1
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Zhang Z, Li Z, Yang M, Zhao F, Han S. Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase. Int J Biol Macromol 2024; 277:134530. [PMID: 39111490 DOI: 10.1016/j.ijbiomac.2024.134530] [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: 06/17/2024] [Revised: 07/25/2024] [Accepted: 08/04/2024] [Indexed: 08/13/2024]
Abstract
Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as directed evolution are often limited by the huge sequence space and combinatorial explosion, making it difficult to obtain optimal mutants. In recent years, machine learning (ML)-guided protein engineering has become an attractive tool because of its ability to comprehensively explore the sequence space of enzymes and discover superior mutants. This study employed ML to perform combinatorial mutation design on the pectin lyase PMGL-Ba from Bacillus licheniformis, aiming to improve its thermostability. First, 18 single-point mutants with enhanced thermostability were identified through semi-rational design. Subsequently, the initial library containing a small number of low-order mutants was utilized to construct an ML model to explore the combinatorial sequence space (theoretically 196,608 mutants) of single-point mutants. The results showed that the ML-predicted second library was successfully enriched with highly thermostable combinatorial mutants. After one iteration of learning, the best-performing combinatorial mutant in the third library, P36, showed a 67-fold and 39-fold increase in half-life at 75 °C and 80 °C, respectively, as well as a 2.1-fold increase in activity. Structural analysis and molecular dynamics simulations provided insights into the improved performance of the engineered enzyme.
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Affiliation(s)
- Zhihui Zhang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Zhixuan Li
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Manli Yang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Fengguang Zhao
- School of Light Industry and Engineering, South China University of Technology, Guangzhou 510006, China
| | - Shuangyan Han
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
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2
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Huang C, Zhang L, Tang T, Wang H, Jiang Y, Ren H, Zhang Y, Fang J, Zhang W, Jia X, You S, Qin B. Application of Directed Evolution and Machine Learning to Enhance the Diastereoselectivity of Ketoreductase for Dihydrotetrabenazine Synthesis. JACS AU 2024; 4:2547-2556. [PMID: 39055154 PMCID: PMC11267543 DOI: 10.1021/jacsau.4c00284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/13/2024] [Accepted: 06/20/2024] [Indexed: 07/27/2024]
Abstract
Biocatalysis is an effective approach for producing chiral drug intermediates that are often difficult to synthesize using traditional chemical methods. A time-efficient strategy is required to accelerate the directed evolution process to achieve the desired enzyme function. In this research, we evaluated machine learning-assisted directed evolution as a potential approach for enzyme engineering, using a moderately diastereoselective ketoreductase library as a model system. Machine learning-assisted directed evolution and traditional directed evolution methods were compared for reducing (±)-tetrabenazine to dihydrotetrabenazine via kinetic resolution facilitated by BsSDR10, a short-chain dehydrogenase/reductase from Bacillus subtilis. Both methods successfully identified variants with significantly improved diastereoselectivity for each isomer of dihydrotetrabenazine. Furthermore, the preparation of (2S,3S,11bS)-dihydrotetrabenazine has been successfully scaled up, with an isolated yield of 40.7% and a diastereoselectivity of 91.3%.
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Affiliation(s)
- Chenming Huang
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Li Zhang
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Tong Tang
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Haijiao Wang
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Yingqian Jiang
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Hanwen Ren
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Yitian Zhang
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Jiali Fang
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Wenhe Zhang
- School
of Life Sciences and Biopharmaceutical Sciences, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Xian Jia
- School
of Pharmaceutical Engineering, Shenyang
Pharmaceutical University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Song You
- School
of Life Sciences and Biopharmaceutical Sciences, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
| | - Bin Qin
- Wuya
College of Innovation, Shenyang Pharmaceutical
University, 103 Wenhua Road, Shenhe, Shenyang 110016, People’s Republic
of China
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3
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Orsi E, Schada von Borzyskowski L, Noack S, Nikel PI, Lindner SN. Automated in vivo enzyme engineering accelerates biocatalyst optimization. Nat Commun 2024; 15:3447. [PMID: 38658554 PMCID: PMC11043082 DOI: 10.1038/s41467-024-46574-4] [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: 12/21/2023] [Accepted: 03/04/2024] [Indexed: 04/26/2024] Open
Abstract
Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.
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Affiliation(s)
- Enrico Orsi
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | | | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam-Golm, Germany.
- Department of Biochemistry, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität, 10117, Berlin, Germany.
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4
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Wang X, Li A, Li X, Cui H. Empowering Protein Engineering through Recombination of Beneficial Substitutions. Chemistry 2024; 30:e202303889. [PMID: 38288640 DOI: 10.1002/chem.202303889] [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: 01/04/2024] [Indexed: 02/24/2024]
Abstract
Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.
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Affiliation(s)
- Xinyue Wang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Anni Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Xiujuan Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Haiyang Cui
- School of Life Sciences, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
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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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Yang L, Liang X, Zhang N, Lu L. STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning. ACS OMEGA 2023; 8:44751-44756. [PMID: 38046324 PMCID: PMC10688154 DOI: 10.1021/acsomega.3c04832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/10/2023] [Accepted: 10/12/2023] [Indexed: 12/05/2023]
Abstract
Protein engineering has made significant contributions to industries such as agriculture, food, and pharmaceuticals. In recent years, directed evolution combined with artificial intelligence has emerged as a cutting-edge R&D approach. However, the application of machine learning techniques can be challenging for those without relevant experience and coding skills. To address this issue, we have developed a web-based protein sequence recommendation system: STAR (Sequence recommendaTion via ARtificial intelligence). Our system utilizes Bayesian optimization as its backbone and includes a filtering step using a regression model to enhance the success rate of recommended sequences. Additionally, we have incorporated an in silico-directed evolution approach to expand the exploration of the protein space. The Web site can be accessed at https://www.FindProteinStar.com/.
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Affiliation(s)
- Likun Yang
- Asymchem Life Science (Tianjin) Co.,
Ltd, Tianjin 300457, P. R. China
| | - Xiaoli Liang
- Asymchem Life Science (Tianjin) Co.,
Ltd, Tianjin 300457, P. R. China
| | - Na Zhang
- Asymchem Life Science (Tianjin) Co.,
Ltd, Tianjin 300457, P. R. China
| | - Lu Lu
- Asymchem Life Science (Tianjin) Co.,
Ltd, Tianjin 300457, P. R. China
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7
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Markus B, C GC, Andreas K, Arkadij K, Stefan L, Gustav O, Elina S, Radka S. Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design. ACS Catal 2023; 13:14454-14469. [PMID: 37942268 PMCID: PMC10629211 DOI: 10.1021/acscatal.3c03417] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology's limitations, unanswered questions, and unmet challenges.
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Affiliation(s)
- Braun Markus
- Department
of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria
| | - Gruber Christian C
- Enzyme
and Drug Discovery, Innophore. 1700 Montgomery Street, San Francisco, California 94111, United States
| | - Krassnigg Andreas
- Enzyme
and Drug Discovery, Innophore. 1700 Montgomery Street, San Francisco, California 94111, United States
| | - Kummer Arkadij
- Moderna,
Inc., 200 Technology
Square, Cambridge, Massachusetts 02139, United States
| | - Lutz Stefan
- Codexis
Inc., 200 Penobscot Drive, Redwood City, California 94063, United States
| | - Oberdorfer Gustav
- Department
of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria
| | - Siirola Elina
- Novartis
Institute for Biomedical Research, Global Discovery Chemistry, Basel CH-4108, Switzerland
| | - Snajdrova Radka
- Novartis
Institute for Biomedical Research, Global Discovery Chemistry, Basel CH-4108, Switzerland
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8
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Arras P, Yoo HB, Pekar L, Clarke T, Friedrich L, Schröter C, Schanz J, Tonillo J, Siegmund V, Doerner A, Krah S, Guarnera E, Zielonka S, Evers A. AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study. Front Mol Biosci 2023; 10:1249247. [PMID: 37842638 PMCID: PMC10575757 DOI: 10.3389/fmolb.2023.1249247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/31/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.
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Affiliation(s)
- Paul Arras
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Han Byul Yoo
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Lukas Pekar
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Thomas Clarke
- Bioinformatics, EMD Serono, Billerica, MA, United States
| | - Lukas Friedrich
- Computational Chemistry and Biologics, Merck Healthcare KGaA, Darmstadt, Germany
| | | | - Jennifer Schanz
- ADCs & Targeted NBE Therapeutics, Merck KGaA, Darmstadt, Germany
| | - Jason Tonillo
- ADCs & Targeted NBE Therapeutics, Merck KGaA, Darmstadt, Germany
| | - Vanessa Siegmund
- Early Protein Supply and Characterization, Merck Healthcare KGaA, Darmstadt, Germany
| | - Achim Doerner
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Simon Krah
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Enrico Guarnera
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Stefan Zielonka
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Andreas Evers
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
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