1
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Deng Y, Wang JX, Ghosh B, Lu Y. Enzymatic CO 2 reduction catalyzed by natural and artificial Metalloenzymes. J Inorg Biochem 2024; 259:112669. [PMID: 39059175 DOI: 10.1016/j.jinorgbio.2024.112669] [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: 05/21/2024] [Revised: 07/04/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
The continuously increasing level of atmospheric CO2 in the atmosphere has led to global warming. Converting CO2 into other carbon compounds could mitigate its atmospheric levels and produce valuable products, as CO2 also serves as a plentiful and inexpensive carbon feedstock. However, the inert nature of CO2 poses a major challenge for its reduction. To meet the challenge, nature has evolved metalloenzymes using transition metal ions like Fe, Ni, Mo, and W, as well as electron-transfer partners for their functions. Mimicking these enzymes, artificial metalloenzymes (ArMs) have been designed using alternative protein scaffolds and various metallocofactors like Ni, Co, Re, Rh, and FeS clusters. Both the catalytic efficiency and the scope of CO2-reduction product of these ArMs have been improved over the past decade. This review first focuses on the natural metalloenzymes that directly reduce CO2 by discussing their structures and active sites, as well as the proposed reaction mechanisms. It then introduces the common strategies for electrochemical, photochemical, or photoelectrochemical utilization of these native enzymes for CO2 reduction and highlights the most recent advancements from the past five years. We also summarize principles of protein design for bio-inspired ArMs, comparing them with native enzymatic systems and outlining challenges and opportunities in enzymatic CO2 reduction.
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Affiliation(s)
- Yunling Deng
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States of America
| | - Jing-Xiang Wang
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States of America
| | - Barshali Ghosh
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States of America
| | - Yi Lu
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, United States of America.
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2
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Dieckhaus H, Kuhlman B. Protein stability models fail to capture epistatic interactions of double point mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.20.608844. [PMID: 39229177 PMCID: PMC11370451 DOI: 10.1101/2024.08.20.608844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
There is strong interest in accurate methods for predicting changes in protein stability resulting from amino acid mutations to the protein sequence. Recombinant proteins must often be stabilized to be used as therapeutics or reagents, and destabilizing mutations are implicated in a variety of diseases. Due to increased data availability and improved modeling techniques, recent studies have shown advancements in predicting changes in protein stability when a single point mutation is made. Less focus has been directed toward predicting changes in protein stability when there are two or more mutations, despite the significance of mutation clusters for disease pathways and protein design studies. Here, we analyze the largest available dataset of double point mutation stability and benchmark several widely used protein stability models on this and other datasets. We identify a blind spot in how predictors are typically evaluated on multiple mutations, finding that, contrary to assumptions in the field, current stability models are unable to consistently capture epistatic interactions between double mutations. We observe one notable deviation from this trend, which is that epistasis-aware models provide marginally better predictions on stabilizing double point mutations. We develop an extension of the ThermoMPNN framework for double mutant modeling as well as a novel data augmentation scheme which mitigates some of the limitations in available datasets. Collectively, our findings indicate that current protein stability models fail to capture the nuanced epistatic interactions between concurrent mutations due to several factors, including training dataset limitations and insufficient model sensitivity. Significance Protein stability is governed in part by epistatic interactions between energetically coupled residues. Prediction of these couplings represents the next frontier in protein stability modeling. In this work, we benchmark protein stability models on a large dataset of double point mutations and identify previously overlooked limitations in model design and evaluation. We also introduce several new strategies to improve modeling of epistatic couplings between protein point mutations.
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3
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Pais DAM, Mayer JPA, Felderer K, Batalha MB, Eichner T, Santos ST, Kumar R, Silva SD, Kaufmann H. Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors. J Comput Aided Mol Des 2024; 38:30. [PMID: 39164492 DOI: 10.1007/s10822-024-00569-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 07/26/2024] [Indexed: 08/22/2024]
Abstract
The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success and ensure robust drug supply beyond approval, it is essential to assess the developability profile of new potential drug candidates as early and broadly as possible in development (Jain et al. MAbs, 2023. https://doi.org/10.1016/j.copbio.2011.06.002 ). Predicting these properties in silico is expected to be the next leap in innovation as it would enable significantly reduced development timelines combined with broader screens at lower costs. However, developing predictive algorithms typically requires substantial datasets generated under very defined conditions, a limiting factor especially for new classes of therapeutic proteins that hold immense clinical promise. Here we describe a strategy for assessing the developability of a novel class of small therapeutic Anticalin® proteins using machine learning in conjunction with a knowledge-driven approach. The knowledge-driven approach considers developability attributes such as aggregation propensity, charge variants, immunogenicity, specificity, thermal stability, hydrophobicity, and potential post-translational modifications, to calculate a holistic developability score. Based on sequence-derived descriptors as input parameters we established novel statistical models designed to predict the developability scores for Anticalin proteins. The best models yielded low root mean square errors across the entire dataset and were further validated by removing input data from individual screening campaigns and predicting developability scores for those drug candidates. The adoption of the described workflow will enable significantly streamlined preclinical development of Anticalin drug candidates and could potentially be applied to other therapeutic protein scaffolds.
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Affiliation(s)
- Daniel A M Pais
- Valgenesis Portugal, Lda, R. Castilho 50 4th Floor, 1250-071, Lisbon, Portugal
| | - Jan-Peter A Mayer
- Pieris Pharmaceuticals GmbH, Carl-Zeiss-Ring 15a, 85737, Ismaning, Germany
| | - Karin Felderer
- Pieris Pharmaceuticals GmbH, Carl-Zeiss-Ring 15a, 85737, Ismaning, Germany
| | - Maria B Batalha
- Valgenesis Portugal, Lda, R. Castilho 50 4th Floor, 1250-071, Lisbon, Portugal
| | - Timo Eichner
- Pieris Pharmaceuticals GmbH, Carl-Zeiss-Ring 15a, 85737, Ismaning, Germany
| | - Sofia T Santos
- Valgenesis Portugal, Lda, R. Castilho 50 4th Floor, 1250-071, Lisbon, Portugal
| | - Raman Kumar
- Pieris Pharmaceuticals GmbH, Carl-Zeiss-Ring 15a, 85737, Ismaning, Germany
| | - Sandra D Silva
- Valgenesis Portugal, Lda, R. Castilho 50 4th Floor, 1250-071, Lisbon, Portugal
| | - Hitto Kaufmann
- Pieris Pharmaceuticals GmbH, Carl-Zeiss-Ring 15a, 85737, Ismaning, Germany.
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4
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Saha R, Chauhan A, Rastogi Verma S. Machine learning: an advancement in biochemical engineering. Biotechnol Lett 2024; 46:497-519. [PMID: 38902585 DOI: 10.1007/s10529-024-03499-8] [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: 11/17/2023] [Revised: 02/24/2024] [Accepted: 05/18/2024] [Indexed: 06/22/2024]
Abstract
One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented.
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Affiliation(s)
- Ritika Saha
- Department of Biotechnology, Delhi Technological University, New Delhi, 110042, India
| | - Ashutosh Chauhan
- Department of Biotechnology, Delhi Technological University, New Delhi, 110042, India
| | - Smita Rastogi Verma
- Department of Biotechnology, Delhi Technological University, New Delhi, 110042, India.
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5
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Wossnig L, Furtmann N, Buchanan A, Kumar S, Greiff V. Best practices for machine learning in antibody discovery and development. Drug Discov Today 2024; 29:104025. [PMID: 38762089 DOI: 10.1016/j.drudis.2024.104025] [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: 12/14/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
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Affiliation(s)
- Leonard Wossnig
- LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
| | - Norbert Furtmann
- R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany
| | - Andrew Buchanan
- Biologics Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Sandeep Kumar
- Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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6
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Schuster J, Kamuju V, Zhou J, Mathaes R. Piston-driven automated liquid handlers. SLAS Technol 2024; 29:100128. [PMID: 38508238 DOI: 10.1016/j.slast.2024.100128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/16/2024] [Accepted: 03/17/2024] [Indexed: 03/22/2024]
Abstract
Laboratory capacities are often limited by time-consuming manual repetitive procedures rather than analysis time itself. While modern instruments are typically equipped with an autosampler, sample preparation often follows manual procedures including many labor-intensive, monotonous tasks. Particularly, for a high number of samples, well plates, and low microliter pipetting, manual preparation is error-prone often requiring repeated experiments. Sampling and sample preparation can account for greater analytical variability than instrument analysis. Repetitive tasks such as liquid handling benefit strongly from technological advances and led to the increasing applications of various automated liquid handlers (ALHs). In this review, we discuss the considerations for ALHs in the microliter range and highlight advantages and challenges when transforming from manual to automated workflows. We strongly focused on differences in liquid handling and outlined advantages due to sensor-controlled pipetting. ALHs can substantially improve costs-effectiveness and laboratory capacity. This is a consequence of increased efficiency, and throughput of laboratories while simultaneously raising data quality. Additionally, ALHs can improve safety, documentation of data, and sustainability. While automation requires careful consideration and resource demanding implementation, we believe it offers numerous advantages and can help to transform modern laboratories.
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Affiliation(s)
- Joachim Schuster
- Lonza Pharma and Biotech, Drug Product Services, Basel, Switzerland.
| | - Vinay Kamuju
- Lonza Pharma and Biotech, Drug Product Services, Basel, Switzerland
| | - Jin Zhou
- Lonza Pharma and Biotech, Drug Product Services, Basel, Switzerland
| | - Roman Mathaes
- Lonza Pharma and Biotech, Drug Product Services, Basel, Switzerland
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7
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Jin R, Ye Q, Wang J, Cao Z, Jiang D, Wang T, Kang Y, Xu W, Hsieh CY, Hou T. AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences. Brief Bioinform 2024; 25:bbae304. [PMID: 38960407 PMCID: PMC11221889 DOI: 10.1093/bib/bbae304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/15/2024] [Accepted: 06/11/2024] [Indexed: 07/05/2024] Open
Abstract
The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.
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Affiliation(s)
- Ruofan Jin
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
- College of Life Science, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Qing Ye
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Zheng Cao
- College of Computer Science and Technology, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Dejun Jiang
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Tianyue Wang
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Wanting Xu
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China
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8
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Yan G, Lu X, Sun R, Zhou W, Zhou H. Intensified perfusion culture (IPC) reduced recombinant protein fragmentation. Biotechnol Prog 2024; 40:e3405. [PMID: 37997628 DOI: 10.1002/btpr.3405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 11/25/2023]
Abstract
Mammalian cells remain the mainstay of biological production host. In industry, cultivating and harvest strategies are sorted in batch mode (e.g., batch, fed-batch, concentrated fed-batch and intensified fed-batch) and continuous mode (e.g., perfusion). To retrieve greater productivity and better product quality, especially for the sensitive products prone to fragmentation, culture modes with various modifications are innovated (e.g., intensified perfusion culture [IPC]). In our study, we demonstrated that the fragmentation of Fc-fusion product (Molecule A) is time-dependent in traditional fed-batch (TFB) culture. The fragmentation proportion increased from 3.8% to 12.4% for Clone A, 0.8% to 1.7% for Clone B and 0.9% to 2.0% for Clone C from Day 10 to Day 14. By applying a novel bioprocess, IPC, which allows continuous feeding of the fresh medium and constant removal of the spent medium without bleeding cells to maintain a defined constant viable cell density, the fragmentation was reduced to 0.3% while the productivity was increased from 2.96 g/L to 15.51 g/L for Clone A. To validate whether the fragmentation reduction is product-sensitive, plasmids carrying the DNA sequences of two other Fc-fusion molecules (Molecule B and Molecule C) were transfected into the host. The results showed consistent fragmentation reducing effect by using IPC. Furthermore, the cultivation scale was expanded to 50 L and 1000 L. A minimum fragmentation level below 0.1% was observed for Molecule C. Our study revealed the capability of IPC in reducing Fc-fusion protein fragmentation and the reproducibility when scaling up while maintaining high productivity.
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Affiliation(s)
- Ge Yan
- Cell Culture Process Development, WuXi Biologics, Shanghai, China
| | - Xun Lu
- Cell Culture Process Development, WuXi Biologics, Shanghai, China
| | - Ruiqiang Sun
- Cell Culture Process Development, WuXi Biologics, Shanghai, China
| | - Weichang Zhou
- Biologics Development, WuXi Biologics, Shanghai, China
| | - Hang Zhou
- Bioprocess Research and Development, WuXi Biologics, Shanghai, China
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9
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de Raffele D, Ilie IM. Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning. Chem Commun (Camb) 2024; 60:632-645. [PMID: 38131333 DOI: 10.1039/d3cc04630c] [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: 12/23/2023]
Abstract
Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.
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Affiliation(s)
- Daria de Raffele
- University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Science Park 904, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands.
- Amsterdam Center for Multiscale Modeling (ACMM), University of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands
| | - Ioana M Ilie
- University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Science Park 904, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands.
- Amsterdam Center for Multiscale Modeling (ACMM), University of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands
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10
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Wang T, Jin X, Lu X, Min X, Ge S, Li S. Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness. Front Genet 2024; 14:1347667. [PMID: 38274106 PMCID: PMC10808456 DOI: 10.3389/fgene.2023.1347667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology.
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Affiliation(s)
- Tianshu Wang
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Xiaocheng Jin
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Xiaoli Lu
- Information and Networking Center, Xiamen University, Xiamen, China
| | - Xiaoping Min
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Shengxiang Ge
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Shaowei Li
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
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11
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Chong JWR, Tang DYY, Leong HY, Khoo KS, Show PL, Chew KW. Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae. Bioengineered 2023; 14:2244232. [PMID: 37578162 PMCID: PMC10431731 DOI: 10.1080/21655979.2023.2244232] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/30/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023] Open
Abstract
Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.
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Affiliation(s)
- Jun Wei Roy Chong
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, Selangor Darul Ehsan, Malaysia
| | - Doris Ying Ying Tang
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, Selangor Darul Ehsan, Malaysia
| | - Hui Yi Leong
- ISCO (Nanjing) Biotech-Company, Nanjing, Jiangning, China
| | - Kuan Shiong Khoo
- Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan
- Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, India
| | - Pau Loke Show
- Department of Chemical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore
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12
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Sharma V, Giammona M, Zubarev D, Tek A, Nugyuen K, Sundberg L, Congiu D, La YH. Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance. J Chem Inf Model 2023; 63:6998-7010. [PMID: 37948621 PMCID: PMC10685446 DOI: 10.1021/acs.jcim.3c01030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/21/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
Advanced computational methods are being actively sought to address the challenges associated with the discovery and development of new combinatorial materials, such as formulations. A widely adopted approach involves domain-informed high-throughput screening of individual components that can be combined together to form a formulation. This manages to accelerate the discovery of new compounds for a target application but still leaves the process of identifying the right "formulation" from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, the Formulation Graph Convolution Network (F-GCN), that can map the structure-composition relationship of the formulation constituents to the property of liquid formulation as a whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on the respective constituent's molar percentage in the formulation, followed by integration into a combined formulation descriptor that represents the complete formulation to an external learning architecture. The use case of the proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary data sets representing electrolyte formulations vs battery performance: one data set is sourced from the literature about Li/Cu half-cells, while the other is obtained by lab experiments related to lithium-iodide full-cell chemistry. The model is shown to predict performance metrics such as Coulombic efficiency (CE) and specific capacity of new electrolyte formulations with the lowest reported errors. The best-performing F-GCN model uses molecular descriptors derived from molecular graphs (GCNs) that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.
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Affiliation(s)
- Vidushi Sharma
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Maxwell Giammona
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Dmitry Zubarev
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Andy Tek
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Khanh Nugyuen
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Linda Sundberg
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Daniele Congiu
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
| | - Young-Hye La
- IBM Almaden Research Center, 650 Harry Rd, San Jose, California 95120, United States
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13
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Arsiccio A, Stratta L, Menzen T. Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study. J Mol Model 2023; 29:377. [PMID: 37968495 DOI: 10.1007/s00894-023-05777-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 10/31/2023] [Indexed: 11/17/2023]
Abstract
CONTEXT Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-principle models, machine learning techniques are easier to implement, and can identify correlations that would be hard to describe at a mechanistic level, but strongly rely on high-quality input training data. Here, we evaluate the potential of the "chaos game" representation to provide input data for machine learning models. The chaos game is an algorithm originally developed for the production of fractal structures, and later on applied also to the representation of biological sequences, such as genes and proteins. Our results show that the combination of the chaos game representation with convolutional neural networks results in comparable accuracy to other machine learning approaches, thus indicating that chaos game representations could be a valid alternative to existing featurization strategies for machine learning models of biological sequences. METHODS We implement the chaos game in Python 3.8.10, and use it to produce fractal as well as novel expanding representations of protein sequences. We then feed the resulting images to a convolutional neural network, built in Python 3.8.10, using TensorFlow 2.9.1, Keras 2.9.0, and the scikit-learn 1.1.1 packages. We select as case study a recently published dataset for the antibody emibetuzumab, with the objective of co-optimizing antibodies variants with both high affinity and low non-specific binding.
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Affiliation(s)
- Andrea Arsiccio
- Coriolis Pharma, Fraunhoferstrasse 18 b, 82152, Martinsried, Germany.
| | - Lorenzo Stratta
- Molecular Engineering Laboratory (molE), Department of Applied Science and Technology, Politecnico di Torino, 24 corso Duca degli Abruzzi, IT-10129, Torino, Italy
| | - Tim Menzen
- Coriolis Pharma, Fraunhoferstrasse 18 b, 82152, Martinsried, Germany
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14
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Yang ZJ, Shao Q, Jiang Y, Jurich C, Ran X, Juarez RJ, Yan B, Stull SL, Gollu A, Ding N. Mutexa: A Computational Ecosystem for Intelligent Protein Engineering. J Chem Theory Comput 2023; 19:7459-7477. [PMID: 37828731 PMCID: PMC10653112 DOI: 10.1021/acs.jctc.3c00602] [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: 06/06/2023] [Indexed: 10/14/2023]
Abstract
Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.
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Affiliation(s)
- Zhongyue J. Yang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute of Chemical Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235, United States
- Data
Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Qianzhen Shao
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yaoyukun Jiang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Christopher Jurich
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute of Chemical Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
| | - Xinchun Ran
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Reecan J. Juarez
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37235, United States
| | - Bailu Yan
- Department
of Biostatistics, Vanderbilt University, Nashville, Tennessee 37205, United States
| | - Sebastian L. Stull
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Anvita Gollu
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Ning Ding
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
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15
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Markus B, C GC, Andreas K, Arkadij K, Stefan L, Gustav O, Elina S, Radka S. Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design. ACS Catal 2023; 13:14454-14469. [PMID: 37942268 PMCID: PMC10629211 DOI: 10.1021/acscatal.3c03417] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology's limitations, unanswered questions, and unmet challenges.
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Affiliation(s)
- Braun Markus
- Department
of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria
| | - Gruber Christian C
- Enzyme
and Drug Discovery, Innophore. 1700 Montgomery Street, San Francisco, California 94111, United States
| | - Krassnigg Andreas
- Enzyme
and Drug Discovery, Innophore. 1700 Montgomery Street, San Francisco, California 94111, United States
| | - Kummer Arkadij
- Moderna,
Inc., 200 Technology
Square, Cambridge, Massachusetts 02139, United States
| | - Lutz Stefan
- Codexis
Inc., 200 Penobscot Drive, Redwood City, California 94063, United States
| | - Oberdorfer Gustav
- Department
of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria
| | - Siirola Elina
- Novartis
Institute for Biomedical Research, Global Discovery Chemistry, Basel CH-4108, Switzerland
| | - Snajdrova Radka
- Novartis
Institute for Biomedical Research, Global Discovery Chemistry, Basel CH-4108, Switzerland
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16
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Qiu Y, Wei GW. Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models. Brief Bioinform 2023; 24:bbad289. [PMID: 37580175 PMCID: PMC10516362 DOI: 10.1093/bib/bbad289] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/14/2023] [Accepted: 07/26/2023] [Indexed: 08/16/2023] Open
Abstract
Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.
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Affiliation(s)
- Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, 48824 MI, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, 48824 MI, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, 48824 MI, USA
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, 48824 MI, USA
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17
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Golinski AW, Schmitz ZD, Nielsen GH, Johnson B, Saha D, Appiah S, Hackel BJ, Martiniani S. Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation. ACS Synth Biol 2023; 12:2600-2615. [PMID: 37642646 PMCID: PMC10829850 DOI: 10.1021/acssynbio.3c00196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability─quantified by expression, solubility, and stability─hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput (HT) developability dataset for 105 of 1020 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a HT dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased recombinant expression through nonlinear dimensionality reduction and we explore the inferred expression landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold expression from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability.
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Affiliation(s)
- Alexander W. Golinski
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Zachary D. Schmitz
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Gregory H. Nielsen
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Bryce Johnson
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Diya Saha
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Sandhya Appiah
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Benjamin J. Hackel
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Stefano Martiniani
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
- Center for Soft Matter Research, Department of Physics, New York University, New York, NY 10003
- Simons Center for Computational Physical Chemistry, Departments of Chemistry, New York University, New York, NY 10003
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10003
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18
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Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. Bioinformatics 2023; 39:btad446. [PMID: 37478351 PMCID: PMC10477941 DOI: 10.1093/bioinformatics/btad446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/21/2023] [Accepted: 07/20/2023] [Indexed: 07/23/2023] Open
Abstract
MOTIVATION Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. RESULTS Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. AVAILABILITY AND IMPLEMENTATION All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.
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Affiliation(s)
- Matthew D Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Marshall A Case
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Emily K Makowski
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
| | - Peter M Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Protein Folding Disease Initiative, University of Michigan, Ann Arbor, MI 48109-2200, United States
- Michigan Alzheimer’s Disease Center, University of Michigan, Ann Arbor, MI 48109-2200, United States
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19
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Park SY, Kim SJ, Park CH, Kim J, Lee DY. Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins. Biotechnol Bioeng 2023; 120:2494-2508. [PMID: 37079452 DOI: 10.1002/bit.28405] [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: 12/01/2022] [Revised: 04/05/2023] [Accepted: 04/10/2023] [Indexed: 04/21/2023]
Abstract
Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as monoclonal antibodies (mAbs). Thus, now it is very important to exploit them for the enhanced reliability, efficiency, and consistency of the RTP-producing culture processes and for the reduced incipient or abrupt faults. It is achievable by AI-based data-driven models (DDMs), which allow us to correlate biological and process conditions and cell culture states. In this work, we provide practical guidelines for choosing the best combination of model elements to design and implement successful DDMs for given hypothetical in-line data sets during mAb-producing Chinese hamster ovary cell culture, as such enabling us to forecast dynamic behaviors of culture performance such as viable cell density, mAb titer as well as glucose, lactate and ammonia concentrations. To do so, we created DDMs that balance computational load with model accuracy and reliability by identifying the best combination of multistep ahead forecasting strategies, input features, and AI algorithms, which is potentially applicable to implementation of interactive DDM within bioprocess digital twins. We believe this systematic study can help bioprocess engineers start developing predictive DDMs with their own data sets and learn how their cell cultures behave in near future, thereby rendering proactive decision possible.
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Affiliation(s)
- Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Sun-Jong Kim
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Cheol-Hwan Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jiyong Kim
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
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20
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Murai T, Matsuda S. Integrated Multimodal Omics and Dietary Approaches for the Management of Neurodegeneration. EPIGENOMES 2023; 7:20. [PMID: 37754272 PMCID: PMC10529483 DOI: 10.3390/epigenomes7030020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/26/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
Neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are caused by a combination of multiple events that damage neuronal function. A well-characterized biomarker of neurodegeneration is the accumulation of proteinaceous aggregates in the brain. However, the gradually worsening symptoms of neurodegenerative diseases are unlikely to be solely due to the result of a mutation in a single gene, but rather a multi-step process involving epigenetic changes. Recently, it has been suggested that a fraction of epigenetic alternations may be correlated to neurodegeneration in the brain. Unlike DNA mutations, epigenetic alterations are reversible, and therefore raise the possibilities for therapeutic intervention, including dietary modifications. Additionally, reactive oxygen species may contribute to the pathogenesis of Alzheimer's disease and Parkinson's disease through epigenetic alternation. Given that the antioxidant properties of plant-derived phytochemicals are likely to exhibit pleiotropic effects against ROS-mediated epigenetic alternation, dietary intervention may be promising for the management of neurodegeneration in these diseases. In this review, the state-of-the-art applications using single-cell multimodal omics approaches, including epigenetics, and dietary approaches for the identification of novel biomarkers and therapeutic approaches for the treatment of neurodegenerative diseases are discussed.
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Affiliation(s)
- Toshiyuki Murai
- Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita 565-0871, Japan;
| | - Satoru Matsuda
- Department of Food Science and Nutrition, Nara Women’s University, Kita-Uoya Nishimachi, Nara 630-8506, Japan
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21
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Lefin N, Herrera-Belén L, Farias JG, Beltrán JF. Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides. Mol Divers 2023:10.1007/s11030-023-10718-3. [PMID: 37626205 DOI: 10.1007/s11030-023-10718-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.
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Affiliation(s)
- Nicolás Lefin
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Lisandra Herrera-Belén
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad Santo Tomás, Temuco, Chile
| | - Jorge G Farias
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile.
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22
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Smith MD, Case MA, Makowski EK, Tessier PM. Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548448. [PMID: 37503142 PMCID: PMC10369870 DOI: 10.1101/2023.07.10.548448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Motivation Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries. Results Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns. Availability All deep sequencing datasets and code to do the analyses presented within are available via GitHub. Contact Peter Tessier, ptessier@umich.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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23
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Mieczkowski CA. The Evolution of Commercial Antibody Formulations. J Pharm Sci 2023; 112:1801-1810. [PMID: 37037341 DOI: 10.1016/j.xphs.2023.03.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/12/2023]
Abstract
It has been nearly four decades since the first therapeutic monoclonal antibodies were approved and made available for widespread human use. Herein, US and EU approved antibody formulations are reviewed, and their nature and compositions are evaluated over time. From 1986 through Jan 2023, significant formulation trends have occurred and to represent this, 165 commercial antibody therapeutic formulations were binned into 5 different periods of time. Overall, we have observed the following: 1) The average formulation pH has decreased in recent years by over 0.5 units along with a decrease in variability that is largely driven by non-high concentration liquid in vial presentations for IV administration, 2) The use of certain excipients and buffers such as histidine, sucrose, metal chelators, arginine and methionine has become significantly more common, whereas formulations that contain phosphate, salt, no sugar or no surfactant have fallen out of favor, 3) Overall formulation space has increasingly become more homogenous and has converged in terms of formulation pH and excipient preferences regardless of formulation concentration, drug product presentation, and route of administration, 4) The average calculated isoelectric point (pI) has decreased 0.26 units, and 5) Overall, the average formulation pH and calculated pI for all commercial antibodies surveyed was 6.0 and 8.4, respectively. These trends and formulation convergence may be driven by multiple factors such as advancements in high-throughput computational and analytical technologies, the increased emphasis and understanding of certain developability attributes and formulation principles during lead selection and formulation development, and the adoption of low-risk development platform approaches.
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24
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Abrantes-Coutinho VE, Santos AO, Holanda BEB, Costa HRA, Oliveira TMBF. Integrating machine learning and electrochemistry to develop a glucose biosensor assembled with Ganoderma applanatum lectin. Bioelectrochemistry 2023; 151:108392. [PMID: 36753946 DOI: 10.1016/j.bioelechem.2023.108392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 01/19/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023]
Abstract
Fungal lectins have enormous biotechnological potential, but limited knowledge about their biochemical and biophysical features prevents their proper use. Herein, we report an innovative alternative to use Ganoderma applanatum lectin (GAL) as a glucose biorecognition element, after identifying the ideal electroanalytical conditions by machine learning studies performed with a homologous agglutinin from the same macrofungus. The research revealed that GAL has moderate resistance to pH (4-8) and temperature (20-60 °C) variations, but its hemagglutinating activity (376.5 HU mg-1 GAL at 20 °C) was better conserved under physiological conditions. Integrating electrochemical data and semi-empirical molecular modeling, biocompatible and electrostatically favorable conditions were found to immobilize the lectin on Prussian blue-modified glassy carbon electrode, after thermal activation of the metal-complex film. The glucose dose-response relationship obtained with the developed biosensor, defined as GAL/ta-PB/GCE, showed a typical Hill equation correlation, suggesting electrodic interactions represented by a sigmoidal mathematical function. GAL/ta-PB/GCE achieved remarkable electroanalytical performance, with emphasis on the detection limit (10.2 pM) and sensitivity (0.012 µA µM-1cm-2). The biosensor was successfully used to quantify glucose in pharmaceutical formulations, reiterating that the association of theoretical and experimental information drives important advances in bioelectrochemical studies.
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Affiliation(s)
| | - André O Santos
- Centro de Ciência e Tecnologia, Universidade Federal do Cariri, 63048-080 Juazeiro do Norte, CE, Brazil
| | - Brenna E B Holanda
- Centro de Ciência e Tecnologia, Universidade Federal do Cariri, 63048-080 Juazeiro do Norte, CE, Brazil
| | - Heryka R A Costa
- Centro de Ciência e Tecnologia, Universidade Federal do Cariri, 63048-080 Juazeiro do Norte, CE, Brazil
| | - Thiago M B F Oliveira
- Centro de Ciência e Tecnologia, Universidade Federal do Cariri, 63048-080 Juazeiro do Norte, CE, Brazil.
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25
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Qian L, Lin X, Gao X, Khan RU, Liao JY, Du S, Ge J, Zeng S, Yao SQ. The Dawn of a New Era: Targeting the "Undruggables" with Antibody-Based Therapeutics. Chem Rev 2023. [PMID: 37186942 DOI: 10.1021/acs.chemrev.2c00915] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The high selectivity and affinity of antibodies toward their antigens have made them a highly valuable tool in disease therapy, diagnosis, and basic research. A plethora of chemical and genetic approaches have been devised to make antibodies accessible to more "undruggable" targets and equipped with new functions of illustrating or regulating biological processes more precisely. In this Review, in addition to introducing how naked antibodies and various antibody conjugates (such as antibody-drug conjugates, antibody-oligonucleotide conjugates, antibody-enzyme conjugates, etc.) work in therapeutic applications, special attention has been paid to how chemistry tools have helped to optimize the therapeutic outcome (i.e., with enhanced efficacy and reduced side effects) or facilitate the multifunctionalization of antibodies, with a focus on emerging fields such as targeted protein degradation, real-time live-cell imaging, catalytic labeling or decaging with spatiotemporal control as well as the engagement of antibodies inside cells. With advances in modern chemistry and biotechnology, well-designed antibodies and their derivatives via size miniaturization or multifunctionalization together with efficient delivery systems have emerged, which have gradually improved our understanding of important biological processes and paved the way to pursue novel targets for potential treatments of various diseases.
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Affiliation(s)
- Linghui Qian
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xuefen Lin
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xue Gao
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, Hangzhou 310058, China
| | - Rizwan Ullah Khan
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jia-Yu Liao
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shubo Du
- School of Bioengineering, Dalian University of Technology, Dalian 116024, China
| | - Jingyan Ge
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Su Zeng
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shao Q Yao
- Department of Chemistry, National University of Singapore, 4 Science Drive 2, Singapore, 117544
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26
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Chen Z, Wang X, Chen X, Huang J, Wang C, Wang J, Wang Z. Accelerating therapeutic protein design with computational approaches toward the clinical stage. Comput Struct Biotechnol J 2023; 21:2909-2926. [PMID: 38213894 PMCID: PMC10781723 DOI: 10.1016/j.csbj.2023.04.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 01/13/2024] Open
Abstract
Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.
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Affiliation(s)
- Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Juyang Huang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Chenglin Wang
- Shenzhen Qiyu Biotechnology Co., Ltd, Shenzhen 518107, China
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
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27
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Wang F, Sangfuang N, McCoubrey LE, Yadav V, Elbadawi M, Orlu M, Gaisford S, Basit AW. Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract. Int J Pharm 2023; 634:122643. [PMID: 36709014 DOI: 10.1016/j.ijpharm.2023.122643] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/26/2023]
Abstract
The oral delivery of peptide therapeutics could facilitate precision treatment of numerous gastrointestinal (GI) and systemic diseases with simple administration for patients. However, the vast majority of licensed peptide drugs are currently administered parenterally due to prohibitive peptide instability in the GI tract. As such, the development of GI-stable peptides is receiving considerable investment. This study provides researchers with the first tool to predict the GI stability of peptide therapeutics based solely on the amino acid sequence. Both unsupervised and supervised machine learning techniques were trained on literature-extracted data describing peptide stability in simulated gastric and small intestinal fluid (SGF and SIF). Based on 109 peptide incubations, classification models for SGF and SIF were developed. The best models utilized k-Nearest Neighbor (for SGF) and XGBoost (for SIF) algorithms, with accuracies of 75.1% (SGF) and 69.3% (SIF), and f1 scores of 84.5% (SGF) and 73.4% (SIF) under 5-fold cross-validation. Feature importance analysis demonstrated that peptides' lipophilicity, rigidity, and size were key determinants of stability. These models are now available to those working on the development of oral peptide therapeutics.
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Affiliation(s)
- Fanjin Wang
- Intract Pharma Ltd. London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | | | | | - Vipul Yadav
- Intract Pharma Ltd. London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Moe Elbadawi
- UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Mine Orlu
- UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Simon Gaisford
- UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Abdul W Basit
- UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK.
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28
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Paganini C, Boyce H, Libort G, Arosio P. High-Yield Production of Extracellular Vesicle Subpopulations with Constant Quality Using Batch-Refeed Cultures. Adv Healthc Mater 2023; 12:e2202232. [PMID: 36479632 DOI: 10.1002/adhm.202202232] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/29/2022] [Indexed: 12/12/2022]
Abstract
The conventional manufacturing of extracellular vesicles (EVs) is characterized by low yields and batch-to-batch variability, hampering fundamental research on EVs and their practical applications. Perfusion operations have huge potential to address these limitations and increase the productivity and quality of EVs. In this study, perfusion cultures are simulated with batch-refeed systems and their productivity is compared with that achieved using batch cultures. It is shown that a shift from batch to batch-refeed system can increase the space-time yields of a target EV subpopulation characterized by CD81 and CD63 biomarkers by threefold. Moreover, it is demonstrated that the method facilitates the consistent production of the target EVs from cells maintained under constant conditions for 13 days. These results indicate that the use of perfusion cultures is a promising strategy to increase the manufacturing yield of EVs and control the production of specific EV subpopulations with constant quality attributes, thereby improving reproducibility.
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Affiliation(s)
- Carolina Paganini
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1-5/10, Zurich, 8093, Switzerland
| | - Hannah Boyce
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1-5/10, Zurich, 8093, Switzerland
| | - Gabriela Libort
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1-5/10, Zurich, 8093, Switzerland
| | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1-5/10, Zurich, 8093, Switzerland
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29
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Zhan H, Zhu X, Qiao Z, Hu J. Graph Neural Tree: A novel and interpretable deep learning-based framework for accurate molecular property predictions. Anal Chim Acta 2023; 1244:340558. [PMID: 36737143 DOI: 10.1016/j.aca.2022.340558] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
Abstract
Determining various properties of molecules is a critical step in drug discovery. Recently, with the improvement of large heterogeneous datasets and the development of deep learning approaches, more and more scientists have turned their attention to neural network-based virtual preliminary screening to reduce the time and monetary cost of drug discovery. However, the poor interpretability of deep learning masks causality, so models' conclusions are often beyond the comprehension of human users, which reduces the credibility of the model and makes it difficult for chemists to further narrow the huge chemical space based on models' results. Thus, this study develops a novel framework consisting of Graph Neural Networks for feature extraction, Curriculum-Based Learning Strategies for optimization, and a Learning Binary Neural Tree (LBNT) for prediction, to improve the performance of neural networks and reveal their decision-making process to chemists. The framework encodes molecular graph data with graph neural networks (GNNs), then retrains the encoder with curriculum-based learning strategies to reduce uncertainty and improve accuracy, and finally uses LBNT as the predictor, which joint retrains with the encoder after independently training, for prediction and visualization. The framework is validated on the public datasets and compared to single GNNs with normal training strategies as well as GNN encoders with common machine learning predictors instead of the LBNT predictor. The result reveals that the proposed framework enhances the point prediction accuracy of the completely trained GNN and reduces its uncertainty through curriculum-based learning, and further improves the accuracy by combining LBNT. Besides, compared with common machine learning tools, the LBNT predictor generally has the best performance because of joint retraining with the GNN encoder. The decision-making process of LBNT is also better and easier to explain than that of other models.
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Affiliation(s)
- Haolin Zhan
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, China; College of Economics and Statistics, Guangzhou University, Guangzhou, China
| | - Xin Zhu
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, China.
| | - Zhiwei Qiao
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, China; Joint Institute of Guangzhou University & Institute of Corrosion Science and Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Jianming Hu
- College of Economics and Statistics, Guangzhou University, Guangzhou, China.
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30
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Condado-Morales I, Sokolova V, Wahlund PO, Heding KE, Auclair S, Kingsbury JS, Arosio P, Lorenzen N. AF4 and PEG Precipitation as Predictive Assays for Antibody Self-Association. Mol Pharm 2023; 20:1323-1330. [PMID: 36668814 DOI: 10.1021/acs.molpharmaceut.2c00946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Monoclonal antibodies (mAbs) are often formulated as high-protein-concentration solutions, which in some cases can exhibit physical stability issues such as high viscosity and opalescence. To ensure that mAb-based drugs can meet their manufacturing, stability, and delivery requirements, it is advantageous to screen for and select mAbs during discovery that are not prone to such behaviors. It has been recently shown that both these macroscopic properties can be predicted to a certain extent from the diffusion interaction parameter (kD), which is a measure of self-association under dilute conditions.1 However, kD can be challenging to measure at the early stage of discovery, where a relatively large amount of a high-purity material, which is required by traditional methods, is often not available. In this study, we demonstrate asymmetric field-flow fractionation (AF4) as a tool to measure self-association and therefore identify antibodies with problematic issues at high concentrations. The principle lies on the ability to concentrate the sample close to the membrane during the injection mode, which can reach formulation-relevant concentrations (>100 mg/mL).2 By analyzing a well-characterized library of commercial antibodies, we show that the measured retention time of the antibodies allows us to pinpoint molecules that exhibit issues at high concentrations. Remarkably, our AF4 assay requires very little (30 μg) sample under dilute conditions and does not need extensive sample purification. Furthermore, we show that a polyethylene glycol (PEG) precipitation assay provides results consistent with AF4 and moreover can further differentiate molecules with issues of opalescence or high viscosity. Overall, our results delineate a two-step strategy for the identification of problematic variants at high concentrations, with AF4 for early developability screening, followed by a PEG assay to validate the problematic molecules and further discriminate between opalescence or high-viscosity issues. This two-step antibody selection strategy enables us to select antibodies early in the discovery process, which are compatible with high-concentration formulations.
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Affiliation(s)
- Itzel Condado-Morales
- Global Research Technology, Novo Nordisk A/S, Maaloev 2760, Denmark.,Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Zürich 8093, Switzerland
| | - Viktoria Sokolova
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Zürich 8093, Switzerland
| | - Per-Olof Wahlund
- Global Research Technology, Novo Nordisk A/S, Maaloev 2760, Denmark
| | | | - Sarah Auclair
- Global CMC Development, Sanofi, Framingham, Massachusetts 02210, United States
| | | | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zürich, Zürich 8093, Switzerland
| | - Nikolai Lorenzen
- Global Research Technology, Novo Nordisk A/S, Maaloev 2760, Denmark
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31
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Qiu Y, Wei GW. Persistent spectral theory-guided protein engineering. NATURE COMPUTATIONAL SCIENCE 2023; 3:149-163. [PMID: 37637776 PMCID: PMC10456983 DOI: 10.1038/s43588-022-00394-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/22/2022] [Indexed: 08/29/2023]
Abstract
While protein engineering, which iteratively optimizes protein fitness by screening the gigantic mutational space, is constrained by experimental capacity, various machine learning models have substantially expedited protein engineering. Three-dimensional protein structures promise further advantages, but their intricate geometric complexity hinders their applications in deep mutational screening. Persistent homology, an established algebraic topology tool for protein structural complexity reduction, fails to capture the homotopic shape evolution during the filtration of a given data. This work introduces a Topology-offered protein Fitness (TopFit) framework to complement protein sequence and structure embeddings. Equipped with an ensemble regression strategy, TopFit integrates the persistent spectral theory, a new topological Laplacian, and two auxiliary sequence embeddings to capture mutation-induced topological invariant, shape evolution, and sequence disparity in the protein fitness landscape. The performance of TopFit is assessed by 34 benchmark datasets with 128,634 variants, involving a vast variety of protein structure acquisition modalities and training set size variations.
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Affiliation(s)
- Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, MI, 48824, USA
- Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA
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32
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Kopp MRG, Grigolato F, Zürcher D, Das TK, Chou D, Wuchner K, Arosio P. Surface-Induced Protein Aggregation and Particle Formation in Biologics: Current Understanding of Mechanisms, Detection and Mitigation Strategies. J Pharm Sci 2023; 112:377-385. [PMID: 36223809 DOI: 10.1016/j.xphs.2022.10.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/05/2022] [Accepted: 10/05/2022] [Indexed: 01/12/2023]
Abstract
Protein stability against aggregation is a major quality concern for the production of safe and effective biopharmaceuticals. Amongst the different drivers of protein aggregation, increasing evidence indicates that interactions between proteins and interfaces represent a major risk factor for the formation of protein aggregates in aqueous solutions. Potentially harmful surfaces relevant to biologics manufacturing and storage include air-water and silicone oil-water interfaces as well as materials from different processing units, storage containers, and delivery devices. The impact of some of these surfaces, for instance originating from impurities, can be difficult to predict and control. Moreover, aggregate formation may additionally be complicated by the simultaneous presence of interfacial, hydrodynamic and mechanical stresses, whose contributions may be difficult to deconvolute. As a consequence, it remains difficult to identify the key chemical and physical determinants and define appropriate analytical methods to monitor and predict protein instability at these interfaces. In this review, we first discuss the main mechanisms of surface-induced protein aggregation. We then review the types of contact materials identified as potentially harmful or detected as potential triggers of proteinaceous particle formation in formulations and discuss proposed mitigation strategies. Finally, we present current methods to probe surface-induced instabilities, which represent a starting point towards assays that can be implemented in early-stage screening and formulation development of biologics.
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Affiliation(s)
- Marie R G Kopp
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Fulvio Grigolato
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Dominik Zürcher
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | | | | | | | - Paolo Arosio
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland.
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33
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Pennell M, Rodriguez OL, Watson CT, Greiff V. The evolutionary and functional significance of germline immunoglobulin gene variation. Trends Immunol 2023; 44:7-21. [PMID: 36470826 DOI: 10.1016/j.it.2022.11.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/07/2022] [Indexed: 12/04/2022]
Abstract
The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.
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Affiliation(s)
- Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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34
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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.
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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
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35
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Fernández-Quintero ML, Ljungars A, Waibl F, Greiff V, Andersen JT, Gjølberg TT, Jenkins TP, Voldborg BG, Grav LM, Kumar S, Georges G, Kettenberger H, Liedl KR, Tessier PM, McCafferty J, Laustsen AH. Assessing developability early in the discovery process for novel biologics. MAbs 2023; 15:2171248. [PMID: 36823021 PMCID: PMC9980699 DOI: 10.1080/19420862.2023.2171248] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/18/2023] [Indexed: 02/25/2023] Open
Abstract
Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.
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Affiliation(s)
- Monica L. Fernández-Quintero
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Anne Ljungars
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Franz Waibl
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Jan Terje Andersen
- Department of Immunology, University of Oslo, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine and Department of Pharmacology, University of Oslo, Oslo, Norway
| | | | - Timothy P. Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Bjørn Gunnar Voldborg
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lise Marie Grav
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA
| | - Guy Georges
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Hubert Kettenberger
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Klaus R. Liedl
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Peter M. Tessier
- Department of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
| | - John McCafferty
- Department of Medicine, Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- Maxion Therapeutics, Babraham Research Campus, Cambridge, UK
| | - Andreas H. Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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Mieczkowski C, Zhang X, Lee D, Nguyen K, Lv W, Wang Y, Zhang Y, Way J, Gries JM. Blueprint for antibody biologics developability. MAbs 2023; 15:2185924. [PMID: 36880643 PMCID: PMC10012935 DOI: 10.1080/19420862.2023.2185924] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
Large-molecule antibody biologics have revolutionized medicine owing to their superior target specificity, pharmacokinetic and pharmacodynamic properties, safety and toxicity profiles, and amenability to versatile engineering. In this review, we focus on preclinical antibody developability, including its definition, scope, and key activities from hit to lead optimization and selection. This includes generation, computational and in silico approaches, molecular engineering, production, analytical and biophysical characterization, stability and forced degradation studies, and process and formulation assessments. More recently, it is apparent these activities not only affect lead selection and manufacturability, but ultimately correlate with clinical progression and success. Emerging developability workflows and strategies are explored as part of a blueprint for developability success that includes an overview of the four major molecular properties that affect all developability outcomes: 1) conformational, 2) chemical, 3) colloidal, and 4) other interactions. We also examine risk assessment and mitigation strategies that increase the likelihood of success for moving the right candidate into the clinic.
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Affiliation(s)
- Carl Mieczkowski
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Xuejin Zhang
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Dana Lee
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Khanh Nguyen
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Wei Lv
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Yanling Wang
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Yue Zhang
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Jackie Way
- Department of Protein Sciences, Hengenix Biotech, Inc, Milpitas, CA, USA
| | - Jean-Michel Gries
- President, Discovery Research, Hengenix Biotech, Inc, Milpitas, CA, USA
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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.
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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.
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Ausserwöger H, Schneider MM, Herling TW, Arosio P, Invernizzi G, Knowles TPJ, Lorenzen N. Non-specificity as the sticky problem in therapeutic antibody development. Nat Rev Chem 2022; 6:844-861. [PMID: 37117703 DOI: 10.1038/s41570-022-00438-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 11/16/2022]
Abstract
Antibodies are highly potent therapeutic scaffolds with more than a hundred different products approved on the market. Successful development of antibody-based drugs requires a trade-off between high target specificity and target binding affinity. In order to better understand this problem, we here review non-specific interactions and explore their fundamental physicochemical origins. We discuss the role of surface patches - clusters of surface-exposed amino acid residues with similar physicochemical properties - as inducers of non-specific interactions. These patches collectively drive interactions including dipole-dipole, π-stacking and hydrophobic interactions to complementary moieties. We elucidate links between these supramolecular assembly processes and macroscopic development issues, such as decreased physical stability and poor in vivo half-life. Finally, we highlight challenges and opportunities for optimizing protein binding specificity and minimizing non-specificity for future generations of therapeutics.
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Zhang W, Wang H, Feng N, Li Y, Gu J, Wang Z. Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics. Antib Ther 2022; 6:13-29. [PMID: 36683767 PMCID: PMC9847343 DOI: 10.1093/abt/tbac029] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/13/2022] Open
Abstract
Developability refers to the likelihood that an antibody candidate will become a manufacturable, safe and efficacious drug. Although the safety and efficacy of a drug candidate will be well considered by sponsors and regulatory agencies, developability in the narrow sense can be defined as the likelihood that an antibody candidate will go smoothly through the chemistry, manufacturing and control (CMC) process at a reasonable cost and within a reasonable timeline. Developability in this sense is the focus of this review. To lower the risk that an antibody candidate with poor developability will move to the CMC stage, the candidate's developability-related properties should be screened, assessed and optimized as early as possible. Assessment of developability at the early discovery stage should be performed in a rapid and high-throughput manner while consuming small amounts of testing materials. In addition to monoclonal antibodies, bispecific antibodies, multispecific antibodies and antibody-drug conjugates, as the derivatives of monoclonal antibodies, should also be assessed for developability. Moreover, we propose that the criterion of developability is relative: expected clinical indication, and the dosage and administration route of the antibody could affect this criterion. We also recommend a general screening process during the early discovery stage of antibody-derived therapeutics. With the advance of artificial intelligence-aided prediction of protein structures and features, computational tools can be used to predict, screen and optimize the developability of antibody candidates and greatly reduce the risk of moving a suboptimal candidate to the development stage.
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Affiliation(s)
- Weijie Zhang
- Biologicals Innovation and Discovery, WuXi Biologicals, 1951 Huifeng West Road, Fengxian District, Shanghai 201400, China
| | - Hao Wang
- Biologicals Innovation and Discovery, WuXi Biologicals, 1951 Huifeng West Road, Fengxian District, Shanghai 201400, China
| | - Nan Feng
- Biologicals Innovation and Discovery, WuXi Biologicals, 1951 Huifeng West Road, Fengxian District, Shanghai 201400, China
| | - Yifeng Li
- Technology and Process Development, WuXi Biologicals, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | - Jijie Gu
- Biologicals Innovation and Discovery, WuXi Biologicals, 1951 Huifeng West Road, Fengxian District, Shanghai 201400, China
| | - Zhuozhi Wang
- To whom correspondence should be addressed. Biologics Innovation and Discovery, WuXi Biologicals, 1951 Huifeng West Road, Fengxian District, Shanghai 201400, China, Phone number: +86-21-50518899
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Qiu Y, Wei GW. CLADE 2.0: Evolution-Driven Cluster Learning-Assisted Directed Evolution. J Chem Inf Model 2022; 62:4629-4641. [PMID: 36154171 DOI: 10.1021/acs.jcim.2c01046] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Directed evolution, a revolutionary biotechnology in protein engineering, optimizes protein fitness by searching an astronomical mutational space via expensive experiments. The cluster learning-assisted directed evolution (CLADE) efficiently explores the mutational space via a combination of unsupervised hierarchical clustering and supervised learning. However, the initial-stage sampling in CLADE treats all clusters equally despite many clusters containing a large portion of non-functional mutations. Recent statistical and deep learning tools enable evolutionary density modeling to access protein fitness in an unsupervised manner. In this work, we construct an ensemble of multiple evolutionary scores to guide the initial sampling in CLADE. The resulting evolutionary score-enhanced CLADE, called CLADE 2.0, efficiently selects a training set within a small informative space using the evolution-driven clustering sampling. CLADE 2.0 is validated by using two benchmark libraries both having 160,000 sequences from four-site mutational combinations. Extensive computational experiments and comparisons with existing cutting-edge methods indicate that CLADE 2.0 is a new state-of-art tool for machine learning-assisted directed evolution.
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Affiliation(s)
- Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States.,Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
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41
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Cano-Garrido O, Serna N, Unzueta U, Parladé E, Mangues R, Villaverde A, Vázquez E. Protein scaffolds in human clinics. Biotechnol Adv 2022; 61:108032. [PMID: 36089254 DOI: 10.1016/j.biotechadv.2022.108032] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/30/2022] [Accepted: 09/03/2022] [Indexed: 11/02/2022]
Abstract
Fundamental clinical areas such as drug delivery and regenerative medicine require biocompatible materials as mechanically stable scaffolds or as nanoscale drug carriers. Among the wide set of emerging biomaterials, polypeptides offer enticing properties over alternative polymers, including full biocompatibility, biodegradability, precise interactivity, structural stability and conformational and functional versatility, all of them tunable by conventional protein engineering. However, proteins from non-human sources elicit immunotoxicities that might bottleneck further development and narrow their clinical applicability. In this context, selecting human proteins or developing humanized protein versions as building blocks is a strict demand to design non-immunogenic protein materials. We review here the expanding catalogue of human or humanized proteins tailored to execute different levels of scaffolding functions and how they can be engineered as self-assembling materials in form of oligomers, polymers or complex networks. In particular, we emphasize those that are under clinical development, revising their fields of applicability and how they have been adapted to offer, apart from mere mechanical support, highly refined functions and precise molecular interactions.
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Affiliation(s)
- Olivia Cano-Garrido
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain
| | - Naroa Serna
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain
| | - Ugutz Unzueta
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain; Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; Biomedical Research Institute Sant Pau (IIB Sant Pau), 08025 Barcelona, Spain; Josep Carreras Leukaemia Research Institute, 08916 Badalona (Barcelona), Spain
| | - Eloi Parladé
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain
| | - Ramón Mangues
- Biomedical Research Institute Sant Pau (IIB Sant Pau), 08025 Barcelona, Spain; Josep Carreras Leukaemia Research Institute, 08916 Badalona (Barcelona), Spain
| | - Antonio Villaverde
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain; Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain.
| | - Esther Vázquez
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain; Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain.
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42
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Rahban M, Zolghadri S, Salehi N, Ahmad F, Haertlé T, Rezaei-Ghaleh N, Sawyer L, Saboury AA. Thermal stability enhancement: Fundamental concepts of protein engineering strategies to manipulate the flexible structure. Int J Biol Macromol 2022; 214:642-654. [DOI: 10.1016/j.ijbiomac.2022.06.154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 01/28/2023]
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43
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Liu Z, Zhang R, Zhang W, Xu Y. Structure-based rational design of hydroxysteroid dehydrogenases for improving and diversifying steroid synthesis. Crit Rev Biotechnol 2022:1-17. [PMID: 35834355 DOI: 10.1080/07388551.2022.2054770] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
A group of steroidogenic enzymes, hydroxysteroid dehydrogenases are involved in steroid metabolism which is very important in the cell: signaling, growth, reproduction, and energy homeostasis. The enzymes show an inherent function in the interconversion of ketosteroids and hydroxysteroids in a position- and stereospecific manner on the steroid nucleus and side-chains. However, the biocatalysis of steroids reaction is a vital and demanding, yet challenging, task to produce the desired enantiopure products with non-natural substrates or non-natural cofactors, and/or in non-physiological conditions. This has driven the use of protein design strategies to improve their inherent biosynthetic efficiency or activate their silent catalytic ability. In this review, the innate features and catalytic characteristics of enzymes based on sequence-structure-function relationships of steroidogenic enzymes are reviewed. Combining structure information and catalytic mechanisms, progress in protein redesign to stimulate potential function, for example, substrate specificity, cofactor dependence, and catalytic stability are discussed.
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Affiliation(s)
- Zhiyong Liu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi, China
| | - Rongzhen Zhang
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi, China
| | - Wenchi Zhang
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yan Xu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi, China
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44
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Narayanan H, Luna M, Sokolov M, Butté A, Morbidelli M. Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Harini Narayanan
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Martin Luna
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | | | | | - Massimo Morbidelli
- DataHow AG, Zürichstrasse 137, 8600 Dübendorf, Switzerland
- Dipartimento di Chimica, Materiali e Ingegneria Chimica, Giulio Natta, Politecnico di Milano, 20131 Milano, Italy
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45
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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.
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Affiliation(s)
| | | | - Michele Vendruscolo
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK; (T.L.); (P.S.)
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46
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Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models. Processes (Basel) 2022. [DOI: 10.3390/pr10040662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general.
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47
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Sankar K, Trainor K, Blazer L, Adams J, Sidhu S, Day T, Meiering E, Maier J. A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics. Mol Inform 2022; 41:e2100240. [PMID: 35277930 DOI: 10.1002/minf.202100240] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 03/11/2022] [Indexed: 11/12/2022]
Abstract
In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.
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48
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Paganini C, Hettich B, Kopp MR, Eördögh A, Capasso Palmiero U, Adamo G, Touzet N, Manno M, Bongiovanni A, Rivera‐Fuentes P, Leroux J, Arosio P. Rapid Characterization and Quantification of Extracellular Vesicles by Fluorescence-Based Microfluidic Diffusion Sizing. Adv Healthc Mater 2022; 11:e2100021. [PMID: 34109753 DOI: 10.1002/adhm.202100021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/19/2021] [Indexed: 12/16/2022]
Abstract
Extracellular vesicles (EVs) are emerging as promising diagnostic and therapeutic tools for a variety of diseases. The characterization of EVs requires a series of orthogonal techniques that are overall time- and material-consuming. Here, a microfluidic device is presented that exploits the combination of diffusion sizing and multiwavelength fluorescence detection to simultaneously provide information on EV size, concentration, and composition. The latter is achieved with the nonspecific staining of lipids and proteins combined with the specific staining of EV markers such as EV-associated tetraspanins via antibodies. The device can be operated as a single-step immunoassay thanks to the integrated separation and quantification of free and EV-bound fluorophores. This microfluidic technique is capable of detecting and quantifying components associated to EV subtypes and impurities and thus to measure EV purity in a time scale of minutes, requiring less than 5 µL of sample and minimal sample handling before the analysis. Moreover, the analysis is performed directly in solution without immobilization steps. Therefore, this method can accelerate screening of EV samples and aid the evaluation of sample reproducibility, representing an important complementary tool to the current array of biophysical methods for EV characterization, particularly valuable for instance for bioprocess development.
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Affiliation(s)
- Carolina Paganini
- Department of Chemistry and Applied Biosciences ETH Zürich Vladimir‐Prelog‐Weg 1–5/10 Zürich 8093 Switzerland
| | - Britta Hettich
- Department of Chemistry and Applied Biosciences ETH Zürich Vladimir‐Prelog‐Weg 1–5/10 Zürich 8093 Switzerland
| | - Marie R.G. Kopp
- Department of Chemistry and Applied Biosciences ETH Zürich Vladimir‐Prelog‐Weg 1–5/10 Zürich 8093 Switzerland
| | - Adam Eördögh
- Department of Chemistry and Applied Biosciences ETH Zürich Vladimir‐Prelog‐Weg 1–5/10 Zürich 8093 Switzerland
- Institute of Chemical Sciences and Engineering EPFL CH C2 425, Bâtiment CH, Station 6 Lausanne CH‐1015 Switzerland
| | - Umberto Capasso Palmiero
- Department of Chemistry and Applied Biosciences ETH Zürich Vladimir‐Prelog‐Weg 1–5/10 Zürich 8093 Switzerland
| | - Giorgia Adamo
- Institute of Biomedical Research and Innovation National Research Council of Italy Via Ugo La Malfa 153 Palermo 90146 Italy
| | - Nicolas Touzet
- Department of Environmental Science IT Sligo Ash Lane Sligo F91 YW50 Ireland
| | - Mauro Manno
- Institute of Biophysics National Research Council of Italy Via Ugo La Malfa 153 Palermo 90146 Italy
| | - Antonella Bongiovanni
- Institute of Biomedical Research and Innovation National Research Council of Italy Via Ugo La Malfa 153 Palermo 90146 Italy
| | - Pablo Rivera‐Fuentes
- Institute of Chemical Sciences and Engineering EPFL CH C2 425, Bâtiment CH, Station 6 Lausanne CH‐1015 Switzerland
| | - Jean‐Christophe Leroux
- Department of Chemistry and Applied Biosciences ETH Zürich Vladimir‐Prelog‐Weg 1–5/10 Zürich 8093 Switzerland
| | - Paolo Arosio
- Department of Chemistry and Applied Biosciences ETH Zürich Vladimir‐Prelog‐Weg 1–5/10 Zürich 8093 Switzerland
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Trenfield SJ, Awad A, McCoubrey LE, Elbadawi M, Goyanes A, Gaisford S, Basit AW. Advancing pharmacy and healthcare with virtual digital technologies. Adv Drug Deliv Rev 2022; 182:114098. [PMID: 34998901 DOI: 10.1016/j.addr.2021.114098] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.
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Khetan R, Curtis R, Deane CM, Hadsund JT, Kar U, Krawczyk K, Kuroda D, Robinson SA, Sormanni P, Tsumoto K, Warwicker J, Martin ACR. Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. MAbs 2022; 14:2020082. [PMID: 35104168 PMCID: PMC8812776 DOI: 10.1080/19420862.2021.2020082] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.
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Affiliation(s)
- Rahul Khetan
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Robin Curtis
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | | | | | - Uddipan Kar
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | | | - Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | | | - Pietro Sormanni
- Chemistry of Health, Yusuf Hamied Department of Chemistry, University of Cambridge
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.,Medical Device Development and Regulation Research Center, School of Engineering, The University of Tokyo, Tokyo, Japan.,Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.,The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Jim Warwicker
- Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
| | - Andrew C R Martin
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
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