS-UPMC, Institut Biologie Paris-Seine, Sorbonne Université, Campus Pierre et Marie Curie, 75005 Paris, France
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43
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Characteristics of microbial community of soil subjected to industrial production of antibiotics. Folia Microbiol (Praha) 2020; 65:1061-1072. [PMID: 32901430 DOI: 10.1007/s12223-020-00819-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 08/30/2020] [Indexed: 10/23/2022]
Abstract
Ecosystems worldwide are exposed to pollutants connected to the industrial production of pharmaceuticals. The objective of this study was to study the composition and characteristics of the soil microbial communities that had been exposed to long-term selection pressure caused by the industrial production of penicillin G. Soil samples from four sites among the penicillin G production plant were analysed using 16S rRNA profiling via Illumina MiSeq platform and were compared with the control samples from four sites outside the plant. Total metagenomic DNA from the impacted soil was also used for the preparation of E. coli T1R-based fosmid library which was consequently qualitatively tested for the presence of penicillin G acylase (PGA)-encoding genes using the method of sequence homology. Analyses of alpha diversity revealed that the long-term antibiotic presence in the soil significantly increased the microbial diversity and richness in terms of Shannon diversity index (p = 0.002) and Chao estimates (p = 0.004). Principal component analysis showed that the two types of communities (on-site and control) could be separated at the phylum, class and genus level. The on-site soil was enriched in Betaproteobacteria, Deltaproteobacteria, Gemmatimonadetes, Acidobacteria and Planctomycetia, while a significant decrease in Actinobacteria was observed. Metagenomic fosmid library revealed high hit rates in identifying PGAs (14 different genes identified) and confirmed the biotechnological potential of soils impacted by anthropogenic activity. This study offers new insights into the changes in microbial communities of soils exposed to anthropogenic activity as well as indicates that those soils may represent a hotspot for biotechnologically interesting targets.
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44
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Procko E. Deep mutagenesis in the study of COVID-19: a technical overview for the proteomics community. Expert Rev Proteomics 2020; 17:633-638. [PMID: 33084449 PMCID: PMC7594187 DOI: 10.1080/14789450.2020.1833721] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 10/05/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION The spike (S) of SARS coronavirus 2 (SARS-CoV-2) engages angiotensin-converting enzyme 2 (ACE2) on a host cell to trigger viral-cell membrane fusion and infection. The extracellular region of ACE2 can be administered as a soluble decoy to compete for binding sites on the receptor-binding domain (RBD) of S, but it has only moderate affinity and efficacy. The RBD, which is targeted by neutralizing antibodies, may also change and adapt through mutation as SARS-CoV-2 becomes endemic, posing challenges for therapeutic and vaccine development. AREAS COVERED Deep mutagenesis is a Big Data approach to characterizing sequence variants. A deep mutational scan of ACE2 expressed on human cells identified mutations that increase S affinity and guided the engineering of a potent and broad soluble receptor decoy. A deep mutational scan of the RBD displayed on the surface of yeast has revealed residues tolerant of mutational changes that may act as a source for drug resistance and antigenic drift. EXPERT OPINION Deep mutagenesis requires a selection of diverse sequence variants; an in vitro evolution experiment that is tracked with next-generation sequencing. The choice of expression system, diversity of the variant library and selection strategy have important consequences for data quality and interpretation.
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Affiliation(s)
- Erik Procko
- Department of Biochemistry and Cancer Center at Illinois, University of Illinois, Urbana, IL, USA
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45
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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: 38] [Impact Index Per Article: 9.5] [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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46
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Thompson S, Zhang Y, Ingle C, Reynolds KA, Kortemme T. Altered expression of a quality control protease in E. coli reshapes the in vivo mutational landscape of a model enzyme. eLife 2020; 9:53476. [PMID: 32701056 PMCID: PMC7377907 DOI: 10.7554/elife.53476] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 07/09/2020] [Indexed: 12/03/2022] Open
Abstract
Protein mutational landscapes are shaped by the cellular environment, but key factors and their quantitative effects are often unknown. Here we show that Lon, a quality control protease naturally absent in common E. coli expression strains, drastically reshapes the mutational landscape of the metabolic enzyme dihydrofolate reductase (DHFR). Selection under conditions that resolve highly active mutants reveals that 23.3% of all single point mutations in DHFR are advantageous in the absence of Lon, but advantageous mutations are largely suppressed when Lon is reintroduced. Protein stability measurements demonstrate extensive activity-stability tradeoffs for the advantageous mutants and provide a mechanistic explanation for Lon’s widespread impact. Our findings suggest possibilities for tuning mutational landscapes by modulating the cellular environment, with implications for protein design and combatting antibiotic resistance.
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Affiliation(s)
- Samuel Thompson
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, United States
| | - Yang Zhang
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, United States
| | - Christine Ingle
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, United States
| | - Kimberly A Reynolds
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, United States.,Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Tanja Kortemme
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, United States.,Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, United States.,Chan Zuckerberg Biohub, San Francisco, United States
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47
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Crean RM, Gardner JM, Kamerlin SCL. Harnessing Conformational Plasticity to Generate Designer Enzymes. J Am Chem Soc 2020; 142:11324-11342. [PMID: 32496764 PMCID: PMC7467679 DOI: 10.1021/jacs.0c04924] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Indexed: 02/08/2023]
Abstract
Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.
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Affiliation(s)
- Rory M. Crean
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
| | - Jasmine M. Gardner
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
| | - Shina C. L. Kamerlin
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
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48
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Park J, Gill KS, Aghajani AA, Heredia JD, Choi H, Oberstein A, Procko E. Engineered receptors for human cytomegalovirus that are orthogonal to normal human biology. PLoS Pathog 2020; 16:e1008647. [PMID: 32559251 PMCID: PMC7329128 DOI: 10.1371/journal.ppat.1008647] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 07/01/2020] [Accepted: 05/20/2020] [Indexed: 12/12/2022] Open
Abstract
A trimeric glycoprotein complex on the surface of human cytomegalovirus (HCMV) binds to platelet-derived growth factor (PDGF) receptor α (PDGFRα) to mediate host cell recognition and fusion of the viral and cellular membranes. Soluble PDGFRα potently neutralizes HCMV in tissue culture, and its potential use as an antiviral therapeutic has the benefit that any escape mutants will likely be attenuated. However, PDGFRα binds multiple PDGF ligands in the human body as part of developmental programs in embryogenesis and continuing through adulthood. Any therapies with soluble receptor therefore come with serious efficacy and safety concerns, especially for the treatment of congenital HCMV. Soluble virus receptors that are orthogonal to human biology might resolve these concerns. This engineering problem is solved by deep mutational scanning on the D2-D3 domains of PDGFRα to identify variants that maintain interactions with the HCMV glycoprotein trimer in the presence of competing PDGF ligands. Competition by PDGFs is conformation-dependent, whereas HCMV trimer binding is independent of proper D2-D3 conformation, and many mutations at the receptor-PDGF interface are suitable for functionally separating trimer from PDGF interactions. Purified soluble PDGFRα carrying a targeted mutation succeeded in displaying wild type affinity for HCMV trimer with a simultaneous loss of PDGF binding, and neutralizes trimer-only and trimer/pentamer-expressing HCMV strains infecting fibroblasts or epithelial cells. Overall, this work makes important progress in the realization of soluble HCMV receptors for clinical application. Human cytomegalovirus (HCMV) causes severe disease in transplant recipients and immunocompromised patients, and infections in a fetus or neonate are responsible for life-long neurological defects. Cell entry is in part mediated by a trimeric glycoprotein complex on the viral surface, which binds tightly to the host receptor PDGFRα. The soluble extracellular region of PDGFRα can be used as an antiviral agent to potently neutralize the virus in vitro. However, PDGFRα ordinarily binds growth factors in the human body to regulate developmental programs, which will limit the in vivo efficacy and safety of soluble PDGFRα. Using saturation mutagenesis and selections in human cell culture, mutations in PDGFRα are identified that eliminate off-target growth factor interactions while preserving HCMV binding and neutralization.
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Affiliation(s)
- Jihye Park
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Kevin Sean Gill
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Ali Asghar Aghajani
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Jeremiah Dallas Heredia
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Hannah Choi
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Adam Oberstein
- Department of Microbiology and Immunology, University of Illinois College of Medicine, Chicago, Illinois, United States of America
| | - Erik Procko
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
- Cancer Center at Illinois (CCIL), University of Illinois, Urbana, Illinois, United States of America
- * E-mail:
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49
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Klesmith JR, Hackel BJ. Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit. Bioinformatics 2020; 35:2707-2712. [PMID: 30590444 DOI: 10.1093/bioinformatics/bty1042] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 12/06/2018] [Accepted: 12/19/2018] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Deep mutational scanning experiments have enabled the measurement of the sequence-function relationship for thousands of mutations in a single experiment. The Protein Analysis and Classifier Toolkit (PACT) is a Python software package that marries the fitness metric of a given mutation within these experiments to sequence and structural features enabling downstream analyses. PACT enables the easy development of user sharable protocols for custom deep mutational scanning experiments as all code is modular and reusable between protocols. Protocols for mutational libraries with single or multiple mutations are included. To exemplify its utility, PACT assessed two deep mutational scanning datasets that measured the tradeoff of enzyme activity and enzyme stability. RESULTS PACT efficiently evaluated classifiers that predict protein mutant function tested on deep mutational scanning screens. We found that the classifiers with the lowest false positive and highest true positive rate assesses sequence homology, contact number and if mutation involves proline. AVAILABILITY AND IMPLEMENTATION PACT and the processed datasets are distributed freely under the terms of the GPL-3 license. The source code is available at GitHub (https://github.com/JKlesmith/PACT). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Justin R Klesmith
- Department of Chemical Engineering and Materials Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Benjamin J Hackel
- Department of Chemical Engineering and Materials Science, University of Minnesota, Twin Cities, Minneapolis, MN, USA
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50
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Chen JZ, Fowler DM, Tokuriki N. Comprehensive exploration of the translocation, stability and substrate recognition requirements in VIM-2 lactamase. eLife 2020; 9:e56707. [PMID: 32510322 PMCID: PMC7308095 DOI: 10.7554/elife.56707] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/06/2020] [Indexed: 12/12/2022] Open
Abstract
Metallo-β-lactamases (MBLs) degrade a broad spectrum of β-lactam antibiotics, and are a major disseminating source for multidrug resistant bacteria. Despite many biochemical studies in diverse MBLs, molecular understanding of the roles of residues in the enzyme's stability and function, and especially substrate specificity, is lacking. Here, we employ deep mutational scanning (DMS) to generate comprehensive single amino acid variant data on a major clinical MBL, VIM-2, by measuring the effect of thousands of VIM-2 mutants on the degradation of three representative classes of β-lactams (ampicillin, cefotaxime, and meropenem) and at two different temperatures (25°C and 37°C). We revealed residues responsible for expression and translocation, and mutations that increase resistance and/or alter substrate specificity. The distribution of specificity-altering mutations unveiled distinct molecular recognition of the three substrates. Moreover, these function-altering mutations are frequently observed among naturally occurring variants, suggesting that the enzymes have continuously evolved to become more potent resistance genes.
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Affiliation(s)
- John Z Chen
- Michael Smith Laboratories, University of British ColumbiaVancouverCanada
| | - Douglas M Fowler
- Department of Genome Sciences, University of WashingtonSeattleUnited States
- Department of Bioengineering, University of WashingtonSeattleUnited States
| | - Nobuhiko Tokuriki
- Michael Smith Laboratories, University of British ColumbiaVancouverCanada
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