1
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Karnchanapandh K, Sanachai K, Poo-Arporn RP, Rungrotmongkol T. Enhancing bezlotoxumab binding to C. difficile toxin B2: insights from computational simulations and mutational analyses for antibody design. J Biomol Struct Dyn 2024:1-11. [PMID: 38511411 DOI: 10.1080/07391102.2024.2329785] [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: 11/09/2023] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
Clostridioides difficile infection (CDI) is a significant concern caused by widespread antibiotic use, resulting in diarrhea and inflammation from the gram-positive anaerobic bacterium C. difficile. Although bezlotoxumab (Bez), a monoclonal antibody (mAb), was developed to address CDI recurrences, the recurrence rate remains high, partly due to reduced neutralization efficiency against toxin B2. In this study, we aimed to enhance the binding of Bez to C. difficile toxin B2 by combining computational simulations and mutational analyses. We identified specific mutations in Bez, including S28R, S31W/K, Y32R, S56W and G103D/S in the heavy chain (Hc), and S32F/H/R/W/Y in the light chain (Lc), which significantly improved binding to toxin B2 and formed critical protein-protein interactions. Through molecular dynamics simulations, several single mutations, such as HcS28R, LcS32H, LcS32R, LcS32W and LcS32Y, exhibited superior binding affinities to toxin B2 compared to Bez wild-type (WT), primarily attributed to Coulombic interactions. Combining the HcS28R mutation with four different mutations at residue LcS32 led to even greater binding affinities in double mutants (MTs), particularly HcS28R/LcS32H, HcS28R/LcS32R and HcS28R/LcS32Y, reinforcing protein-protein binding. Analysis of per-residue decomposition free energy highlighted key residues contributing significantly to enhanced binding interactions, emphasizing the role of electrostatic interactions. These findings offer insights into rational Bez MT design for improved toxin B2 binding, providing a foundation for developing more effective antibodies to neutralize toxin B2 and combat-related infections.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Kun Karnchanapandh
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
| | - Kamonpan Sanachai
- Department of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
| | - Rungtiva P Poo-Arporn
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
| | - Thanyada Rungrotmongkol
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
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2
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Zhou P, Gao C, Song W, Wei W, Wu J, Liu L, Chen X. Engineering status of protein for improving microbial cell factories. Biotechnol Adv 2024; 70:108282. [PMID: 37939975 DOI: 10.1016/j.biotechadv.2023.108282] [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: 05/12/2023] [Revised: 10/23/2023] [Accepted: 11/05/2023] [Indexed: 11/10/2023]
Abstract
With the development of metabolic engineering and synthetic biology, microbial cell factories (MCFs) have provided an efficient and sustainable method to synthesize a series of chemicals from renewable feedstocks. However, the efficiency of MCFs is usually limited by the inappropriate status of protein. Thus, engineering status of protein is essential to achieve efficient bioproduction with high titer, yield and productivity. In this review, we summarize the engineering strategies for metabolic protein status, including protein engineering for boosting microbial catalytic efficiency, protein modification for regulating microbial metabolic capacity, and protein assembly for enhancing microbial synthetic capacity. Finally, we highlight future challenges and prospects of improving microbial cell factories by engineering status of protein.
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Affiliation(s)
- Pei Zhou
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Cong Gao
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Wei Song
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China
| | - Wanqing Wei
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jing Wu
- School of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China
| | - Liming Liu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xiulai Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.
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3
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Faiz M, Khan SJ, Azim F, Ejaz N. Disclosing the locale of transmembrane proteins within cellular alcove by machine learning approach: systematic review and meta analysis. J Biomol Struct Dyn 2023:1-16. [PMID: 37768108 DOI: 10.1080/07391102.2023.2260490] [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: 02/21/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
Protein subcellular localization is a promising research question in Proteomics and associated fields, including Biological Sciences, Biomedical Engineering, Computational Biology, Bioinformatics, Proteomics, Artificial Intelligence, and Biophysics. However, computational techniques are preferred to explore this attribute for a massive number of proteins. The byproduct of this conjunction yields diversified location identifiers of proteins. These protein subcellular localization identifiers are unique regarding the database used, organisms, Machine Learning Technique, and accuracy. Despite the availability of these identifiers, the majority of the work has been done on the subcellular localization of proteins and, less work has been done specifically on locations of transmembrane proteins. This systematic review accounts for computational techniques implemented on transmembrane protein localization. Moreover, a literature search on PubMed, Science Direct, and IEEE Databases disclosed no systematic review or meta-analysis on the cell's transmembrane protein locale. A Systematic review was formed under the guidelines of PRISMA by using Science Direct, PubMed, and IEEE Databases. Journal publications from 2000 to 2023 were taken into consideration and screened. This review has focused only on computational studies rather than experimental techniques. 1004 studies were reviewed and were categorized as relevant and non-relevant according to inclusion and exclusion criteria. All the screening was done through Endnote after importing citations. This systematic review characterizes the gap in targeting the locale of the transmembrane protein and will aid researchers in exploring its new horizons.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mehwish Faiz
- Department of Biomedical Engineering, Ziauddin University (FESTM), Karachi, Pakistan
- Department of Electrical Engineering, Ziauddin University, (FESTM), Karachi, Pakistan
| | - Saad Jawaid Khan
- Department of Biomedical Engineering, Ziauddin University (FESTM), Karachi, Pakistan
| | - Fahad Azim
- Department of Electrical Engineering, Ziauddin University, (FESTM), Karachi, Pakistan
| | - Nazia Ejaz
- Balochistan University of Engineering and Technology, Khuzdar, Pakistan
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4
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Chenna A, Khan WH, Dash R, Saraswat S, Chugh A, Rathore AS, Goel G. An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins. Proteins 2023; 91:1222-1234. [PMID: 37283297 DOI: 10.1002/prot.26511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/17/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023]
Abstract
The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25μM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.
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Affiliation(s)
- Akshay Chenna
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Wajihul Hasan Khan
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Virology Unit, Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India
| | - Rozaleen Dash
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Saurabh Saraswat
- Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi, India
| | - Archana Chugh
- Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Gaurav Goel
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
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5
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Nezhad NG, Rahman RNZRA, Normi YM, Oslan SN, Shariff FM, Leow TC. Recent advances in simultaneous thermostability-activity improvement of industrial enzymes through structure modification. Int J Biol Macromol 2023; 232:123440. [PMID: 36708895 DOI: 10.1016/j.ijbiomac.2023.123440] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/17/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023]
Abstract
Engineered thermostable microbial enzymes are widely employed to catalyze chemical reactions in numerous industrial sectors. Although high thermostability is a prerequisite of industrial applications, enzyme activity is usually sacrificed during thermostability improvement. Therefore, it is vital to select the common and compatible strategies between thermostability and activity improvement to reduce mutants̕ libraries and screening time. Three functional protein engineering approaches, including directed evolution, rational design, and semi-rational design, are employed to manipulate protein structure on a genetic basis. From a structural standpoint, integrative strategies such as increasing substrate affinity; introducing electrostatic interaction; removing steric hindrance; increasing flexibility of the active site; N- and C-terminal engineering; and increasing intramolecular and intermolecular hydrophobic interactions are well-known to improve simultaneous activity and thermostability. The current review aims to analyze relevant strategies to improve thermostability and activity simultaneously to circumvent the thermostability and activity trade-off of industrial enzymes.
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Affiliation(s)
- Nima Ghahremani Nezhad
- Enzyme and Microbial Research Center, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Raja Noor Zaliha Raja Abd Rahman
- Enzyme and Microbial Research Center, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Yahaya M Normi
- Enzyme and Microbial Research Center, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Siti Nurbaya Oslan
- Enzyme and Microbial Research Center, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Fairolniza Mohd Shariff
- Enzyme and Microbial Research Center, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Thean Chor Leow
- Enzyme and Microbial Research Center, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia; Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
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6
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Farheen J, Hosmane NS, Zhao R, Zhao Q, Iqbal MZ, Kong X. Nanomaterial-assisted CRISPR gene-engineering - A hallmark for triple-negative breast cancer therapeutics advancement. Mater Today Bio 2022; 16:100450. [PMID: 36267139 PMCID: PMC9576993 DOI: 10.1016/j.mtbio.2022.100450] [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: 07/16/2022] [Revised: 09/16/2022] [Accepted: 10/02/2022] [Indexed: 11/05/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is the most violent class of tumor and accounts for 20-24% of total breast carcinoma, in which frequently rare mutation occurs in high frequency. The poor prognosis, recurrence, and metastasis in the brain, heart, liver and lungs decline the lifespan of patients by about 21 months, emphasizing the need for advanced treatment. Recently, the adaptive immunity mechanism of archaea and bacteria, called clustered regularly interspaced short palindromic repeats (CRISPR) combined with nanotechnology, has been utilized as a potent gene manipulating tool with an extensive clinical application in cancer genomics due to its easeful usage and cost-effectiveness. However, CRISPR/Cas are arguably the efficient technology that can be made efficient via organic material-assisted approaches. Despite the efficacy of the CRISPR/Cas@nano complex, problems regarding successful delivery, biodegradability, and toxicity remain to render its medical implications. Therefore, this review is different in focus from past reviews by (i) detailing all possible genetic mechanisms of TNBC occurrence; (ii) available treatments and gene therapies for TNBC; (iii) overview of the delivery system and utilization of CRISPR-nano complex in TNBC, and (iv) recent advances and related toxicity of CRISPR-nano complex towards clinical trials for TNBC.
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Affiliation(s)
- Jabeen Farheen
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
- Zhejiang-Mauritius Joint Research Centre for Biomaterials and Tissue Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
| | - Narayan S. Hosmane
- Department of Chemistry & Biochemistry, Northern Illinois University, DeKalb, IL, 60115, USA
| | - Ruibo Zhao
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
- Zhejiang-Mauritius Joint Research Centre for Biomaterials and Tissue Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
- Department of Materials, Imperial College London, London, SW7 2AZ, UK
| | - Qingwei Zhao
- Research Center for Clinical Pharmacy & Key Laboratory for Drug Evaluation and Clinical Research of Zhejiang Province, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, PR China
| | - M. Zubair Iqbal
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
- Zhejiang-Mauritius Joint Research Centre for Biomaterials and Tissue Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
| | - Xiangdong Kong
- Institute of Smart Biomedical Materials, School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
- Zhejiang-Mauritius Joint Research Centre for Biomaterials and Tissue Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, PR China
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7
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Kuang D, Issakova D, Kim J. Learning Proteome Domain Folding Using LSTMs in an Empirical Kernel Space. J Mol Biol 2022; 434:167686. [PMID: 35716781 DOI: 10.1016/j.jmb.2022.167686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/08/2022] [Accepted: 06/10/2022] [Indexed: 11/30/2022]
Abstract
The recognition of protein structural folds is the starting point for protein function inference and for many structural prediction tools. We previously introduced the idea of using empirical comparisons to create a data-augmented feature space called PESS (Protein Empirical Structure Space)1 as a novel approach for protein structure prediction. Here, we extend the previous approach by generating the PESS feature space over fixed-length subsequences of query peptides, and applying a sequential neural network model, with one long short-term memory cell layer followed by a fully connected layer. Using this approach, we show that only a small group of domains as a training set is needed to achieve near state-of-the-art accuracy on fold recognition. Our method improves on the previous approach by reducing the training set required and improving the model's ability to generalize across species, which will help fold prediction for newly discovered proteins.
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Affiliation(s)
- Da Kuang
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA.
| | - Dina Issakova
- Department of Biology, University of Pennsylvania, Philadelphia, USA.
| | - Junhyong Kim
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA; Department of Biology, University of Pennsylvania, Philadelphia, USA.
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8
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Chen H, Ma L, Dai H, Fu Y, Wang H, Zhang Y. Advances in Rational Protein Engineering toward Functional Architectures and Their Applications in Food Science. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:4522-4533. [PMID: 35353517 DOI: 10.1021/acs.jafc.2c00232] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Protein biomolecules including enzymes, cagelike proteins, and specific peptides have been continuously exploited as functional biomaterials applied in catalysis, nutrient delivery, and food preservation in food-related areas. However, natural proteins usually function well in physiological conditions, not industrial conditions, or may possess undesirable physical and chemical properties. Currently, rational protein design as a valuable technology has attracted extensive attention for the rational engineering or fabrication of ideal protein biomaterials with novel properties and functionality. This article starts with the underlying knowledge of protein folding and assembly and is followed by the introduction of the principles and strategies for rational protein design. Basic strategies for rational protein engineering involving experienced protein tailoring, computational prediction, computation redesign, and de novo protein design are summarized. Then, we focus on the recent progress of rational protein engineering or design in the application of food science, and a comprehensive summary ranging from enzyme manufacturing to cagelike protein nanocarriers engineering and antimicrobial peptides preparation is given. Overall, this review highlights the importance of rational protein engineering in food biomaterial preparation which could be beneficial for food science.
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Affiliation(s)
- Hai Chen
- College of Food Science, Southwest University, Chongqing 400715, China
| | - Liang Ma
- College of Food Science, Southwest University, Chongqing 400715, China
| | - Hongjie Dai
- College of Food Science, Southwest University, Chongqing 400715, China
| | - Yu Fu
- College of Food Science, Southwest University, Chongqing 400715, China
| | - Hongxia Wang
- College of Food Science, Southwest University, Chongqing 400715, China
| | - Yuhao Zhang
- College of Food Science, Southwest University, Chongqing 400715, China
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9
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Dolgikh B, Woldring D. Site-wise Diversification of Combinatorial Libraries Using Insights from Structure-guided Stability Calculations. Methods Mol Biol 2022; 2491:63-73. [PMID: 35482184 DOI: 10.1007/978-1-0716-2285-8_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Many auspicious clinical and industrial accomplishments have improved the human condition by means of protein engineering. Despite these achievements, our incomplete understanding of the sequence-structure-function relationship prevents rapid innovation. To tackle this problem, we must develop and integrate new and existing technologies. To date, directed evolution and rational design have dominated as protein engineering principles. Even so, prior to screening for novel or improved functions, a large collection of variants, within a protein library, exist along an ambiguous mutational terrain. Complicating things further, the choice of where to initialize investigation along a vast sequence space becomes even more difficult given that the majority of any sequence lacks function entirely. Unfortunately, even when considering functionally relevant positions, random substitutions can prove to be destabilizing, causing a hindrance to an otherwise function-inducing, stability-reliant folding process. To enhance productivity in the field, we seek to address this issue of destabilization, and subsequent disfunction, at protein-protein and protein-ligand interacting regions. Herein, the process of choosing amenable positions - and amino acids at those positions - allows for a refined, knowledge-based approach to combinatorial library design. Using structural data, we perform computational stability prediction with FoldX's PositionScan and Rosetta's ddG_monomer in tandem, allowing for the refinement of our thermodynamic stability data through the comparison of results. In turn, we provide a process for selecting in silico predicted mutually stabilizing positions and avoiding overly destabilizing ones that guides the site-wise diversification of combinatorial libraries.
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Affiliation(s)
- Benedikt Dolgikh
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Daniel Woldring
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.
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10
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The development of natural and designed protein nanocages for encapsulation and delivery of active compounds. Food Hydrocoll 2021. [DOI: 10.1016/j.foodhyd.2021.107004] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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11
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Defresne M, Barbe S, Schiex T. Protein Design with Deep Learning. Int J Mol Sci 2021; 22:11741. [PMID: 34769173 PMCID: PMC8584038 DOI: 10.3390/ijms222111741] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/23/2021] [Accepted: 10/26/2021] [Indexed: 12/21/2022] Open
Abstract
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.
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Affiliation(s)
- Marianne Defresne
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France; (M.D.); (S.B.)
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
| | - Sophie Barbe
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France; (M.D.); (S.B.)
| | - Thomas Schiex
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
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12
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Wu L, Qin L, Nie Y, Xu Y, Zhao YL. Computer-aided understanding and engineering of enzymatic selectivity. Biotechnol Adv 2021; 54:107793. [PMID: 34217814 DOI: 10.1016/j.biotechadv.2021.107793] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/26/2021] [Accepted: 06/28/2021] [Indexed: 12/26/2022]
Abstract
Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.
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Affiliation(s)
- Lunjie Wu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Lei Qin
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yao Nie
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Suqian Industrial Technology Research Institute of Jiangnan University, Suqian 223814, China.
| | - Yan Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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