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Jiang R, Yuan S, Zhou Y, Wei Y, Li F, Wang M, Chen B, Yu H. Strategies to overcome the challenges of low or no expression of heterologous proteins in Escherichia coli. Biotechnol Adv 2024; 75:108417. [PMID: 39038691 DOI: 10.1016/j.biotechadv.2024.108417] [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: 03/21/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 07/24/2024]
Abstract
Protein expression is a critical process in diverse biological systems. For Escherichia coli, a widely employed microbial host in industrial catalysis and healthcare, researchers often face significant challenges in constructing recombinant expression systems. To maximize the potential of E. coli expression systems, it is essential to address problems regarding the low or absent production of certain target proteins. This article presents viable solutions to the main factors posing challenges to heterologous protein expression in E. coli, which includes protein toxicity, the intrinsic influence of gene sequences, and mRNA structure. These strategies include specialized approaches for managing toxic protein expression, addressing issues related to mRNA structure and codon bias, advanced codon optimization methodologies that consider multiple factors, and emerging optimization techniques facilitated by big data and machine learning.
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Affiliation(s)
- Ruizhao Jiang
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Key Laboratory of Industrial Biocatalysis (Tsinghua University), the Ministry of Education, Beijing 100084, China
| | - Shuting Yuan
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Key Laboratory of Industrial Biocatalysis (Tsinghua University), the Ministry of Education, Beijing 100084, China
| | - Yilong Zhou
- Tanwei College, Tsinghua University, Beijing 100084, China
| | - Yuwen Wei
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Key Laboratory of Industrial Biocatalysis (Tsinghua University), the Ministry of Education, Beijing 100084, China
| | - Fulong Li
- Beijing Evolyzer Co.,Ltd., 100176, China
| | | | - Bo Chen
- Beijing Evolyzer Co.,Ltd., 100176, China
| | - Huimin Yu
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Key Laboratory of Industrial Biocatalysis (Tsinghua University), the Ministry of Education, Beijing 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China.
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2
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Laksmi FA, Dewi KS, Nuryana I, Yulianti SE, Ramadhan KP, Hadi MI, Nugraha Y. High-level expression of codon-optimized Taq DNA polymerase under the control of rhaBAD promoter. Anal Biochem 2024; 692:115581. [PMID: 38815728 DOI: 10.1016/j.ab.2024.115581] [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: 01/03/2024] [Revised: 05/24/2024] [Accepted: 05/25/2024] [Indexed: 06/01/2024]
Abstract
A DNA polymerase from Thermus aquaticus remains the most popular among DNA polymerases. It was widely applied in various fields involving the application of polymerase chain reaction (PCR), implying the high commercial value of this enzyme. For this reason, an attempt to obtain a high yield of Taq DNA polymerase is continuously conducted. In this study, the l-rhamnose-inducible promoter rhaBAD was utilized due to its ability to produce recombinant protein under tight control in E. coli expression system. Instead of full-length Taq polymerase, an N-terminal deletion of Taq polymerase was selected. To obtain a high-level expression, we attempted to optimize the codon by reducing the rare codon and GC content, and in a second attempt, we optimized the culture conditions for protein expression. The production of Taq polymerase using the optimum culture condition improved the level of expression by up to 3-fold. This approach further proved that a high level of recombinant protein expression could be achieved by yielding a purified Taq polymerase of about 8.5 mg/L of culture. This is the first research publication on the production of Taq polymerase with N-terminal deletion in E. coli with the control of the rhaBAD promoter system.
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Affiliation(s)
- Fina Amreta Laksmi
- Research Center for Applied Microbiology, National Research and Innovation Agency, Jalan Raya Bogor KM 46, Cibinong, Bogor, 16911, West Java, Indonesia.
| | - Kartika Sari Dewi
- Research Center for Genetic Engineering, National Research and Innovation Agency, Jalan Raya Bogor KM 46, Cibinong, Bogor, 16911, West Java, Indonesia
| | - Isa Nuryana
- Research Center for Applied Microbiology, National Research and Innovation Agency, Jalan Raya Bogor KM 46, Cibinong, Bogor, 16911, West Java, Indonesia
| | - Siti Eka Yulianti
- Research Center for Applied Microbiology, National Research and Innovation Agency, Jalan Raya Bogor KM 46, Cibinong, Bogor, 16911, West Java, Indonesia
| | - Kharisma Panji Ramadhan
- Research Center for Applied Microbiology, National Research and Innovation Agency, Jalan Raya Bogor KM 46, Cibinong, Bogor, 16911, West Java, Indonesia
| | - Moch Irfan Hadi
- Department of Biology, Sunan Ampel State Islamic University, Surabaya, Indonesia
| | - Yudhi Nugraha
- Research Center for Molecular Biology Eijkman, National Research and Innovation Agency, Jalan Raya Bogor KM 46, Cibinong, Bogor, 16911, West Java, Indonesia.
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3
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Bashour H, Smorodina E, Pariset M, Zhong J, Akbar R, Chernigovskaya M, Lê Quý K, Snapkow I, Rawat P, Krawczyk K, Sandve GK, Gutierrez-Marcos J, Gutierrez DNZ, Andersen JT, Greiff V. Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability. Commun Biol 2024; 7:922. [PMID: 39085379 PMCID: PMC11291509 DOI: 10.1038/s42003-024-06561-3] [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: 01/25/2024] [Accepted: 07/05/2024] [Indexed: 08/02/2024] Open
Abstract
Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.
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Affiliation(s)
- Habib Bashour
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Jahn Zhong
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Division of Genetics, Department Biology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Khang Lê Quý
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Igor Snapkow
- Department of Chemical Toxicology, Norwegian Institute of Public Health, Oslo, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | | | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Pharmacology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Precision Immunotherapy Alliance (PRIMA), University of Oslo, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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4
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Taguchi H, Niwa T. Reconstituted Cell-free Translation Systems for Exploring Protein Folding and Aggregation. J Mol Biol 2024; 436:168726. [PMID: 39074633 DOI: 10.1016/j.jmb.2024.168726] [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: 03/25/2024] [Revised: 07/18/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
Protein folding is crucial for achieving functional three-dimensional structures. However, the process is often hampered by aggregate formation, necessitating the presence of chaperones and quality control systems within the cell to maintain protein homeostasis. Despite a long history of folding studies involving the denaturation and subsequent refolding of translation-completed purified proteins, numerous facets of cotranslational folding, wherein nascent polypeptides are synthesized by ribosomes and folded during translation, remain unexplored. Cell-free protein synthesis (CFPS) systems are invaluable tools for studying cotranslational folding, offering a platform not only for elucidating mechanisms but also for large-scale analyses to identify aggregation-prone proteins. This review provides an overview of the extensive use of CFPS in folding studies to date. In particular, we discuss a comprehensive aggregation formation assay of thousands of Escherichia coli proteins conducted under chaperone-free conditions using a reconstituted translation system, along with its derived studies.
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Affiliation(s)
- Hideki Taguchi
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, S2-19, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan.
| | - Tatsuya Niwa
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, S2-19, 4259 Nagatsuta, Midori-ku, Yokohama 226-8501, Japan
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5
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Zhang X, Hu X, Zhang T, Yang L, Liu C, Xu N, Wang H, Sun W. PLM_Sol: predicting protein solubility by benchmarking multiple protein language models with the updated Escherichia coli protein solubility dataset. Brief Bioinform 2024; 25:bbae404. [PMID: 39179250 PMCID: PMC11343611 DOI: 10.1093/bib/bbae404] [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: 04/29/2024] [Revised: 07/19/2024] [Accepted: 08/07/2024] [Indexed: 08/26/2024] Open
Abstract
Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.
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Affiliation(s)
- Xuechun Zhang
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- University of Chinese Academy of Sciences, No. 1 Yanqihu East Rd, Huairou District, Beijing 101408, China
| | - Xiaoxuan Hu
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- University of Chinese Academy of Sciences, No. 1 Yanqihu East Rd, Huairou District, Beijing 101408, China
| | - Tongtong Zhang
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- University of Chinese Academy of Sciences, No. 1 Yanqihu East Rd, Huairou District, Beijing 101408, China
| | - Ling Yang
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- University of Chinese Academy of Sciences, No. 1 Yanqihu East Rd, Huairou District, Beijing 101408, China
| | - Chunhong Liu
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- University of Chinese Academy of Sciences, No. 1 Yanqihu East Rd, Huairou District, Beijing 101408, China
| | - Ning Xu
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- University of Chinese Academy of Sciences, No. 1 Yanqihu East Rd, Huairou District, Beijing 101408, China
| | - Haoyi Wang
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- University of Chinese Academy of Sciences, No. 1 Yanqihu East Rd, Huairou District, Beijing 101408, China
- Beijing Institute for Stem Cell and Regenerative Medicine, A 3 Datun Road, Chaoyang District, Beijing 100100, China
| | - Wen Sun
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, A 3 Datun Road, Chaoyang District, Beijing 100100, China
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Tsopanakis V, Anastasiadou E, Mikkelsen MD, Meyer AS, Pavlidis IV. Identification and characterization of a novel thermostable PL7 alginate lyase from a submarine volcanic metagenomic library. Enzyme Microb Technol 2024; 180:110486. [PMID: 39038418 DOI: 10.1016/j.enzmictec.2024.110486] [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: 05/17/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 07/24/2024]
Abstract
Seaweed biomass is as an abundant and renewable source of complex polysaccharides, including alginate which has a variety of applications. A sustainable method for exploiting alginate towards the production of valuable oligosaccharides is through enzymatic processing, using alginate lyases. Industrial refinement methods demand robust enzymes. Metagenomic libraries from extreme environments are a new source of unique enzymes with great industrial potential. Herein we report the identification of a new thermostable alginate lyase with only 58 % identity to known sequences, identified by mining a metagenomic library obtained from the hydrothermal vents of the volcano Kolumbo in the Aegean Sea (Kolumbo Alginate Lyase, KAlLy). Sequence analysis and biochemical characterization of KAlLy showed that this new alginate lyase is a Polysaccharide Lyase of family 7 (PL7) enzyme with endo- and exo-action on alginate and poly-mannuronic acid, with high activity at 60°C (56 ± 8 U/mg) and high thermostability (half-life time of 30 h at 50°C). The response surface methodology analysis revealed that the reaction optimum conditions with poly-mannuronic acid as substrate are 44°C, pH of 5.5 with 440 mM NaCl. This novel alginate lyase is a valuable addition to the toolbox of alginate modifying enzymes, due to its diverse sequence and its good thermal stability.
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Affiliation(s)
- Vasileios Tsopanakis
- Department of Chemistry, University of Crete, Voutes University Campus, Heraklion 70013, Greece
| | - Elena Anastasiadou
- Department of Chemistry, University of Crete, Voutes University Campus, Heraklion 70013, Greece
| | - Maria D Mikkelsen
- Protein Chemistry and Enzyme Technology Section, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby DK-2800 Kgs, Denmark
| | - Anne S Meyer
- Protein Chemistry and Enzyme Technology Section, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby DK-2800 Kgs, Denmark
| | - Ioannis V Pavlidis
- Department of Chemistry, University of Crete, Voutes University Campus, Heraklion 70013, Greece.
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7
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Yuan Q, Tian C, Song Y, Ou P, Zhu M, Zhao H, Yang Y. GPSFun: geometry-aware protein sequence function predictions with language models. Nucleic Acids Res 2024; 52:W248-W255. [PMID: 38738636 PMCID: PMC11223820 DOI: 10.1093/nar/gkae381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/14/2024] Open
Abstract
Knowledge of protein function is essential for elucidating disease mechanisms and discovering new drug targets. However, there is a widening gap between the exponential growth of protein sequences and their limited function annotations. In our prior studies, we have developed a series of methods including GraphPPIS, GraphSite, LMetalSite and SPROF-GO for protein function annotations at residue or protein level. To further enhance their applicability and performance, we now present GPSFun, a versatile web server for Geometry-aware Protein Sequence Function annotations, which equips our previous tools with language models and geometric deep learning. Specifically, GPSFun employs large language models to efficiently predict 3D conformations of the input protein sequences and extract informative sequence embeddings. Subsequently, geometric graph neural networks are utilized to capture the sequence and structure patterns in the protein graphs, facilitating various downstream predictions including protein-ligand binding sites, gene ontologies, subcellular locations and protein solubility. Notably, GPSFun achieves superior performance to state-of-the-art methods across diverse tasks without requiring multiple sequence alignments or experimental protein structures. GPSFun is freely available to all users at https://bio-web1.nscc-gz.cn/app/GPSFun with user-friendly interfaces and rich visualizations.
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Affiliation(s)
- Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Chong Tian
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Yidong Song
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Peihua Ou
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Mingming Zhu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Huiying Zhao
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510000, China
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8
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Nestl BM, Nebel BA, Resch V, Schürmann M, Tischler D. The Development and Opportunities of Predictive Biotechnology. Chembiochem 2024; 25:e202300863. [PMID: 38713151 DOI: 10.1002/cbic.202300863] [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: 12/22/2023] [Revised: 04/05/2024] [Indexed: 05/08/2024]
Abstract
Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.
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Affiliation(s)
- Bettina M Nestl
- Joint working group on biotransformations of the Association for General and Applied Microbiology VAAM, the Society for Chemical Engineering, Biotechnology DECHEMA, Theodor-Heuss-Allee 25, 60486, Frankfurt, Germany
- Innophore GmbH, Am Eisernen Tor 3, 8010, Graz, Austria
| | - Bernd A Nebel
- Innophore GmbH, Am Eisernen Tor 3, 8010, Graz, Austria
| | - Verena Resch
- Innophore GmbH, Am Eisernen Tor 3, 8010, Graz, Austria
| | - Martin Schürmann
- Joint working group on biotransformations of the Association for General and Applied Microbiology VAAM, the Society for Chemical Engineering, Biotechnology DECHEMA, Theodor-Heuss-Allee 25, 60486, Frankfurt, Germany
- InnoSyn B. V., Urmonderbaan 22, 6167 RD, Geleen, The Netherlands
- SynSilico B. V., Urmonderbaan 22, 6167 RD, Geleen, The Netherlands
| | - Dirk Tischler
- Joint working group on biotransformations of the Association for General and Applied Microbiology VAAM, the Society for Chemical Engineering, Biotechnology DECHEMA, Theodor-Heuss-Allee 25, 60486, Frankfurt, Germany
- Microbial Biotechnology, Ruhr University Bochum, Universitätsstrasse 150, 44780, Bochum, Germany
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9
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Roy A, Swetha RG, Basu S, Biswas R, Ramaiah S, Anbarasu A. Integrating pan-genome and reverse vaccinology to design multi-epitope vaccine against Herpes simplex virus type-1. 3 Biotech 2024; 14:176. [PMID: 38855144 PMCID: PMC11153438 DOI: 10.1007/s13205-024-04022-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/27/2024] [Indexed: 06/11/2024] Open
Abstract
Herpes simplex virus type-1 (HSV-1), the etiological agent of sporadic encephalitis and recurring oral (sometimes genital) infections in humans, affects millions each year. The evolving viral genome reduces susceptibility to existing antivirals and, thus, necessitates new therapeutic strategies. Immunoinformatics strategies have shown promise in designing novel vaccine candidates in the absence of a clinically licensed vaccine to prevent HSV-1. However, to encourage clinical translation, the HSV-1 pan-genome was integrated with the reverse-vaccinology pipeline for rigorous screening of universal vaccine candidates. Viral targets were screened from 104 available complete genomes. Among 364 proteins, envelope glycoprotein D being an outer membrane protein with a high antigenicity score (> 0.4) and solubility (> 0.6) was selected for epitope screening. A total of 17 T-cell and 4 B-cell epitopes with highly antigenic, immunogenic, non-toxic properties and high global population coverage were identified. Furthermore, 8 vaccine constructs were designed using different combinations of epitopes and suitable linkers. VC-8 was identified as the most potential vaccine candidate regarding chemical and structural stability. Molecular docking revealed high interactive affinity (low binding energy: - 56.25 kcal/mol) of VC-8 with the target elicited by firm intermolecular H-bonds, salt-bridges, and hydrophobic interactions, which was validated with simulations. Compatibility of the vaccine candidate to be expressed in pET-29(a) + plasmid was established by in silico cloning studies. Immune simulations confirmed the potential of VC-8 to trigger robust B-cell, T-cell, cytokine, and antibody-mediated responses, thereby suggesting a promising candidate for the future of HSV-1 prevention. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-024-04022-6.
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Affiliation(s)
- Aditi Roy
- Medical and Biological Computing Laboratory, School of Biosciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014 India
- Department of Biotechnology, SBST, VIT, Vellore, Tamil Nadu 632014 India
| | - Rayapadi G. Swetha
- Medical and Biological Computing Laboratory, School of Biosciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014 India
- Department of Biosciences, SBST, VIT, Vellore, Tamil Nadu 632014 India
| | - Soumya Basu
- Department of Biotechnology, NIST University, Berhampur, Odisha 761008 India
| | - Rhitam Biswas
- Medical and Biological Computing Laboratory, School of Biosciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014 India
- Department of Biotechnology, SBST, VIT, Vellore, Tamil Nadu 632014 India
| | - Sudha Ramaiah
- Medical and Biological Computing Laboratory, School of Biosciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014 India
- Department of Biosciences, SBST, VIT, Vellore, Tamil Nadu 632014 India
| | - Anand Anbarasu
- Medical and Biological Computing Laboratory, School of Biosciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014 India
- Department of Biotechnology, SBST, VIT, Vellore, Tamil Nadu 632014 India
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10
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Kaur B, Karnwal A, Bansal A, Malik T. An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus. BIOMED RESEARCH INTERNATIONAL 2024; 2024:4066641. [PMID: 38962403 PMCID: PMC11221950 DOI: 10.1155/2024/4066641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/30/2024] [Accepted: 06/01/2024] [Indexed: 07/05/2024]
Abstract
The zoonotic viruses pose significant threats to public health. Nipah virus (NiV) is an emerging virus transmitted from bats to humans. The NiV causes severe encephalitis and acute respiratory distress syndrome, leading to high mortality rates, with fatality rates ranging from 40% to 75%. The first emergence of the disease was found in Malaysia in 1998-1999 and later in Bangladesh, Cambodia, Timor-Leste, Indonesia, Singapore, Papua New Guinea, Vietnam, Thailand, India, and other South and Southeast Asian nations. Currently, no specific vaccines or antiviral drugs are available. The potential advantages of epitope-based vaccines include their ability to elicit specific immune responses while minimizing potential side effects. The epitopes have been identified from the conserved region of viral proteins obtained from the UniProt database. The selection of conserved epitopes involves analyzing the genetic sequences of various viral strains. The present study identified two B cell epitopes, seven cytotoxic T lymphocyte (CTL) epitopes, and seven helper T lymphocyte (HTL) epitope interactions from the NiV proteomic inventory. The antigenic and physiological properties of retrieved protein were analyzed using online servers ToxinPred, VaxiJen v2.0, and AllerTOP. The final vaccine candidate has a total combined coverage range of 80.53%. The tertiary structure of the constructed vaccine was optimized, and its stability was confirmed with the help of molecular simulation. Molecular docking was performed to check the binding affinity and binding energy of the constructed vaccine with TLR-3 and TLR-5. Codon optimization was performed in the constructed vaccine within the Escherichia coli K12 strain, to eliminate the danger of codon bias. However, these findings must require further validation to assess their effectiveness and safety. The development of vaccines and therapeutic approaches for virus infection is an ongoing area of research, and it may take time before effective interventions are available for clinical use.
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Affiliation(s)
- Beant Kaur
- School of Bioengineering and BiosciencesLovely Professional University, Phagwara, Punjab 144411, India
| | - Arun Karnwal
- School of Bioengineering and BiosciencesLovely Professional University, Phagwara, Punjab 144411, India
| | - Anu Bansal
- School of Bioengineering and BiosciencesLovely Professional University, Phagwara, Punjab 144411, India
| | - Tabarak Malik
- Department of Biomedical SciencesInstitute of HealthJimma University, Jimma, Ethiopia
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11
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Harding-Larsen D, Madsen CD, Teze D, Kittilä T, Langhorn MR, Gharabli H, Hobusch M, Otalvaro FM, Kırtel O, Bidart GN, Mazurenko S, Travnik E, Welner DH. GASP: A Pan-Specific Predictor of Family 1 Glycosyltransferase Acceptor Specificity Enabled by a Pipeline for Substrate Feature Generation and Large-Scale Experimental Screening. ACS OMEGA 2024; 9:27278-27288. [PMID: 38947828 PMCID: PMC11209901 DOI: 10.1021/acsomega.4c01583] [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: 02/19/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 07/02/2024]
Abstract
Glycosylation represents a major chemical challenge; while it is one of the most common reactions in Nature, conventional chemistry struggles with stereochemistry, regioselectivity, and solubility issues. In contrast, family 1 glycosyltransferase (GT1) enzymes can glycosylate virtually any given nucleophilic group with perfect control over stereochemistry and regioselectivity. However, the appropriate catalyst for a given reaction needs to be identified among the tens of thousands of available sequences. Here, we present the glycosyltransferase acceptor specificity predictor (GASP) model, a data-driven approach to the identification of reactive GT1:acceptor pairs. We trained a random forest-based acceptor predictor on literature data and validated it on independent in-house generated data on 1001 GT1:acceptor pairs, obtaining an AUROC of 0.79 and a balanced accuracy of 72%. The performance was stable even in the case of completely new GT1s and acceptors not present in the training data set, highlighting the pan-specificity of GASP. Moreover, the model is capable of parsing all known GT1 sequences, as well as all chemicals, the latter through a pipeline for the generation of 153 chemical features for a given molecule taking the CID or SMILES as input (freely available at https://github.com/degnbol/GASP). To investigate the power of GASP, the model prediction probability scores were compared to GT1 substrate conversion yields from a newly published data set, with the top 50% of GASP predictions corresponding to reactions with >50% synthetic yields. The model was also tested in two comparative case studies: glycosylation of the antihelminth drug niclosamide and the plant defensive compound DIBOA. In the first study, the model achieved an 83% hit rate, outperforming a hit rate of 53% from a random selection assay. In the second case study, the hit rate of GASP was 50%, and while being lower than the hit rate of 83% using expert-selected enzymes, it provides a reasonable performance for the cases when an expert opinion is unavailable. The hierarchal importance of the generated chemical features was investigated by negative feature selection, revealing properties related to cyclization and atom hybridization status to be the most important characteristics for accurate prediction. Our study provides a GT1:acceptor predictor which can be trained on other data sets enabled by the automated feature generation pipelines. We also release the new in-house generated data set used for testing of GASP to facilitate the future development of GT1 activity predictors and their robust benchmarking.
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Affiliation(s)
- David Harding-Larsen
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Christian Degnbol Madsen
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
- The
University of Melbourne Faculty of Science, Melbourne Integrative
Genomics, University of Melbourne, Building 184, Royal Parade, Parkville
3010, Melbourne, VIC 3052, Australia
| | - David Teze
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Tiia Kittilä
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | | | - Hani Gharabli
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Mandy Hobusch
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Felipe Mejia Otalvaro
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Onur Kırtel
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Gonzalo Nahuel Bidart
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Stanislav Mazurenko
- Department
of Experimental Biology and RECETOX, Faculty of Science, Masarykova Univerzita, Kamenice 5/A4, Brno 625 00, Czech Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, Brno 656
91, Czech Republic
| | - Evelyn Travnik
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
| | - Ditte Hededam Welner
- DTU
Biosustain, Technical University of Denmark, Kemitorvet 220, Lyngby, Denmark 2800
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12
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Elisée E, Ducrot L, Méheust R, Bastard K, Fossey-Jouenne A, Grogan G, Pelletier E, Petit JL, Stam M, de Berardinis V, Zaparucha A, Vallenet D, Vergne-Vaxelaire C. A refined picture of the native amine dehydrogenase family revealed by extensive biodiversity screening. Nat Commun 2024; 15:4933. [PMID: 38858403 PMCID: PMC11164908 DOI: 10.1038/s41467-024-49009-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 05/20/2024] [Indexed: 06/12/2024] Open
Abstract
Native amine dehydrogenases offer sustainable access to chiral amines, so the search for scaffolds capable of converting more diverse carbonyl compounds is required to reach the full potential of this alternative to conventional synthetic reductive aminations. Here we report a multidisciplinary strategy combining bioinformatics, chemoinformatics and biocatalysis to extensively screen billions of sequences in silico and to efficiently find native amine dehydrogenases features using computational approaches. In this way, we achieve a comprehensive overview of the initial native amine dehydrogenase family, extending it from 2,011 to 17,959 sequences, and identify native amine dehydrogenases with non-reported substrate spectra, including hindered carbonyls and ethyl ketones, and accepting methylamine and cyclopropylamine as amine donor. We also present preliminary model-based structural information to inform the design of potential (R)-selective amine dehydrogenases, as native amine dehydrogenases are mostly (S)-selective. This integrated strategy paves the way for expanding the resource of other enzyme families and in highlighting enzymes with original features.
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Affiliation(s)
- Eddy Elisée
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Laurine Ducrot
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Raphaël Méheust
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Karine Bastard
- School of Pharmacy, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, 2006, Australia
| | - Aurélie Fossey-Jouenne
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Gideon Grogan
- York Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York, YO10 5DD, UK
| | - Eric Pelletier
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Jean-Louis Petit
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Mark Stam
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Véronique de Berardinis
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Anne Zaparucha
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - David Vallenet
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France.
| | - Carine Vergne-Vaxelaire
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France.
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13
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Li B, Ming D. GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling. BMC Bioinformatics 2024; 25:204. [PMID: 38824535 DOI: 10.1186/s12859-024-05820-8] [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: 12/20/2023] [Accepted: 05/29/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance. RESULTS In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set. CONCLUSIONS GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .
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Affiliation(s)
- Bin Li
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 South Puzhu Road, Jiangbei New District, Nanjing, 211816, Jiangsu, People's Republic of China
| | - Dengming Ming
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 South Puzhu Road, Jiangbei New District, Nanjing, 211816, Jiangsu, People's Republic of China.
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14
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Love AC, Purdy TN, Hubert FM, Kirwan EJ, Holland DC, Moore BS. Discovery of Latent Cannabichromene Cyclase Activity in Marine Bacterial Flavoenzymes. ACS Synth Biol 2024; 13:1343-1354. [PMID: 38459634 PMCID: PMC11031283 DOI: 10.1021/acssynbio.4c00051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2024]
Abstract
Production of phytocannabinoids remains an area of active scientific interest due to the growing use of cannabis by the public and the underexplored therapeutic potential of the over 100 minor cannabinoids. While phytocannabinoids are biosynthesized by Cannabis sativa and other select plants and fungi, structural analogs and stereoisomers can only be accessed synthetically or through heterologous expression. To date, the bioproduction of cannabinoids has required eukaryotic hosts like yeast since key, native oxidative cyclization enzymes do not express well in bacterial hosts. Here, we report that two marine bacterial flavoenzymes, Clz9 and Tcz9, perform oxidative cyclization reactions on phytocannabinoid precursors to efficiently generate cannabichromene scaffolds. Furthermore, Clz9 and Tcz9 express robustly in bacteria and display significant tolerance to organic solvent and high substrate loading, thereby enabling fermentative production of cannabichromenic acid in Escherichia coli and indicating their potential for biocatalyst development.
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Affiliation(s)
- Anna C. Love
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
| | - Trevor N. Purdy
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
| | - Felix M. Hubert
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
| | - Ella J. Kirwan
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
| | - Darren C. Holland
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
| | - Bradley S. Moore
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
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15
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Tripp A, Braun M, Wieser F, Oberdorfer G, Lechner H. Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering. Chembiochem 2024:e202400092. [PMID: 38634409 DOI: 10.1002/cbic.202400092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 04/19/2024]
Abstract
Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.
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Affiliation(s)
- Adrian Tripp
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Markus Braun
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Florian Wieser
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
| | - Gustav Oberdorfer
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
- BioTechMed, Graz, Austria
| | - Horst Lechner
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010, Graz, Austria
- BioTechMed, Graz, Austria
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16
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Rizarullah, Aditama R, Giri-Rachman EA, Hertadi R. Designing a Novel Multiepitope Vaccine from the Human Papilloma Virus E1 and E2 Proteins for Indonesia with Immunoinformatics and Molecular Dynamics Approaches. ACS OMEGA 2024; 9:16547-16562. [PMID: 38617694 PMCID: PMC11007845 DOI: 10.1021/acsomega.4c00425] [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: 01/12/2024] [Revised: 03/04/2024] [Accepted: 03/08/2024] [Indexed: 04/16/2024]
Abstract
One of the deadliest malignant cancer in women globally is cervical cancer. Specifically, cervical cancer is the second most common type of cancer in Indonesia. The main infectious agent of cervical cancer is the human papilloma virus (HPV). Although licensed prophylactic vaccines are available, cervical cancer cases are on the rise. Therapy using multiepitope-based vaccines is a very promising therapy for cervical cancer. This study aimed to develop a multiepitope vaccine based on the E1 and E2 proteins of HPV 16, 18, 45, and 52 using in silico. In this study, we develop a novel multiepitope vaccine candidate using an immunoinformatic approach. We predicted the epitopes of the cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) and evaluated their immunogenic properties. Population coverage analysis of qualified epitopes was conducted to determine the successful use of the vaccine worldwide. The epitopes were constructed into a multiepitope vaccine by using AAY linkers between the CTL epitopes and GPGPG linkers between the HTL epitopes. The tertiary structure of the multiepitope vaccine was modeled with AlphaFold and was evaluated by Prosa-web. The results of vaccine construction were analyzed for B-cell epitope prediction, molecular docking with Toll like receptor-4 (TLR4), and molecular dynamics simulation. The results of epitope prediction obtained 4 CTL epitopes and 7 HTL epitopes that are eligible for construction of multiepitope vaccines. Prediction of the physicochemical properties of multiepitope vaccines obtained good results for recombinant protein production. The interaction showed that the interaction of the multiepitope vaccine-TLR4 complex is stable based on the binding free energy value -106.5 kcal/mol. The results of the immune response simulation show that multiepitope vaccine candidates could activate the adaptive and humoral immune systems and generate long-term B-cell memory. According to these results, the development of a multiepitope vaccine with a reverse vaccinology approach is a breakthrough to develop potential cervical cancer therapeutic vaccines.
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Affiliation(s)
- Rizarullah
- Biochemistry
and Biomolecular Engineering Research Division, Faculty of Mathematics
and Natural Sciences, Bandung Institute
of Technology, Jl. Ganesa No. 10, Bandung 40132, Indonesia
- Department
of Biochemistry, Faculty of Medicine, Abulyatama
University, Jl. Blangbintang Lama, Aceh Besar 23372, Indonesia
| | - Reza Aditama
- Biochemistry
and Biomolecular Engineering Research Division, Faculty of Mathematics
and Natural Sciences, Bandung Institute
of Technology, Jl. Ganesa No. 10, Bandung 40132, Indonesia
| | - Ernawati Arifin Giri-Rachman
- Genetics
and Molecular Biotechnology Research Division, School of Life Sciences
and Technology, Bandung Institute of Technology, Jl. Ganesa No. 10, Bandung 40132, Indonesia
| | - Rukman Hertadi
- Biochemistry
and Biomolecular Engineering Research Division, Faculty of Mathematics
and Natural Sciences, Bandung Institute
of Technology, Jl. Ganesa No. 10, Bandung 40132, Indonesia
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17
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Li W, Lin H, Huang Z, Xie S, Zhou Y, Gong R, Jiang Q, Xiang C, Huang J. DOTAD: A Database of Therapeutic Antibody Developability. Interdiscip Sci 2024:10.1007/s12539-024-00613-2. [PMID: 38530613 DOI: 10.1007/s12539-024-00613-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 01/25/2024] [Accepted: 01/27/2024] [Indexed: 03/28/2024]
Abstract
The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.
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Affiliation(s)
- Wenzhen Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hongyan Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shiyang Xie
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Rong Gong
- School of Computer Science and Technology, Aba Teachers University, Aba, 623002, China
| | - Qianhu Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - ChangCheng Xiang
- School of Computer Science and Technology, Aba Teachers University, Aba, 623002, China.
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China.
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18
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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19
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Mostafa K, Yerlikaya BA, Abdulla MF, Aydin A, Yerlikaya S, Kavas M. Genome-wide analysis of PvMADS in common bean and functional characterization of PvMADS31 in Arabidopsis thaliana as a player in abiotic stress responses. THE PLANT GENOME 2024; 17:e20432. [PMID: 38327143 DOI: 10.1002/tpg2.20432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/15/2023] [Accepted: 01/02/2024] [Indexed: 02/09/2024]
Abstract
Changing climatic conditions with rising temperatures and altered precipitation patterns pose significant challenges to agricultural productivity, particularly for common bean crops. Transcription factors (TFs) are crucial regulators that can mitigate the impact of biotic and abiotic stresses on crop production. The MADS-box TFs family has been implicated in various plant physiological processes, including stress-responsive mechanisms. However, their role in common bean and their response to stressful conditions remain poorly understood. Here, we identified 35 MADS-box gene family members in common bean, with conserved MADS-box domains and other functional domains. Gene duplication events were observed, suggesting the significance of duplication in the evolutionary development of gene families. The analysis of promoter regions revealed diverse elements, including stress-responsive elements, indicating their potential involvement in stress responses. Notably, PvMADS31, a member of the PvMADS-box gene family, demonstrated rapid upregulation under various abiotic stress conditions, including NaCl, polyethylene glycol, drought, and abscisic acid (ABA) treatments. Transgenic plants overexpressing PvMADS31 displayed enhanced lateral root development, root elongation, and seed germination under stress conditions. Furthermore, PvMADS31 overexpression in Arabidopsis resulted in improved drought tolerance, likely attributed to the enhanced scavenging of ROS and increased proline accumulation. These findings suggest that PvMADS31 might play a crucial role in modulating seed germination, root development, and stress responses, potentially through its involvement in auxin and ABA signaling pathways. Overall, this study provides valuable insights into the potential roles of PvMADS-box genes in abiotic stress responses in common bean, offering prospects for crop improvement strategies to enhance resilience under changing environmental conditions.
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Affiliation(s)
- Karam Mostafa
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Turkey
- The Central Laboratory for Date Palm Research and Development, Agricultural Research Center (ARC), Giza, Egypt
| | - Bayram Ali Yerlikaya
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Turkey
| | - Mohamed Farah Abdulla
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Turkey
| | - Abdullah Aydin
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Turkey
| | - Seher Yerlikaya
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Turkey
| | - Musa Kavas
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Turkey
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20
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Golosova NN, Matveev AL, Tikunova NV, Khlusevich YA, Kozlova YN, Morozova VV, Babkin IV, Ushakova TA, Zhirakovskaya EV, Panina EA, Ryabchikova EI, Tikunov AY. Bacteriophage vB_SepP_134 and Endolysin LysSte_134_1 as Potential Staphylococcus-Biofilm-Removing Biological Agents. Viruses 2024; 16:385. [PMID: 38543751 PMCID: PMC10975630 DOI: 10.3390/v16030385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/19/2024] [Accepted: 02/28/2024] [Indexed: 05/23/2024] Open
Abstract
Bacteria of the genus Staphylococcus are significant challenge for medicine, as many species are resistant to multiple antibiotics and some are even to all of the antibiotics we use. One of the approaches to developing new therapeutics to treat staphylococcal infections is the use of bacteriophages specific to these bacteria or the lytic enzymes of such bacteriophages, which are capable of hydrolyzing the cell walls of these bacteria. In this study, a new bacteriophage vB_SepP_134 (St 134) specific to Staphylococcus epidermidis was described. This podophage, with a genome of 18,275 bp, belongs to the Andhravirus genus. St 134 was able to infect various strains of 12 of the 21 tested coagulase-negative Staphylococcus species and one clinical strain from the Staphylococcus aureus complex. The genes encoding endolysin (LysSte134_1) and tail tip lysin (LysSte134_2) were identified in the St 134 genome. Both enzymes were cloned and produced in Escherichia coli cells. The endolysin LysSte134_1 demonstrated catalytic activity against peptidoglycans isolated from S. aureus, S. epidermidis, Staphylococcus haemolyticus, and Staphylococcus warneri. LysSte134_1 was active against S. aureus and S. epidermidis planktonic cells and destroyed the biofilms formed by clinical strains of S. aureus and S. epidermidis.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Artem Y. Tikunov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia; (N.N.G.); (N.V.T.); (Y.A.K.); (Y.N.K.); (V.V.M.); (I.V.B.); (T.A.U.); (E.A.P.); (E.I.R.)
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21
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Rahbar MR, Nezafat N, Morowvat MH, Savardashtaki A, Ghoshoon MB, Mehrabani-Zeinabad K, Ghasemi Y. Targeting Efficient Features of Urate Oxidase to Increase Its Solubility. Appl Biochem Biotechnol 2024:10.1007/s12010-023-04819-w. [PMID: 38308671 DOI: 10.1007/s12010-023-04819-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2023] [Indexed: 02/05/2024]
Abstract
With the demand for mass production of protein drugs, solubility has become a serious issue. Extrinsic and intrinsic factors both affect this property. A homotetrameric cofactor-free urate oxidase (UOX) is not sufficiently soluble. To engineer UOX for optimum solubility, it is important to identify the most effective factor that influences solubility. The most effective feature to target for protein engineering was determined by measuring various solubility-related factors of UOX. A large library of homologous sequences was obtained from the databases. The data was reduced to six enzymes from different organisms. On the basis of various sequence- and structure-derived elements, the most and the least soluble enzymes were defined. To determine the best protein engineering target for modification, features of the most and least soluble enzymes were compared. Metabacillus fastidiosus UOX was the most soluble enzyme, while Agrobacterium globiformis UOX was the least soluble. According to the comparison-constant method, positive surface patches caused by arginine residue distribution are appropriate targets for modification. Two Arg to Ala mutations were introduced to the least soluble enzyme to test this hypothesis. These mutations significantly enhanced the mutant's solubility. While different algorithms produced conflicting results, it was difficult to determine which proteins were most and least soluble. Solubility prediction requires multiple algorithms based on these controversies. Protein surfaces should be investigated regionally rather than globally, and both sequence and structural data should be considered. Several other biotechnological products could be engineered using the data reduction and comparison-constant methods used in this study.
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Affiliation(s)
- Mohammad Reza Rahbar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Navid Nezafat
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran
| | - Mohammad Hossein Morowvat
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran
| | - Amir Savardashtaki
- Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Bagher Ghoshoon
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran
| | - Kamran Mehrabani-Zeinabad
- Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Younes Ghasemi
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran.
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22
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Golosova NN, Khlusevich YA, Morozova VV, Matveev AL, Kozlova YN, Tikunov AY, Panina EA, Tikunova NV. Characterization of a Thermostable Endolysin of the Aeribacillus Phage AeriP45 as a Potential Staphylococcus Biofilm-Removing Agent. Viruses 2024; 16:93. [PMID: 38257793 PMCID: PMC10819204 DOI: 10.3390/v16010093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/06/2024] [Accepted: 01/06/2024] [Indexed: 01/24/2024] Open
Abstract
Multidrug-resistant Gram-positive bacteria, including bacteria from the genus Staphylococcus, are currently a challenge for medicine. Therefore, the development of new antimicrobials is required. Promising candidates for new antistaphylococcal drugs are phage endolysins, including endolysins from thermophilic phages against other Gram-positive bacteria. In this study, the recombinant endolysin LysAP45 from the thermophilic Aeribacillus phage AP45 was obtained and characterized. The recombinant endolysin LysAP45 was produced in Escherichia coli M15 cells. It was shown that LysAP45 is able to hydrolyze staphylococcal peptidoglycans from five species and eleven strains. Thermostability tests showed that LysAP45 retained its hydrolytic activity after incubation at 80 °C for at least 30 min. The enzymatically active domain of the recombinant endolysin LysAP45 completely disrupted biofilms formed by multidrug-resistant S. aureus, S. haemolyticus, and S. epidermidis. The results suggested that LysAP45 is a novel thermostable antimicrobial agent capable of destroying biofilms formed by various species of multidrug-resistant Staphylococcus. An unusual putative cell-binding domain was found at the C-terminus of LysAP45. No domains with similar sequences were found among the described endolysins.
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Affiliation(s)
| | | | | | | | | | | | | | - Nina V. Tikunova
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; (N.N.G.); (V.V.M.); (A.L.M.); (Y.N.K.); (A.Y.T.); (E.A.P.)
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23
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Boob AG, Chen J, Zhao H. Enabling pathway design by multiplex experimentation and machine learning. Metab Eng 2024; 81:70-87. [PMID: 38040110 DOI: 10.1016/j.ymben.2023.11.006] [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: 09/14/2023] [Revised: 11/01/2023] [Accepted: 11/25/2023] [Indexed: 12/03/2023]
Abstract
The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.
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Affiliation(s)
- Aashutosh Girish Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Junyu Chen
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
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24
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Sun C, Hu B, Li Y, Wu Z, Zhou J, Li J, Chen J, Du G, Zhao X. Efficient stereoselective hydroxylation of deoxycholic acid by the robust whole-cell cytochrome P450 CYP107D1 biocatalyst. Synth Syst Biotechnol 2023; 8:741-748. [PMID: 38107826 PMCID: PMC10722395 DOI: 10.1016/j.synbio.2023.11.008] [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: 10/15/2023] [Revised: 11/12/2023] [Accepted: 11/18/2023] [Indexed: 12/19/2023] Open
Abstract
Deoxycholic acid (DCA) has been authorized by the Federal Drug Agency for cosmetic reduction of redundant submental fat. The hydroxylated product (6β-OH DCA) was developed to improve the solubility and pharmaceutic properties of DCA for further applications. Herein, a combinatorial catalytic strategy was applied to construct a powerful Cytochrome P450 biocatalyst (CYP107D1, OleP) to convert DCA to 6β-OH DCA. Firstly, the weak expression of OleP was significantly improved using pRSFDuet-1 plasmid in the E. coli C41 (DE3) strain. Next, the supply of heme was enhanced by the moderate overexpression of crucial genes in the heme biosynthetic pathway. In addition, a new biosensor was developed to select the appropriate redox partner. Furthermore, a cost-effective whole-cell catalytic system was constructed, resulting in the highest reported conversion rate of 6β-OH DCA (from 4.8% to 99.1%). The combinatorial catalytic strategies applied in this study provide an efficient method to synthesize high-value-added hydroxylated compounds by P450s.
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Affiliation(s)
- Chixiang Sun
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Baodong Hu
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Yanchun Li
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Zhimeng Wu
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Jingwen Zhou
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Jianghua Li
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Jian Chen
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Xinrui Zhao
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
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25
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G P, Rathi B, Santoshi S. Translational and structural vaccinomics approach to design a multi-epitope vaccine against NOL4 autologous antigen of small cell lung cancer. Immunol Res 2023; 71:909-928. [PMID: 37410306 DOI: 10.1007/s12026-023-09404-1] [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: 02/24/2023] [Accepted: 06/22/2023] [Indexed: 07/07/2023]
Abstract
Small cell lung cancer (SCLC) is one of the most common cancers and it is the sixth common cause for cancer-related deaths. The high plasticity and metastasis have been a major challenge for humanity to treat the disease. Hence, a vaccine for SCLC has become an urgent need of the hour due to public health concern. Implementation of immunoinformatics technique is one of the best way to find a suitable vaccine candidate. Immunoinformatics tools can be used to overcome the limitations and difficulties of traditional vaccinological techniques. Multi-epitope cancer vaccines have become a next-generation technique in vaccinology which can be used to stimulate more potent immune response against a particular antigen by eliminating undesirable molecules. In this study, we used multiple computational and immunoinformatics approach to design a novel multi-epitope vaccine for small cell lung cancer. Nucleolar protein 4 (NOL4) is an autologous cancer-testis antigen overexpressed in SCLC cells. Seventy-five percent humoral immunity have been identified for this particular antigen. In this study, we mapped immunogenic cytotoxic T lymphocyte, helper T lymphocyte, and interferon-gamma epitopes present in NOL4 antigen and designed a multi-epitope-based vaccine using the predicted epitopes. The designed vaccine was antigenic, non-allergenic, and non-toxic with 100% applicability on human population. The chimeric vaccine construct showed stable and significant interaction with endosomal and plasmalemmal toll-like receptors in molecular docking and protein-peptide interaction analysis, thus assuring a strong potent immune response against the vaccine upon administration. Therefore, these preliminary results can be used to carry out further experimental investigations.
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Affiliation(s)
- Pavithran G
- Amity Institute of Microbial Technology, Amity University Uttar Pradesh, Noida, India
| | - Bhawna Rathi
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India.
| | - Seneha Santoshi
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
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26
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Guntuboina C, Das A, Mollaei P, Kim S, Barati Farimani A. PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction. J Phys Chem Lett 2023; 14:10427-10434. [PMID: 37956397 PMCID: PMC10683064 DOI: 10.1021/acs.jpclett.3c02398] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 11/04/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023]
Abstract
Recent advances in language models have enabled the protein modeling community with a powerful tool that uses transformers to represent protein sequences as text. This breakthrough enables a sequence-to-property prediction for peptides without relying on explicit structural data. Inspired by the recent progress in the field of large language models, we present PeptideBERT, a protein language model specifically tailored for predicting essential peptide properties such as hemolysis, solubility, and nonfouling. The PeptideBERT utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. Through fine-tuning the pretrained model for the three downstream tasks, our model is state of the art (SOTA) in predicting hemolysis, which is crucial for determining a peptide's potential to induce red blood cells as well as nonfouling properties. Leveraging primarily shorter sequences and a data set with negative samples predominantly associated with insoluble peptides, our model showcases remarkable performance.
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Affiliation(s)
- Chakradhar Guntuboina
- Department
of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Adrita Das
- Department
of Biomedical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Parisa Mollaei
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Seongwon Kim
- Department
of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Amir Barati Farimani
- Department
of Biomedical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
- Department
of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
- Machine
Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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27
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Elalouf A, Kedarya T, Elalouf H, Rosenfeld A. Computational design and evaluation of mRNA- and protein-based conjugate vaccines for influenza A and SARS-CoV-2 viruses. J Genet Eng Biotechnol 2023; 21:120. [PMID: 37966525 PMCID: PMC10651613 DOI: 10.1186/s43141-023-00574-x] [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: 06/02/2023] [Accepted: 10/26/2023] [Indexed: 11/16/2023]
Abstract
BACKGROUND Israel confirmed the first case of "flurona"-a co-infection of seasonal flu (IAV) and SARS-CoV-2 in an unvaccinated pregnant woman. This twindemic has been confirmed in multiple countries and underscores the importance of managing respiratory viral illnesses. RESULTS The novel conjugate vaccine was designed by joining four hemagglutinin, three neuraminidase, and four S protein of B-cell epitopes, two hemagglutinin, three neuraminidase, and four S proteins of MHC-I epitopes, and three hemagglutinin, nine neuraminidase, and five S proteins of MHC-II epitopes with linkers and adjuvants. The constructed conjugate vaccine was found stable, non-toxic, non-allergic, and antigenic with 0.6466 scores. The vaccine contained 14.87% alpha helix, 29.85% extended strand, 9.64% beta-turn, and 45.64% random coil, which was modeled to a 3D structure with 94.7% residues in the most favored region of the Ramachandran plot and Z-score of -3.33. The molecular docking of the vaccine with TLR3 represented -1513.9 kcal/mol of binding energy with 39 hydrogen bonds and 514 non-bonded contacts, and 1.582925e-07 of eigenvalue complex. Immune stimulation prediction showed the conjugate vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies. CONCLUSION The in silico-designed vaccine against IAV and SARS-CoV-2 showed good population coverage and immune response with predicted T- and B-cell epitopes, favorable molecular docking, Ramachandran plot results, and good protein expression. It fulfilled safety criteria, indicating potential for preclinical studies and experimental clinical trials.
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Affiliation(s)
- Amir Elalouf
- Department of Management, Bar-Ilan University, 5290002, Ramat Gan, Israel.
| | - Tomer Kedarya
- Department of Management, Bar-Ilan University, 5290002, Ramat Gan, Israel
| | - Hadas Elalouf
- Information Science Department, Bar-Ilan University, 5290002, Ramat Gan, Israel
| | - Ariel Rosenfeld
- Information Science Department, Bar-Ilan University, 5290002, Ramat Gan, Israel
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28
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Kouba P, Kohout P, Haddadi F, Bushuiev A, Samusevich R, Sedlar J, Damborsky J, Pluskal T, Sivic J, Mazurenko S. Machine Learning-Guided Protein Engineering. ACS Catal 2023; 13:13863-13895. [PMID: 37942269 PMCID: PMC10629210 DOI: 10.1021/acscatal.3c02743] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/20/2023] [Indexed: 11/10/2023]
Abstract
Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.
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Affiliation(s)
- Petr Kouba
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Faculty of
Electrical Engineering, Czech Technical
University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic
| | - Pavel Kohout
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Faraneh Haddadi
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Anton Bushuiev
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Raman Samusevich
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Jiri Sedlar
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Jiri Damborsky
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Tomas Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Josef Sivic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Stanislav Mazurenko
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
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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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Ryu G, Kim GB, Yu T, Lee SY. Deep learning for metabolic pathway design. Metab Eng 2023; 80:130-141. [PMID: 37734652 DOI: 10.1016/j.ymben.2023.09.012] [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: 08/19/2023] [Revised: 09/17/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023]
Abstract
The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.
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Affiliation(s)
- Gahyeon Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Taeho Yu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea; Graduate School of Engineering Biology, KAIST, Daejeon, 34141, Republic of Korea.
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Hayat C, Shahab M, Khan SA, Liang C, Duan X, Khan H, Zheng G, Ul-Haq Z. Design of a novel multiple epitope-based vaccine: an immunoinformatics approach to combat monkeypox. J Biomol Struct Dyn 2023; 41:9344-9355. [PMID: 36331082 PMCID: PMC9718376 DOI: 10.1080/07391102.2022.2141887] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
Abstract
Monkeypox virus is an infectious agent that causes fever, Pneumonitis encephalitis, rash, lymphadenopathy and bacterial infection. The current outbreak of monkeypox has reawakened the global health concern. In the current situation of increasing viral infection, no vaccine or drug is available for monkeypox. Thus, there is an urgent need for viable vaccine development to prevent viral transmission by boosting human immunity. Herein, using immunoinformatics approaches, a multi-epitope vaccine was constructed for the Monkeypox virus. In this connection, B-Cell and T-cell epitopes were identified and joined with the help of adjutants and linkers. The vaccine construct was selected based on promising vaccine candidates and immunogenic potential. Further epitopes were selected based on antigenicity score, non-allergenicity and good immunological properties. Molecular docking reveals strong interactions between TLR-9 and the predicted vaccine construct. Finally, molecular dynamics simulations were performed to evaluate the stability and compactness of the constructed vaccine. The MD simulation results demonstrated the significant stability of the polypeptide vaccine construct. The predicted vaccine represented good stability, expression, immunostimulatory capabilities and significant solubility. Design vaccine was verified as efficient in different computer-based immune response investigations. Additionally, the constructed vaccine also represents a good population coverage in computer base analysis.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Chandni Hayat
- Department of Biochemistry, Computational Medicinal Chemistry Laboratory, UCSS, Abdul Wali Khan University, Mardan, Pakistan
| | - Muhammad Shahab
- State Key Laboratories Of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Salman Ali Khan
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Chaoqun Liang
- State Key Laboratories Of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Xiuyuan Duan
- State Key Laboratories Of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Haleema Khan
- Department of Chemistry, UCSS, Abdul Wali Khan University, Mardan, Pakistan
| | - Guojun Zheng
- State Key Laboratories Of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Zaheer Ul-Haq
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
- Third World Center for Science and Technology, H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
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32
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Elalouf A, Yaniv-Rosenfeld A. Immunoinformatic-guided designing and evaluating protein and mRNA-based vaccines against Cryptococcus neoformans for immunocompromised patients. J Genet Eng Biotechnol 2023; 21:108. [PMID: 37882985 PMCID: PMC10603020 DOI: 10.1186/s43141-023-00560-3] [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: 06/02/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023]
Abstract
BACKGROUND Cryptococcus neoformans is a fungal pathogen that can cause serious meningoencephalitis in individuals with compromised immune systems due to HIV/AIDS (human immunodeficiency virus/acquired immunodeficiency syndrome), liver cirrhosis, and transplantation. Mannoproteins (MPs), glycoproteins in the C. neoformans capsule, crucially impact virulence by mediating adhesion to lung cells and modulating immune response via cytokine induction and phagocytosis influence. Therefore, creating a vaccine that can generate targeted antibodies to fight infection and prevent fungal illnesses is essential. RESULTS This research aims to create a unique, stable, and safe vaccine through bioinformatics methodologies, aiming at epitopes of T and B cells found in the MP of C. neoformans. Based on toxicity, immunogenicity, and antigenicity, this research predicted novel T cells (GNPVGGNVT, NPVGGNVTT, QTSYARLLS, TSVGNGIAS, WVMPGDYTN, AAATGSSSSGSTGSG, GSTGSGSGSAAAGST, SGSTGSGSGSAAAGS, SSGSTGSGSGSAAAG, and SSSGSTGSGSGSAAA) and B cell (ANGSTSTFQQRYTGTYTNGDGSLGTWTQGETVTPQTAYSTPATSNCKTYTSVGNGIASLALSNAGSNSTAAATNSSSGGASAAATGSSSSGSTGSGSGSAAAGSTAAASSSGDSSSSTSAAMSNGI, HGATGLGNPVGGNVTT, TMGPTNPSEPTLGTAI, GNPVGGNVTTNATGSD, and NSTAAATNSSSGGASA) epitopes for a multiple-epitope vaccine and constructed a vaccine subunit with potential immunogenic properties. The present study used four linkers (AAY, GPGPG, KK, and EAAAK linkers) to connect the epitopes and adjuvant. After constructing the vaccine, it was confronted with receptor docking and simulation analysis. Subsequently, the vaccine was cloned into the vector of Escherichia coli pET-28a ( +) by ligation process for the expression using the SnapGene tool, which confirmed a significant immune response. To assess the constructed vaccine's properties, multiple computational tools were employed. Based on the MP sequence, the tools evaluated the antigenicity, immunogenicity, cytokine-inducing capacity, allergenicity, toxicity, population coverage, and solubility. CONCLUSION Eventually, the results revealed a promising multi-epitope vaccine as a potential candidate for addressing global C. neoformans infection, particularly in immunocompromised patients. Yet, additional in vitro and in vivo investigations are necessary to validate its safety and effectiveness.
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Affiliation(s)
- Amir Elalouf
- Department of Management, Bar-Ilan University, Ramat Gan, 5290002, Israel.
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Chen JP, Gong JS, Su C, Li H, Xu ZH, Shi JS. Improving the soluble expression of difficult-to-express proteins in prokaryotic expression system via protein engineering and synthetic biology strategies. Metab Eng 2023; 78:99-114. [PMID: 37244368 DOI: 10.1016/j.ymben.2023.05.007] [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: 04/09/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023]
Abstract
Solubility and folding stability are key concerns for difficult-to-express proteins (DEPs) restricted by amino acid sequences and superarchitecture, resolved by the precise distribution of amino acids and molecular interactions as well as the assistance of the expression system. Therefore, an increasing number of tools are available to achieve efficient expression of DEPs, including directed evolution, solubilization partners, chaperones, and affluent expression hosts, among others. Furthermore, genome editing tools, such as transposons and CRISPR Cas9/dCas9, have been developed and expanded to construct engineered expression hosts capable of efficient expression ability of soluble proteins. Accounting for the accumulated knowledge of the pivotal factors in the solubility and folding stability of proteins, this review focuses on advanced technologies and tools of protein engineering, protein quality control systems, and the redesign of expression platforms in prokaryotic expression systems, as well as advances of the cell-free expression technologies for membrane proteins production.
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Affiliation(s)
- Jin-Ping Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
| | - Jin-Song Gong
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China.
| | - Chang Su
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Heng Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Zheng-Hong Xu
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, School of Biotechnology, Jiangnan University, Wuxi, 214122, PR China; Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
| | - Jin-Song Shi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
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Baradaran M, Mahdavinia M, Naderi Soorki M, Jorfi S. Identification, Characterization, and Modeling of a Bioinsecticide Protein Isolated from Scorpion Venom gland: A Three-Finger Protein. IRANIAN BIOMEDICAL JOURNAL 2023; 27:158-66. [PMID: 37553755 PMCID: PMC10507287 DOI: 10.52547/ibj.3885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/24/2023] [Indexed: 08/10/2023]
Abstract
Background The majority of insecticides target sodium channels. The increasing emergence of resistance to the current insecticides has persuaded researchers to search for alternative compounds. Scorpion venom gland as a reservoir of peptides or proteins, which selectively target insect sodium channels. These proteins would be an appropriate source for finding new suitable anti-insect components. Methods Transcriptome of venom gland of scorpion Mesobuthus eupeus was obtained by RNA extraction and complementary DNA library synthesis. The obtained transcriptome was blasted against protein databases to find insect toxins against sodium channel based on the statistically significant similarity in sequence. Physicochemical properties of the identified protein were calculated using bioinformatics software. The three-dimensional structure of this protein was determined using homology modeling, and the final structure was assessed by molecular dynamics simulation. Results The sodium channel blocker found in the transcriptome of M. eupeus venom gland was submitted to the GenBank under the name of meuNa10, a stable hydrophilic protein consisting of 69 amino acids, with the molecular weight of 7721.77 g/mol and pI of 8.7. The tertiary structure of meuNa10 revealed a conserved LCN-type cysteine-stabilized alpha/beta domain stabilized by eight cysteine residues. The meuNa10 is a member of the 3FP superfamily consisting of three finger-like beta strands. Conclusion This study identified meuNa10 as a small insect sodium channel-interacting protein with some physicochemical properties, including stability and water-solubility, which make it a good candidate for further in vivo and in vitro experiments in order to develop a new bioinsecticide.
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Affiliation(s)
- Masoumeh Baradaran
- Toxicology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Masoud Mahdavinia
- Department of Toxicology, School of Pharmacy, Toxicology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Maryam Naderi Soorki
- Department of Biology, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Sahand Jorfi
- Environmental Technologies Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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35
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Baradaran M, Mahdavinia M, Naderi Soorki M, Jorfi S. Identification, Characterization, and Modeling of a Bioinsecticide Protein Isolated from Scorpion Venom gland: A Three-Finger Protein. IRANIAN BIOMEDICAL JOURNAL 2023; 27:158-66. [PMID: 37553755 PMCID: PMC10507287 DOI: 10.61186/ibj.3885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/24/2023] [Indexed: 12/17/2023]
Abstract
Background The majority of insecticides target sodium channels. The increasing emergence of resistance to the current insecticides has persuaded researchers to search for alternative compounds. Scorpion venom gland as a reservoir of peptides or proteins, which selectively target insect sodium channels. These proteins would be an appropriate source for finding new suitable anti-insect components. Methods Transcriptome of venom gland of scorpion Mesobuthus eupeus was obtained by RNA extraction and complementary DNA library synthesis. The obtained transcriptome was blasted against protein databases to find insect toxins against sodium channel based on the statistically significant similarity in sequence. Physicochemical properties of the identified protein were calculated using bioinformatics software. The three-dimensional structure of this protein was determined using homology modeling, and the final structure was assessed by molecular dynamics simulation. Results The sodium channel blocker found in the transcriptome of M. eupeus venom gland was submitted to the GenBank under the name of meuNa10, a stable hydrophilic protein consisting of 69 amino acids, with the molecular weight of 7721.77 g/mol and pI of 8.7. The tertiary structure of meuNa10 revealed a conserved LCN-type cysteine-stabilized alpha/beta domain stabilized by eight cysteine residues. The meuNa10 is a member of the 3FP superfamily consisting of three finger-like beta strands. Conclusion This study identified meuNa10 as a small insect sodium channel-interacting protein with some physicochemical properties, including stability and water-solubility, which make it a good candidate for further in vivo and in vitro experiments in order to develop a new bioinsecticide.
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Affiliation(s)
- Masoumeh Baradaran
- Toxicology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Masoud Mahdavinia
- Department of Toxicology, School of Pharmacy, Toxicology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Maryam Naderi Soorki
- Department of Biology, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Sahand Jorfi
- Environmental Technologies Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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36
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Ouellet S, Ferguson L, Lau AZ, Lim TKY. CysPresso: a classification model utilizing deep learning protein representations to predict recombinant expression of cysteine-dense peptides. BMC Bioinformatics 2023; 24:200. [PMID: 37193950 PMCID: PMC10189939 DOI: 10.1186/s12859-023-05327-8] [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: 01/12/2023] [Accepted: 05/08/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Cysteine-dense peptides (CDPs) are an attractive pharmaceutical scaffold that display extreme biochemical properties, low immunogenicity, and the ability to bind targets with high affinity and selectivity. While many CDPs have potential and confirmed therapeutic uses, synthesis of CDPs is a challenge. Recent advances have made the recombinant expression of CDPs a viable alternative to chemical synthesis. Moreover, identifying CDPs that can be expressed in mammalian cells is crucial in predicting their compatibility with gene therapy and mRNA therapy. Currently, we lack the ability to identify CDPs that will express recombinantly in mammalian cells without labour intensive experimentation. To address this, we developed CysPresso, a novel machine learning model that predicts recombinant expression of CDPs based on primary sequence. RESULTS We tested various protein representations generated by deep learning algorithms (SeqVec, proteInfer, AlphaFold2) for their suitability in predicting CDP expression and found that AlphaFold2 representations possessed the best predictive features. We then optimized the model by concatenation of AlphaFold2 representations, time series transformation with random convolutional kernels, and dataset partitioning. CONCLUSION Our novel model, CysPresso, is the first to successfully predict recombinant CDP expression in mammalian cells and is particularly well suited for predicting recombinant expression of knottin peptides. When preprocessing the deep learning protein representation for supervised machine learning, we found that random convolutional kernel transformation preserves more pertinent information relevant for predicting expressibility than embedding averaging. Our study showcases the applicability of deep learning-based protein representations, such as those provided by AlphaFold2, in tasks beyond structure prediction.
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Affiliation(s)
| | - Larissa Ferguson
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Angus Z Lau
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Tony K Y Lim
- , Vancouver, Canada.
- Department of Pharmacology, University of Cambridge, Cambridge, UK.
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Chen Z, Wang X, Chen X, Huang J, Wang C, Wang J, Wang Z. Accelerating therapeutic protein design with computational approaches toward the clinical stage. Comput Struct Biotechnol J 2023; 21:2909-2926. [PMID: 38213894 PMCID: PMC10781723 DOI: 10.1016/j.csbj.2023.04.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 01/13/2024] Open
Abstract
Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.
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Affiliation(s)
- Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Juyang Huang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Chenglin Wang
- Shenzhen Qiyu Biotechnology Co., Ltd, Shenzhen 518107, China
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
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Rahmani F, Imani Fooladi AA, Ajoudanifar H, Soleimani NA. In silico and experimental methods for designing a potent anticancer arazyme-herceptin fusion protein in HER2-positive breast cancer. J Mol Model 2023; 29:160. [PMID: 37103612 DOI: 10.1007/s00894-023-05562-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 04/17/2023] [Indexed: 04/28/2023]
Abstract
CONTEXT Breast cancer is the most prevalent type of malignancies among women worldwide and is associated with serious physical and mental consequences. Current chemotherapies may lack successful outcomes; thus, the development of targeted recombinant immunotoxins is plausible. The predicted B cell and T cell epitopes of arazyme of the fusion protein are able to elicit immune response. The results of codon adaptation tool of herceptin-arazyme have improved from 0.4 to 1. The in silico immune simulation results showed significant response for immune cells. In conclusion, our findings show that the known multi-epitope fusion protein may activate humoral and cellular immune responses and maybe a possible candidate for breast cancer treatment. METHODS In this study, the selected monoclonal antibody constituting herceptin and the bacterial metalloprotease, arazyme, was used with different peptide linkers to design a novel fusion protein to predict different B cell and T cell epitopes by the means of the relevant databases. Modeler 10.1 and I-TASSER online server were used to predict and validate the 3D structure and then docked to HER2-receptor using HADDOCK2.4 web server. The molecular dynamics (MD) simulations of the arazyme-linker-herceptin-HER2 complex were performed by GROMACS 2019.6 software. The sequence of arazyme-herceptin was optimized for the expression in prokaryotic host using online servers and cloned into pET-28a plasmid. The recombinant pET28a was transferred into the Escherichia coli BL21DE3. Expression and binding affinity of arazyme-herceptin and arazyme to human breast cancer cell lines (SK-BR-3/HER2 + and MDA-MB-468/HER2 -) were validated by the SDS-PAGE and cell‑ELISA, respectively.
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Affiliation(s)
- Farideh Rahmani
- Department of Microbiology, Damghan Branch, Islamic Azad University, Damghan, Iran
| | - Abbas Ali Imani Fooladi
- Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
| | - Hatef Ajoudanifar
- Department of Microbiology, Damghan Branch, Islamic Azad University, Damghan, Iran
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Kim H, Seo J. A Novel Strategy to Identify Endolysins with Lytic Activity against Methicillin-Resistant Staphylococcus aureus. Int J Mol Sci 2023; 24:ijms24065772. [PMID: 36982851 PMCID: PMC10059956 DOI: 10.3390/ijms24065772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 03/22/2023] Open
Abstract
The increasing prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in the dairy industry has become a fundamental concern. Endolysins are bacteriophage-derived peptidoglycan hydrolases that induce the rapid lysis of host bacteria. Herein, we evaluated the lytic activity of endolysin candidates against S. aureus and MRSA. To identify endolysins, we used a bioinformatical strategy with the following steps: (1) retrieval of genetic information, (2) annotation, (3) selection of MRSA, (4) selection of endolysin candidates, and (5) evaluation of protein solubility. We then characterized the endolysin candidates under various conditions. Approximately 67% of S. aureus was detected as MRSA, and 114 putative endolysins were found. These 114 putative endolysins were divided into three groups based on their combinations of conserved domains. Considering protein solubility, we selected putative endolysins 117 and 177. Putative endolysin 117 was the only successfully overexpressed endolysin, and it was renamed LyJH1892. LyJH1892 showed potent lytic activity against both methicillin-susceptible S. aureus and MRSA and showed broad lytic activity against coagulase-negative staphylococci. In conclusion, this study demonstrates a rapid strategy for the development of endolysin against MRSA. This strategy could also be used to combat other antibiotic-resistant bacteria.
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Yu T, Boob AG, Volk MJ, Liu X, Cui H, Zhao H. Machine learning-enabled retrobiosynthesis of molecules. Nat Catal 2023. [DOI: 10.1038/s41929-022-00909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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Wang C, Zou Q. Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE. BMC Biol 2023; 21:12. [PMID: 36694239 PMCID: PMC9875434 DOI: 10.1186/s12915-023-01510-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work. RESULTS In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects. CONCLUSIONS DeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .
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Affiliation(s)
- Chao Wang
- grid.411307.00000 0004 1790 5236School of Software Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Quan Zou
- grid.54549.390000 0004 0369 4060Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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Current insights into protein solubility: A review of its importance for alternative proteins. Food Hydrocoll 2022. [DOI: 10.1016/j.foodhyd.2022.108416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Shey RA, Ghogomu SM, Nebangwa DN, Shintouo CM, Yaah NE, Yengo BN, Nkemngo FN, Esoh KK, Tchatchoua NMT, Mbachick TT, Dede AF, Lemoge AA, Ngwese RA, Asa BF, Ayong L, Njemini R, Vanhamme L, Souopgui J. Rational design of a novel multi-epitope peptide-based vaccine against Onchocerca volvulus using transmembrane proteins. FRONTIERS IN TROPICAL DISEASES 2022. [DOI: 10.3389/fitd.2022.1046522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Almost a decade ago, it was recognized that the global elimination of onchocerciasis by 2030 will not be feasible without, at least, an effective prophylactic and/or therapeutic vaccine to complement chemotherapy and vector control strategies. Recent advances in computational immunology (immunoinformatics) have seen the design of novel multi-epitope onchocerciasis vaccine candidates which are however yet to be evaluated in clinical settings. Still, continued research to increase the pool of vaccine candidates, and therefore the chance of success in a clinical trial remains imperative. Here, we designed a multi-epitope vaccine candidate by assembling peptides from 14 O. volvulus (Ov) proteins using an immunoinformatics approach. An initial 126 Ov proteins, retrieved from the Wormbase database, and at least 90% similar to orthologs in related nematode species of economic importance, were screened for localization, presence of transmembrane domain, and antigenicity using different web servers. From the 14 proteins retained after the screening, 26 MHC-1 and MHC-II (T-cell) epitopes, and linear B-lymphocytes epitopes were predicted and merged using suitable linkers. The Mycobacterium tuberculosis Resuscitation-promoting factor E (RPFE_MYCTU), which is an agonist of TLR4, was then added to the N-terminal of the vaccine candidate as a built-in adjuvant. Immune simulation analyses predicted strong B-cell and IFN-γ based immune responses which are necessary for protection against O. volvulus infection. Protein-protein docking and molecular dynamic simulation predicted stable interactions between the 3D structure of the vaccine candidate and human TLR4. These results show that the designed vaccine candidate has the potential to stimulate both humoral and cellular immune responses and should therefore be subject to further laboratory investigation.
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Peñas-Utrilla D, Marcos E. Identifying well-folded de novo proteins in the new era of accurate structure prediction. Front Mol Biosci 2022; 9:991380. [PMID: 36275629 PMCID: PMC9581288 DOI: 10.3389/fmolb.2022.991380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/20/2022] [Indexed: 11/29/2022] Open
Abstract
Computational de novo protein design tailors proteins for target structures and oligomerisation states with high stability, which allows overcoming many limitations of natural proteins when redesigned for new functions. Despite significant advances in the field over the past decade, it remains challenging to predict sequences that will fold as stable monomers in solution or binders to a particular protein target; thereby requiring substantial experimental resources to identify proteins with the desired properties. To overcome this, here we leveraged the large amount of design data accumulated in the last decade, and the breakthrough in protein structure prediction from last year to investigate on improved ways of selecting promising designs before experimental testing. We collected de novo proteins from previous studies, 518 designed as monomers of different folds and 2112 as binders against the Botulinum neurotoxin, and analysed their structures with AlphaFold2, RoseTTAFold and fragment quality descriptors in combination with other properties related to surface interactions. These features showed high complementarity in rationalizing the experimental results, which allowed us to generate quite accurate machine learning models for predicting well-folded monomers and binders with a small set of descriptors. Cross-validating designs with varied orthogonal computational techniques should guide us for identifying design imperfections, rescuing designs and making more robust design selections before experimental testing.
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Zheng AJL, Thermou A, Daskalogianni C, Malbert-Colas L, Karakostis K, Le Sénéchal R, Trang Dinh V, Tovar Fernandez MC, Apcher S, Chen S, Blondel M, Fahraeus R. The nascent polypeptide-associated complex (NAC) controls translation initiation in cis by recruiting nucleolin to the encoding mRNA. Nucleic Acids Res 2022; 50:10110-10122. [PMID: 36107769 PMCID: PMC9508830 DOI: 10.1093/nar/gkac751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/10/2022] [Indexed: 11/20/2022] Open
Abstract
Protein aggregates and abnormal proteins are toxic and associated with neurodegenerative diseases. There are several mechanisms to help cells get rid of aggregates but little is known on how cells prevent aggregate-prone proteins from being synthesised. The EBNA1 of the Epstein-Barr virus (EBV) evades the immune system by suppressing its own mRNA translation initiation in order to minimize the production of antigenic peptides for the major histocompatibility (MHC) class I pathway. Here we show that the emerging peptide of the disordered glycine–alanine repeat (GAr) within EBNA1 dislodges the nascent polypeptide-associated complex (NAC) from the ribosome. This results in the recruitment of nucleolin to the GAr-encoding mRNA and suppression of mRNA translation initiation in cis. Suppressing NAC alpha (NACA) expression prevents nucleolin from binding to the GAr mRNA and overcomes GAr-mediated translation inhibition. Taken together, these observations suggest that EBNA1 exploits a nascent protein quality control pathway to regulate its own rate of synthesis that is based on sensing the nascent GAr peptide by NAC followed by the recruitment of nucleolin to the GAr-encoding RNA sequence.
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Affiliation(s)
- Alice J L Zheng
- Inserm UMRS 1131, Institut de Génétique Moléculaire, Université de Paris, Hôpital St. Louis , F-75010 Paris , France
| | - Aikaterini Thermou
- Inserm UMRS 1131, Institut de Génétique Moléculaire, Université de Paris, Hôpital St. Louis , F-75010 Paris , France
- ICCVS, University of Gdańsk , Science, ul. Wita Stwosza 63 , 80-308 Gdańsk , Poland
| | - Chrysoula Daskalogianni
- Inserm UMRS 1131, Institut de Génétique Moléculaire, Université de Paris, Hôpital St. Louis , F-75010 Paris , France
- ICCVS, University of Gdańsk , Science, ul. Wita Stwosza 63 , 80-308 Gdańsk , Poland
| | - Laurence Malbert-Colas
- Inserm UMRS 1131, Institut de Génétique Moléculaire, Université de Paris, Hôpital St. Louis , F-75010 Paris , France
| | - Konstantinos Karakostis
- Inserm UMRS 1131, Institut de Génétique Moléculaire, Université de Paris, Hôpital St. Louis , F-75010 Paris , France
| | - Ronan Le Sénéchal
- Inserm UMR 1078, Université de Bretagne Occidentale (UBO), Etablissement Français du Sang (EFS) Bretagne, CHRU Brest , 29200 , Brest , France
| | - Van Trang Dinh
- Inserm UMR 1078, Université de Bretagne Occidentale (UBO), Etablissement Français du Sang (EFS) Bretagne, CHRU Brest , 29200 , Brest , France
| | - Maria C Tovar Fernandez
- Inserm UMRS 1131, Institut de Génétique Moléculaire, Université de Paris, Hôpital St. Louis , F-75010 Paris , France
- ICCVS, University of Gdańsk , Science, ul. Wita Stwosza 63 , 80-308 Gdańsk , Poland
| | - Sébastien Apcher
- Institut Gustave Roussy, Université Paris Sud, Unité 1015 département d’immunologie , 114, rue Edouard Vaillant , 94805 Villejuif , France
| | - Sa Chen
- Department of Medical Biosciences, Building 6M, Umeå University , 901 85 Umeå , Sweden
| | - Marc Blondel
- Inserm UMR 1078, Université de Bretagne Occidentale (UBO), Etablissement Français du Sang (EFS) Bretagne, CHRU Brest , 29200 , Brest , France
| | - Robin Fahraeus
- Inserm UMRS 1131, Institut de Génétique Moléculaire, Université de Paris, Hôpital St. Louis , F-75010 Paris , France
- Department of Medical Biosciences, Building 6M, Umeå University , 901 85 Umeå , Sweden
- RECAMO, Masaryk Memorial Cancer Institute , Zluty kopec 7 , 65653 Brno , Czech Republic
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Expression of Codon-Optimized Gene Encoding Murine Moloney Leukemia Virus Reverse Transcriptase in Escherichia coli. Protein J 2022; 41:515-526. [PMID: 35933571 PMCID: PMC9362449 DOI: 10.1007/s10930-022-10066-5] [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] [Accepted: 07/10/2022] [Indexed: 10/27/2022]
Abstract
Moloney murine leukemia virus reverse transcriptase (MMLV-RT) is the most frequently used enzyme in molecular biology for cDNA synthesis. To date, reverse transcription coupled with Polymerase Chain Reaction, known as RT-PCR, has been popular as an excellent approach for the detection of SARS-CoV-2 during the COVID-19 pandemic. In this study, we aimed to improve the enzymatic production and performance of MMLV-RT by optimizing both codon and culture conditions in E. coli expression system. By applying the optimized codon and culture conditions, the enzyme was successfully overexpressed and increased at high level based on the result of SDS-PAGE and Western blotting. The total amount of MMLV-RT has improved 85-fold from 0.002 g L-1 to 0.175 g L-1 of culture. One-step purification by nickel affinity chromatography has been performed to generate the purified enzyme for further analysis of qualitative and quantitative RT activity. Overall, our investigation provides useful strategies to enhance the recombinant enzyme of MMLV-RT in both production and performance. More importantly, the enzyme has shown promising activity to be used for RT-PCR assay.
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Santos SP, Garcés LFS, Silva FS, Santiago LF, Pinheiro CS, Alcantara-Neves NM, Pacheco LG. Engineering an optimized expression operating unit for improved recombinant protein production in Escherichia coli. Protein Expr Purif 2022; 199:106150. [DOI: 10.1016/j.pep.2022.106150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 10/31/2022]
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Karaiyan P, Chang CCH, Chan ES, Tey BT, Ramanan RN, Ooi CW. In silico screening and heterologous expression of soluble dimethyl sulfide monooxygenases of microbial origin in Escherichia coli. Appl Microbiol Biotechnol 2022; 106:4523-4537. [PMID: 35713659 PMCID: PMC9259527 DOI: 10.1007/s00253-022-12008-8] [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: 02/20/2022] [Revised: 05/30/2022] [Accepted: 06/01/2022] [Indexed: 11/28/2022]
Abstract
Abstract Sequence-based screening has been widely applied in the discovery of novel microbial enzymes. However, majority of the sequences in the genomic databases were annotated using computational approaches and lacks experimental characterization. Hence, the success in obtaining the functional biocatalysts with improved characteristics requires an efficient screening method that considers a wide array of factors. Recombinant expression of microbial enzymes is often hampered by the undesirable formation of inclusion body. Here, we present a systematic in silico screening method to identify the proteins expressible in soluble form and with the desired biological properties. The screening approach was adopted in the recombinant expression of dimethyl sulfide (DMS) monooxygenase in Escherichia coli. DMS monooxygenase, a two-component enzyme consisting of DmoA and DmoB subunits, was used as a model protein. The success rate of producing soluble and active DmoA is 71% (5 out of 7 genes). Interestingly, the soluble recombinant DmoA enzymes exhibited the NADH:FMN oxidoreductase activity in the absence of DmoB (second subunit), and the cofactor FMN, suggesting that DmoA is also an oxidoreductase. DmoA originated from Janthinobacterium sp. AD80 showed the maximum NADH oxidation activity (maximum reaction rate: 6.6 µM/min; specific activity: 133 µM/min/mg). This novel finding may allow DmoA to be used as an oxidoreductase biocatalyst for various industrial applications. The in silico gene screening methodology established from this study can increase the success rate of producing soluble and functional enzymes while avoiding the laborious trial and error involved in the screening of a large pool of genes available. Key points • A systematic gene screening method was demonstrated. • DmoA is also an oxidoreductase capable of oxidizing NADH and reducing FMN. • DmoA oxidizes NADH in the absence of external FMN. Supplementary Information The online version contains supplementary material available at 10.1007/s00253-022-12008-8.
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Affiliation(s)
- Prasanth Karaiyan
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
| | - Catherine Ching Han Chang
- Arkema Thiochemicals Sdn. Bhd., Jalan PJU 1A/7A OASIS Ara Damansara, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia
| | - Eng-Seng Chan
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
| | - Beng Ti Tey
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.,Advanced Engineering Platform, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
| | - Ramakrishnan Nagasundara Ramanan
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia. .,Arkema Thiochemicals Sdn. Bhd., Jalan PJU 1A/7A OASIS Ara Damansara, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
| | - Chien Wei Ooi
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia. .,Advanced Engineering Platform, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.
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Vasina M, Velecký J, Planas-Iglesias J, Marques SM, Skarupova J, Damborsky J, Bednar D, Mazurenko S, Prokop Z. Tools for computational design and high-throughput screening of therapeutic enzymes. Adv Drug Deliv Rev 2022; 183:114143. [PMID: 35167900 DOI: 10.1016/j.addr.2022.114143] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/04/2022] [Accepted: 02/09/2022] [Indexed: 12/16/2022]
Abstract
Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Jan Velecký
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Sergio M Marques
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Jana Skarupova
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic; Enantis, INBIT, Kamenice 34, Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
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50
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Sankar K, Trainor K, Blazer L, Adams J, Sidhu S, Day T, Meiering E, Maier J. A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics. Mol Inform 2022; 41:e2100240. [PMID: 35277930 DOI: 10.1002/minf.202100240] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 03/11/2022] [Indexed: 11/12/2022]
Abstract
In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.
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