1
|
Barletta GP, Tandiana R, Soler M, Fortuna S, Rocchia W. Locuaz: an in silico platform for protein binders optimization. Bioinformatics 2024; 40:btae492. [PMID: 39107888 PMCID: PMC11324344 DOI: 10.1093/bioinformatics/btae492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 07/28/2024] [Accepted: 08/03/2024] [Indexed: 08/16/2024] Open
Abstract
MOTIVATION Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. RESULTS In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on High Performance Computing (HPC) systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/. The data underlying this article are available at https://github.com/pgbarletta/suppl_info_locuaz.
Collapse
Affiliation(s)
- German P Barletta
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
- The Abdus Salam International Centre for Theoretical Physics (ICTP), Str. Costiera, 11, Trieste, Friuli-Venezia Giulia, 34151, Italy
| | - Rika Tandiana
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
| | - Miguel Soler
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
- Dipartimento di Scienze Matematiche, Informatiche e Fisiche (DMIF), University of Udine, Via delle Scienze, 206, Udine, Friuli-Venezia Giulia, 33100, Italy
| | - Sara Fortuna
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
- Cresset, New Cambridge House, Litlington, Royston, SG8-0SS, United Kingdom
| | - Walter Rocchia
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
| |
Collapse
|
2
|
Natsrita P, Charoenkwan P, Shoombuatong W, Mahalapbutr P, Faksri K, Chareonsudjai S, Rungrotmongkol T, Pipattanaboon C. Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes. Sci Rep 2024; 14:17165. [PMID: 39060292 PMCID: PMC11282219 DOI: 10.1038/s41598-024-67487-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new computational framework to predict potent and stable NAbs against DENV-1 to DENV-4 using only antibody (CDR-H3) and epitope sequences as input. Specifically, our proposed computational framework employed sequence-based ML and molecular dynamic simulation (MD) methods to achieve more accurate identification. First, we built a novel dataset (n = 1108) by compiling the interactions of CDR-H3 and epitope sequences with the half maximum inhibitory concentration (IC50) values, which represent neutralizing activities. Second, we achieved an accurately predictive ML model that showed high AUC values of 0.879 and 0.885 by tenfold cross-validation and independent tests, respectively. Finally, our computational framework could be applied to filter approximately 2.5 million unseen antibodies into two final candidates that showed strong and stable binding to all four serotypes. In addition, the most potent and stable candidate (1B3B9_V21) was evaluated for its development potential as a therapeutic agent by molecular docking and MD simulations. This study provides an antibody computational approach to facilitate the high-throughput identification of NAbs and accelerate the development of therapeutic antibodies.
Collapse
Affiliation(s)
- Piyatida Natsrita
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Panupong Mahalapbutr
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Kiatichai Faksri
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
- Research and Diagnostic Center for Emerging Infectious Diseases, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Sorujsiri Chareonsudjai
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand
| | - Thanyada Rungrotmongkol
- Center of Excellent in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Chonlatip Pipattanaboon
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand.
| |
Collapse
|
3
|
Gialama D, Vadukul DM, Thrush RJ, Radford SE, Aprile FA. A Potent Sybody Selectively Inhibits α-Synuclein Amyloid Formation by Binding to the P1 Region. J Med Chem 2024; 67:9857-9868. [PMID: 38842931 PMCID: PMC11215725 DOI: 10.1021/acs.jmedchem.3c02408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/25/2024] [Accepted: 05/17/2024] [Indexed: 06/07/2024]
Abstract
Increasing research efforts focus on exploiting antibodies to inhibit the amyloid formation of neurodegenerative proteins. Nevertheless, it is challenging to discover antibodies that inhibit this process in a specific manner. Using ribosome display, we screened for synthetic single-domain antibodies, i.e., sybodies, of the P1 region of α-synuclein (residues 36-42), a protein that forms amyloid in Parkinson's disease and multiple-system atrophy. Hits were assessed for direct binding to a P1 peptide and the inhibition of amyloid formation. We discovered a sybody, named αSP1, that inhibits amyloid formation of α-synuclein at substoichiometric concentrations in a specific manner, even within highly crowded heterogeneous mixtures. Fluorescence resonance energy transfer-based binding assays and seeding experiments with and without αSP1 further demonstrate the importance of the P1 region for both primary and secondary nucleation mechanisms of amyloid assembly.
Collapse
Affiliation(s)
- Dimitra Gialama
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
| | - Devkee M. Vadukul
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
| | - Rebecca J. Thrush
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
- Institute
of Chemical Biology, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
| | - Sheena E. Radford
- Astbury
Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, University of Leeds, Leeds LS2 9JT, U.K.
| | - Francesco A. Aprile
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
- Institute
of Chemical Biology, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
| |
Collapse
|
4
|
Adediwura VA, Koirala K, Do HN, Wang J, Miao Y. Understanding the impact of binding free energy and kinetics calculations in modern drug discovery. Expert Opin Drug Discov 2024; 19:671-682. [PMID: 38722032 PMCID: PMC11108734 DOI: 10.1080/17460441.2024.2349149] [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: 07/27/2023] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs. AREAS COVERED End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations. EXPERT OPINION The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.
Collapse
Affiliation(s)
- Victor A. Adediwura
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kushal Koirala
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hung N. Do
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
- Present address: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Jinan Wang
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yinglong Miao
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| |
Collapse
|
5
|
Tandiana R, Barletta GP, Soler MA, Fortuna S, Rocchia W. Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔ G Predictions. J Chem Theory Comput 2024; 20:2630-2642. [PMID: 38445482 DOI: 10.1021/acs.jctc.3c01225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.
Collapse
Affiliation(s)
- Rika Tandiana
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
| | - German P Barletta
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
- The Abdus Salam International Centre for Theoretical Physics─ICTP, Strada Costiera 11, 34151 Trieste, Italy
| | - Miguel Angel Soler
- Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Universita' di Udine, Via delle Scienze 206, 33100 Udine, Italy
| | - Sara Fortuna
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
| | - Walter Rocchia
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
| |
Collapse
|
6
|
Yaghoobizadeh F, Roayaei Ardakani M, Ranjbar MM, Khosravi M, Galehdari H. Development of a potent recombinant scFv antibody against the SARS-CoV-2 by in-depth bioinformatics study: Paving the way for vaccine/diagnostics development. Comput Biol Med 2024; 170:108091. [PMID: 38295473 DOI: 10.1016/j.compbiomed.2024.108091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches? METHOD Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (VH/VL). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the VH-VL. Finally, this scFv was analyzed/optimized using various web servers. RESULTS Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs). CONCLUSIONS Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen.
Collapse
Affiliation(s)
- Fatemeh Yaghoobizadeh
- Department of Biology, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Khouzestan, 6135783151, Iran.
| | - Mohammad Roayaei Ardakani
- Department of Biology, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Khouzestan, 6135783151, Iran.
| | | | - Mohammad Khosravi
- Department of Pathobiology, Faculty of Veterinary Medicine, Shahid Chamran University of Ahvaz, Ahvaz, Khouzestan, 6135783151, Iran.
| | - Hamid Galehdari
- Department of Biology, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Khouzestan, 6135783151, Iran.
| |
Collapse
|
7
|
Gallo E. Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances. Mol Biotechnol 2024:10.1007/s12033-024-01064-2. [PMID: 38308755 DOI: 10.1007/s12033-024-01064-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/02/2024] [Indexed: 02/05/2024]
Abstract
Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.
Collapse
Affiliation(s)
- Eugenio Gallo
- Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
| |
Collapse
|
8
|
Bigi A, Napolitano L, Vadukul DM, Chiti F, Cecchi C, Aprile FA, Cascella R. A single-domain antibody detects and neutralises toxic Aβ 42 oligomers in the Alzheimer's disease CSF. Alzheimers Res Ther 2024; 16:13. [PMID: 38238842 PMCID: PMC10795411 DOI: 10.1186/s13195-023-01361-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 11/29/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Amyloid-β42 (Aβ42) aggregation consists of a complex chain of nucleation events producing soluble oligomeric intermediates, which are considered the major neurotoxic agents in Alzheimer's disease (AD). Cerebral lesions in the brain of AD patients start to develop 20 years before symptom onset; however, no preventive strategies, effective treatments, or specific and sensitive diagnostic tests to identify people with early-stage AD are currently available. In addition, the isolation and characterisation of neurotoxic Aβ42 oligomers are particularly difficult because of their transient and heterogeneous nature. To overcome this challenge, a rationally designed method generated a single-domain antibody (sdAb), named DesAb-O, targeting Aβ42 oligomers. METHODS We investigated the ability of DesAb-O to selectively detect preformed Aβ42 oligomers both in vitro and in cultured neuronal cells, by using dot-blot, ELISA immunoassay and super-resolution STED microscopy, and to counteract the toxicity induced by the oligomers, monitoring their interaction with neuronal membrane and the resulting mitochondrial impairment. We then applied this approach to CSF samples (CSFs) from AD patients as compared to age-matched control subjects. RESULTS DesAb-O was found to selectively detect synthetic Aβ42 oligomers both in vitro and in cultured cells, and to neutralise their associated neuronal dysfunction. DesAb-O can also identify Aβ42 oligomers present in the CSFs of AD patients with respect to healthy individuals, and completely prevent cell dysfunction induced by the administration of CSFs to neuronal cells. CONCLUSIONS Taken together, our data indicate a promising method for the improvement of an early diagnosis of AD and for the generation of novel therapeutic approaches based on sdAbs for the treatment of AD and other devastating neurodegenerative conditions.
Collapse
Affiliation(s)
- Alessandra Bigi
- Department of Experimental and Clinical Biomedical Sciences, Section of Biochemistry, University of Florence, Florence, Italy
| | - Liliana Napolitano
- Department of Experimental and Clinical Biomedical Sciences, Section of Biochemistry, University of Florence, Florence, Italy
| | - Devkee M Vadukul
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London, UK
| | - Fabrizio Chiti
- Department of Experimental and Clinical Biomedical Sciences, Section of Biochemistry, University of Florence, Florence, Italy
| | - Cristina Cecchi
- Department of Experimental and Clinical Biomedical Sciences, Section of Biochemistry, University of Florence, Florence, Italy
| | - Francesco A Aprile
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London, UK
- Institute of Chemical Biology, Molecular Sciences Research Hub, Imperial College London, London, UK
| | - Roberta Cascella
- Department of Experimental and Clinical Biomedical Sciences, Section of Biochemistry, University of Florence, Florence, Italy.
| |
Collapse
|
9
|
Baronaite U, Cachat E. Preparation of Chromobodies for the Detection of Cell Surface Epitopes. Methods Mol Biol 2024; 2774:303-314. [PMID: 38441773 DOI: 10.1007/978-1-0716-3718-0_20] [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: 03/07/2024]
Abstract
Chromobodies are nanobodies genetically fused to fluorescent proteins, which were developed to visualize endogenous intracellular antigens. These versatile bioimaging nanotools can also be used to detect cell surface epitopes, and we describe here how we use them as an alternative to conjugated antibodies. This way, we routinely test the binding efficiency of nanobodies for their cognate cell surface antigens, before integrating them as sensing domains into complex synthetic receptor architectures.
Collapse
Affiliation(s)
- Ugne Baronaite
- Centre for Engineering Biology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom
- Institute for Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom
| | - Elise Cachat
- Centre for Engineering Biology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
- Institute for Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
| |
Collapse
|
10
|
Fernández-Quintero ML, Pomarici ND, Fischer ALM, Hoerschinger VJ, Kroell KB, Riccabona JR, Kamenik AS, Loeffler JR, Ferguson JA, Perrett HR, Liedl KR, Han J, Ward AB. Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines. Antibodies (Basel) 2023; 12:67. [PMID: 37873864 PMCID: PMC10594513 DOI: 10.3390/antib12040067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/25/2023] Open
Abstract
Antibodies and other new antibody-like formats have emerged as one of the most rapidly growing classes of biotherapeutic proteins. Understanding the structural features that drive antibody function and, consequently, their molecular recognition is critical for engineering antibodies. Here, we present the structural architecture of conventional IgG antibodies alongside other formats. We emphasize the importance of considering antibodies as conformational ensembles in solution instead of focusing on single-static structures because their functions and properties are strongly governed by their dynamic nature. Thus, in this review, we provide an overview of the unique structural and dynamic characteristics of antibodies with respect to their antigen recognition, biophysical properties, and effector functions. We highlight the numerous technical advances in antibody structure prediction and design, enabled by the vast number of experimentally determined high-quality structures recorded with cryo-EM, NMR, and X-ray crystallography. Lastly, we assess antibody and vaccine design strategies in the context of structure and dynamics.
Collapse
Affiliation(s)
- Monica L. Fernández-Quintero
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Nancy D. Pomarici
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna-Lena M. Fischer
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Valentin J. Hoerschinger
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Katharina B. Kroell
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Jakob R. Riccabona
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna S. Kamenik
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Johannes R. Loeffler
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - James A. Ferguson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hailee R. Perrett
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Klaus R. Liedl
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Julianna Han
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Andrew B. Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| |
Collapse
|
11
|
Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
Collapse
Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| |
Collapse
|
12
|
Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
Collapse
Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| |
Collapse
|
13
|
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.
Collapse
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.
| |
Collapse
|
14
|
Zhang Y, Li Q, Luo L, Duan C, Shen J, Wang Z. Application of germline antibody features to vaccine development, antibody discovery, antibody optimization and disease diagnosis. Biotechnol Adv 2023; 65:108143. [PMID: 37023966 DOI: 10.1016/j.biotechadv.2023.108143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/26/2023] [Accepted: 03/29/2023] [Indexed: 04/08/2023]
Abstract
Although the efficacy and commercial success of vaccines and therapeutic antibodies have been tremendous, designing and discovering new drug candidates remains a labor-, time- and cost-intensive endeavor with high risks. The main challenges of vaccine development are inducing a strong immune response in broad populations and providing effective prevention against a group of highly variable pathogens. Meanwhile, antibody discovery faces several great obstacles, especially the blindness in antibody screening and the unpredictability of the developability and druggability of antibody drugs. These challenges are largely due to poorly understanding of germline antibodies and the antibody responses to pathogen invasions. Thanks to the recent developments in high-throughput sequencing and structural biology, we have gained insight into the germline immunoglobulin (Ig) genes and germline antibodies and then the germline antibody features associated with antigens and disease manifestation. In this review, we firstly outline the broad associations between germline antibodies and antigens. Moreover, we comprehensively review the recent applications of antigen-specific germline antibody features, physicochemical properties-associated germline antibody features, and disease manifestation-associated germline antibody features on vaccine development, antibody discovery, antibody optimization, and disease diagnosis. Lastly, we discuss the bottlenecks and perspectives of current and potential applications of germline antibody features in the biotechnology field.
Collapse
Affiliation(s)
- Yingjie Zhang
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Qing Li
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Liang Luo
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Changfei Duan
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Jianzhong Shen
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Zhanhui Wang
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China.
| |
Collapse
|
15
|
Soler MA, Minovski N, Rocchia W, Fortuna S. Replica-exchange optimization of antibody fragments. Comput Biol Chem 2023; 103:107819. [PMID: 36657284 DOI: 10.1016/j.compbiolchem.2023.107819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/16/2022] [Accepted: 01/13/2023] [Indexed: 01/15/2023]
Abstract
In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.
Collapse
Affiliation(s)
- Miguel A Soler
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy; Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, Udine, Italy
| | - Nikola Minovski
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia; Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, Trieste, Italy
| | - Walter Rocchia
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy
| | - Sara Fortuna
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy; Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, Trieste, Italy.
| |
Collapse
|
16
|
Parkinson J, Hard R, Wang W. The RESP AI model accelerates the identification of tight-binding antibodies. Nat Commun 2023; 14:454. [PMID: 36709319 PMCID: PMC9884274 DOI: 10.1038/s41467-023-36028-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 01/13/2023] [Indexed: 01/30/2023] Open
Abstract
High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the KD of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development.
Collapse
Affiliation(s)
- Jonathan Parkinson
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0359, USA
| | - Ryan Hard
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0359, USA
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0359, USA. .,Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, 92093-0359, USA.
| |
Collapse
|
17
|
Svilenov HL, Arosio P, Menzen T, Tessier P, Sormanni P. Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties. MAbs 2023; 15:2164459. [PMID: 36629855 PMCID: PMC9839375 DOI: 10.1080/19420862.2022.2164459] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/22/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.
Collapse
Affiliation(s)
- Hristo L. Svilenov
- Laboratory of General Biochemistry and Physical Pharmacy, Faculty of Pharmaceutical Sciences, Ghent University, Gent, Belgium
| | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Tim Menzen
- Coriolis Pharma Research GmbH, Martinsried, 82152, Germany
| | - Peter 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, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| |
Collapse
|
18
|
Comeau SR, Thorsteinson N, Kumar S. Structural Considerations in Affinity Maturation of Antibody-Based Biotherapeutic Candidates. Methods Mol Biol 2023; 2552:309-321. [PMID: 36346600 DOI: 10.1007/978-1-0716-2609-2_17] [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: 06/16/2023]
Abstract
Affinity maturation is an important stage in biologic drug discovery as is the natural process of generating an immune response inside the human body. In this chapter, we describe in silico approaches to affinity maturation via a worked example. Both advantages and limitations of the computational methods used are critically examined. Furthermore, construction of affinity maturation libraries and how their outputs might be implemented in an experimental setting are also described. It should be noted that structure-based design of biologic drugs is an emerging field and the tools currently available require further development. Furthermore, there are no standardized structure-based strategies yet for antibody affinity maturation as this research relies heavily on scientific logic as well as creative intuition.
Collapse
Affiliation(s)
- Stephen R Comeau
- Computational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Nels Thorsteinson
- Scientific Services Manager, Biologics, Chemical Computing Group ULC, Montreal, QC, Canada
| | - Sandeep Kumar
- Computational Biochemistry and Bioinformatics Group, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA.
| |
Collapse
|
19
|
Abstract
In the computational design of antibodies, the interaction analysis between target antigen and antibody is an essential process to obtain feedback for validation and optimization of the design. Kinetic and thermodynamic parameters as well as binding affinity (KD) allow for a more detailed evaluation and understanding of the molecular recognition. In this chapter, we summarize the conventional experimental methods which can calculate KD value (ELISA, FP), analyze a binding activity to actual cells (FCM), and evaluate the kinetic and thermodynamic parameters (ITC, SPR, BLI), including high-throughput analysis and a recently developed experimental technique.
Collapse
Affiliation(s)
- Aki Tanabe
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- AIDS Research Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, 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.
| |
Collapse
|
20
|
Bai Z, Wang J, Li J, Yuan H, Wang P, Zhang M, Feng Y, Cao X, Cao X, Kang G, de Marco A, Huang H. Design of nanobody-based bispecific constructs by in silico affinity maturation and umbrella sampling simulations. Comput Struct Biotechnol J 2022; 21:601-613. [PMID: 36659922 PMCID: PMC9822835 DOI: 10.1016/j.csbj.2022.12.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Random mutagenesis is the natural opportunity for proteins to evolve and biotechnologically it has been exploited to create diversity and identify variants with improved characteristics in the mutant pools. Rational mutagenesis based on biophysical assumptions and supported by computational power has been proposed as a faster and more predictable strategy to reach the same aim. In this work we confirm that substantial improvements in terms of both affinity and stability of nanobodies can be obtained by using combinations of algorithms, even for binders with already high affinity and elevated thermal stability. Furthermore, in silico approaches allowed the development of an optimized bispecific construct able to bind simultaneously the two clinically relevant antigens TNF-α and IL-23 and, by means of its enhanced avidity, to inhibit effectively the apoptosis of TNF-α-sensitive L929 cells. The results revealed that salt bridges, hydrogen bonds, aromatic-aromatic and cation-pi interactions had a critical role in increasing affinity. We provided a platform for the construction of high-affinity bispecific constructs based on nanobodies that can have relevant applications for the control of all those biological mechanisms in which more than a single antigen must be targeted to increase the treatment effectiveness and avoid resistance mechanisms.
Collapse
Affiliation(s)
- Zixuan Bai
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China,Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Jiewen Wang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China,Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China,Institute of Shaoxing, Tianjin University, Zhejiang 312300, China
| | - Jiaqi Li
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China,Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China,Institute of Shaoxing, Tianjin University, Zhejiang 312300, China
| | - Haibin Yuan
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China,Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Ping Wang
- Tianjin Modern Innovative TCM Technology Co. Ltd., Tianjin, China
| | - Miao Zhang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China,Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China,China Resources Biopharmaceutical Company Limited, Beijing, China
| | - Yuanhang Feng
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China,Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Xiangtong Cao
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Xiangan Cao
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Guangbo Kang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China,Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China,Institute of Shaoxing, Tianjin University, Zhejiang 312300, China,Corresponding authors at: Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China.
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Nova Gorica, Slovenia,Corresponding author.
| | - He Huang
- Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China,Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China,Institute of Shaoxing, Tianjin University, Zhejiang 312300, China,Corresponding authors at: Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China.
| |
Collapse
|
21
|
Robert PA, Akbar R, Frank R, Pavlović M, Widrich M, Snapkov I, Slabodkin A, Chernigovskaya M, Scheffer L, Smorodina E, Rawat P, Mehta BB, Vu MH, Mathisen IF, Prósz A, Abram K, Olar A, Miho E, Haug DTT, Lund-Johansen F, Hochreiter S, Haff IH, Klambauer G, Sandve GK, Greiff V. Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction. NATURE COMPUTATIONAL SCIENCE 2022; 2:845-865. [PMID: 38177393 DOI: 10.1038/s43588-022-00372-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/09/2022] [Indexed: 01/06/2024]
Abstract
Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.
Collapse
Affiliation(s)
- Philippe A Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Michael Widrich
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Igor Snapkov
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrei Slabodkin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Puneet Rawat
- 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, Oslo, Norway
| | | | - Aurél Prósz
- Danish Cancer Society Research Center, Translational Cancer Genomics, Copenhagen, Denmark
| | - Krzysztof Abram
- The Novo Nordisk Foundation Center for Biosustainability, Autoflow, DTU Biosustain and IT University of Copenhagen, Copenhagen, Denmark
| | - Alex Olar
- Department of Complex Systems in Physics, Eötvös Loránd University, Budapest, Hungary
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
- aiNET GmbH, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | | | - Sepp Hochreiter
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
- Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
| | | | - Günter Klambauer
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | | | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
| |
Collapse
|
22
|
Kulenkampff K, Emin D, Staats R, Zhang YP, Sakhnini L, Kouli A, Rimon O, Lobanova E, Williams-Gray CH, Aprile FA, Sormanni P, Klenerman D, Vendruscolo M. An antibody scanning method for the detection of α-synuclein oligomers in the serum of Parkinson's disease patients. Chem Sci 2022; 13:13815-13828. [PMID: 36544716 PMCID: PMC9710209 DOI: 10.1039/d2sc00066k] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 09/16/2022] [Indexed: 01/16/2023] Open
Abstract
Misfolded α-synuclein oligomers are closely implicated in the pathology of Parkinson's disease and related synucleinopathies. The elusive nature of these aberrant assemblies makes it challenging to develop quantitative methods to detect them and modify their behavior. Existing detection methods use antibodies to bind α-synuclein aggregates in biofluids, although it remains challenging to raise antibodies against α-synuclein oligomers. To address this problem, we used an antibody scanning approach in which we designed a panel of 9 single-domain epitope-specific antibodies against α-synuclein. We screened these antibodies for their ability to inhibit the aggregation process of α-synuclein, finding that they affected the generation of α-synuclein oligomers to different extents. We then used these antibodies to investigate the size distribution and morphology of soluble α-synuclein aggregates in serum and cerebrospinal fluid samples from Parkinson's disease patients. Our results indicate that the approach that we present offers a promising route for the development of antibodies to characterize soluble α-synuclein aggregates in biofluids.
Collapse
Affiliation(s)
- Klara Kulenkampff
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCambridge CB2 1EWUK
| | - Derya Emin
- Yusuf Hamied Department of Chemistry, University of CambridgeCambridgeCB2 1EWUK,UK Dementia Research Institute, University of CambridgeCambridgeCB2 0XYUK
| | - Roxine Staats
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCambridge CB2 1EWUK
| | - Yu P. Zhang
- Yusuf Hamied Department of Chemistry, University of CambridgeCambridgeCB2 1EWUK
| | - Laila Sakhnini
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCambridge CB2 1EWUK
| | - Antonina Kouli
- John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of CambridgeUK
| | - Oded Rimon
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCambridge CB2 1EWUK
| | - Evgeniia Lobanova
- Yusuf Hamied Department of Chemistry, University of CambridgeCambridgeCB2 1EWUK,UK Dementia Research Institute, University of CambridgeCambridgeCB2 0XYUK
| | - Caroline H. Williams-Gray
- John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of CambridgeUK
| | - Francesco A. Aprile
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCambridge CB2 1EWUK
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCambridge CB2 1EWUK
| | - David Klenerman
- Yusuf Hamied Department of Chemistry, University of CambridgeCambridgeCB2 1EWUK,UK Dementia Research Institute, University of CambridgeCambridgeCB2 0XYUK
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCambridge CB2 1EWUK
| |
Collapse
|
23
|
Aguilar Rangel M, Bedwell A, Costanzi E, Taylor RJ, Russo R, Bernardes GJL, Ricagno S, Frydman J, Vendruscolo M, Sormanni P. Fragment-based computational design of antibodies targeting structured epitopes. SCIENCE ADVANCES 2022; 8:eabp9540. [PMID: 36367941 PMCID: PMC9651861 DOI: 10.1126/sciadv.abp9540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.
Collapse
Affiliation(s)
- Mauricio Aguilar Rangel
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Alice Bedwell
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Elisa Costanzi
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy
| | - Ross J. Taylor
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Rosaria Russo
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milano 20122, Italy
| | - Gonçalo J. L. Bernardes
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Stefano Ricagno
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy
- Institute of Molecular and Translational Cardiology, IRCCS Policlinico San Donato, Milan 20097, Italy
| | - Judith Frydman
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Pietro Sormanni
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| |
Collapse
|
24
|
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.
Collapse
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
| |
Collapse
|
25
|
Zhu F, Bourguet FA, Bennett WFD, Lau EY, Arrildt KT, Segelke BW, Zemla AT, Desautels TA, Faissol DM. Large-scale application of free energy perturbation calculations for antibody design. Sci Rep 2022; 12:12489. [PMID: 35864134 PMCID: PMC9302960 DOI: 10.1038/s41598-022-14443-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/07/2022] [Indexed: 01/02/2023] Open
Abstract
Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.
Collapse
Affiliation(s)
- Fangqiang Zhu
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA.
| | - Feliza A Bourguet
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - William F D Bennett
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Edmond Y Lau
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Kathryn T Arrildt
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Brent W Segelke
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Adam T Zemla
- Global Security Computing Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Thomas A Desautels
- Computational Engineering Division, Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, USA
| | - Daniel M Faissol
- Computational Engineering Division, Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, USA.
| |
Collapse
|
26
|
Designing antibodies as therapeutics. Cell 2022; 185:2789-2805. [PMID: 35868279 DOI: 10.1016/j.cell.2022.05.029] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 12/25/2022]
Abstract
Antibody therapeutics are a large and rapidly expanding drug class providing major health benefits. We provide a snapshot of current antibody therapeutics including their formats, common targets, therapeutic areas, and routes of administration. Our focus is on selected emerging directions in antibody design where progress may provide a broad benefit. These topics include enhancing antibodies for cancer, antibody delivery to organs such as the brain, gastrointestinal tract, and lungs, plus antibody developability challenges including immunogenicity risk assessment and mitigation and subcutaneous delivery. Machine learning has the potential, albeit as yet largely unrealized, for a transformative future impact on antibody discovery and engineering.
Collapse
|
27
|
Wang A, Durrant JD. Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery. Molecules 2022; 27:molecules27144623. [PMID: 35889494 PMCID: PMC9319651 DOI: 10.3390/molecules27144623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/17/2022] [Accepted: 07/18/2022] [Indexed: 01/27/2023] Open
Abstract
We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users’ local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps—which run calculations “in the cloud” rather than on users’ local computers—browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software.
Collapse
|
28
|
Merkuleva YA, Shcherbakov DN, Ilyichev AA. Methods to Produce Monoclonal Antibodies for the Prevention and Treatment of Viral Infections. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2022; 48:256-272. [PMID: 35637780 PMCID: PMC9134727 DOI: 10.1134/s1068162022020169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/07/2021] [Accepted: 06/17/2021] [Indexed: 11/23/2022]
Abstract
A viral threat can arise suddenly and quickly turn into a major epidemic or pandemic. In such a case, it is necessary to develop effective means of therapy and prevention in a short time. Vaccine development takes decades, and the use of antiviral compounds is often ineffective and unsafe. A quick response may be the use of convalescent plasma, but a number of difficulties associated with it forced researchers to switch to the development of safer and more effective drugs based on monoclonal antibodies (mAbs). In order to provide protection, such drugs must have a key characteristic-neutralizing properties, i.e., the ability to block viral infection. Currently, there are several approaches to produce mAbs in the researchers' toolkit, however, none of them may serve as a gold standard. Each approach has its own advantages and disadvantages. The choice of the method depends both on the characteristics of the virus and on time constraints and technical challenges. This review provides a comparative analysis of modern methods to produce neutralizing mAbs and describes current trends in the design of antibodies for therapy and prevention of viral diseases.
Collapse
Affiliation(s)
- Yu. A. Merkuleva
- Vector State Research Center of Virology and Biotechnology, Rospotrebnadzor, World-Class Genomic Research Center for Biological Safety and Technological Independence, Federal Scientific and Technical Program for the Development of Genetic Technologies, 630559 Koltsovo, Novosibirsk oblast Russia
| | - D. N. Shcherbakov
- Vector State Research Center of Virology and Biotechnology, Rospotrebnadzor, World-Class Genomic Research Center for Biological Safety and Technological Independence, Federal Scientific and Technical Program for the Development of Genetic Technologies, 630559 Koltsovo, Novosibirsk oblast Russia
| | - A. A. Ilyichev
- Vector State Research Center of Virology and Biotechnology, Rospotrebnadzor, World-Class Genomic Research Center for Biological Safety and Technological Independence, Federal Scientific and Technical Program for the Development of Genetic Technologies, 630559 Koltsovo, Novosibirsk oblast Russia
| |
Collapse
|
29
|
Löhr T, Sormanni P, Vendruscolo M. Conformational Entropy as a Potential Liability of Computationally Designed Antibodies. Biomolecules 2022; 12:718. [PMID: 35625644 PMCID: PMC9138470 DOI: 10.3390/biom12050718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/06/2022] [Accepted: 05/08/2022] [Indexed: 01/28/2023] Open
Abstract
In silico antibody discovery is emerging as a viable alternative to traditional in vivo and in vitro approaches. Many challenges, however, remain open to enabling the properties of designed antibodies to match those produced by the immune system. A major question concerns the structural features of computer-designed complementarity determining regions (CDRs), including the role of conformational entropy in determining the stability and binding affinity of the designed antibodies. To address this problem, we used enhanced-sampling molecular dynamics simulations to compare the free energy landscapes of single-domain antibodies (sdAbs) designed using structure-based (DesAb-HSA-D3) and sequence-based approaches (DesAbO), with that of a nanobody derived from llama immunization (Nb10). Our results indicate that the CDR3 of DesAbO is more conformationally heterogeneous than those of both DesAb-HSA-D3 and Nb10, and the CDR3 of DesAb-HSA-D3 is slightly more dynamic than that of Nb10, which is the original scaffold used for the design of DesAb-HSA-D3. These differences underline the challenges in the rational design of antibodies by revealing the presence of conformational substates likely to have different binding properties and to generate a high entropic cost upon binding.
Collapse
Affiliation(s)
| | | | - Michele Vendruscolo
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK; (T.L.); (P.S.)
| |
Collapse
|
30
|
Krebs SK, Rakotoarinoro N, Stech M, Zemella A, Kubick S. A CHO-Based Cell-Free Dual Fluorescence Reporter System for the Straightforward Assessment of Amber Suppression and scFv Functionality. Front Bioeng Biotechnol 2022; 10:873906. [PMID: 35573244 PMCID: PMC9098822 DOI: 10.3389/fbioe.2022.873906] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 03/25/2022] [Indexed: 11/29/2022] Open
Abstract
Incorporation of noncanonical amino acids (ncAAs) with bioorthogonal reactive groups by amber suppression allows the generation of synthetic proteins with desired novel properties. Such modified molecules are in high demand for basic research and therapeutic applications such as cancer treatment and in vivo imaging. The positioning of the ncAA-responsive codon within the protein’s coding sequence is critical in order to maintain protein function, achieve high yields of ncAA-containing protein, and allow effective conjugation. Cell-free ncAA incorporation is of particular interest due to the open nature of cell-free systems and their concurrent ease of manipulation. In this study, we report a straightforward workflow to inquire ncAA positions in regard to incorporation efficiency and protein functionality in a Chinese hamster ovary (CHO) cell-free system. As a model, the well-established orthogonal translation components Escherichia coli tyrosyl-tRNA synthetase (TyrRS) and tRNATyrCUA were used to site-specifically incorporate the ncAA p-azido-l-phenylalanine (AzF) in response to UAG codons. A total of seven ncAA sites within an anti-epidermal growth factor receptor (EGFR) single-chain variable fragment (scFv) N-terminally fused to the red fluorescent protein mRFP1 and C-terminally fused to the green fluorescent protein sfGFP were investigated for ncAA incorporation efficiency and impact on antigen binding. The characterized cell-free dual fluorescence reporter system allows screening for ncAA incorporation sites with high incorporation efficiency that maintain protein activity. It is parallelizable, scalable, and easy to operate. We propose that the established CHO-based cell-free dual fluorescence reporter system can be of particular interest for the development of antibody-drug conjugates (ADCs).
Collapse
Affiliation(s)
- Simon K. Krebs
- Fraunhofer Institute for Cell Therapy and Immunology (IZI), Branch Bioanalytics and Bioprocesses (IZI-BB), Potsdam, Germany
- Institute for Biotechnology, Technical University of Berlin, Berlin, Germany
| | - Nathanaël Rakotoarinoro
- Fraunhofer Institute for Cell Therapy and Immunology (IZI), Branch Bioanalytics and Bioprocesses (IZI-BB), Potsdam, Germany
- Institute of Pharmacy, Freie Universität Berlin, Berlin, Germany
| | - Marlitt Stech
- Fraunhofer Institute for Cell Therapy and Immunology (IZI), Branch Bioanalytics and Bioprocesses (IZI-BB), Potsdam, Germany
| | - Anne Zemella
- Fraunhofer Institute for Cell Therapy and Immunology (IZI), Branch Bioanalytics and Bioprocesses (IZI-BB), Potsdam, Germany
| | - Stefan Kubick
- Fraunhofer Institute for Cell Therapy and Immunology (IZI), Branch Bioanalytics and Bioprocesses (IZI-BB), Potsdam, Germany
- Institute of Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany
- Faculty of Health Sciences, Joint Faculty of the Brandenburg University of Technology Cottbus - Senftenberg, the Brandenburg Medical School Theodor Fontane and the University of Potsdam, Potsdam, Germany
- *Correspondence: Stefan Kubick,
| |
Collapse
|
31
|
Hummer AM, Abanades B, Deane CM. Advances in computational structure-based antibody design. Curr Opin Struct Biol 2022; 74:102379. [PMID: 35490649 DOI: 10.1016/j.sbi.2022.102379] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/28/2022] [Accepted: 03/17/2022] [Indexed: 12/12/2022]
Abstract
Antibodies are currently the most important class of biotherapeutics and are used to treat numerous diseases. Recent advances in computational methods are ushering in a new era of antibody design, driven in part by accurate structure prediction. Previously, structure-based antibody design has been limited to a relatively small number of cases where accurate structures or models of both the target antigen and antibody were available. As we move towards a time where it is possible to accurately model most antibodies and antigens, and to reliably predict their binding site, there is vast potential for true computational antibody design. In this review, we describe the latest methods that promise to launch a paradigm shift towards entirely in silico structure-based antibody design.
Collapse
Affiliation(s)
- Alissa M Hummer
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. https://twitter.com/@AlissaHummer
| | - Brennan Abanades
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. https://twitter.com/@brennanaba
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Schneider MM, Emmenegger M, Xu CK, Condado Morales I, Meisl G, Turelli P, Zografou C, Zimmermann MR, Frey BM, Fiedler S, Denninger V, Jacquat RP, Madrigal L, Ilsley A, Kosmoliaptsis V, Fiegler H, Trono D, Knowles TP, Aguzzi A. Microfluidic characterisation reveals broad range of SARS-CoV-2 antibody affinity in human plasma. Life Sci Alliance 2022; 5:e202101270. [PMID: 34848436 PMCID: PMC8645332 DOI: 10.26508/lsa.202101270] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/13/2021] [Accepted: 11/15/2021] [Indexed: 12/31/2022] Open
Abstract
The clinical outcome of SARS-CoV-2 infections, which can range from asymptomatic to lethal, is crucially shaped by the concentration of antiviral antibodies and by their affinity to their targets. However, the affinity of polyclonal antibody responses in plasma is difficult to measure. Here we used microfluidic antibody affinity profiling (MAAP) to determine the aggregate affinities and concentrations of anti-SARS-CoV-2 antibodies in plasma samples of 42 seropositive individuals, 19 of which were healthy donors, 20 displayed mild symptoms, and 3 were critically ill. We found that dissociation constants, K d, of anti-receptor-binding domain antibodies spanned 2.5 orders of magnitude from sub-nanomolar to 43 nM. Using MAAP we found that antibodies of seropositive individuals induced the dissociation of pre-formed spike-ACE2 receptor complexes, which indicates that MAAP can be adapted as a complementary receptor competition assay. By comparison with cytopathic effect-based neutralisation assays, we show that MAAP can reliably predict the cellular neutralisation ability of sera, which may be an important consideration when selecting the most effective samples for therapeutic plasmapheresis and tracking the success of vaccinations.
Collapse
Affiliation(s)
- Matthias M Schneider
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK
| | - Marc Emmenegger
- Institute of Neuropathology, University of Zurich, Zurich, Switzerland
| | - Catherine K Xu
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK
| | | | - Georg Meisl
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK
| | - Priscilla Turelli
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Chryssa Zografou
- Institute of Neuropathology, University of Zurich, Zurich, Switzerland
| | - Manuela R Zimmermann
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK
| | - Beat M Frey
- Regional Blood Transfusion Service Zurich, Swiss Red Cross, Schlieren, Switzerland
| | | | - Viola Denninger
- Fluidic Analytics, Unit A, Paddocks Business Centre, Cambridge, UK
| | - Raphaël Pb Jacquat
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK
| | - Lidia Madrigal
- Institute of Neuropathology, University of Zurich, Zurich, Switzerland
| | - Alison Ilsley
- Fluidic Analytics, Unit A, Paddocks Business Centre, Cambridge, UK
| | - Vasilis Kosmoliaptsis
- Department of Surgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Organ Donation and Transplantation, University of Cambridge, Cambridge, UK
| | - Heike Fiegler
- Fluidic Analytics, Unit A, Paddocks Business Centre, Cambridge, UK
| | - Didier Trono
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tuomas Pj Knowles
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK
| | - Adriano Aguzzi
- Institute of Neuropathology, University of Zurich, Zurich, Switzerland
| |
Collapse
|
34
|
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.
Collapse
|
35
|
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.
Collapse
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
| |
Collapse
|
36
|
Das TK, Chou DK, Jiskoot W, Arosio P. Nucleation in protein aggregation in biotherapeutic development: a look into the heart of the event. J Pharm Sci 2022; 111:951-959. [DOI: 10.1016/j.xphs.2022.01.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/24/2022] [Accepted: 01/24/2022] [Indexed: 12/26/2022]
|
37
|
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.
Collapse
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.
| |
Collapse
|
38
|
Prates-Syed WA, Chaves LCS, Crema KP, Vuitika L, Lira A, Côrtes N, Kersten V, Guimarães FEG, Sadraeian M, Barroso da Silva FL, Cabral-Marques O, Barbuto JAM, Russo M, Câmara NOS, Cabral-Miranda G. VLP-Based COVID-19 Vaccines: An Adaptable Technology against the Threat of New Variants. Vaccines (Basel) 2021; 9:1409. [PMID: 34960155 PMCID: PMC8708688 DOI: 10.3390/vaccines9121409] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/18/2021] [Accepted: 11/20/2021] [Indexed: 12/23/2022] Open
Abstract
Virus-like particles (VLPs) are a versatile, safe, and highly immunogenic vaccine platform. Recently, there are developmental vaccines targeting SARS-CoV-2, the causative agent of COVID-19. The COVID-19 pandemic affected humanity worldwide, bringing out incomputable human and financial losses. The race for better, more efficacious vaccines is happening almost simultaneously as the virus increasingly produces variants of concern (VOCs). The VOCs Alpha, Beta, Gamma, and Delta share common mutations mainly in the spike receptor-binding domain (RBD), demonstrating convergent evolution, associated with increased transmissibility and immune evasion. Thus, the identification and understanding of these mutations is crucial for the production of new, optimized vaccines. The use of a very flexible vaccine platform in COVID-19 vaccine development is an important feature that cannot be ignored. Incorporating the spike protein and its variations into VLP vaccines is a desirable strategy as the morphology and size of VLPs allows for better presentation of several different antigens. Furthermore, VLPs elicit robust humoral and cellular immune responses, which are safe, and have been studied not only against SARS-CoV-2 but against other coronaviruses as well. Here, we describe the recent advances and improvements in vaccine development using VLP technology.
Collapse
Affiliation(s)
- Wasim A. Prates-Syed
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
- Institute of Research and Education in Child Health (PENSI), São Paulo 01228200, SP, Brazil
| | - Lorena C. S. Chaves
- Department of Microbiology and Immunology, School of Medicine, Emory University, Claudia Nance Rollins Building, Atlanta, GA 30329, USA;
| | - Karin P. Crema
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
- Institute of Research and Education in Child Health (PENSI), São Paulo 01228200, SP, Brazil
| | - Larissa Vuitika
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
| | - Aline Lira
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
- Institute of Research and Education in Child Health (PENSI), São Paulo 01228200, SP, Brazil
| | - Nelson Côrtes
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
- Institute of Research and Education in Child Health (PENSI), São Paulo 01228200, SP, Brazil
| | - Victor Kersten
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
| | | | - Mohammad Sadraeian
- São Carlos Institute of Physics, IFSC-USP, São Carlos 13566590, SP, Brazil; (F.E.G.G.); (M.S.)
- Institute for Biomedical Materials & Devices (IBMD), Faculty of Science, University of Technology, Sydney, NSW 2007, Australia
| | - Fernando L. Barroso da Silva
- Department of Biomolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto 14040903, SP, Brazil;
- Department of Chemical and Biomolecular Engeneering, North Carolina State University, Raleigh, NC 27695, USA
| | - Otávio Cabral-Marques
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo 05508000, SP, Brazil
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Children’s Medical Center, Tehran 1419733151, Iran
| | - José A. M. Barbuto
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
- Laboratory of Medical Investigation in Pathogenesis and Targeted Therapy in Onco-Immuno-Hematology (LIM-31), Department of Hematology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo 0124690, SP, Brazil
| | - Momtchilo Russo
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
| | - Niels O. S. Câmara
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
| | - Gustavo Cabral-Miranda
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo 05508000, SP, Brazil; (W.A.P.-S.); (K.P.C.); (L.V.); (A.L.); (N.C.); (V.K.); (O.C.-M.); (J.A.M.B.); (M.R.); (N.O.S.C.)
- Institute of Research and Education in Child Health (PENSI), São Paulo 01228200, SP, Brazil
| |
Collapse
|
39
|
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.
Collapse
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.
| |
Collapse
|
40
|
Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences. Sci Rep 2021; 11:21362. [PMID: 34725391 PMCID: PMC8560851 DOI: 10.1038/s41598-021-00669-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/05/2021] [Indexed: 01/01/2023] Open
Abstract
The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.
Collapse
|
41
|
Lin J, Figazzolo C, Metrick MA, Sormanni P, Vendruscolo M. Computational maturation of a single-domain antibody against Aβ42 aggregation. Chem Sci 2021; 12:13940-13948. [PMID: 35475123 PMCID: PMC8901120 DOI: 10.1039/d1sc03898b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 08/23/2021] [Indexed: 02/02/2023] Open
Abstract
The expansion of structural databases and the increase in computing power are enabling approaches for antibody discovery based on computational design. It has already been shown that it is possible to use this approach to generate antibodies for specific epitopes on challenging targets. Here we describe an application of this procedure for antibody maturation through the computational design of mutational variants of increased potency. We illustrate this procedure in the case of a single-domain antibody targeting an epitope in the N-terminal region of Aβ42, a peptide whose aggregation is closely associated with Alzheimer's disease. We show that this approach enables the generation of an antibody variant with over 200-fold increased potency against the primary nucleation process in Aβ42 aggregation. Our results thus demonstrate that potentiated antibody variants can be obtained by computational maturation. A computational maturation method enables the generation of an antibody variant with over 200-fold increased potency against the primary nucleation process in Aβ42 aggregation.![]()
Collapse
Affiliation(s)
- Jiacheng Lin
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge Cambridge CB2 1EW UK
| | - Chiara Figazzolo
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge Cambridge CB2 1EW UK
| | - Michael A Metrick
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge Cambridge CB2 1EW UK
| | - Pietro Sormanni
- 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
| |
Collapse
|
42
|
Nilvebrant J, Ereño-Orbea J, Gorelik M, Julian MC, Tessier PM, Julien JP, Sidhu SS. Systematic Engineering of Optimized Autonomous Heavy-Chain Variable Domains. J Mol Biol 2021; 433:167241. [PMID: 34508727 DOI: 10.1016/j.jmb.2021.167241] [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: 06/28/2021] [Revised: 08/16/2021] [Accepted: 09/03/2021] [Indexed: 01/06/2023]
Abstract
Autonomous heavy-chain variable (VH) domains are the smallest functional antibody fragments, and they possess unique features, including small size and convex paratopes, which provide enhanced targeting of concave epitopes that are difficult to access with larger conventional antibodies. However, human VH domains have evolved to fold and function with a light chain partner, and alone, they typically suffer from low stability and high aggregation propensity. Development of autonomous human VH domains, in which aggregation propensity is reduced without compromising antigen recognition, has proven challenging. Here, we used an autonomous human VH domain as a scaffold to construct phage-displayed synthetic libraries in which aspartate was systematically incorporated at different paratope positions. In selections, the library yielded many anti-EphA1 receptor VH domains, which were characterized in detail. Structural analyses of a parental anti-EphA1 VH domain and an improved variant provided insights into the effects of aspartate and other substitutions on preventing aggregation while retaining function. Our naïve libraries and in vitro selection procedures offer a systematic approach to generating highly functional autonomous human VH domains that resist aggregation and could be used for basic research and biomedical applications.
Collapse
Affiliation(s)
- Johan Nilvebrant
- Banting and Best Department of Medical Research and Department of Molecular Genetics, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - June Ereño-Orbea
- Program in Molecular Medicine, The Hospital for Sick Children Research Institute and Departments of Biochemistry and Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Maryna Gorelik
- Banting and Best Department of Medical Research and Department of Molecular Genetics, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Mark C Julian
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Peter M Tessier
- Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; Departments of Chemical Engineering, Pharmaceutical Sciences, and Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jean-Philippe Julien
- Program in Molecular Medicine, The Hospital for Sick Children Research Institute and Departments of Biochemistry and Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Sachdev S Sidhu
- Banting and Best Department of Medical Research and Department of Molecular Genetics, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
| |
Collapse
|
43
|
Narayanan H, Dingfelder F, Condado Morales I, Patel B, Heding KE, Bjelke JR, Egebjerg T, Butté A, Sokolov M, Lorenzen N, Arosio P. Design of Biopharmaceutical Formulations Accelerated by Machine Learning. Mol Pharm 2021; 18:3843-3853. [PMID: 34519511 DOI: 10.1021/acs.molpharmaceut.1c00469] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced algorithms from the domain of machine learning are highly beneficial in complementing analytical screening and rational design. Here, we propose a Bayesian optimization algorithm to accelerate the design of biopharmaceutical formulations. We demonstrate the power of this approach by identifying the formulation that optimizes the thermal stability of three tandem single-chain Fv variants within 25 experiments, a number which is less than one-third of the experiments that would be required by a classical DoE method and several orders of magnitude smaller compared to detailed experimental analysis of full combinatorial space. We further show the advantage of this method over conventional approaches to efficiently transfer historical information as prior knowledge for the development of new biologics or when new buffer agents are available. Moreover, we highlight the benefit of our technique in engineering multiple biophysical properties by simultaneously optimizing both thermal and interface stabilities. This optimization minimizes the amount of surfactant in the formulation, which is important to decrease the risks associated with corresponding degradation processes. Overall, this method can provide high speed of converging to optimal conditions, the ability to transfer prior knowledge, and the identification of new nonlinear combinations of excipients. We envision that these features can lead to a considerable acceleration in formulation design and to parallelization of operations during drug development.
Collapse
Affiliation(s)
- Harini Narayanan
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, 8093 Zurich, Switzerland
| | - Fabian Dingfelder
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, 8093 Zurich, Switzerland.,Department of Biophysics and Injectable Formulation, Global Research Technologies, Novo Nordisk A/S, Måløv 2760, Denmark
| | - Itzel Condado Morales
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, 8093 Zurich, Switzerland.,Department of Biophysics and Injectable Formulation, Global Research Technologies, Novo Nordisk A/S, Måløv 2760, Denmark
| | - Bhargav Patel
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, 8093 Zurich, Switzerland
| | - Kristine Enemærke Heding
- Department of Biophysics and Injectable Formulation, Global Research Technologies, Novo Nordisk A/S, Måløv 2760, Denmark
| | - Jais Rose Bjelke
- Department of Purification Technologies, Global Research Technologies, Novo Nordisk A/S, Måløv 2760, Denmark
| | - Thomas Egebjerg
- Department of Mammalian Expression, Global Research Technologies, Novo Nordisk A/S, Måløv 2760, Denmark
| | | | | | - Nikolai Lorenzen
- Department of Biophysics and Injectable Formulation, Global Research Technologies, Novo Nordisk A/S, Måløv 2760, Denmark
| | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, 8093 Zurich, Switzerland
| |
Collapse
|
44
|
Prabakaran R, Rawat P, Yasuo N, Sekijima M, Kumar S, Gromiha MM. Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations. Proteins 2021; 90:405-417. [PMID: 34460128 DOI: 10.1002/prot.26230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 06/30/2021] [Accepted: 08/24/2021] [Indexed: 12/14/2022]
Abstract
Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val ➔ Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.
Collapse
Affiliation(s)
- R Prabakaran
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Puneet Rawat
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India
| | - Nobuaki Yasuo
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Masakazu Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, Connecticut, USA
| | - M Michael Gromiha
- Protein Bioinformatics Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India.,Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| |
Collapse
|
45
|
Beutgen VM, Schmelter C, Pfeiffer N, Grus FH. Contribution of the Commensal Microflora to the Immunological Homeostasis and the Importance of Immune-Related Drug Development for Clinical Applications. Int J Mol Sci 2021; 22:8896. [PMID: 34445599 PMCID: PMC8396286 DOI: 10.3390/ijms22168896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022] Open
Abstract
Not long ago, self-reactive immune activity was considered as pathological trait. A paradigm shift has now led to the recognition of autoimmune processes as part of natural maintenance of molecular homeostasis. The immune system is assigned further roles beneath the defense against pathogenic organisms. Regarding the humoral immune system, the investigation of natural autoantibodies that are frequently found in healthy individuals has led to further hypotheses involving natural autoimmunity in other processes as the clearing of cellular debris or decrease in inflammatory processes. However, their role and origin have not been entirely clarified, but accumulating evidence links their formation to immune reactions against the gut microbiome. Antibodies targeting highly conserved proteins of the commensal microflora are suggested to show self-reactive properties, following the paradigm of the molecular mimicry. Here, we discuss recent findings, which demonstrate potential links of the commensal microflora to the immunological homeostasis and highlight the possible implications for various diseases. Furthermore, specific components of the immune system, especially antibodies, have become a focus of attention for the medical management of various diseases and provide attractive treatment options in the future. Nevertheless, the development and optimization of such macromolecules still represents a very time-consuming task, shifting the need to more medical agents with simple structural properties and low manufacturing costs. Synthesizing only the biologically active sites of antibodies has become of great interest for the pharmaceutical industry and offers a wide range of therapeutic application areas as it will be discussed in the present review article.
Collapse
Affiliation(s)
| | | | | | - Franz H. Grus
- Experimental and Translational Ophthalmology, Department of Ophthalmology, University Medical Center, 55131 Mainz, Germany; (V.M.B.); (C.S.); (N.P.)
| |
Collapse
|
46
|
Computational and Rational Design of Single-Chain Antibody against Tick-Borne Encephalitis Virus for Modifying Its Specificity. Viruses 2021; 13:v13081494. [PMID: 34452359 PMCID: PMC8402911 DOI: 10.3390/v13081494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/09/2021] [Accepted: 06/23/2021] [Indexed: 12/27/2022] Open
Abstract
Tick-borne encephalitis virus (TBEV) causes 5−7 thousand cases of human meningitis and encephalitis annually. The neutralizing and protective antibody ch14D5 is a potential therapeutic agent. This antibody exhibits a high affinity for binding with the D3 domain of the glycoprotein E of the Far Eastern subtype of the virus, but a lower affinity for the D3 domains of the Siberian and European subtypes. In this study, a 2.2-fold increase in the affinity of single-chain antibody sc14D5 to D3 proteins of the Siberian and European subtypes of the virus was achieved using rational design and computational modeling. This improvement can be further enhanced in the case of the bivalent binding of the full-length chimeric antibody containing the identified mutation.
Collapse
|
47
|
Abstract
Measurement of biological systems containing biomolecules and bioparticles is a key task in the fields of analytical chemistry, biology, and medicine. Driven by the complex nature of biological systems and unprecedented amounts of measurement data, artificial intelligence (AI) in measurement science has rapidly advanced from the use of silicon-based machine learning (ML) for data mining to the development of molecular computing with improved sensitivity and accuracy. This review presents an overview of fundamental ML methodologies and discusses their applications in disease diagnostics, biomarker discovery, and imaging analysis. We next provide the working principles of molecular computing using logic gates and arithmetical devices, which can be employed for in situ detection, computation, and signal transduction for biological systems. This review concludes by summarizing the strengths and limitations of AI-involved biological measurement in fundamental and applied research.
Collapse
Affiliation(s)
- Chao Liu
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiashu Sun
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
48
|
Bauer J, Mathias S, Kube S, Otte K, Garidel P, Gamer M, Blech M, Fischer S, Karow-Zwick AR. Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties along the entire pharmaceutical process chain. MAbs 2021; 12:1787121. [PMID: 32658605 PMCID: PMC7531517 DOI: 10.1080/19420862.2020.1787121] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The discovery of therapeutic monoclonal antibodies (mAbs) primarily focuses on their biological activity favoring the selection of highly potent drug candidates. These candidates, however, may have physical or chemical attributes that lead to unfavorable chemistry, manufacturing, and control (CMC) properties, such as low product titers, conformational and colloidal instabilities, or poor solubility, which can hamper or even prevent development and manufacturing. Hence, there is an urgent need to consider the developability of mAb candidates during lead identification and optimization. This work provides a comprehensive proof of concept study for the significantly improved developability of a mAb variant that was optimized with the help of sophisticated in silico tools relative to its difficult-to-develop parental counterpart. Interestingly, a single amino acid substitution in the variable domain of the light chain resulted in a three-fold increased product titer after stable expression in Chinese hamster ovary cells. Microscopic investigations revealed that wild type mAb-producing cells displayed potential antibody inclusions, while the in silico optimized variant-producing cells showed a rescued phenotype. Notably, the drug substance of the in silico optimized variant contained substantially reduced levels of aggregates and fragments after downstream process purification. Finally, formulation studies unraveled a significantly enhanced colloidal stability of the in silico optimized variant while its folding stability and potency were maintained. This study emphasizes that implementation of bioinformatics early in lead generation and optimization of biotherapeutics reduces failures during subsequent development activities and supports the reduction of project timelines and resources.
Collapse
Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Sven Mathias
- Institute of Applied Biotechnology, University of Applied Sciences Biberach , Biberach/Riss, Germany.,Early Stage Bioprocess Development, Bioprocess Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Sebastian Kube
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Kerstin Otte
- Institute of Applied Biotechnology, University of Applied Sciences Biberach , Biberach/Riss, Germany
| | - Patrick Garidel
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Martin Gamer
- Early Stage Bioprocess Development, Bioprocess Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Michaela Blech
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Simon Fischer
- Cell Line Development, Bioprocess Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| | - Anne R Karow-Zwick
- Early Stage Pharmaceutical Development, Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG , Biberach/Riss, Germany
| |
Collapse
|
49
|
Wang H, Yan K, Wang R, Yang Y, Shen Y, Yu C, Chen L. Antibody heavy chain CDR3 length-dependent usage of human IGHJ4 and IGHJ6 germline genes. Antib Ther 2021; 4:101-108. [PMID: 34195544 PMCID: PMC8237691 DOI: 10.1093/abt/tbab010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/07/2021] [Accepted: 06/10/2021] [Indexed: 12/26/2022] Open
Abstract
Therapeutic antibody discovery using synthetic diversity has been proved productive, especially for target proteins not suitable for traditional animal immunization-based antibody discovery approaches. Recently, many lines of evidences suggest that the quality of synthetic diversity design limits the development success of synthetic antibody hits. The aim of our study is to understand the quality limitation and to properly address the challenges with a better design. Using VH3–23 as a model framework, we observed and quantitatively mapped CDR-H3 loop length-dependent usage of human IGHJ4 and IGHJ6 germline genes in the natural human immune repertoire. Skewed usage of DH2-JH6 and DH3-JH6 rearrangements was quantitatively determined in a CDR-H3 length-dependent manner in natural human antibodies with long CDR-H3 loops. Structural modeling suggests choices of JH help to stabilize antibody CDR-H3 loop and JH only partially contributes to the paratope. Our observations shed light on the design of next-generation synthetic diversity with improved probability of success.
Collapse
Affiliation(s)
- Huimin Wang
- College of Life Science and Technology, Beijing University of Chemical Technology, #15 Beisanhuandong Rd, Chaoyang District, Beijing 100029, China
| | - Kai Yan
- Biotherapeutics, Biocytogen Pharmaceuticals (Beijing) Co. Ltd., #12 Baoshennan St, Daxing District, Beijing 102629, China
| | - Ruixue Wang
- Biotherapeutics, Biocytogen Pharmaceuticals (Beijing) Co. Ltd., #12 Baoshennan St, Daxing District, Beijing 102629, China
| | - Yi Yang
- Biotherapeutics, Biocytogen Pharmaceuticals (Beijing) Co. Ltd., #12 Baoshennan St, Daxing District, Beijing 102629, China
| | - Yuelei Shen
- Biotherapeutics, Biocytogen Pharmaceuticals (Beijing) Co. Ltd., #12 Baoshennan St, Daxing District, Beijing 102629, China
| | - Changyuan Yu
- College of Life Science and Technology, Beijing University of Chemical Technology, #15 Beisanhuandong Rd, Chaoyang District, Beijing 100029, China
| | - Lei Chen
- Biotherapeutics, Biocytogen Pharmaceuticals (Beijing) Co. Ltd., #12 Baoshennan St, Daxing District, Beijing 102629, China
| |
Collapse
|
50
|
Evolution of KIPPIS as a versatile platform for evaluating intracellularly functional peptide aptamers. Sci Rep 2021; 11:11758. [PMID: 34083659 PMCID: PMC8175380 DOI: 10.1038/s41598-021-91287-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 05/18/2021] [Indexed: 11/18/2022] Open
Abstract
Chimeric proteins have been widely used to evaluate intracellular protein–protein interactions (PPIs) in living cells with various readouts. By combining an interleukin-3-dependent murine cells and chimeric proteins containing a receptor tyrosine kinase c-kit, we previously established a c-kit-based PPI screening (KIPPIS) system to evaluate and select protein binders. In the KIPPIS components, proteins of interest are connected with a chemically inducible helper module and the intracellular domain of the growth-signaling receptor c-kit, which detects PPIs based on cell proliferation as a readout. In this system, proteins of interest can be incorporated into chimeric proteins without any scaffold proteins, which would be advantageous for evaluating interaction between small peptides/domains. To prove this superiority, we apply KIPPIS to 6 peptide aptamer–polypeptide pairs, which are derived from endogenous, synthetic, and viral proteins. Consequently, all of the 6 peptide aptamer–polypeptide interactions are successfully detected by cell proliferation. The detection sensitivity can be modulated in a helper ligand-dependent manner. The assay results of KIPPIS correlate with the activation levels of Src, which is located downstream of c-kit-mediated signal transduction. Control experiments reveal that KIPPIS clearly discriminates interacting aptamers from non-interacting ones. Thus, KIPPIS proves to be a versatile platform for evaluating the binding properties of peptide aptamers.
Collapse
|