1
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Howes JM, Harper MT. Application of the Cellular Thermal Shift Assay (CETSA) to validate drug target engagement in platelets. Platelets 2024; 35:2354833. [PMID: 38767506 DOI: 10.1080/09537104.2024.2354833] [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: 03/08/2024] [Accepted: 05/06/2024] [Indexed: 05/22/2024]
Abstract
Small molecule drugs play a major role in the study of human platelets. Effective action of a drug requires it to bind to one or more targets within the platelet (target engagement). However, although in vitro assays with isolated proteins can be used to determine drug affinity to these targets, additional factors affect target engagement and its consequences in an intact platelet, including plasma membrane permeability, intracellular metabolism or compartmentalization, and level of target expression. Mechanistic interpretation of the effect of drugs on platelet activity requires comprehensive investigation of drug binding in the proper cellular context, i.e. in intact platelets. The Cellular Thermal Shift Assay (CETSA) is a valuable method to investigate target engagement within complex cellular environments. The assay is based on the principle that drug binding to a target protein increases that protein's thermal stability. In this technical report, we describe the application of CETSA to platelets. We highlight CETSA as a quick and informative technique for confirming the direct binding of drugs to platelet protein targets, providing a platform for understanding the mechanism of action of drugs in platelets, and which will be a valuable tool for investigating platelet signaling and function.
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Affiliation(s)
| | - Matthew T Harper
- Department of Pharmacology, University of Cambridge, Cambridge, UK
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2
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Chu SKS, Narang K, Siegel JB. Protein stability prediction by fine-tuning a protein language model on a mega-scale dataset. PLoS Comput Biol 2024; 20:e1012248. [PMID: 39038042 DOI: 10.1371/journal.pcbi.1012248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 06/13/2024] [Indexed: 07/24/2024] Open
Abstract
Protein stability plays a crucial role in a variety of applications, such as food processing, therapeutics, and the identification of pathogenic mutations. Engineering campaigns commonly seek to improve protein stability, and there is a strong interest in streamlining these processes to enable rapid optimization of highly stabilized proteins with fewer iterations. In this work, we explore utilizing a mega-scale dataset to develop a protein language model optimized for stability prediction. ESMtherm is trained on the folding stability of 528k natural and de novo sequences derived from 461 protein domains and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. ESMtherm performs reasonably on small protein domains and generalizes to sequences distal from the training set. Lastly, we discuss our model's limitations compared to other state-of-the-art methods in generalizing to larger protein scaffolds. Our results highlight the need for large-scale stability measurements on a diverse dataset that mirrors the distribution of sequence lengths commonly observed in nature.
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Affiliation(s)
- Simon K S Chu
- Biophysics Graduate Program, University of California Davis, Davis, California, United States of America
| | - Kush Narang
- College of Biological Sciences, University of California Davis, Davis, California, United States of America
| | - Justin B Siegel
- Genome Center, University of California Davis, Davis, California, United States of America
- Department of Chemistry, University of California Davis, Davis, California, United States of America
- Department of Biochemistry and Molecular Medicine, University of California Davis, Davis, California, United States of America
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3
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Albayati SH, Nezhad NG, Taki AG, Rahman RNZRA. Efficient and easible biocatalysts: Strategies for enzyme improvement. A review. Int J Biol Macromol 2024; 276:133978. [PMID: 39038570 DOI: 10.1016/j.ijbiomac.2024.133978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/19/2024] [Accepted: 07/16/2024] [Indexed: 07/24/2024]
Abstract
Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.
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Affiliation(s)
- Samah Hashim Albayati
- Enzyme and Microbial Technology Research Centre, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Nima Ghahremani Nezhad
- Enzyme and Microbial Technology Research Centre, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Anmar Ghanim Taki
- Department of Radiology Techniques, Health and Medical Techniques College, Alnoor University, Mosul, Iraq
| | - Raja Noor Zaliha Raja Abd Rahman
- Enzyme and Microbial Technology Research Centre, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; Institute Bioscience, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
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4
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Altangerel N, Neuman BW, Hemmer PR, Yakovlev VV, Sokolov AV, Scully MO. A Novel Non-Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution. SMALL METHODS 2024; 8:e2301191. [PMID: 38485686 PMCID: PMC11260246 DOI: 10.1002/smtd.202301191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 02/14/2024] [Indexed: 05/04/2024]
Abstract
Amino-acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.
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Affiliation(s)
| | | | | | | | | | - Marlan O Scully
- Texas A&M University, College Station, TX, 77843, USA
- Baylor University, Waco, TX, 76798, USA
- Princeton University, Princeton, NJ, 08544, USA
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5
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Norton-Baker B, Denton MCR, Murphy NP, Fram B, Lim S, Erickson E, Gauthier NP, Beckham GT. Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline. Sci Rep 2024; 14:14449. [PMID: 38914665 PMCID: PMC11196671 DOI: 10.1038/s41598-024-64938-0] [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: 04/08/2024] [Accepted: 06/14/2024] [Indexed: 06/26/2024] Open
Abstract
As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.
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Grants
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-AC36-08GO28308 Advanced Materials and Manufacturing Technologies Office (AMMTO)
- DE-AC36-08GO28308 Advanced Materials and Manufacturing Technologies Office (AMMTO)
- DE-AC36-08GO28308 Advanced Materials and Manufacturing Technologies Office (AMMTO)
- DE-AC36-08GO28308 Advanced Materials and Manufacturing Technologies Office (AMMTO)
- U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office (BETO)
- Bio-Optimized Technologies to keep Thermoplastics out of Landfills and the Environment (BOTTLE) Consortium
- Dana-Farber Cancer Institute
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Affiliation(s)
- Brenna Norton-Baker
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
- BOTTLE Consortium, Golden, CO, USA
- Agile BioFoundry, Emeryville, CA, USA
| | - Mackenzie C R Denton
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
- BOTTLE Consortium, Golden, CO, USA
| | - Natasha P Murphy
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
- BOTTLE Consortium, Golden, CO, USA
| | - Benjamin Fram
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuel Lim
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Erika Erickson
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
- BOTTLE Consortium, Golden, CO, USA
| | - Nicholas P Gauthier
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Gregg T Beckham
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA.
- BOTTLE Consortium, Golden, CO, USA.
- Agile BioFoundry, Emeryville, CA, USA.
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6
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Srivastava S, Sandhu N, Liu J, Xie YH. AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids. Bioengineering (Basel) 2024; 11:482. [PMID: 38790349 PMCID: PMC11117800 DOI: 10.3390/bioengineering11050482] [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: 03/03/2024] [Revised: 05/05/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for elucidating the molecular makeup of materials. It possesses the unique characteristics of single-molecule sensitivity and extremely high specificity. However, the true potential of SERS, particularly in capturing the biochemical content of particles, remains underexplored. In this study, we harnessed transformer neural networks to interpret SERS spectra, aiming to discern the amino acid profiles within proteins. By training the network on the SERS profiles of 20 amino acids of human proteins, we explore the feasibility of predicting the predominant proteins within the µL-scale detection volume of SERS. Our results highlight a consistent alignment between the model's predictions and the protein's known amino acid compositions, deepening our understanding of the inherent information contained within SERS spectra. For instance, the model achieved low root mean square error (RMSE) scores and minimal deviation in the prediction of amino acid compositions for proteins such as Bovine Serum Albumin (BSA), ACE2 protein, and CD63 antigen. This novel methodology offers a robust avenue not only for protein analytics but also sets a precedent for the broader realm of spectral analyses across diverse material categories. It represents a solid step forward to establishing SERS-based proteomics.
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Affiliation(s)
| | | | | | - Ya-Hong Xie
- Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA; (S.S.); (N.S.); (J.L.)
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7
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Chu H, Tian Z, Hu L, Zhang H, Chang H, Bai J, Liu D, Lu L, Cheng J, Jiang H. High-Temperature Tolerance Protein Engineering through Deep Evolution. BIODESIGN RESEARCH 2024; 6:0031. [PMID: 38572349 PMCID: PMC10988389 DOI: 10.34133/bdr.0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/12/2024] [Indexed: 04/05/2024] Open
Abstract
Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.
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Affiliation(s)
- Huanyu Chu
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
| | - Zhenyang Tian
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
- Tianjin Zhonghe Gene Technology Co., LTD, Tianjin 300308, P. R. China
| | - Lingling Hu
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
- College of Biotechnology,
Tianjin University of Science and Technology, Tianjin 300457, P. R. China
| | - Hejian Zhang
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
- College of Biotechnology,
Tianjin University of Science and Technology, Tianjin 300457, P. R. China
| | - Hong Chang
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
- College of Biotechnology,
Tianjin University of Science and Technology, Tianjin 300457, P. R. China
| | - Jie Bai
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
- College of Biotechnology,
Tianjin University of Science and Technology, Tianjin 300457, P. R. China
| | - Dingyu Liu
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
| | - Lina Lu
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
| | - Jian Cheng
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
| | - Huifeng Jiang
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, P. R. China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
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8
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Szadkowska M, Kocot AM, Sowik D, Wyrzykowski D, Jankowska E, Kozlowski LP, Makowska J, Plotka M. Molecular characterization of the PhiKo endolysin from Thermus thermophilus HB27 bacteriophage phiKo and its cryptic lytic peptide RAP-29. Front Microbiol 2024; 14:1303794. [PMID: 38312500 PMCID: PMC10836841 DOI: 10.3389/fmicb.2023.1303794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/12/2023] [Indexed: 02/06/2024] Open
Abstract
Introduction In the era of increasing bacterial resistance to antibiotics, new bactericidal substances are sought, and lysins derived from extremophilic organisms have the undoubted advantage of being stable under harsh environmental conditions. The PhiKo endolysin is derived from the phiKo bacteriophage infecting Gram-negative extremophilic bacterium Thermus thermophilus HB27. This enzyme shows similarity to two previously investigated thermostable type-2 amidases, the Ts2631 and Ph2119 from Thermus scotoductus bacteriophages, that revealed high lytic activity not only against thermophiles but also against Gram-negative mesophilic bacteria. Therefore, antibacterial potential of the PhiKo endolysin was investigated in the study presented here. Methods Enzyme activity was assessed using turbidity reduction assays (TRAs) and antibacterial tests. Differential scanning calorimetry was applied to evaluate protein stability. The Collection of Anti-Microbial Peptides (CAMP) and Antimicrobial Peptide Calculator and Predictor (APD3) were used to predict regions with antimicrobial potential in the PhiKo primary sequence. The minimum inhibitory concentration (MIC) of the RAP-29 synthetic peptide was determined against Gram-positive and Gram-negative selected strains, and mechanism of action was investigated with use of membrane potential sensitive fluorescent dye 3,3'-Dipropylthiacarbocyanine iodide (DiSC3(5)). Results and discussion The PhiKo endolysin is highly thermostable with melting temperature of 91.70°C. However, despite its lytic effect against such extremophiles as: T. thermophilus, Thermus flavus, Thermus parvatiensis, Thermus scotoductus, and Deinococcus radiodurans, PhiKo showed moderate antibacterial activity against mesophiles. Consequently, its protein sequence was searched for regions with potential antibacterial activity. A highly positively charged region was identified and synthetized (PhiKo105-133). The novel RAP-29 peptide lysed mesophilic strains of staphylococci and Gram-negative bacteria, reducing the number of cells by 3.7-7.1 log units and reaching the minimum inhibitory concentration values in the range of 2-31 μM. This peptide is unstructured in an aqueous solution but forms an α-helix in the presence of detergents. Moreover, it binds lipoteichoic acid and lipopolysaccharide, and causes depolarization of bacterial membranes. The RAP-29 peptide is a promising candidate for combating bacterial pathogens. The existence of this cryptic peptide testifies to a much wider panel of antimicrobial peptides than thought previously.
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Affiliation(s)
- Monika Szadkowska
- Laboratory of Extremophiles Biology, Department of Microbiology, University of Gdańsk, Gdańsk, Poland
| | - Aleksandra Maria Kocot
- Laboratory of Extremophiles Biology, Department of Microbiology, University of Gdańsk, Gdańsk, Poland
| | - Daria Sowik
- Department of Biomedical Chemistry, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Dariusz Wyrzykowski
- Department of General and Inorganic Chemistry, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Elzbieta Jankowska
- Department of Biomedical Chemistry, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Lukasz Pawel Kozlowski
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Joanna Makowska
- Department of General and Inorganic Chemistry, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | - Magdalena Plotka
- Laboratory of Extremophiles Biology, Department of Microbiology, University of Gdańsk, Gdańsk, Poland
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9
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Ghaedizadeh S, Zeinali M, Dabirmanesh B, Rasekh B, Khajeh K, Banaei-Moghaddam AM. Rational design engineering of a more thermostable Sulfurihydrogenibium yellowstonense carbonic anhydrase for potential application in carbon dioxide capture technologies. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2024; 1872:140962. [PMID: 37716447 DOI: 10.1016/j.bbapap.2023.140962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/18/2023] [Accepted: 09/06/2023] [Indexed: 09/18/2023]
Abstract
Implementing hyperthermostable carbonic anhydrases into CO2 capture and storage technologies in order to increase the rate of CO2 absorption from the industrial flue gases is of great importance from technical and economical points of view. The present study employed a combination of in silico tools to further improve thermostability of a known thermostable carbonic anhydrase from Sulfurihydrogenibium yellowstonense. Experimental results showed that our rationally engineered K100G mutant not only retained the overall structure and catalytic efficiency but also showed a 3 °C increase in the melting temperature and a two-fold improvement in the enzyme half-life at 85 °C. Based on the molecular dynamics simulation results, rearrangement of salt bridges and hydrogen interactions network causes a reduction in local flexibility of the K100G variant. In conclusion, our study demonstrated that thermostability can be improved through imposing local structural rigidity by engineering a single-point mutation on the surface of the enzyme.
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Affiliation(s)
- Shima Ghaedizadeh
- Laboratory of Genomics and Epigenomics (LGE), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Majid Zeinali
- Microbiology and Biotechnology Research Group, Research Institute of Petroleum Industry (RIPI), Tehran, Iran.
| | - Bahareh Dabirmanesh
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Behnam Rasekh
- Microbiology and Biotechnology Research Group, Research Institute of Petroleum Industry (RIPI), Tehran, Iran
| | - Khosrow Khajeh
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Ali Mohammad Banaei-Moghaddam
- Laboratory of Genomics and Epigenomics (LGE), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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10
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Platero-Rochart D, Krivobokova T, Gastegger M, Reibnegger G, Sánchez-Murcia PA. Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning. J Chem Inf Model 2023; 63:4623-4632. [PMID: 37479222 PMCID: PMC10430765 DOI: 10.1021/acs.jcim.3c00772] [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: 05/19/2023] [Indexed: 07/23/2023]
Abstract
The prediction of enzyme activity is one of the main challenges in catalysis. With computer-aided methods, it is possible to simulate the reaction mechanism at the atomic level. However, these methods are usually expensive if they are to be used on a large scale, as they are needed for protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein, we test different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase ofIdeonella sakaiensis. As a training data set, we use steered quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulation snapshots and their corresponding pulling work values. We have explored three algorithms together with three chemical representations. As an outcome, our trained models are able to predict pulling works along the steered QM/MM MD simulations with a mean absolute error below 3 kcal mol-1 and a score value above 0.90. More challenging is the prediction of the energy maximum with a single geometry. Whereas the use of the initial snapshot of the QM/MM MD trajectory as input geometry yields a very poor prediction of the reaction energy barrier, the use of an intermediate snapshot of the former trajectory brings the score value above 0.40 with a low mean absolute error (ca. 3 kcal mol-1). Altogether, we have faced in this work some initial challenges of the final goal of getting an efficient workflow for the semiautomatic prediction of enzyme-catalyzed energy barriers and catalytic efficiencies.
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Affiliation(s)
- Daniel Platero-Rochart
- Laboratory
of Computer-Aided Molecular Design, Division of Medicinal Chemistry,
Otto-Loewi Research Center, Medical University
of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria
| | - Tatyana Krivobokova
- Department
of Statistics and Operations Research, University
of Vienna, Oskar-Morgenstern-Platz 1, A-1090 Vienna, Austria
| | - Michael Gastegger
- Institute
of Software Engineering and Theoretical Computer Science, Machine
Learning Group, Technische Universität, 10587 Berlin, Germany
| | - Gilbert Reibnegger
- Laboratory
of Computer-Aided Molecular Design, Division of Medicinal Chemistry,
Otto-Loewi Research Center, Medical University
of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria
| | - Pedro A. Sánchez-Murcia
- Laboratory
of Computer-Aided Molecular Design, Division of Medicinal Chemistry,
Otto-Loewi Research Center, Medical University
of Graz, Neue Stiftingtalstraße 6/III, A-8010 Graz, Austria
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11
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Xue Z, Quan S. Understanding the Stabilization Mechanism of a Thermostable Mutant of Hygromycin B Phosphotransferase by Protein Sector-Guided Dynamic Analysis. ACS OMEGA 2023; 8:25739-25748. [PMID: 37521677 PMCID: PMC10372938 DOI: 10.1021/acsomega.3c00373] [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: 01/18/2023] [Accepted: 06/12/2023] [Indexed: 08/01/2023]
Abstract
Point mutations can exert beneficial effects on proteins, including stabilization. The stabilizing effects of mutations are typically attributed to changes in free energy and residue interactions. However, these explanations lack detail and physical insights, which hinder the mechanistic study of protein stabilization and prevent accurate computational prediction of stabilizing mutations. Here, we investigate the physical mechanism underlying the enhanced thermostability of a Hygromycin B phosphotransferase mutant, Hph5. We find that the unpredictable mutation A118V induces rotation of F199, allowing it to establish an aromatic-aromatic interaction with W235. In contrast, the predictable mutation T246A acts through static hydrophobic interactions within the protein core. These discoveries were accelerated by a residue-coevolution-based theory, which links mutational effects to stability-associated local structures, providing valuable guidance for mechanistic exploration. The established workflow will benefit the development of accurate stability prediction programs and can be used to mine a protein stability database for undiscovered physical mechanisms.
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Affiliation(s)
- Zixiao Xue
- State
Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation
Center for Biomanufacturing (SCICB), East
China University of Science and Technology, Shanghai 200237, China
| | - Shu Quan
- State
Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation
Center for Biomanufacturing (SCICB), East
China University of Science and Technology, Shanghai 200237, China
- Shanghai
Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai 200237, China
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12
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Hardebeck S, Schreiber S, Adick A, Langer K, Jose J. A FRET-Based Assay for the Identification of PCNA Inhibitors. Int J Mol Sci 2023; 24:11858. [PMID: 37511614 PMCID: PMC10380293 DOI: 10.3390/ijms241411858] [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: 07/05/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Proliferating cell nuclear antigen (PCNA) is the key regulator of human DNA metabolism. One important interaction partner is p15, involved in DNA replication and repair. Targeting the PCNA-p15 interaction is a promising therapeutic strategy against cancer. Here, a Förster resonance energy transfer (FRET)-based assay for the analysis of the PCNA-p15 interaction was developed. Next to the application as screening tool for the identification and characterization of PCNA-p15 interaction inhibitors, the assay is also suitable for the investigation of mutation-induced changes in their affinity. This is particularly useful for analyzing disease associated PCNA or p15 variants at the molecular level. Recently, the PCNA variant C148S has been associated with Ataxia-telangiectasia-like disorder type 2 (ATLD2). ATLD2 is a neurodegenerative disease based on defects in DNA repair due to an impaired PCNA. Incubation time dependent FRET measurements indicated no effect on PCNAC148S-p15 affinity, but on PCNA stability. The impaired stability and increased aggregation behavior of PCNAC148S was confirmed by intrinsic tryptophan fluorescence, differential scanning fluorimetry (DSF) and asymmetrical flow field-flow fractionation (AF4) measurements. The analysis of the disease associated PCNA variant demonstrated the versatility of the interaction assay as developed.
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Affiliation(s)
- Sarah Hardebeck
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, Pharmacampus, 48149 Münster, Germany
| | - Sebastian Schreiber
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, Pharmacampus, 48149 Münster, Germany
| | - Annika Adick
- University of Münster, Institute for Pharmaceutical Technology and Biopharmacy, Pharmacampus, 48149 Münster, Germany
| | - Klaus Langer
- University of Münster, Institute for Pharmaceutical Technology and Biopharmacy, Pharmacampus, 48149 Münster, Germany
| | - Joachim Jose
- University of Münster, Institute of Pharmaceutical and Medicinal Chemistry, Pharmacampus, 48149 Münster, Germany
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13
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Ouyang B, Wang G, Zhang N, Zuo J, Huang Y, Zhao X. Recent Advances in β-Glucosidase Sequence and Structure Engineering: A Brief Review. Molecules 2023; 28:4990. [PMID: 37446652 DOI: 10.3390/molecules28134990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
β-glucosidases (BGLs) play a crucial role in the degradation of lignocellulosic biomass as well as in industrial applications such as pharmaceuticals, foods, and flavors. However, the application of BGLs has been largely hindered by issues such as low enzyme activity, product inhibition, low stability, etc. Many approaches have been developed to engineer BGLs to improve these enzymatic characteristics to facilitate industrial production. In this article, we review the recent advances in BGL engineering in the field, including the efforts from our laboratory. We summarize and discuss the BGL engineering studies according to the targeted functions as well as the specific strategies used for BGL engineering.
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Affiliation(s)
- Bei Ouyang
- College of Life Science, Jiangxi Normal University, Nanchang 330022, China
| | - Guoping Wang
- College of Life Science, Jiangxi Normal University, Nanchang 330022, China
| | - Nian Zhang
- College of Life Science, Jiangxi Normal University, Nanchang 330022, China
| | - Jiali Zuo
- School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China
| | - Yunhong Huang
- College of Life Science, Jiangxi Normal University, Nanchang 330022, China
| | - Xihua Zhao
- College of Life Science, Jiangxi Normal University, Nanchang 330022, China
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14
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Chan M, Siegel JB, Vater A. Design to Data for mutants of B-glucosidase B from Paenibacillus polymyxa : V311D, F248N, Y166H, Y166K, M221K. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.10.540081. [PMID: 37214998 PMCID: PMC10197662 DOI: 10.1101/2023.05.10.540081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Engaging computational tools for protein design is gaining traction in the enzyme engineering community. However, current design and modeling algorithms have limited functionality predictive capacities for enzymes due to limitations of the dataset in terms of size and data quality. This study aims to expand training datasets for improved algorithm development with the addition of five rationally designed single-point enzyme variants. β-glucosidase B variants were modeled in Foldit Standalone and then produced and assayed for thermal stability and kinetic parameters. Functional parameters: thermal stability (T M ) and Michaelis-Menten constants ( k cat , K M , and k cat /K M ) of five variants, V311D, Y166H, M221K, F248N, and Y166K, were added into the Design2Data database. As a case study, evaluation of this small mutant set finds mutational effect trends that both corroborate and contradict findings from larger studies examining the entire dataset.
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15
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Vasina M, Kovar D, Damborsky J, Ding Y, Yang T, deMello A, Mazurenko S, Stavrakis S, Prokop Z. In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning. Biotechnol Adv 2023; 66:108171. [PMID: 37150331 DOI: 10.1016/j.biotechadv.2023.108171] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - David Kovar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Yun Ding
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Tianjin Yang
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland; Department of Biochemistry, University of Zurich, 8057 Zurich, Switzerland
| | - Andrew deMello
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
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16
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Peccati F, Alunno-Rufini S, Jiménez-Osés G. Accurate Prediction of Enzyme Thermostabilization with Rosetta Using AlphaFold Ensembles. J Chem Inf Model 2023; 63:898-909. [PMID: 36647575 PMCID: PMC9930118 DOI: 10.1021/acs.jcim.2c01083] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Thermostability enhancement is a fundamental aspect of protein engineering as a biocatalyst's half-life is key for its industrial and biotechnological application, particularly at high temperatures and under harsh conditions. Thermostability changes upon mutation originate from modifications of the free energy of unfolding (ΔGu), making thermostabilization extremely challenging to predict with computational methods. In this contribution, we combine global conformational sampling with energy prediction using AlphaFold and Rosetta to develop a new computational protocol for the quantitative prediction of thermostability changes upon laboratory evolution of acyltransferase LovD and lipase LipA. We highlight how using an ensemble of protein conformations rather than a single three-dimensional model is mandatory for accurate thermostability predictions. By comparing our approaches with existing ones, we show that ensembles based on AlphaFold models provide more accurate and robust calculated thermostability trends than ensembles based solely on crystallographic structures as the latter introduce a strong distortion (scaffold bias) in computed thermostabilities. Eliminating this bias is critical for computer-guided enzyme design and evaluating the effect of multiple mutations on protein stability.
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Affiliation(s)
- Francesca Peccati
- Basque
Research and Technology Alliance (BRTA), Center for Cooperative Research in Biosciences (CIC bioGUNE), Bizkaia Technology Park, Building
800, 48160Derio, Spain,
| | - Sara Alunno-Rufini
- Basque
Research and Technology Alliance (BRTA), Center for Cooperative Research in Biosciences (CIC bioGUNE), Bizkaia Technology Park, Building
800, 48160Derio, Spain
| | - Gonzalo Jiménez-Osés
- Basque
Research and Technology Alliance (BRTA), Center for Cooperative Research in Biosciences (CIC bioGUNE), Bizkaia Technology Park, Building
800, 48160Derio, Spain,Ikerbasque, Basque
Foundation for Science, 48013Bilbao, Spain,
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17
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Hernández IM, Dehouck Y, Bastolla U, López-Blanco JR, Chacón P. Predicting protein stability changes upon mutation using a simple orientational potential. Bioinformatics 2023; 39:6984713. [PMID: 36629451 PMCID: PMC9850275 DOI: 10.1093/bioinformatics/btad011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 11/17/2022] [Accepted: 01/10/2023] [Indexed: 01/12/2023] Open
Abstract
MOTIVATION Structure-based stability prediction upon mutation is crucial for protein engineering and design, and for understanding genetic diseases or drug resistance events. For this task, we adopted a simple residue-based orientational potential that considers only three backbone atoms, previously applied in protein modeling. Its application to stability prediction only requires parametrizing 12 amino acid-dependent weights using cross-validation strategies on a curated dataset in which we tried to reduce the mutations that belong to protein-protein or protein-ligand interfaces, extreme conditions and the alanine over-representation. RESULTS Our method, called KORPM, accurately predicts mutational effects on an independent benchmark dataset, whether the wild-type or mutated structure is used as starting point. Compared with state-of-the-art methods on this balanced dataset, our approach obtained the lowest root mean square error (RMSE) and the highest correlation between predicted and experimental ΔΔG measures, as well as better receiver operating characteristics and precision-recall curves. Our method is almost anti-symmetric by construction, and it performs thus similarly for the direct and reverse mutations with the corresponding wild-type and mutated structures. Despite the strong limitations of the available experimental mutation data in terms of size, variability, and heterogeneity, we show competitive results with a simple sum of energy terms, which is more efficient and less prone to overfitting. AVAILABILITY AND IMPLEMENTATION https://github.com/chaconlab/korpm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Iván Martín Hernández
- Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry, CSIC, 28006 Madrid, Spain
| | - Yves Dehouck
- Bioinformatic Unit, Centro de Biología Molecular “Severo Ochoa,” CSIC-UAM Cantoblanco, Madrid 28049, Spain
| | - Ugo Bastolla
- Bioinformatic Unit, Centro de Biología Molecular “Severo Ochoa,” CSIC-UAM Cantoblanco, Madrid 28049, Spain
| | - José Ramón López-Blanco
- Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry, CSIC, 28006 Madrid, Spain
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18
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Tang R, Gasvoda KL, Rabin J, Alsberg E. Protein conformation stabilized by newly formed turns for thermal resilience. Biophys J 2023; 122:82-89. [PMID: 36419349 PMCID: PMC9822789 DOI: 10.1016/j.bpj.2022.11.2936] [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: 11/27/2021] [Revised: 08/30/2022] [Accepted: 11/21/2022] [Indexed: 11/24/2022] Open
Abstract
Thermally stable or resilient proteins are usually stabilized at intermediate states during thermal stress to prevent irreversible denaturation. However, the mechanism by which their conformations are stabilized to resist high temperature remains elusive. Herein, we investigate the conformational and thermal stability of transforming growth factor-β1 (TGF-β1), a key signaling molecule in numerous biological pathways. We report that the TGF-β1 molecule is thermally resilient as it gradually denatures during thermal treatment when the temperature increases to 90°C-100°C but recovers native folding when the temperature decreases. Using this protein as a model, further studies show the maintenance of its bioactive functional properties after thermal stress, as demonstrated by differentiation induction of NIH/3T3 fibroblasts and human mesenchymal stem cells into myofibroblasts and chondrocytes, respectively. Molecular dynamic simulations revealed that although the protein's secondary structure is unstable under thermal stress, its conformation is partially stabilized by newly formed turns. Given the importance and/or prevalence of TGF-β1 in biological processes, potential therapeutics, and the human diet, our findings encourage consideration of its thermostability for biomedical applications and nutrition.
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Affiliation(s)
- Rui Tang
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
| | - Kaelyn L Gasvoda
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois
| | - Jacob Rabin
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Eben Alsberg
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio; Departments of Mechanical & Industrial Engineering, Orthopedics, and Pharmacology and Regenerative Medicine, University of Illinois at Chicago, Chicago, Illinois; Department of Orthopedic Surgery, Case Western Reserve University, Cleveland, Ohio.
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19
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Harmalkar A, Rao R, Richard Xie Y, Honer J, Deisting W, Anlahr J, Hoenig A, Czwikla J, Sienz-Widmann E, Rau D, Rice AJ, Riley TP, Li D, Catterall HB, Tinberg CE, Gray JJ, Wei KY. Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features. MAbs 2023; 15:2163584. [PMID: 36683173 PMCID: PMC9872953 DOI: 10.1080/19420862.2022.2163584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/14/2022] [Accepted: 12/26/2022] [Indexed: 01/24/2023] Open
Abstract
Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ , of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ = 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Roshan Rao
- Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Yuxuan Richard Xie
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jonas Honer
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Wibke Deisting
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Jonas Anlahr
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Anja Hoenig
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Julia Czwikla
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Eva Sienz-Widmann
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Doris Rau
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Austin J. Rice
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Timothy P. Riley
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Danqing Li
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | | | | | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Kathy Y. Wei
- Therapeutic Discovery, Amgen Research, Amgen Inc, South San Francisco, CA, USA
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20
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Protein Function Analysis through Machine Learning. Biomolecules 2022; 12:biom12091246. [PMID: 36139085 PMCID: PMC9496392 DOI: 10.3390/biom12091246] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/22/2022] [Accepted: 08/31/2022] [Indexed: 11/16/2022] Open
Abstract
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.
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21
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Engineering functional thermostable proteins using ancestral sequence reconstruction. J Biol Chem 2022; 298:102435. [PMID: 36041629 PMCID: PMC9525910 DOI: 10.1016/j.jbc.2022.102435] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 11/20/2022] Open
Abstract
Natural proteins are often only slightly more stable in the native state than the denatured state, and an increase in environmental temperature can easily shift the balance towards unfolding. Therefore, the engineering of proteins to improve protein stability is an area of intensive research. Thermostable proteins are required to withstand industrial process conditions, for increased shelf-life of protein therapeutics, for developing robust 'biobricks' for synthetic biology applications, and for research purposes (e.g. structure determination). In addition, thermostability buffers the often destabilizing effects of mutations introduced to improve other properties. Rational design approaches to engineering thermostability require structural information, but even with advanced computational methods, it is challenging to predict or parameterize all the relevant structural factors with sufficient precision to anticipate the results of a given mutation. Directed evolution is an alternative when structures are unavailable but requires extensive screening of mutant libraries. Recently however, bioinspired approaches based on phylogenetic analyses have shown great promise. Leveraging the rapid expansion in sequence data and bioinformatic tools, ancestral sequence reconstruction (ASR) can generate highly stable folds for novel applications in industrial chemistry, medicine, and synthetic biology. This review provides an overview of the factors important for successful inference of thermostable proteins by ASR and what it can reveal about the determinants of stability in proteins.
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22
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Oxidative Implications of Substituting a Conserved Cysteine Residue in Sugar Beet Phytoglobin BvPgb 1.2. Antioxidants (Basel) 2022; 11:antiox11081615. [PMID: 36009334 PMCID: PMC9404779 DOI: 10.3390/antiox11081615] [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: 06/29/2022] [Revised: 08/09/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
Phytoglobins (Pgbs) are plant-originating heme proteins of the globin superfamily with varying degrees of hexacoordination. Pgbs have a conserved cysteine residue, the role of which is poorly understood. In this paper, we investigated the functional and structural role of cysteine in BvPgb1.2, a Class 1 Pgb from sugar beet (Beta vulgaris), by constructing an alanine-substituted mutant (Cys86Ala). The substitution had little impact on structure, dimerization, and heme loss as determined by X-ray crystallography, size-exclusion chromatography, and an apomyoglobin-based heme-loss assay, respectively. The substitution significantly affected other important biochemical properties. The autoxidation rate increased 16.7- and 14.4-fold for the mutant versus the native protein at 25 °C and 37 °C, respectively. Thermal stability similarly increased for the mutant by ~2.5 °C as measured by nano-differential scanning fluorimetry. Monitoring peroxidase activity over 7 days showed a 60% activity decrease in the native protein, from 33.7 to 20.2 U/mg protein. When comparing the two proteins, the mutant displayed a remarkable enzymatic stability as activity remained relatively constant throughout, albeit at a lower level, ~12 U/mg protein. This suggests that cysteine plays an important role in BvPgb1.2 function and stability, despite having seemingly little effect on its tertiary and quaternary structure.
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23
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Glycine Substitution of Residues with Unfavored Dihedral Angles Improves Protein Thermostability. Catalysts 2022. [DOI: 10.3390/catal12080898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Single mutations that can substantially enhance stability are highly desirable for protein engineering. However, it is generally rare for this kind of mutant to emerge from directed evolution experiments. This study used computational approaches to identify hotspots in a diacylglycerol-specific lipase for mutagenesis with functional hotspot and sequence consensus strategies, followed by ∆∆G calculations for all possible mutations using the Rosetta ddg_monomer protocol. Single mutants with significant ∆∆G changes (≤−2.5 kcal/mol) were selected for expression and characterization. Three out of seven tested mutants showed a significantly enhanced thermostability, with Q282W and A292G in the catalytic pocket and D245G located on the opposite surface of the protein. Remarkably, A292G increased the T5015 (the temperature at which 50% of the enzyme activity was lost after a 15 min of incubation) by ~7 °C, concomitant with a twofold increase in enzymatic activity at the optimal reaction temperature. Structural analysis showed that both A292 and D245 adopted unfavored dihedral angles in the wild-type (WT) enzyme. Substitution of them by glycine might release a steric strain to increase the stability. In sum, substitution by glycine might be a promising strategy to improve protein thermostability.
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24
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Zhu F, Bourguet FA, Bennett WFD, Lau EY, Arrildt KT, Segelke BW, Zemla AT, Desautels TA, Faissol DM. Large-scale application of free energy perturbation calculations for antibody design. Sci Rep 2022; 12:12489. [PMID: 35864134 PMCID: PMC9302960 DOI: 10.1038/s41598-022-14443-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/07/2022] [Indexed: 01/02/2023] Open
Abstract
Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.
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Affiliation(s)
- Fangqiang Zhu
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA.
| | - Feliza A Bourguet
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - William F D Bennett
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Edmond Y Lau
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Kathryn T Arrildt
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Brent W Segelke
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Adam T Zemla
- Global Security Computing Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Thomas A Desautels
- Computational Engineering Division, Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Daniel M Faissol
- Computational Engineering Division, Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, USA.
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25
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Casadio R, Savojardo C, Fariselli P, Capriotti E, Martelli PL. Turning Failures into Applications: The Problem of Protein ΔΔG Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2449:169-185. [PMID: 35507262 DOI: 10.1007/978-1-0716-2095-3_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
After nearly two decades of research in the field of computational methods based on machine learning and knowledge-based potentials for ΔG and ΔΔG prediction upon variations, we now realize that all the approaches are poorly performing when tested on specific cases and that there is large space for improvement. Why this is so? Is it wrong the underlying assumption that experimental protein thermodynamics in solution reflects the thermodynamics of a single protein? Both machine learning and knowledge-based computational methods are rigorous and we know the solid theory behind. We are now in a critical situation, which suggests that predictions of protein instability upon variation should be considered with care. In the following, we will show how to cope with the problem of understanding which protein positions may be of interest for biotechnological and biomedical purposes. By applying a consensus procedure, we indicate possible strategies for the result interpretation.
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Affiliation(s)
- Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
| | - Castrense Savojardo
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Turin, Italy
| | - Emidio Capriotti
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
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26
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Markthaler D, Fleck M, Stankiewicz B, Hansen N. Exploring the Effect of Enhanced Sampling on Protein Stability Prediction. J Chem Theory Comput 2022; 18:2569-2583. [PMID: 35298174 DOI: 10.1021/acs.jctc.1c01012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.
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Affiliation(s)
- Daniel Markthaler
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Maximilian Fleck
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Bartosz Stankiewicz
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
| | - Niels Hansen
- Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany
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27
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Chee WKD, Yeoh JW, Dao VL, Poh CL. Thermogenetics: Applications come of age. Biotechnol Adv 2022; 55:107907. [PMID: 35041863 DOI: 10.1016/j.biotechadv.2022.107907] [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: 10/12/2021] [Revised: 12/13/2021] [Accepted: 01/09/2022] [Indexed: 12/20/2022]
Abstract
Temperature is a ubiquitous physical cue that is non-invasive, penetrative and easy to apply. In the growing field of thermogenetics, through beneficial repurposing of natural thermosensing mechanisms, synthetic biology is bringing new opportunities to design and build robust temperature-sensitive (TS) sensors which forms a thermogenetic toolbox of well characterised biological parts. Recent advancements in technological platforms available have expedited the discovery of novel or de novo thermosensors which are increasingly deployed in many practical temperature-dependent biomedical, industrial and biosafety applications. In all, the review aims to convey both the exhilarating recent technological developments underlying the advancement of thermosensors and the exciting opportunities the nascent thermogenetic field holds for biomedical and biotechnology applications.
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Affiliation(s)
- Wai Kit David Chee
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore; NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
| | - Jing Wui Yeoh
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore; NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
| | - Viet Linh Dao
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore; NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
| | - Chueh Loo Poh
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore; NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore.
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28
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García‐Marquina G, Núñez‐Franco R, Peccati F, Tang Y, Jiménez‐Osés G, López‐Gallego F. Deconvoluting the Directed Evolution Pathway of Engineered Acyltransferase LovD. ChemCatChem 2022. [DOI: 10.1002/cctc.202101349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Guillermo García‐Marquina
- Center for cooperative Research in Biomaterials (CIC biomaGUNE) - Basque Research and Technology Alliance (BRTA) Heterogeneous Biocatalysis laboratory Paseo de Miramón, 182 20014 Donostia-San Sebastián Spain
- Universidad de La Rioja Departamento de Química Centro de Investigación en Síntesis Química Madre de Dios, 53 E-26006 Logroño Spain
| | - Reyes Núñez‐Franco
- Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA) Computational Chemistry Laboratory Bizkaia Technology Park Building 800 48160 Derio Spain
| | - Francesca Peccati
- Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA) Computational Chemistry Laboratory Bizkaia Technology Park Building 800 48160 Derio Spain
| | - Yi Tang
- Department of Chemical and Biomolecular Engineering University of California 607 Charles E. Young Drive East 90095 Los Angeles, CA USA
| | - Gonzalo Jiménez‐Osés
- Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA) Computational Chemistry Laboratory Bizkaia Technology Park Building 800 48160 Derio Spain
- lkerbasque Basque Foundation for Science Plaza Euskadi 5 48009 Bilbao Spain
| | - Fernando López‐Gallego
- Center for cooperative Research in Biomaterials (CIC biomaGUNE) - Basque Research and Technology Alliance (BRTA) Heterogeneous Biocatalysis laboratory Paseo de Miramón, 182 20014 Donostia-San Sebastián Spain
- lkerbasque Basque Foundation for Science Plaza Euskadi 5 48009 Bilbao Spain
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29
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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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30
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Samaga YBL, Raghunathan S, Priyakumar UD. SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation. J Phys Chem B 2021; 125:10657-10671. [PMID: 34546056 DOI: 10.1021/acs.jpcb.1c04913] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new Stransitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.
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Affiliation(s)
- Yashas B L Samaga
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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31
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Xing YN, Tan J, Wang Y, Wang J. Enhancing the thermostability of a mono- and diacylglycerol lipase from Malassizia globose by stabilizing a flexible loop in the catalytic pocket. Enzyme Microb Technol 2021; 149:109849. [PMID: 34311886 DOI: 10.1016/j.enzmictec.2021.109849] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/05/2021] [Accepted: 06/08/2021] [Indexed: 11/29/2022]
Abstract
A lipase from Malassizia globose, named SMG1, is highly desirable for industrial application due to its substrate specificity towards mono- and diacylglycerol. To improve its thermostability, we constructed a mutant library using an error-prone polymerase chain reaction, which was screened for both initial and residual enzymatic activity. Selected mutants were further studied using purified proteins for their kinetic thermostability at 45 ℃, T50 (the temperature at which the enzyme loses half of its activity), and the optimal reaction temperature. Results showed that the majority of mutations with improved thermostability were on the protein surface. D245N and L270P showed the most significant thermostability enhancement with an approximately 3 ℃ increase in T50 compared to wild-type (WT). In addition, combining these two mutations resulted in an increase of T50 by 5 °C. Also, the optimal reaction temperatures of L270P and this double mutant are 10 ℃ higher than WT. The double mutant showed an approximately 100-fold increase in half-life at 45 ℃ and higher enzymatic activities at 30 ℃ and above compared to WT. High-temperature unfolding molecular dynamics simulation suggested that the double mutant stabilized a flexible loop in the catalytic pocket.
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Affiliation(s)
- Yan-Ni Xing
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Jie Tan
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China
| | - Yonghua Wang
- School of Food Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
| | - Jiaqi Wang
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China.
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32
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Louis BBV, Abriata LA. Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures. Mol Biotechnol 2021; 63:863-884. [PMID: 34101125 PMCID: PMC8443528 DOI: 10.1007/s12033-021-00349-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/01/2021] [Indexed: 11/26/2022]
Abstract
Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the limitations of prediction methods and of the datasets used to train them, which result in low reliability for actual applications despite globally capturing trends. Here, we review these and other issues by analyzing one of the most detailed, carefully curated datasets of melting temperature change (ΔTm) upon mutation for proteins with high-resolution structures. After examining the composition of this dataset to discuss imbalances and biases, we inspect several of its entries assisted by an online app for data navigation and structure display and aided by a neural network that predicts ΔTm with accuracy close to that of programs available to this end. We pose that the ΔTm predictions of our network, and also likely those of other programs, account only for a baseline-like general effect of each type of amino acid substitution which then requires substantial corrections to reproduce the actual stability changes. The corrections are very different for each specific case and arise from fine structural details which are not well represented in the dataset and which, despite appearing reasonable upon visual inspection of the structures, are hard to encode and parametrize. Based on these observations, additional analyses, and a review of recent literature, we propose recommendations for developers of stability prediction methods and for efforts aimed at improving the datasets used for training. We leave our interactive interface for analysis available online at http://lucianoabriata.altervista.org/papersdata/proteinstability2021/s1626navigation.html so that users can further explore the dataset and baseline predictions, possibly serving as a tool useful in the context of structural biology and protein biotechnology research and as material for education in protein biophysics.
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Affiliation(s)
- Benjamin B V Louis
- Master of Life Sciences Engineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland
| | - Luciano A Abriata
- Laboratory for Biomolecular Modeling, School of Life Sciences, École Polytechnique Fédérale de Lausanne, and Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland.
- Protein Production and Structure Core Facility, School of Life Sciences, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.
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33
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Xu H, Qing X, Wang Q, Li C, Lai L. Dimerization of PHGDH via the catalytic unit is essential for its enzymatic function. J Biol Chem 2021; 296:100572. [PMID: 33753166 PMCID: PMC8081924 DOI: 10.1016/j.jbc.2021.100572] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 03/12/2021] [Accepted: 03/18/2021] [Indexed: 11/25/2022] Open
Abstract
Human D-3-phosphoglycerate dehydrogenase (PHGDH), a key enzyme in de novo serine biosynthesis, is amplified in various cancers and serves as a potential target for anticancer drug development. To facilitate this process, more information is needed on the basic biochemistry of this enzyme. For example, PHGDH was found to form tetramers in solution and the structure of its catalytic unit (sPHGDH) was solved as a dimer. However, how the oligomeric states affect PHGDH enzyme activity remains elusive. We studied the dependence of PHGDH enzymatic activity on its oligomeric states. We found that sPHGDH forms a mixture of monomers and dimers in solution with a dimer dissociation constant of ∼0.58 μM, with the enzyme activity depending on the dimer content. We computationally identified hotspot residues at the sPHGDH dimer interface. Single-point mutants at these sites disrupt dimer formation and abolish enzyme activity. Molecular dynamics simulations showed that dimer formation facilitates substrate binding and maintains the correct conformation required for enzyme catalysis. We further showed that the full-length PHGDH exists as a dynamic mixture of monomers, dimers, and tetramers in solution with enzyme concentration-dependent activity. Mutations that can completely disrupt the sPHGDH dimer show different abilities to interrupt the full-length PHGDH tetramer. Among them, E108A and I121A can also disrupt the oligomeric structures of the full-length PHGDH and abolish its enzyme activity. Our study indicates that disrupting the oligomeric structure of PHGDH serves as a novel strategy for PHGDH drug design and the hotspot residues identified can guide the design process.
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Affiliation(s)
- Hanyu Xu
- BNLMS, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Xiaoyu Qing
- BNLMS, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Qian Wang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, China
| | - Chunmei Li
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Luhua Lai
- BNLMS, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing, China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
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34
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Mol V, Bennett M, Sánchez BJ, Lisowska BK, Herrgård MJ, Nielsen AT, Leak DJ, Sonnenschein N. Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism. Metab Eng 2021; 65:123-134. [PMID: 33753231 DOI: 10.1016/j.ymben.2021.03.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 03/01/2021] [Indexed: 12/27/2022]
Abstract
Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.
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Affiliation(s)
- Viviënne Mol
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Martyn Bennett
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
| | - Benjamín J Sánchez
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Beata K Lisowska
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; BioInnovation Institute, Copenhagen N, Denmark
| | - Alex Toftgaard Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - David J Leak
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom.
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.
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35
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Li G, Qin Y, Fontaine NT, Ng Fuk Chong M, Maria‐Solano MA, Feixas F, Cadet XF, Pandjaitan R, Garcia‐Borràs M, Cadet F, Reetz MT. Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation. Chembiochem 2021; 22:904-914. [PMID: 33094545 PMCID: PMC7984044 DOI: 10.1002/cbic.202000612] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/22/2020] [Indexed: 12/15/2022]
Abstract
Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside-the-box, predictions not found in other state-of-the-art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.
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Affiliation(s)
- Guangyue Li
- State Key Laboratory for Biology of Plant Diseases and Insect Pests Key Laboratory of Control of Biological Hazard Factors (Plant Origin) for Agri-product Quality and Safety Ministry of Agriculture, Institute of Plant ProtectionChinese Academy of Agricultural SciencesBeijing100081P. R. China
| | - Youcai Qin
- State Key Laboratory for Biology of Plant Diseases and Insect Pests Key Laboratory of Control of Biological Hazard Factors (Plant Origin) for Agri-product Quality and Safety Ministry of Agriculture, Institute of Plant ProtectionChinese Academy of Agricultural SciencesBeijing100081P. R. China
| | - Nicolas T. Fontaine
- PEACCELArtificial Intelligence Department6 Square Albin Cachot, Box 4275013ParisFrance) .
| | - Matthieu Ng Fuk Chong
- PEACCELArtificial Intelligence Department6 Square Albin Cachot, Box 4275013ParisFrance) .
| | - Miguel A. Maria‐Solano
- Institut de Química Computacional i Catàlisi and Departament de QuímicaUniversitat de Girona Campus Montilivi17003Girona, CataloniaSpain) .
| | - Ferran Feixas
- Institut de Química Computacional i Catàlisi and Departament de QuímicaUniversitat de Girona Campus Montilivi17003Girona, CataloniaSpain) .
| | - Xavier F. Cadet
- PEACCELArtificial Intelligence Department6 Square Albin Cachot, Box 4275013ParisFrance) .
| | - Rudy Pandjaitan
- PEACCELArtificial Intelligence Department6 Square Albin Cachot, Box 4275013ParisFrance) .
| | - Marc Garcia‐Borràs
- Institut de Química Computacional i Catàlisi and Departament de QuímicaUniversitat de Girona Campus Montilivi17003Girona, CataloniaSpain) .
| | - Frederic Cadet
- PEACCELArtificial Intelligence Department6 Square Albin Cachot, Box 4275013ParisFrance) .
| | - Manfred T. Reetz
- Department of ChemistryPhilipps-Universität35032MarburgGermany) .
- Max-Planck-Institut fuer Kohlenforschung45470MülheimGermany
- Tianjin Institute of Industrial BiotechnologyChinese Academy of Sciences32 West 7th Avenue, Tianjin Airport Economic Area300308TianjinP. R. China
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36
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Mesbahuddin MS, Ganesan A, Kalyaanamoorthy S. Engineering stable carbonic anhydrases for CO2 capture: a critical review. Protein Eng Des Sel 2021; 34:6356912. [PMID: 34427656 DOI: 10.1093/protein/gzab021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 07/16/2021] [Indexed: 11/14/2022] Open
Abstract
Targeted inhibition of misregulated protein-protein interactions (PPIs) has been a promising area of investigation in drug discovery and development for human diseases. However, many constraints remain, including shallow binding surfaces and dynamic conformation changes upon interaction. A particularly challenging aspect is the undesirable off-target effects caused by inherent structural similarity among the protein families. To tackle this problem, phage display has been used to engineer PPIs for high-specificity binders with improved binding affinity and greatly reduced undesirable interactions with closely related proteins. Although general steps of phage display are standardized, library design is highly variable depending on experimental contexts. Here in this review, we examined recent advances in the structure-based combinatorial library design and the advantages and limitations of different approaches. The strategies described here can be explored for other protein-protein interactions and aid in designing new libraries or improving on previous libraries.
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Affiliation(s)
| | - Aravindhan Ganesan
- School of Pharmacy, University of Waterloo, Waterloo, Ontario N2G 1C5, Canada
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37
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Strokach A, Lu TY, Kim PM. ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations. J Mol Biol 2021; 433:166810. [PMID: 33450251 DOI: 10.1016/j.jmb.2021.166810] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/19/2020] [Accepted: 01/03/2021] [Indexed: 12/21/2022]
Abstract
The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.
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Affiliation(s)
- Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Tian Yu Lu
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Philip M Kim
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada.
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Baghban R, Farajnia S, Ghasemi Y, Hoseinpoor R, Safary A, Mortazavi M, Zarghami N. Mutational Analysis of Ocriplasmin to Reduce Proteolytic and Autolytic Activity in Pichia pastoris. Biol Proced Online 2020; 22:25. [PMID: 33308171 PMCID: PMC7734836 DOI: 10.1186/s12575-020-00138-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 12/01/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Ocriplasmin (Jetrea) is using for the treatment of symptomatic vitreomacular adhesion. This enzyme undergoes rapid inactivation and limited activity duration as a result of its autolytic nature after injection within the eye. Moreover, the proteolytic activity can cause photoreceptor damage, which may result in visual impairment in more serious cases. RESULTS The present research aimed to reduce the disadvantages of ocriplasmin using site-directed mutagenesis. To reduce the autolytic activity of ocriplasmin in the first variant, lysine 156 changed to glutamic acid and, in the second variant for the proteolytic activity reduction, alanine 59 mutated to threonine. The third variant contained both mutations. Expression of wild type and three mutant variants of ocriplasmin constructs were done in the Pichia pastoris expression system. The mutant variants were analyzed in silico and in vitro and compared to the wild type. The kinetic parameters of ocriplasmin variants showed both variants with K156E substitution were more resistant to autolytic degradation than wild-type. These variants also exhibited reduced Kcat and Vmax values. An increase in their Km values, leading to a decreased catalytic efficiency (the Kcat/Km ratio) of autolytic and mixed variants. Moreover, in the variant with A59T mutation, Kcat and Vmax values have reduced compared to wild type. The mix variants showed the most increase in Km value (almost 2-fold) as well as reduced enzymatic affinity to the substrate. Thus, the results indicated that combined mutations at the ocriplasmin sequence were more effective compared with single mutations. CONCLUSIONS The results indicated such variants represent valuable tools for the investigation of therapeutic strategies aiming at the non-surgical resolution of vitreomacular adhesion.
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Affiliation(s)
- Roghayyeh Baghban
- Medical Biotechnology Department, Faculty of Advanced Medical Science, Tabriz University of Medical Sciences, Tabriz, Iran
- Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
- Poostchi Ophthalmology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Biotechnology Research Center, Tabriz University of Medical Sciences, Daneshgah Ave, Tabriz, Iran
| | - Safar Farajnia
- Biotechnology Research Center, Tabriz University of Medical Sciences, Daneshgah Ave, Tabriz, Iran.
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Younes Ghasemi
- Department of Pharmaceutical Biotechnology, Faculty of Pharmacy and Pharmaceutical Sciences Research Center, Shiraz University of Medical Science, Shiraz, Iran
| | - Reyhaneh Hoseinpoor
- Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azam Safary
- Connective Tissue Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mojtaba Mortazavi
- Department of Biotechnology, Institute of Science and High Technology and Environmental Science, Graduate University of Advanced Technology, Kerman, Iran
| | - Nosratollah Zarghami
- Medical Biotechnology Department, Faculty of Advanced Medical Science, Tabriz University of Medical Sciences, Tabriz, Iran
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Mazurenko S. Predicting protein stability and solubility changes upon mutations: data perspective. ChemCatChem 2020. [DOI: 10.1002/cctc.202000933] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Stanislav Mazurenko
- Loschmidt Laboratories Department of Experimental Biology and RECETOX Faculty of Science Masaryk University Zerotinovo nam. 617/9 601 77 Brno Czech Republic
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