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Manning MC, Holcomb RE, Payne RW, Stillahn JM, Connolly BD, Katayama DS, Liu H, Matsuura JE, Murphy BM, Henry CS, Crommelin DJA. Stability of Protein Pharmaceuticals: Recent Advances. Pharm Res 2024; 41:1301-1367. [PMID: 38937372 DOI: 10.1007/s11095-024-03726-x] [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: 03/25/2024] [Accepted: 06/03/2024] [Indexed: 06/29/2024]
Abstract
There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'
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Affiliation(s)
- Mark Cornell Manning
- Legacy BioDesign LLC, Johnstown, CO, USA.
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA.
| | - Ryan E Holcomb
- Legacy BioDesign LLC, Johnstown, CO, USA
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA
| | - Robert W Payne
- Legacy BioDesign LLC, Johnstown, CO, USA
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA
| | - Joshua M Stillahn
- Legacy BioDesign LLC, Johnstown, CO, USA
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA
| | | | | | | | | | | | - Charles S Henry
- Department of Chemistry, Colorado State University, Fort Collins, CO, USA
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2
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Cárdenas-Guerra RE, Montes-Flores O, Nava-Pintor EE, Reséndiz-Cardiel G, Flores-Pucheta CI, Rodríguez-Gavaldón YI, Arroyo R, Bottazzi ME, Hotez PJ, Ortega-López J. Chagasin from Trypanosoma cruzi as a molecular scaffold to express epitopes of TSA-1 as soluble recombinant chimeras. Protein Expr Purif 2024; 218:106458. [PMID: 38423156 DOI: 10.1016/j.pep.2024.106458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/13/2024] [Accepted: 02/21/2024] [Indexed: 03/02/2024]
Abstract
Trypanosoma cruzi is the causative agent of Chagas disease, a global public health problem. New therapeutic drugs and biologics are needed. The TSA-1 recombinant protein of T. cruzi is one such promising antigen for developing a therapeutic vaccine. However, it is overexpressed in E. coli as inclusion bodies, requiring an additional refolding step. As an alternative, in this study, we propose the endogenous cysteine protease inhibitor chagasin as a molecular scaffold to generate chimeric proteins. These proteins will contain combinations of two of the five conserved epitopes (E1 to E5) of TSA-1 in the L4 and L6 chagasin loops. Twenty chimeras (Q1-Q20) were designed, and their solubility was predicted using bioinformatics tools. Nine chimeras with different degrees of solubility were selected and expressed in E. coli BL21 (DE3). Western blot assays with anti-6x-His and anti-chagasin antibodies confirmed the expression of soluble recombinant chimeras. Both theoretically and experimentally, the Q12 (E5-E3) chimera was the most soluble, and the Q20 (E4-E5) the most insoluble protein. Q4 (E5-E1) and Q8 (E5-E2) chimeras were classified as proteins with medium solubility that exhibited the highest yield in the soluble fraction. Notably, Q4 has a yield of 239 mg/L, well above the yield of recombinant chagasin (16.5 mg/L) expressed in a soluble form. The expression of the Q4 chimera was scaled up to a 7 L fermenter obtaining a yield of 490 mg/L. These data show that chagasin can serve as a molecular scaffold for the expression of TSA-1 epitopes in the form of soluble chimeras.
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Affiliation(s)
- Rosa Elena Cárdenas-Guerra
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Octavio Montes-Flores
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Edgar Ezequiel Nava-Pintor
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Gerardo Reséndiz-Cardiel
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Claudia Ivonne Flores-Pucheta
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Yasmín Irene Rodríguez-Gavaldón
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Rossana Arroyo
- Departamento de Infectómica y Patogénesis Molecular, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Maria Elena Bottazzi
- Texas Children's Hospital Center for Vaccine Development, Department of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Peter J Hotez
- Texas Children's Hospital Center for Vaccine Development, Department of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Jaime Ortega-López
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico.
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3
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Ali M, Greenig M, Oeller M, Atkinson M, Xu X, Sormanni P. Automated optimization of the solubility of a hyper-stable α-amylase. Open Biol 2024; 14:240014. [PMID: 38745462 PMCID: PMC11293438 DOI: 10.1098/rsob.240014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/26/2024] [Accepted: 03/27/2024] [Indexed: 05/16/2024] Open
Abstract
Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm's capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.
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Affiliation(s)
- Montader Ali
- Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, UK
| | - Matthew Greenig
- Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, UK
| | - Marc Oeller
- Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, UK
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried82152, Germany
| | - Misha Atkinson
- Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, UK
| | - Xing Xu
- Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, UK
| | - Pietro Sormanni
- Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, UK
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Shad M, Nazir A, Usman M, Akhtar MW, Sajjad M. Investigating the effect of SUMO fusion on solubility and stability of amylase-catalytic domain from Pyrococcus abyssi. Int J Biol Macromol 2024; 266:131310. [PMID: 38569986 DOI: 10.1016/j.ijbiomac.2024.131310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 02/09/2024] [Accepted: 03/30/2024] [Indexed: 04/05/2024]
Abstract
Alpha amylase belonging to starch hydrolyzing enzymes has significant contributions to different industrial processes. The enzyme production through recombinant DNA technology faces certain challenges related to their expression, solubility and purification, which can be overcome through fusion tags. This study explored the influence of SUMO, a protein tag reported to enhance the solubility and stability of target proteins when fused to the N-terminal of the catalytic domain of amylase from Pyrococcus abyssi (PaAD). The insoluble expression of PaAD in E. coli was overcome when the enzyme was expressed in a fusion state (S-PaAD) and culture was cultivated at 18 °C. Moreover, the activity of S-PaAD increased by 1.5-fold as compared to that of PaAD. The ligand binding and enzyme activity assays against different substrates demonstrated that it was more active against 1 % glycogen and amylopectin. The analysis of the hydrolysates through HPLC demonstrated that the enzyme activity is mainly amylolytic, producing longer oligosaccharides as the major end product. The secondary structure analyses by temperature ramping in CD spectroscopy and MD simulation demonstrated the enzymes in the free, as well as fusion state, were stable at 90 °C. The soluble production, thermostability and broad substrate specificity make this enzyme a promising choice for various foods, feed, textiles, detergents, pharmaceuticals, and many industrial applications.
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Affiliation(s)
- Mohsin Shad
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, P.O. 54590, Pakistan; Structural Biology, The Rosalind Franklin Institute, Harwell Science & Innovation Campus, Didcot OX11 0QS, United Kingdom
| | - Arshia Nazir
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, P.O. 54590, Pakistan
| | - Muhammad Usman
- Department of Plant Pathology, University of Agriculture, Faisalabad, P.O. 38000, Pakistan
| | - Muhammad Waheed Akhtar
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, P.O. 54590, Pakistan
| | - Muhammad Sajjad
- School of Biological Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, P.O. 54590, Pakistan.
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5
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Röntgen A, Toprakcioglu Z, Tomkins JE, Vendruscolo M. Modulation of α-synuclein in vitro aggregation kinetics by its alternative splice isoforms. Proc Natl Acad Sci U S A 2024; 121:e2313465121. [PMID: 38324572 PMCID: PMC10873642 DOI: 10.1073/pnas.2313465121] [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: 08/05/2023] [Accepted: 12/20/2023] [Indexed: 02/09/2024] Open
Abstract
The misfolding and aggregation of α-synuclein is linked to a family of neurodegenerative disorders known as synucleinopathies, the most prominent of which is Parkinson's disease (PD). Understanding the aggregation process of α-synuclein from a mechanistic point of view is thus of key importance. SNCA, the gene encoding α-synuclein, comprises six exons and produces various isoforms through alternative splicing. The most abundant isoform is expressed as a 140-amino acid protein (αSyn-140), while three other isoforms, αSyn-126, αSyn-112, and αSyn-98, are generated by skipping exon 3, exon 5, or both exons, respectively. In this study, we performed a detailed biophysical characterization of the aggregation of these four isoforms. We found that αSyn-112 and αSyn-98 exhibit accelerated aggregation kinetics compared to αSyn-140 and form distinct aggregate morphologies, as observed by transmission electron microscopy. Moreover, we observed that the presence of relatively small amounts of αSyn-112 accelerates the aggregation of αSyn-140, significantly reducing the aggregation half-time. These results indicate a potential role of alternative splicing in the pathological aggregation of α-synuclein and provide insights into how this process could be associated with the development of synucleinopathies.
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Affiliation(s)
- Alexander Röntgen
- Centre for Misfolding Diseases, Yusuf HamiedDepartment of Chemistry, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - Zenon Toprakcioglu
- Centre for Misfolding Diseases, Yusuf HamiedDepartment of Chemistry, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - James E. Tomkins
- Centre for Misfolding Diseases, Yusuf HamiedDepartment of Chemistry, University of Cambridge, CambridgeCB2 1EW, United Kingdom
- Aligning Science Across Parkinson’s Collaborative Research Network, Chevy Chase, MD20815
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf HamiedDepartment of Chemistry, University of Cambridge, CambridgeCB2 1EW, United Kingdom
- Aligning Science Across Parkinson’s Collaborative Research Network, Chevy Chase, MD20815
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Rahbar MR, Nezafat N, Morowvat MH, Savardashtaki A, Ghoshoon MB, Mehrabani-Zeinabad K, Ghasemi Y. Targeting Efficient Features of Urate Oxidase to Increase Its Solubility. Appl Biochem Biotechnol 2024:10.1007/s12010-023-04819-w. [PMID: 38308671 DOI: 10.1007/s12010-023-04819-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2023] [Indexed: 02/05/2024]
Abstract
With the demand for mass production of protein drugs, solubility has become a serious issue. Extrinsic and intrinsic factors both affect this property. A homotetrameric cofactor-free urate oxidase (UOX) is not sufficiently soluble. To engineer UOX for optimum solubility, it is important to identify the most effective factor that influences solubility. The most effective feature to target for protein engineering was determined by measuring various solubility-related factors of UOX. A large library of homologous sequences was obtained from the databases. The data was reduced to six enzymes from different organisms. On the basis of various sequence- and structure-derived elements, the most and the least soluble enzymes were defined. To determine the best protein engineering target for modification, features of the most and least soluble enzymes were compared. Metabacillus fastidiosus UOX was the most soluble enzyme, while Agrobacterium globiformis UOX was the least soluble. According to the comparison-constant method, positive surface patches caused by arginine residue distribution are appropriate targets for modification. Two Arg to Ala mutations were introduced to the least soluble enzyme to test this hypothesis. These mutations significantly enhanced the mutant's solubility. While different algorithms produced conflicting results, it was difficult to determine which proteins were most and least soluble. Solubility prediction requires multiple algorithms based on these controversies. Protein surfaces should be investigated regionally rather than globally, and both sequence and structural data should be considered. Several other biotechnological products could be engineered using the data reduction and comparison-constant methods used in this study.
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Affiliation(s)
- Mohammad Reza Rahbar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Navid Nezafat
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran
| | - Mohammad Hossein Morowvat
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran
| | - Amir Savardashtaki
- Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Bagher Ghoshoon
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran
| | - Kamran Mehrabani-Zeinabad
- Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Younes Ghasemi
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran.
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7
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Trier NH, Friis T. Production of Antibodies to Peptide Targets Using Hybridoma Technology. Methods Mol Biol 2024; 2821:135-156. [PMID: 38997486 DOI: 10.1007/978-1-0716-3914-6_11] [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] [Indexed: 07/14/2024]
Abstract
Hybridoma technology is a well-established and indispensable tool for generating high-quality monoclonal antibodies and has become one of the most common methods for monoclonal antibody production. In this process, antibody-producing B cells are isolated from mice following immunization of mice with a specific immunogen and fused with an immortal myeloma cell line to form antibody-producing hybridoma cell lines. Hybridoma-derived monoclonal antibodies not only serve as powerful research and diagnostic reagents but have also emerged as the most rapidly expanding class of therapeutic biologicals. In spite of the development of new high-throughput monoclonal antibody generation technologies, hybridoma technology still is applied for antibody production due to its ability to preserve innate functions of immune cells and to preserve natural cognate antibody paring information. In this chapter, an overview of hybridoma technology and the laboratory procedures used for hybridoma production and antibody screening of peptide-specific antibodies are presented.
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Affiliation(s)
| | - Tina Friis
- Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark
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Oeller M, Kang RJD, Bolt HL, Gomes Dos Santos AL, Weinmann AL, Nikitidis A, Zlatoidsky P, Su W, Czechtizky W, De Maria L, Sormanni P, Vendruscolo M. Sequence-based prediction of the intrinsic solubility of peptides containing non-natural amino acids. Nat Commun 2023; 14:7475. [PMID: 37978172 PMCID: PMC10656490 DOI: 10.1038/s41467-023-42940-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Non-natural amino acids are increasingly used as building blocks in the development of peptide-based drugs as they expand the available chemical space to tailor function, half-life and other key properties. However, while the chemical space of modified amino acids (mAAs) such as residues containing post-translational modifications (PTMs) is potentially vast, experimental methods for measuring the developability properties of mAA-containing peptides are expensive and time consuming. To facilitate developability programs through computational methods, we present CamSol-PTM, a method that enables the fast and reliable sequence-based prediction of the intrinsic solubility of mAA-containing peptides in aqueous solution at room temperature. From a computational screening of 50,000 mAA-containing variants of three peptides, we selected five different small-size mAAs for a total number of 37 peptide variants for experimental validation. We demonstrate the accuracy of the predictions by comparing the calculated and experimental solubility values. Our results indicate that the computational screening of mAA-containing peptides can extend by over four orders of magnitude the ability to explore the solubility chemical space of peptides and confirm that our method can accurately assess the solubility of peptides containing mAAs. This method is available as a web server at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolptm .
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Affiliation(s)
- Marc Oeller
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Ryan J D Kang
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Hannah L Bolt
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ana L Gomes Dos Santos
- Advanced Drug Delivery, Pharmaceutical Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Annika Langborg Weinmann
- Early Chemical Development, Pharmaceutical Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Antonios Nikitidis
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Pavol Zlatoidsky
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Wu Su
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Werngard Czechtizky
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Leonardo De Maria
- Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
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Garfagnini T, Bemporad F, Harries D, Chiti F, Friedler A. Amyloid Aggregation Is Potently Slowed Down by Osmolytes Due to Compaction of Partially Folded State. J Mol Biol 2023; 435:168281. [PMID: 37734431 DOI: 10.1016/j.jmb.2023.168281] [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: 03/25/2023] [Revised: 07/30/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
Amyloid aggregation is a key process in amyloidoses and neurodegenerative diseases. Hydrophobicity is one of the major driving forces for this type of aggregation, as an increase in hydrophobicity generally correlates with aggregation susceptibility and rate. However, most experimental systems in vitro and prediction tools in silico neglect the contribution of protective osmolytes present in the cellular environment. Here, we assessed the role of hydrophobic mutations in amyloid aggregation in the presence of osmolytes. To achieve this goal, we used the model protein human muscle acylphosphatase (mAcP) and mutations to leucine that increased its hydrophobicity without affecting its thermodynamic stability. Osmolytes significantly slowed down the aggregation kinetics of the hydrophobic mutants, with an effect larger than that observed on the wild-type protein. The effect increased as the mutation site was closer to the middle of the protein sequence. We propose that the preferential exclusion of osmolytes from mutation-introduced hydrophobic side-chains quenches the aggregation potential of the ensemble of partially unfolded states of the protein by inducing its compaction and inhibiting its self-assembly with other proteins. Our results suggest that including the effect of the cellular environment in experimental setups and predictive softwares, for both mechanistic studies and drug design, is essential in order to obtain a more complete combination of the driving forces of amyloid aggregation.
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Affiliation(s)
- Tommaso Garfagnini
- Institute of Chemistry, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 9190401, Israel
| | - Francesco Bemporad
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence 50134, Italy
| | - Daniel Harries
- Institute of Chemistry, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 9190401, Israel; The Fritz Haber Research Center, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 9190401, Israel
| | - Fabrizio Chiti
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence 50134, Italy
| | - Assaf Friedler
- Institute of Chemistry, The Hebrew University of Jerusalem, Edmond J. Safra Campus at Givat Ram, Jerusalem 9190401, Israel.
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Muhammad SA, Guo J, Noor K, Mustafa A, Amjad A, Bai B. Pangenomic and immunoinformatics based analysis of Nipah virus revealed CD4 + and CD8 + T-Cell epitopes as potential vaccine candidates. Front Pharmacol 2023; 14:1290436. [PMID: 38035008 PMCID: PMC10682379 DOI: 10.3389/fphar.2023.1290436] [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: 09/07/2023] [Accepted: 10/31/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction: Nipah (NiV) is the zoonotic deadly bat-borne virus that causes neurological and respiratory infections which ultimately lead to death. There are 706 infected cases reported up till now especially in Asia, out of which 409 patients died. There is no vaccine and effective treatment available for NiV infections and we have to timely design such strategies as world could not bear another pandemic situation. Methods: In this study, we screened viral proteins of NiV strains based on pangenomics analysis, antigenicity, molecular weight, and sub-cellular localization. The immunoproteomics based approach was used to predict T-cell epitopes of MHC class-I and II as potential vaccine candidates. These epitopes are capable to activate CD4+, CD8+, and T-cell dependent B-lymphocytes. Results: The two surface proteins including fusion glycoprotein (F) and attachment glycoprotein (G) are antigenic with molecular weights of 60 kDa and 67 kDa respectively. Three epitopes of F protein (VNYNSEGIA, PNFILVRNT, and IKMIPNVSN) were ranked and selected based on the binding affinity with MHC class-I, and 3 epitopes (VILNKRYYS, ILVRNTLIS, and VKLQETAEK) with MHC-II molecules. Similarly, for G protein, 3 epitopes each for MHC-I (GKYDKVMPY, ILKPKLISY, and KNKIWCISL) and MHC-II (LRNIEKGKY, FLIDRINWI, and FLLKNKIWC) with substantial binding energies were predicted. Based on the physicochemical properties, all these epitopes are non-toxic, hydrophilic, and stable. Conclusion: Our vaccinomics and system-level investigation could help to trigger the host immune system to prevent NiV infection.
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Affiliation(s)
- Syed Aun Muhammad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | - Jinlei Guo
- School of Intelligent Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang, China
| | - Komal Noor
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | - Aymen Mustafa
- University of Health Sciences Lahore, Lahore, Pakistan
| | - Anam Amjad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | - Baogang Bai
- School of Information and Technology, Wenzhou Business College, Wenzhou, China
- Zhejiang Province Engineering Research Center of Intelligent Medicine, Wenzhou, China
- The 1st School of Medical, School of Information and Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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11
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Pang KT, Yang YS, Zhang W, Ho YS, Sormanni P, Michaels TCT, Walsh I, Chia S. Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics. Biotechnol Adv 2023; 67:108192. [PMID: 37290583 DOI: 10.1016/j.biotechadv.2023.108192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/09/2023] [Accepted: 06/01/2023] [Indexed: 06/10/2023]
Abstract
In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.
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Affiliation(s)
- Kuin Tian Pang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technology University, Singapore
| | - Yuan Sheng Yang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Wei Zhang
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Pietro Sormanni
- Chemistry of Health, Yusuf Hamied Department of Chemistry, University of Cambridge, United Kingdom
| | - Thomas C T Michaels
- Department of Biology, Institute of Biochemistry, ETH Zurich, Otto-Stern-Weg 3, 8093 Zurich, Switzerland; Bringing Materials to Life Initiative, ETH Zurich, Switzerland
| | - Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
| | - Sean Chia
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
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12
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Ausserwöger H, Krainer G, Welsh TJ, Thorsteinson N, de Csilléry E, Sneideris T, Schneider MM, Egebjerg T, Invernizzi G, Herling TW, Lorenzen N, Knowles TPJ. Surface patches induce nonspecific binding and phase separation of antibodies. Proc Natl Acad Sci U S A 2023; 120:e2210332120. [PMID: 37011217 PMCID: PMC10104583 DOI: 10.1073/pnas.2210332120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 02/06/2023] [Indexed: 04/05/2023] Open
Abstract
Nonspecific interactions are a key challenge in the successful development of therapeutic antibodies. The tendency for nonspecific binding of antibodies is often difficult to reduce by rational design, and instead, it is necessary to rely on comprehensive screening campaigns. To address this issue, we performed a systematic analysis of the impact of surface patch properties on antibody nonspecificity using a designer antibody library as a model system and single-stranded DNA as a nonspecificity ligand. Using an in-solution microfluidic approach, we find that the antibodies tested bind to single-stranded DNA with affinities as high as KD = 1 µM. We show that DNA binding is driven primarily by a hydrophobic patch in the complementarity-determining regions. By quantifying the surface patches across the library, the nonspecific binding affinity is shown to correlate with a trade-off between the hydrophobic and total charged patch areas. Moreover, we show that a change in formulation conditions at low ionic strengths leads to DNA-induced antibody phase separation as a manifestation of nonspecific binding at low micromolar antibody concentrations. We highlight that phase separation is driven by a cooperative electrostatic network assembly mechanism of antibodies with DNA, which correlates with a balance between positive and negative charged patches. Importantly, our study demonstrates that both nonspecific binding and phase separation are controlled by the size of the surface patches. Taken together, these findings highlight the importance of surface patches and their role in conferring antibody nonspecificity and its macroscopic manifestation in phase separation.
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Affiliation(s)
- Hannes Ausserwöger
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - Georg Krainer
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - Timothy J. Welsh
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - Nels Thorsteinson
- Research and Development, Chemical Computing Group, Montreal, QuebecH3A 2R7, Canada
| | - Ella de Csilléry
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - Tomas Sneideris
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - Matthias M. Schneider
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - Thomas Egebjerg
- Global Research Technologies, Novo Nordisk A/S2760Måløv, Denmark
| | | | - Therese W. Herling
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, CambridgeCB2 1EW, United Kingdom
| | - Nikolai Lorenzen
- Global Research Technologies, Novo Nordisk A/S2760Måløv, Denmark
| | - Tuomas P. J. Knowles
- Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, CambridgeCB2 1EW, United Kingdom
- Department of Physics, Cavendish Laboratory, University of Cambridge, CambridgeCB3 0HE, United Kingdom
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13
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Rosace A, Bennett A, Oeller M, Mortensen MM, Sakhnini L, Lorenzen N, Poulsen C, Sormanni P. Automated optimisation of solubility and conformational stability of antibodies and proteins. Nat Commun 2023; 14:1937. [PMID: 37024501 PMCID: PMC10079162 DOI: 10.1038/s41467-023-37668-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.
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Affiliation(s)
- Angelo Rosace
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Master in Bioinformatics for Health Sciences, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institute for Research in Biomedicine (IRB), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Anja Bennett
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Department of Mammalian Expression, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
- BRIC, Faculty of Health and Medical Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Marc Oeller
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
| | - Mie M Mortensen
- Department of Purification Technologies, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
- Faculty of Engineering and Science, Department of Biotechnology, Chemistry and Environmental Engineering, University of Aalborg, Fredrik Bajers Vej 7H, 9220, Aalborg, Denmark
| | - Laila Sakhnini
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, 2760, Denmark
| | - Nikolai Lorenzen
- Department of Biophysics and Injectable Formulation 2, Global Research Technologies, Novo Nordisk A/S, Måløv, 2760, Denmark
| | - Christian Poulsen
- Department of Mammalian Expression, Global Research Technologies, Novo Nordisk A/S, Novo Nordisk Park 1, 2760, Måløv, Denmark
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield road, CB2 1EW, Cambridge, UK.
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14
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Oeller M, Kang R, Bell R, Ausserwöger H, Sormanni P, Vendruscolo M. Sequence-based prediction of pH-dependent protein solubility using CamSol. Brief Bioinform 2023; 24:7017367. [PMID: 36719110 PMCID: PMC10025429 DOI: 10.1093/bib/bbad004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/10/2022] [Accepted: 10/16/2022] [Indexed: 02/01/2023] Open
Abstract
Solubility is a property of central importance for the use of proteins in research in molecular and cell biology and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are material intensive and time consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of solubility for large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the development of one such method to include in the predictions the effect of the pH on solubility. We illustrate the resulting pH-dependent predictions on a variety of antibodies and other proteins to demonstrate that these predictions achieve an accuracy comparable with that of experimental methods. We make this method publicly available at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolph, as the version 3.0 of CamSol.
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Affiliation(s)
- Marc Oeller
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Ryan Kang
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Rosie Bell
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Hannes Ausserwöger
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
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15
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Porosk L, Härk HH, Bicev RN, Gaidutšik I, Nebogatova J, Armolik EJ, Arukuusk P, da Silva ER, Langel Ü. Aggregation Limiting Cell-Penetrating Peptides Derived from Protein Signal Sequences. Int J Mol Sci 2023; 24:ijms24054277. [PMID: 36901707 PMCID: PMC10002422 DOI: 10.3390/ijms24054277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease (ND) and the leading cause of dementia. It is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in the biological alterations and the causes of the disease. One of the hallmarks of the AD is the progression of plaques of aggregated amyloid-β (Aβ) or neurofibrillary tangles of Tau. Currently there is no efficient treatment for the AD. Nevertheless, several breakthroughs in revealing the mechanisms behind progression of the AD have led to the discovery of possible therapeutic targets. Some of these include the reduction in inflammation in the brain, and, although highly debated, limiting of the aggregation of the Aβ. In this work we show that similarly to the Neural cell adhesion molecule 1 (NCAM1) signal sequence, other Aβ interacting protein sequences, especially derived from Transthyretin, can be used successfully to reduce or target the amyloid aggregation/aggregates in vitro. The modified signal peptides with cell-penetrating properties reduce the Aβ aggregation and are predicted to have anti-inflammatory properties. Furthermore, we show that by expressing the Aβ-EGFP fusion protein, we can efficiently assess the potential for reduction in aggregation, and the CPP properties of peptides in mammalian cells.
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Affiliation(s)
- Ly Porosk
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
- Correspondence:
| | - Heleri Heike Härk
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | - Renata Naporano Bicev
- Departamento de Biofísica, Universidade Federal de São Paulo, São Paulo 04023-062, Brazil
| | - Ilja Gaidutšik
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | | | - Eger-Jasper Armolik
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | - Piret Arukuusk
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | | | - Ülo Langel
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
- Department Biochemistry and Biophysics, Stockholm University, S.Arrheniusv. 16B, Room C472, 106 91 Stockholm, Sweden
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16
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Yang YX, Wang P, Zhu BT. Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas. J Mol Graph Model 2023; 118:108364. [PMID: 36356467 DOI: 10.1016/j.jmgm.2022.108364] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022]
Abstract
Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.
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Affiliation(s)
- Yong Xiao Yang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Pan Wang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Bao Ting Zhu
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China.
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17
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Ausserwöger H, Schneider MM, Herling TW, Arosio P, Invernizzi G, Knowles TPJ, Lorenzen N. Non-specificity as the sticky problem in therapeutic antibody development. Nat Rev Chem 2022; 6:844-861. [PMID: 37117703 DOI: 10.1038/s41570-022-00438-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 11/16/2022]
Abstract
Antibodies are highly potent therapeutic scaffolds with more than a hundred different products approved on the market. Successful development of antibody-based drugs requires a trade-off between high target specificity and target binding affinity. In order to better understand this problem, we here review non-specific interactions and explore their fundamental physicochemical origins. We discuss the role of surface patches - clusters of surface-exposed amino acid residues with similar physicochemical properties - as inducers of non-specific interactions. These patches collectively drive interactions including dipole-dipole, π-stacking and hydrophobic interactions to complementary moieties. We elucidate links between these supramolecular assembly processes and macroscopic development issues, such as decreased physical stability and poor in vivo half-life. Finally, we highlight challenges and opportunities for optimizing protein binding specificity and minimizing non-specificity for future generations of therapeutics.
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18
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Mahajan SP, Ruffolo JA, Frick R, Gray JJ. Hallucinating structure-conditioned antibody libraries for target-specific binders. Front Immunol 2022; 13:999034. [PMID: 36341416 PMCID: PMC9635398 DOI: 10.3389/fimmu.2022.999034] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022] Open
Abstract
Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the FvHallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, FvHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the FvHallucinator designs amino acid substitutions at the VH-VL interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens FvHallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs predicted to improve upon the binding affinity and interfacial properties of the original antibody. Thus, the FvHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.
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Affiliation(s)
- Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jeffrey A. Ruffolo
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, United States
| | - Rahel Frick
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, United States
- *Correspondence: Jeffrey J. Gray,
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19
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Sarma S, Herrera SM, Xiao X, Hudalla GA, Hall CK. Computational Design and Experimental Validation of ACE2-Derived Peptides as SARS-CoV-2 Receptor Binding Domain Inhibitors. J Phys Chem B 2022; 126:8129-8139. [PMID: 36219223 PMCID: PMC9578369 DOI: 10.1021/acs.jpcb.2c03918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/28/2022] [Indexed: 01/05/2023]
Abstract
The COVID-19 pandemic has caused significant social and economic disruption across the globe. Cellular entry of SARS-CoV-2 into the human body is mediated via binding of the Receptor Binding Domain (RBD) on the viral Spike protein (SARS-CoV-2 RBD) to Angiotensin-Converting Enzyme 2 (ACE2) expressed on host cells. Molecules that can disrupt ACE2:RBD interactions are attractive therapeutic candidates to prevent virus entry into human cells. A computational strategy that combines our Peptide Binding Design (PepBD) algorithm with atomistic molecular dynamics simulations was used to design new inhibitory peptide candidates via sequence iteration starting with a 23-mer peptide, referred to as SBP1. SBP1 is derived from a region of the ACE2 Peptidase Domain α1 helix that binds to the SARS-CoV-2 RBD of the initial Wuhan-Hu-1 strain. Three peptides demonstrated a solution-phase RBD-binding dissociation constant in the micromolar range during tryptophan fluorescence quenching experiments, one peptide did not bind, and one was insoluble at micromolar concentrations. However, in competitive ELISA assays, none of these peptides could outcompete ACE2 binding to SARS-CoV-2-RBD up to concentrations of 50 μM, similar to the parent SBP1 peptide which also failed to outcompete ACE2:RBD binding. Molecular dynamics simulations suggest that P4 would have a good binding affinity for the RBD domain of Beta-B.1.351, Gamma-P.1, Kappa-B.1.617.1, Delta-B.1.617.2, and Omicron-B.1.1.529 variants, but not the Alpha variant. Consistent with this, P4 bound Kappa-B.1.617.1 and Delta-B.1.617.2 RBD with micromolar affinity in tryptophan fluorescence quenching experiments. Collectively, these data show that while relatively short unstructured peptides can bind to SARS-CoV-2 RBD with moderate affinity, they are incapable of outcompeting the strong interactions between RBD and ACE2.
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Affiliation(s)
- Sudeep Sarma
- Department
of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina27695-7905, United States
| | - Stephanie M. Herrera
- J.
Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Biomedical Sciences J293, P.O Box 116131, Gainesville, Florida32611, United States
| | - Xingqing Xiao
- Department
of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina27695-7905, United States
| | - Gregory A. Hudalla
- J.
Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Biomedical Sciences J293, P.O Box 116131, Gainesville, Florida32611, United States
| | - Carol K. Hall
- Department
of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina27695-7905, United States
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20
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Qing R, Hao S, Smorodina E, Jin D, Zalevsky A, Zhang S. Protein Design: From the Aspect of Water Solubility and Stability. Chem Rev 2022; 122:14085-14179. [PMID: 35921495 PMCID: PMC9523718 DOI: 10.1021/acs.chemrev.1c00757] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Indexed: 12/13/2022]
Abstract
Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.
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Affiliation(s)
- Rui Qing
- State
Key Laboratory of Microbial Metabolism, School of Life Sciences and
Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shilei Hao
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Key
Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Eva Smorodina
- Department
of Immunology, University of Oslo and Oslo
University Hospital, Oslo 0424, Norway
| | - David Jin
- Avalon GloboCare
Corp., Freehold, New Jersey 07728, United States
| | - Arthur Zalevsky
- Laboratory
of Bioinformatics Approaches in Combinatorial Chemistry and Biology, Shemyakin−Ovchinnikov Institute of Bioorganic
Chemistry RAS, Moscow 117997, Russia
| | - Shuguang Zhang
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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21
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Martin WR, Cheng F. A rational design of a multi-epitope vaccine against SARS-CoV-2 which accounts for the glycan shield of the spike glycoprotein. J Biomol Struct Dyn 2022; 40:7099-7113. [PMID: 33715598 PMCID: PMC9003619 DOI: 10.1080/07391102.2021.1894986] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 02/19/2021] [Indexed: 02/06/2023]
Abstract
The ongoing global health crisis caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus which leads to Coronavirus Disease 2019 (COVID-19) has impacted not only the health of people everywhere, but the economy in nations across the world. While vaccine candidates and therapeutics are currently undergoing clinical trials, there is a lack of proven effective treatments or cures for COVID-19. In this study, we have presented a synergistic computational platform, including molecular dynamics simulations and immunoinformatics techniques, to rationally design a multi-epitope vaccine candidate for COVID-19. This platform combines epitopes across Linear B Lymphocytes (LBL), Cytotoxic T Lymphocytes (CTL) and Helper T Lymphocytes (HTL) derived from both mutant and wild-type spike glycoproteins from SARS-CoV-2 with diverse protein conformations. In addition, this vaccine construct also takes the considerable glycan shield of the spike glycoprotein into account, which protects it from immune response. We have identified a vaccine candidate (a 35.9 kDa protein), named COVCCF, which is composed of 5 LBL, 6 HTL, and 6 CTL epitopes from the spike glycoprotein of SARS-CoV-2. Using multi-dose immune simulations, COVCCF induces elevated levels of immunoglobulin activity (IgM, IgG1, IgG2), and induces strong responses from B lymphocytes, CD4 T-helper lymphocytes, and CD8 T-cytotoxic lymphocytes. COVCCF induces cytokines important to innate immunity, including IFN-γ, IL4, and IL10. Additionally, COVCCF has ideal pharmacokinetic properties and low immune-related toxicities. In summary, this study provides a powerful, computational vaccine design platform for rapid development of vaccine candidates (including COVCCF) for effective prevention of COVID-19.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- William R. Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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22
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Waibl F, Fernández-Quintero ML, Wedl FS, Kettenberger H, Georges G, Liedl KR. Comparison of hydrophobicity scales for predicting biophysical properties of antibodies. Front Mol Biosci 2022; 9:960194. [PMID: 36120542 PMCID: PMC9475378 DOI: 10.3389/fmolb.2022.960194] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
While antibody-based therapeutics have grown to be one of the major classes of novel medicines, some antibody development candidates face significant challenges regarding expression levels, solubility, as well as stability and aggregation, under physiological and storage conditions. A major determinant of those properties is surface hydrophobicity, which promotes unspecific interactions and has repeatedly proven problematic in the development of novel antibody-based drugs. Multiple computational methods have been devised for in-silico prediction of antibody hydrophobicity, often using hydrophobicity scales to assign values to each amino acid. Those approaches are usually validated by their ability to rank potential therapeutic antibodies in terms of their experimental hydrophobicity. However, there is significant diversity both in the hydrophobicity scales and in the experimental methods, and consequently in the performance of in-silico methods to predict experimental results. In this work, we investigate hydrophobicity of monoclonal antibodies using hydrophobicity scales. We implement several scoring schemes based on the solvent-accessibility and the assigned hydrophobicity values, and compare the different scores and scales based on their ability to predict retention times from hydrophobic interaction chromatography. We provide an overview of the strengths and weaknesses of several commonly employed hydrophobicity scales, thereby improving the understanding of hydrophobicity in antibody development. Furthermore, we test several datasets, both publicly available and proprietary, and find that the diversity of the dataset affects the performance of hydrophobicity scores. We expect that this work will provide valuable guidelines for the optimization of biophysical properties in future drug discovery campaigns.
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Affiliation(s)
- Franz Waibl
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | | | - Florian S. Wedl
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Hubert Kettenberger
- Large Molecule Research, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany
| | - Guy Georges
- Large Molecule Research, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany
| | - Klaus R. Liedl
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
- *Correspondence: Klaus R. Liedl,
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23
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Gadhave K, Kapuganti SK, Mishra PM, Giri R. p53 TAD2 Domain (38-61) Forms Amyloid-like Aggregates in Isolation. ACS Chem Neurosci 2022; 13:2281-2287. [PMID: 35856925 DOI: 10.1021/acschemneuro.1c00860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
A strong association between protein aggregation and human diseases (such as Alzheimer's, Parkinson's, and Huntington's disease) is well demonstrated. Misfolding and aggregation of p53, a central transcriptional mediator, has been revealed by various experimental evidence in different types of cancers. Aggregation studies focusing on different p53 domains, mostly, the central core domain and its mutants under the influence of various environmental conditions, and the p53 transactivation domain (TAD) (1-63) have been reported. However, the specific subdomains responsible for p53 aggregation are not known. p53 TADs interact with diverse cellular factors to modulate the function of p53 and elicit appropriate cellular responses under different stress conditions. In this study, the aggregation of the p53 TAD2 domain (38-61) has been studied in isolation. The aggregates were generated in vitro under acidic pH conditions after in silico scoring for amyloidogenic tendency and characterized using dye-based assays (ThT and bis-ANS fluorescence), CD spectroscopy, and microscopy (scanning electron microscoy, transmission electron microscopy, and atomic force microscopy). It was observed that p53 TAD2 forms characteristic β-sheet-rich amyloid-like fibrils. Via a reductionist approach, this study highlights the nature of p53 TAD2 domain (38-61) aggregation.
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Affiliation(s)
- Kundlik Gadhave
- School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Mandi, Himachal Pradesh 175005, India
| | - Shivani K Kapuganti
- School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Mandi, Himachal Pradesh 175005, India
| | - Pushpendra Mani Mishra
- School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Mandi, Himachal Pradesh 175005, India
| | - Rajanish Giri
- School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, Mandi, Himachal Pradesh 175005, India
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24
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Poly (ethylene) glycol (PEG) precipitation of glycosylated and non-glycosylated monoclonal antibodies. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.07.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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25
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The effect of mutation on an aggregation-prone protein: An in vivo, in vitro, and in silico analysis. Proc Natl Acad Sci U S A 2022; 119:e2200468119. [PMID: 35613051 PMCID: PMC9295795 DOI: 10.1073/pnas.2200468119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Aggregation of initially stably structured proteins is involved in more than 20 human amyloid diseases. Despite intense research, however, how this class of proteins assembles into amyloid fibrils remains poorly understood, principally because of the complex effects of amino acid substitutions on protein stability, solubility, and aggregation propensity. We address this question using β2-microglobulin (β2m) as a model system, focusing on D76N-β2m that is involved in hereditary amyloidosis. This amino acid substitution causes the aggregation-resilient wild-type protein to become highly aggregation prone in vitro, although the mechanism by which this occurs remained elusive. Here, we identify the residues key to protecting β2m from aggregation by coupling aggregation with antibiotic resistance in E. coli using a tripartite β-lactamase assay (TPBLA). By performing saturation mutagenesis at three different sites (D53X-, D76X-, and D98X-β2m) we show that residue 76 has a unique ability to drive β2m aggregation in vivo and in vitro. Using a randomly mutated D76N-β2m variant library, we show that all of the mutations found to improve protein behavior involve residues in a single aggregation-prone region (APR) (residues 60 to 66). Surprisingly, no correlation was found between protein stability and protein aggregation rate or yield, with several mutations in the APR decreasing aggregation without affecting stability. Together, the results demonstrate the power of the TPBLA to develop proteins that are resilient to aggregation and suggest a model for D76N-β2m aggregation involving the formation of long-range couplings between the APR and Asn76 in a nonnative state.
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26
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Korona D, Dirnberger B, Giachello CNG, Queiroz RML, Popovic R, Müller KH, Minde DP, Deery MJ, Johnson G, Firth LC, Earley FG, Russell S, Lilley KS. Drosophila nicotinic acetylcholine receptor subunits and their native interactions with insecticidal peptide toxins. eLife 2022; 11:74322. [PMID: 35575460 PMCID: PMC9110030 DOI: 10.7554/elife.74322] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 04/19/2022] [Indexed: 12/14/2022] Open
Abstract
Drosophila nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels that represent a target for insecticides. Peptide neurotoxins are known to block nAChRs by binding to their target subunits, however, a better understanding of this mechanism is needed for effective insecticide design. To facilitate the analysis of nAChRs we used a CRISPR/Cas9 strategy to generate null alleles for all ten nAChR subunit genes in a common genetic background. We studied interactions of nAChR subunits with peptide neurotoxins by larval injections and styrene maleic acid lipid particles (SMALPs) pull-down assays. For the null alleles, we determined the effects of α-Bungarotoxin (α-Btx) and ω-Hexatoxin-Hv1a (Hv1a) administration, identifying potential receptor subunits implicated in the binding of these toxins. We employed pull-down assays to confirm α-Btx interactions with the Drosophila α5 (Dα5), Dα6, Dα7 subunits. Finally, we report the localisation of fluorescent tagged endogenous Dα6 during Drosophila CNS development. Taken together, this study elucidates native Drosophila nAChR subunit interactions with insecticidal peptide toxins and provides a resource for the in vivo analysis of insect nAChRs.
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Affiliation(s)
- Dagmara Korona
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom
| | - Benedict Dirnberger
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom.,Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.,Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom
| | - Carlo N G Giachello
- Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom
| | - Rayner M L Queiroz
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Rebeka Popovic
- MRC Toxicology Unit, Gleeson Building, University of Cambridge, Tennis Court Road, Cambridge, United Kingdom
| | - Karin H Müller
- Cambridge Advanced Imaging Centre, Department of Physiology, Development and Neuroscience/Anatomy Building, University of Cambridge, Cambridge, United Kingdom
| | - David-Paul Minde
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Michael J Deery
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Glynnis Johnson
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom
| | - Lucy C Firth
- Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom
| | - Fergus G Earley
- Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom
| | - Steven Russell
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, United Kingdom
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27
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Swanson S, Sivaraman V, Grigoryan G, Keating AE. Tertiary motifs as building blocks for the design of protein‐binding peptides. Protein Sci 2022; 31:e4322. [PMID: 35634780 PMCID: PMC9088223 DOI: 10.1002/pro.4322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 11/07/2022]
Affiliation(s)
- Sebastian Swanson
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Venkatesh Sivaraman
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Gevorg Grigoryan
- Department of Computer Science Dartmouth College Hanover New Hampshire USA
| | - Amy E. Keating
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
- Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
- Koch Center for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts USA
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28
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Spassov VZ, Kemmish H, Yan L. Two physics‐based models for
pH
‐dependent calculations of protein solubility. Protein Sci 2022; 31:e4299. [PMID: 35481654 PMCID: PMC8996476 DOI: 10.1002/pro.4299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/01/2022] [Accepted: 02/28/2022] [Indexed: 11/11/2022]
Abstract
When engineering a protein for its biological function, many physicochemical properties are also optimized throughout the engineering process, and the protein's solubility is among the most important properties to consider. Here, we report two novel computational methods to calculate the pH-dependent protein solubility, and to rank the solubility of mutants. The first is an empirical method developed for fast ranking of the solubility of a large number of mutants of a protein. It takes into account electrostatic solvation energy term calculated using Generalized Born approximation, hydrophobic patches, protein charge, and charge asymmetry, as well as the changes of protein stability upon mutation. This method has been tested on over 100 mutations for 17 globular proteins, as well as on 44 variants of five different antibodies. The prediction rate is over 80%. The antibody tests showed a Pearson correlation coefficient, R, with experimental data from .83 to .91. The second method is based on a novel, completely force-field-based approach using CHARMm program modules to calculate the binding energy of the protein to a part of the crystal lattice, generated from X-ray structure. The method predicted with very high accuracy the solubility of Ribonuclease SA and its 3K and 5K mutants as a function of pH without any parameter adjustments of the existing BIOVIA Discovery Studio binding affinity model. Our methods can be used for rapid screening of large numbers of design candidates based on solubility, and to guide the design of solution conditions for antibody formulation.
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Affiliation(s)
- Velin Z. Spassov
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
| | - Helen Kemmish
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
| | - Lisa Yan
- BIOVIA Dassault Systemes, 5005 Wateridge Vista Drive San Diego California USA
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29
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Khetan R, Curtis R, Deane CM, Hadsund JT, Kar U, Krawczyk K, Kuroda D, Robinson SA, Sormanni P, Tsumoto K, Warwicker J, Martin ACR. Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. MAbs 2022; 14:2020082. [PMID: 35104168 PMCID: PMC8812776 DOI: 10.1080/19420862.2021.2020082] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.
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Affiliation(s)
- Rahul Khetan
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Robin Curtis
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | | | | | - Uddipan Kar
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | | | - Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | | | - Pietro Sormanni
- Chemistry of Health, Yusuf Hamied Department of Chemistry, University of Cambridge
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.,The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Jim Warwicker
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Andrew C R Martin
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
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30
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Xiao X, Sarma S, Menegatti S, Crook N, Magness ST, Hall CK. In Silico Identification and Experimental Validation of Peptide-Based Inhibitors Targeting Clostridium difficile Toxin A. ACS Chem Biol 2022; 17:118-128. [PMID: 34965093 DOI: 10.1021/acschembio.1c00743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Clostridium difficile infection is mediated by two major exotoxins: toxins A (TcdA) and B (TcdB). Inhibiting the biocatalytic activities of these toxins with targeted peptide-based drugs can reduce the risk of C. difficile infection. In this work, we used a computational strategy that integrates a peptide binding design (PepBD) algorithm and explicit-solvent atomistic molecular dynamics simulation to determine promising toxin A-targeting peptides that can recognize and bind to the catalytic site of the TcdA glucosyltransferase domain (GTD). Our simulation results revealed that two out of three in silico discovered peptides, viz. the neutralizing peptides A (NPA) and B (NPB), exhibit lower binding free energies when bound to the TcdA GTD than the phage-display discovered peptide, viz. the reference peptide (RP). These peptides may serve as potential inhibitors against C. difficile infection. The efficacy of the peptides RP, NPA, and NPB to neutralize the cytopathic effects of TcdA was tested in vitro in human jejunum cells. Both phage-display peptide RP and in silico peptide NPA were found to exhibit strong toxin-neutralizing properties, thereby preventing the TcdA toxicity. However, the in silico peptide NPB demonstrates a relatively low efficacy against TcdA.
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Affiliation(s)
- Xingqing Xiao
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Sudeep Sarma
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Stefano Menegatti
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
- Biomanufacturing Training and Education Center (BTEC), North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Nathan Crook
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Scott T Magness
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, United States
| | - Carol K Hall
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
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31
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Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2313:57-113. [PMID: 34478132 DOI: 10.1007/978-1-0716-1450-1_4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.
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32
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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33
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Han X, Shih J, Lin Y, Chai Q, Cramer SM. Development of QSAR models for in silico screening of antibody solubility. MAbs 2022; 14:2062807. [PMID: 35442164 PMCID: PMC9037471 DOI: 10.1080/19420862.2022.2062807] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R2 of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.
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Affiliation(s)
- Xuan Han
- Department of Chemical and Biological Engineering and Center for Biotechnology and interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - James Shih
- Biotechnology Discovery Research, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Yuhao Lin
- Research Information & Digital Solutions, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Qing Chai
- Biotechnology Discovery Research, Eli Lilly Biotechnology Center, San Diego, California, USA
| | - Steven M Cramer
- Department of Chemical and Biological Engineering and Center for Biotechnology and interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York, USA
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34
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Heads JT, Kelm S, Tyson K, Lawson ADG. A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers. MAbs 2022; 14:2138092. [PMID: 36418193 PMCID: PMC9704409 DOI: 10.1080/19420862.2022.2138092] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.
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Affiliation(s)
- James T. Heads
- UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK,CONTACT James T. Heads UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK
| | | | - Kerry Tyson
- UCB Pharma, 208 Bath Road, SloughSL1 3WE, UK
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35
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Pons Royo MDC, Beulay JL, Valery E, Jungbauer A, Satzer P. Design of millidevices to expedite apparent solubility measurements. REACT CHEM ENG 2022. [DOI: 10.1039/d2re00022a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
A fast, automated and accurate millidevice for determination of the apparent solubility of proteins and impurities and different industrially relevant precipitating agents.
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Affiliation(s)
- Maria del Carme Pons Royo
- Department of Innovation, Novasep, 81 Boulevard de la Moselle, 54340 Pompey, France
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, 1190 Vienna, Austria
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 18, 1190 Vienna, Austria
| | - Jean-Luc Beulay
- Department of Innovation, Novasep, 81 Boulevard de la Moselle, 54340 Pompey, France
| | - Eric Valery
- Department of Innovation, Novasep, 81 Boulevard de la Moselle, 54340 Pompey, France
| | - Alois Jungbauer
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, 1190 Vienna, Austria
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 18, 1190 Vienna, Austria
| | - Peter Satzer
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, 1190 Vienna, Austria
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36
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Waibl F, Kraml J, Fernández-Quintero ML, Loeffler JR, Liedl KR. Explicit solvation thermodynamics in ionic solution: extending grid inhomogeneous solvation theory to solvation free energy of salt-water mixtures. J Comput Aided Mol Des 2022; 36:101-116. [PMID: 35031880 PMCID: PMC8907097 DOI: 10.1007/s10822-021-00429-y] [Citation(s) in RCA: 6] [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: 09/14/2021] [Accepted: 10/28/2021] [Indexed: 12/03/2022]
Abstract
Hydration thermodynamics play a fundamental role in fields ranging from the pharmaceutical industry to environmental research. Numerous methods exist to predict solvation thermodynamics of compounds ranging from small molecules to large biomolecules. Arguably the most precise methods are those based on molecular dynamics (MD) simulations in explicit solvent. One theory that has seen increased use is inhomogeneous solvation theory (IST). However, while many applications require accurate description of salt-water mixtures, no implementation of IST is currently able to estimate solvation properties involving more than one solvent species. Here, we present an extension to grid inhomogeneous solvation theory (GIST) that can take salt contributions into account. At the example of carbazole in 1 M NaCl solution, we compute the solvation energy as well as first and second order entropies. While the effect of the first order ion entropy is small, both the water-water and water-ion entropies contribute strongly. We show that the water-ion entropies are efficiently approximated using the Kirkwood superposition approximation. However, this approach cannot be applied to the water-water entropy. Furthermore, we test the quantitative validity of our method by computing salting-out coefficients and comparing them to experimental data. We find a good correlation to experimental salting-out constants, while the absolute values are overpredicted due to the approximate second order entropy. Since ions are frequently used in MD, either to neutralize the system or as a part of the investigated process, our method greatly extends the applicability of GIST. The use-cases range from biopharmaceuticals, where many assays require high salt concentrations, to environmental research, where solubility in sea water is important to model the fate of organic substances.
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Affiliation(s)
- Franz Waibl
- Department of General, Inorganic, and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, 6020, Innsbruck, Austria
| | - Johannes Kraml
- Department of General, Inorganic, and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, 6020, Innsbruck, Austria
| | - Monica L Fernández-Quintero
- Department of General, Inorganic, and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, 6020, Innsbruck, Austria
| | - Johannes R Loeffler
- Department of General, Inorganic, and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, 6020, Innsbruck, Austria
| | - Klaus R Liedl
- Department of General, Inorganic, and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, 6020, Innsbruck, Austria.
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37
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Raybould MIJ, Deane CM. The Therapeutic Antibody Profiler for Computational Developability Assessment. Methods Mol Biol 2022; 2313:115-125. [PMID: 34478133 DOI: 10.1007/978-1-0716-1450-1_5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.
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Affiliation(s)
- Matthew I J Raybould
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.
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38
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Oeller M, Sormanni P, Vendruscolo M. An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement. Sci Rep 2021; 11:21932. [PMID: 34753962 PMCID: PMC8578320 DOI: 10.1038/s41598-021-01126-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/18/2021] [Indexed: 02/02/2023] Open
Abstract
The solubility of proteins correlates with a variety of their properties, including function, production yield, pharmacokinetics, and formulation at high concentrations. High solubility is therefore a key requirement for the development of protein-based reagents for applications in life sciences, biotechnology, diagnostics, and therapeutics. Accurate solubility measurements, however, remain challenging and resource intensive, which limits their throughput and hence their applicability at the early stages of development pipelines, when long-lists of candidates are typically available in minute amounts. Here, we present an automated method based on the titration of a crowding agent (polyethylene glycol, PEG) to quantitatively assess relative solubility of proteins using about 200 µg of purified material. Our results demonstrate that this method is accurate and economical in material requirement and costs of reagents, which makes it suitable for high-throughput screening. This approach is freely-shared and based on a low cost, open-source liquid-handling robot. We anticipate that this method will facilitate the assessment of the developability of proteins and make it substantially more accessible.
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Affiliation(s)
- Marc Oeller
- grid.5335.00000000121885934Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK
| | - Pietro Sormanni
- Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK.
| | - Michele Vendruscolo
- Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Cambridge, UK.
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39
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Tomar DS, Licari G, Bauer J, Singh SK, Li L, Kumar S. Stress-dependent flexibility of a full-length human monoclonal antibody: Insights from molecular dynamics to support biopharmaceutical development. J Pharm Sci 2021; 111:628-637. [PMID: 34742728 DOI: 10.1016/j.xphs.2021.10.039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/30/2021] [Accepted: 10/30/2021] [Indexed: 01/15/2023]
Abstract
After several decades of advancements in drug discovery, product development of biopharmaceuticals remains a time- and resource-consuming endeavor. One of the main reasons is associated to the lack of fundamental understanding of conformational dynamics of such biologic entities, and how they respond to various stresses encountered during manufacturing. In this work, we have studied the conformational dynamics of human IgG1κ b12 monoclonal antibody (mAb) using molecular dynamics simulations. The hundreds of nanoseconds long trajectories reveal that b12 mAb is highly flexible. Its variable domains show greater conformational fluctuations than the constant domains. Additionally, it collapses towards a more globular shape in response to thermal stress, leading to decrease in the total solvent exposed surface area and radius of gyration. This behavior is more pronounced for the deglycosylated b12 mAb, and it appears to correlate with increase in inter-domain contacts between specific regions of the antibody. Conformational fluctuations also cause temporary formation and disruption of hydrophobic and charged patches on the antibody surface, which is particularly important for the prediction of CMC properties during development phases of antibody-based biotherapeutics. The insights gained through these simulations may help the development of biologic drugs, especially with regards to manufacturing processes where antibodies may undergo significant thermal stress.
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Affiliation(s)
- Dheeraj S Tomar
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 700 Chesterfield Parkway West, Chesterfield, MO, 63017, USA
| | - Giuseppe Licari
- Pharmaceuticals Development Biologicals, Boehringer Ingelheim Pharmaceuticals, Inc., D-88397 Biberach an der Riss, Germany
| | - Joschka Bauer
- Pharmaceuticals Development Biologicals, Boehringer Ingelheim Pharmaceuticals, Inc., D-88397 Biberach an der Riss, Germany
| | - Satish K Singh
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 700 Chesterfield Parkway West, Chesterfield, MO, 63017, USA
| | - Li Li
- Biotherapeutics Pharmaceutical Sciences Research and Development, Pfizer Inc., 1 Burtt Road, Andover, Massachusetts, 01810, USA
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT 06877.
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40
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Xiao X, Wang Y, Seroski DT, Wong KM, Liu R, Paravastu AK, Hudalla GA, Hall CK. De novo design of peptides that coassemble into β sheet-based nanofibrils. SCIENCE ADVANCES 2021; 7:eabf7668. [PMID: 34516924 PMCID: PMC8442925 DOI: 10.1126/sciadv.abf7668] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Peptides’ hierarchical coassembly into nanostructures enables controllable fabrication of multicomponent biomaterials. In this work, we describe a computational and experimental approach to design pairs of charge-complementary peptides that selectively coassemble into β-sheet nanofibers when mixed together but remain unassembled when isolated separately. The key advance is a peptide coassembly design (PepCAD) algorithm that searches for pairs of coassembling peptides. Six peptide pairs are identified from a pool of ~106 candidates via the PepCAD algorithm and then subjected to DMD/PRIME20 simulations to examine their co-/self-association kinetics. The five pairs that spontaneously aggregate in kinetic simulations selectively coassemble in biophysical experiments, with four forming β-sheet nanofibers and one forming a stable nonfibrillar aggregate. Solid-state NMR, which is applied to characterize the coassembling pairs, suggests that the in silico peptides exhibit a higher degree of structural order than the previously reported CATCH(+/−) peptides.
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Affiliation(s)
- Xingqing Xiao
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695-7905, USA
| | - Yiming Wang
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695-7905, USA
| | - Dillon T. Seroski
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Kong M. Wong
- Department of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Renjie Liu
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Anant K. Paravastu
- Department of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Gregory A. Hudalla
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Carol K. Hall
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695-7905, USA
- Corresponding author.
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41
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Machine Learning Attempts for Predicting Human Subcutaneous Bioavailability of Monoclonal Antibodies. Pharm Res 2021; 38:451-460. [PMID: 33710513 DOI: 10.1007/s11095-021-03022-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 02/22/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE One knowledge gap related to subcutaneous (SC) delivery is unpredictable and variable bioavailability. This study was aimed to develop machine learning methods to predict whether mAb's bioavailability was ≥70% or below, without completely knowing the mechanism and causality between inputs and outputs. METHODS A database of mAb SC products was built. The model training and validation were accomplished based on this database and a set of the inputs (product properties) were mapped to the output (bioavailability) using different machine learning algorithms. Dimensionality reduction was undertaken using principal component analysis (PCA). RESULTS The bioavailability of the mAb products being investigated varied from 35% to 90%. The tree-based methods, including random forest (RF), Adaptive Boost (AdaBoost), and decision tree (DT) presented the best predictability and generalization power on bioavailability classification. The models based on Multi-layer perceptron (MLP), Gaussian Naïve Bayes (GaussianNB), and k nearest neighbor (kNN) algorithms also provided acceptable prediction accuracy. CONCLUSION Machine learning could be a potential tool to predict mAb's bioavailability. Since all input features were acquired using theoretical calculations and predictions rather than experiments, the models may be particularly applicable to some early-stage research activities such as mAb molecule triage, design/optimization, mutant screening, molecule selection, and formulation design.
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42
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Bhandari BK, Gardner PP, Lim CS. Solubility-Weighted Index: fast and accurate prediction of protein solubility. Bioinformatics 2021; 36:4691-4698. [PMID: 32559287 PMCID: PMC7750957 DOI: 10.1093/bioinformatics/btaa578] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 05/05/2020] [Accepted: 06/12/2020] [Indexed: 12/14/2022] Open
Abstract
Motivation Recombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified. Results We have discovered that global structural flexibility, which can be modeled by normalized B-factors, accurately predicts the solubility of 12 216 recombinant proteins expressed in Escherichia coli. We have optimized these B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the ‘Solubility-Weighted Index’ (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed ‘SoDoPE’ (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximizing both protein expression and solubility. Availability and implementation The SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper_2020. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bikash K Bhandari
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
| | - Paul P Gardner
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand.,Biomolecular Interaction Centre, University of Canterbury, Christchurch, New Zealand
| | - Chun Shen Lim
- Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand
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43
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Sangpheak K, Waraho-Zhmayev D, Haonoo K, Torpaiboon S, Teacharsripaiboon T, Rungrotmongkol T, Poo-Arporn RP. Investigation of interactions between binding residues and solubility of grafted humanized anti-VEGF IgG antibodies expressed as full-length format in the cytoplasm of a novel engineered E. coli SHuffle strain. RSC Adv 2021; 11:6035-6048. [PMID: 35423148 PMCID: PMC8694825 DOI: 10.1039/d0ra08534k] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 01/25/2021] [Indexed: 12/29/2022] Open
Abstract
Monoclonal antibodies (mAbs) are one of the fastest-growing areas of biopharmaceutical industry and have been widely used for a broad spectrum of diseases. Meanwhile, the immunogenicity of non-human derived antibodies can generate side effects by inducing the human immune response to produce human anti-mouse-immunoglobulin antibody (HAMA). In this work, we aim to reduce the immunogenicity of muMAb A.4.6.1 by substitute human sequences for murine sequences. Humanized antibodies are constructed by grafting, specificity determining residues (SDR), complementary determining regions (CDR), and chimeric region of muMAb A.4.6.1, onto variable domain of Trastuzumab (Herceptin). The interactions between grafted antibodies and their target, Vascular endothelial growth factor (VEGF), were theoretically investigated by molecular dynamics simulation in order to evaluate the antibodies-antigen binding behavior. The obtained protein-protein interactions and calculated binding free energy suggested that the SDR-VEGF complex presented a significantly greater binding affinity, number of contact and total number of H-bonds compared to CDR and chimeric mAbs, significantly. Moreover, the Camsol program predicted that the solubility of SDR mAb exhibits the greatest solubility. This result was supported by performing a western blot analysis of the grafted mAbs with soluble and insoluble fractions in order to evaluate their solubility, in which SDR was found to have a much lower amount of insoluble proteins. Consequently, the enhanced binding affinity and solubility of the designed SDR was achieved by the single S106D mutation using computational methods. With the aim of low immunogenicity, high solubility, and high affinity, this SDR humanized antibody was expected to have greater efficacy than murine or chimeric antibodies for future use in humans.
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Affiliation(s)
- Kanyani Sangpheak
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi 10140 Bangkok Thailand
| | - Dujduan Waraho-Zhmayev
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi 10140 Bangkok Thailand
| | - Korakod Haonoo
- Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi 10140 Bangkok Thailand
| | - Sarun Torpaiboon
- Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi 10140 Bangkok Thailand
| | - Tarin Teacharsripaiboon
- Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi 10140 Bangkok Thailand
| | - Thanyada Rungrotmongkol
- Biocatalyst and Environmental Biotechnology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University 10330 Bangkok Thailand.,Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University 10330 Bangkok Thailand
| | - Rungtiva P Poo-Arporn
- Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi 10140 Bangkok Thailand
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44
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Soler MA, Medagli B, Wang J, Oloketuyi S, Bajc G, Huang H, Fortuna S, de Marco A. Effect of Humanizing Mutations on the Stability of the Llama Single-Domain Variable Region. Biomolecules 2021; 11:biom11020163. [PMID: 33530572 PMCID: PMC7911018 DOI: 10.3390/biom11020163] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/21/2021] [Accepted: 01/24/2021] [Indexed: 11/24/2022] Open
Abstract
In vivo clinical applications of nanobodies (VHHs) require molecules that induce minimal immunoresponse and therefore possess sequences as similar as possible to the human VH domain. Although the relative sequence variability in llama nanobodies has been used to identify scaffolds with partially humanized signature, the transformation of the Camelidae hallmarks in the framework2 still represents a major problem. We assessed a set of mutants in silico and experimentally to elucidate what is the contribution of single residues to the VHH stability and how their combinations affect the mutant nanobody stability. We described at molecular level how the interaction among residues belonging to different structural elements enabled a model llama nanobody (C8WT, isolated from a naïve library) to be functional and maintain its stability, despite the analysis of its primary sequence would classify it as aggregation-prone. Five chimeras formed by grafting CDRs isolated from different nanobodies into C8WT scaffold were successfully expressed as soluble proteins and both tested clones preserved their antigen binding specificity. We identified a nanobody with human hallmarks that seems suitable for humanizing selected camelid VHHs by grafting heterologous CDRs in its scaffold and could serve for the preparation of a synthetic library of human-like single domains.
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Affiliation(s)
- Miguel A. Soler
- CONCEPT Lab, Italian Institute of Technology (IIT), 16152 Genova, Italy
- Correspondence: (M.A.S.); (A.d.M.); Tel.: +386-05-3315295 (A.d.M.); Fax: +386-05-90-99-722 (A.d.M.)
| | - Barbara Medagli
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, 34127 Trieste, Italy; (B.M.); (S.F.)
| | - Jiewen Wang
- Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering & Technology, Tianjin University, Tianjin 300072, China; (J.W.); (H.H.)
| | - Sandra Oloketuyi
- Lab of Environmental and Life Sciences, University of Nova Gorica, 5000 Rožna Dolina-Nova Gorica, Slovenia;
| | - Gregor Bajc
- Department of Biology, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - He Huang
- Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering & Technology, Tianjin University, Tianjin 300072, China; (J.W.); (H.H.)
| | - Sara Fortuna
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, 34127 Trieste, Italy; (B.M.); (S.F.)
| | - Ario de Marco
- Lab of Environmental and Life Sciences, University of Nova Gorica, 5000 Rožna Dolina-Nova Gorica, Slovenia;
- Correspondence: (M.A.S.); (A.d.M.); Tel.: +386-05-3315295 (A.d.M.); Fax: +386-05-90-99-722 (A.d.M.)
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45
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Makowski EK, Wu L, Gupta P, Tessier PM. Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods. MAbs 2021; 13:1895540. [PMID: 34313532 PMCID: PMC8346245 DOI: 10.1080/19420862.2021.1895540] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/05/2021] [Accepted: 02/22/2021] [Indexed: 11/30/2022] Open
Abstract
There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.
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Affiliation(s)
- Emily K. Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Lina Wu
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering
| | - Priyanka Gupta
- Department of Biochemistry and Biophysics, Rensselaer Polytechnic Institute, Troy, NY, USA
- Biotherapeutics Discovery Department, Boehringer Ingelheim, Ridgefield, CT, USA
| | - Peter M. Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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46
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Lai PK, Fernando A, Cloutier TK, Kingsbury JS, Gokarn Y, Halloran KT, Calero-Rubio C, Trout BL. Machine Learning Feature Selection for Predicting High Concentration Therapeutic Antibody Aggregation. J Pharm Sci 2020; 110:1583-1591. [PMID: 33346034 DOI: 10.1016/j.xphs.2020.12.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/25/2020] [Accepted: 12/11/2020] [Indexed: 02/03/2023]
Abstract
Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation behaviors of 21 approved monospecific monoclonal antibodies at high concentration (150 mg/mL), yielding a correlation coefficient of 0.71 on validation tests with only two features using a linear model. The nearest neighbors and support vector regression models further improved the performance, which have correlation coefficients of 0.86 and 0.80, respectively. This framework can be extended to train other models that predict different physical properties.
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Affiliation(s)
- Pin-Kuang Lai
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Amendra Fernando
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Theresa K Cloutier
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Yatin Gokarn
- Biologics Development, Sanofi, Framingham, MA, USA
| | | | | | - Bernhardt L Trout
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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47
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Carrara SC, Ulitzka M, Grzeschik J, Kornmann H, Hock B, Kolmar H. From cell line development to the formulated drug product: The art of manufacturing therapeutic monoclonal antibodies. Int J Pharm 2020; 594:120164. [PMID: 33309833 DOI: 10.1016/j.ijpharm.2020.120164] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/23/2020] [Accepted: 12/06/2020] [Indexed: 02/07/2023]
Abstract
Therapeutic monoclonal antibodies and related products have steadily grown to become the dominant product class within the biopharmaceutical market. Production of antibodies requires special precautions to ensure safety and efficacy of the product. In particular, minimizing antibody product heterogeneity is crucial as drug substance variants may impair the activity, efficacy, safety, and pharmacokinetic properties of an antibody, consequently resulting in the failure of a product in pre-clinical and clinical development. This review will cover the manufacturing and formulation challenges and advances of therapeutic monoclonal antibodies, focusing on improved processes to minimize variants and ensure batch-to-batch consistency. Processes put in place by regulatory agencies, such as Quality-by-Design (QbD) and current Good Manufacturing Practices (cGMP), and how their implementation has aided drug development in pharmaceutical companies will be reviewed. Advances in formulation and considerations on the intended use of a therapeutic antibody, including the route of administration and patient compliance, will be discussed.
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Affiliation(s)
- Stefania C Carrara
- Institute for Organic Chemistry and Biochemistry, Technische Universität Darmstadt, Alarich-Weiss-Strasse 4, D-64287 Darmstadt, Germany; Ferring Darmstadt Laboratory, Alarich-Weiss-Strasse 4, D-64287 Darmstadt, Germany
| | - Michael Ulitzka
- Institute for Organic Chemistry and Biochemistry, Technische Universität Darmstadt, Alarich-Weiss-Strasse 4, D-64287 Darmstadt, Germany; Ferring Darmstadt Laboratory, Alarich-Weiss-Strasse 4, D-64287 Darmstadt, Germany
| | - Julius Grzeschik
- Ferring Darmstadt Laboratory, Alarich-Weiss-Strasse 4, D-64287 Darmstadt, Germany
| | - Henri Kornmann
- Ferring International Center SA, CH-1162 Saint-Prex, Switzerland
| | - Björn Hock
- Ferring International Center SA, CH-1162 Saint-Prex, Switzerland.
| | - Harald Kolmar
- Institute for Organic Chemistry and Biochemistry, Technische Universität Darmstadt, Alarich-Weiss-Strasse 4, D-64287 Darmstadt, Germany.
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48
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Zaman M, Andreasen M. Cross-talk between individual phenol-soluble modulins in Staphylococcus aureus biofilm enables rapid and efficient amyloid formation. eLife 2020; 9:59776. [PMID: 33259287 PMCID: PMC7732344 DOI: 10.7554/elife.59776] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 11/30/2020] [Indexed: 12/11/2022] Open
Abstract
The infective ability of the opportunistic pathogen Staphylococcus aureus, recognized as the most frequent cause of biofilm-associated infections, is associated with biofilm-mediated resistance to host immune response. Phenol-soluble modulins (PSM) comprise the structural scaffold of S. aureus biofilms through self-assembly into functional amyloids, but the role of individual PSMs during biofilm formation remains poorly understood and the molecular pathways of PSM self-assembly are yet to be identified. Here we demonstrate high degree of cooperation between individual PSMs during functional amyloid formation. PSMα3 initiates the aggregation, forming unstable aggregates capable of seeding other PSMs resulting in stable amyloid structures. Using chemical kinetics we dissect the molecular mechanism of aggregation of individual PSMs showing that PSMα1, PSMα3 and PSMβ1 display secondary nucleation whereas PSMβ2 aggregates through primary nucleation and elongation. Our findings suggest that various PSMs have evolved to ensure fast and efficient biofilm formation through cooperation between individual peptides.
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Affiliation(s)
- Masihuz Zaman
- Aarhus University, Department of Biomedicine, Aarhus, Denmark
| | - Maria Andreasen
- Aarhus University, Department of Biomedicine, Aarhus, Denmark
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49
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Ikenoue T, Aprile FA, Sormanni P, Ruggeri FS, Perni M, Heller GT, Haas CP, Middel C, Limbocker R, Mannini B, Michaels TCT, Knowles TPJ, Dobson CM, Vendruscolo M. A rationally designed bicyclic peptide remodels Aβ42 aggregation in vitro and reduces its toxicity in a worm model of Alzheimer's disease. Sci Rep 2020; 10:15280. [PMID: 32943652 PMCID: PMC7498612 DOI: 10.1038/s41598-020-69626-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 06/26/2020] [Indexed: 01/01/2023] Open
Abstract
Bicyclic peptides have great therapeutic potential since they can bridge the gap between small molecules and antibodies by combining a low molecular weight of about 2 kDa with an antibody-like binding specificity. Here we apply a recently developed in silico rational design strategy to produce a bicyclic peptide to target the C-terminal region (residues 31–42) of the 42-residue form of the amyloid β peptide (Aβ42), a protein fragment whose aggregation into amyloid plaques is linked with Alzheimer’s disease. We show that this bicyclic peptide is able to remodel the aggregation process of Aβ42 in vitro and to reduce its associated toxicity in vivo in a C. elegans worm model expressing Aβ42. These results provide an initial example of a computational approach to design bicyclic peptides to target specific epitopes on disordered proteins.
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Affiliation(s)
- Tatsuya Ikenoue
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.,Department of Chemistry, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Francesco A Aprile
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.,Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London, W12 0BZ, UK
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Francesco S Ruggeri
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Michele Perni
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Gabriella T Heller
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Christian P Haas
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Christoph Middel
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Ryan Limbocker
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.,Department of Chemistry and Life Science, United States Military Academy, West Point, NY, 10996, USA
| | - Benedetta Mannini
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Thomas C T Michaels
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Tuomas P J Knowles
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Christopher M Dobson
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
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50
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Raimundo AF, Ferreira S, Farrim MI, Santos CN, Menezes R. Heterologous Expression of Immature Forms of Human Islet Amyloid Polypeptide in Yeast Triggers Intracellular Aggregation and Cytotoxicity. Front Microbiol 2020; 11:2035. [PMID: 33013747 PMCID: PMC7496629 DOI: 10.3389/fmicb.2020.02035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 07/31/2020] [Indexed: 01/23/2023] Open
Abstract
Diabetes is a major public health issue that has attained alarming levels worldwide. Pancreatic aggregates of human islet amyloid polypeptide (IAPP) represent a major histopathological hallmark of type 2 diabetes. IAPP is expressed in β-cells as pre-pro-IAPP (ppIAPP) that is first processed to pro-IAPP (pIAPP) and finally to its mature form (matIAPP), being released upon glucose stimulation together with insulin. Impairment and overload of the IAPP processing machinery seem to be associated with the accumulation of immature IAPP species and the formation of toxic intracellular oligomers, which have been associated with β-cell dyshomeostasis and apoptosis. Nevertheless, the pathological importance of these immature IAPP forms for the assembly and cytotoxicity of these oligomers is not completely understood. Here, we describe the generation and characterization of unprecedented Saccharomyces cerevisiae models recapitulating IAPP intracellular oligomerization. Expression of green fluorescent protein (GFP) fusions of human ppIAPP, pIAPP, and matIAPP proved to be toxic in yeast cells at different extents, with ppIAPP exerting the most deleterious effect on yeast growth and cell viability. Although expression of all IAPP constructs induced the formation of intracellular aggregates in yeast cells, our data point out the accumulation of insoluble oligomeric species enriched in immature ppIAPP as the trigger of the high toxicity mediated by this construct in cells expressing ppIAPP-GFP. In addition, MS/MS analysis indicated that oligomeric species found in the ppIAPP-GFP lysates contain the N-terminal sequence of the propeptide fused to GFP. These models represent powerful tools for future research focused on the relevance of immature forms in IAPP-induced toxicity. Furthermore, they are extremely useful in high-throughput screenings for genetic and chemical modulators of IAPP aggregation.
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Affiliation(s)
- Ana F Raimundo
- iBET - Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal.,CEDOC - Chronic Diseases Research Center, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal.,ITQB-NOVA - Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Sofia Ferreira
- iBET - Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal.,CEDOC - Chronic Diseases Research Center, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Maria I Farrim
- CEDOC - Chronic Diseases Research Center, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Cláudia N Santos
- iBET - Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal.,CEDOC - Chronic Diseases Research Center, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal.,ITQB-NOVA - Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Regina Menezes
- iBET - Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal.,CEDOC - Chronic Diseases Research Center, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal.,ITQB-NOVA - Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
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