Abstract:Nanobodies, a subclass of antibodies found in camelids, are a versatile molecular binding scaffold composed of a single polypeptide chain. The small size of nanobodies bestows multiple therapeutic advantages (stability, tumor penetration) with the first therapeutic approval in 2018 cementing the clinical viability of this format. Structured data and sequence information of nanobodies will enable the accelerated clinical development of nanobody-based therapeutics. Though the nanobody sequence and structure data… Show more
“…Some of the databases that can be used for the analysis of nanobody-derived therapeutics are the Single Domain Antibody Database 170 (sdAb-DB), Integrated Nanobody Database for Immunoinformatics 11 (INDI Nanobodies DB), Non-redundant Nanobody database 171 and database of Institute Collection and Analysis of Nanobodies 172 (iCAN). These databases host large collections of natural and synthetic camelid single-domain antibody sequences from literature sources and other online repositories.…”
Section: Future Perspectives In Biopharmaceutical Informaticsmentioning
confidence: 99%
“…For instance, structural modeling can provide a conformational dimension to millions of sequences drawn from NGS, 9 whereas contrasting naturally sourced and therapeutically developed molecules can provide insights on commonalities and divergences between the two sources. 10 A good example of such an integrated approach is the INDI database, 11 which contains data for antibody-cognate nanobodies (single-domain antibodies VHH) collected from all major public sources, encompassing patents, 8 NCBI GenBank, Protein Data Bank (PDB), and NGS/AIRR 12 supplemented by manual curation from the scientific literature. The sequences and structures of antibodies from these heterogeneous sources are linked with textual information into an antibody-specific database.…”
Section: Introductionmentioning
confidence: 99%
“… Integrated Nanobody Database for Immunoinformatics (INDI) Database with structure data and sequence information of nanobodies created using an integrated curation approach from several sources. http://research.naturalantibody.com/nanobodies 11 Structure Databases 9. Protein Data Bank (PDB) 3D structure data for large biological molecules (proteins, DNA, and RNA).…”
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.
“…Some of the databases that can be used for the analysis of nanobody-derived therapeutics are the Single Domain Antibody Database 170 (sdAb-DB), Integrated Nanobody Database for Immunoinformatics 11 (INDI Nanobodies DB), Non-redundant Nanobody database 171 and database of Institute Collection and Analysis of Nanobodies 172 (iCAN). These databases host large collections of natural and synthetic camelid single-domain antibody sequences from literature sources and other online repositories.…”
Section: Future Perspectives In Biopharmaceutical Informaticsmentioning
confidence: 99%
“…For instance, structural modeling can provide a conformational dimension to millions of sequences drawn from NGS, 9 whereas contrasting naturally sourced and therapeutically developed molecules can provide insights on commonalities and divergences between the two sources. 10 A good example of such an integrated approach is the INDI database, 11 which contains data for antibody-cognate nanobodies (single-domain antibodies VHH) collected from all major public sources, encompassing patents, 8 NCBI GenBank, Protein Data Bank (PDB), and NGS/AIRR 12 supplemented by manual curation from the scientific literature. The sequences and structures of antibodies from these heterogeneous sources are linked with textual information into an antibody-specific database.…”
Section: Introductionmentioning
confidence: 99%
“… Integrated Nanobody Database for Immunoinformatics (INDI) Database with structure data and sequence information of nanobodies created using an integrated curation approach from several sources. http://research.naturalantibody.com/nanobodies 11 Structure Databases 9. Protein Data Bank (PDB) 3D structure data for large biological molecules (proteins, DNA, and RNA).…”
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.
“…There are several databases compiling nanobody sequences (but not structures) from a variety of data sources. INDI ( 14 ) contains more than 11 million nanobody sequences, including, at time of writing, sequences derived from 805 PDB structures, but does not provide experimentally resolved structures. sdAb-DB ( 15 ) contains 1452 single-domain antibody sequences, of which 195 are derived from experimentally resolved structures, but similarly does not provide the corresponding structures.…”
In 2013, we released the Structural Antibody Database (SAbDab), a publicly available repository of experimentally determined antibody structures. In the interim, the rapid increase in the number of antibody structure depositions to the Protein Data Bank, driven primarily by increased interest in antibodies as biotherapeutics, has led us to implement several improvements to the original database infrastructure. These include the development of SAbDab-nano, a sub-database that tracks nanobodies (heavy chain-only antibodies) which have seen a particular growth in attention from both the academic and pharmaceutical research communities over the past few years. Both SAbDab and SAbDab-nano are updated weekly, comprehensively annotated with the latest features described here, and are freely accessible at opig.stats.ox.ac.uk/webapps/newsabdab/.
“… 69 Indeed, only large and exhaustive data will allow us to perform subsampling studies 27 , 54 for determining the minimal dataset size necessary to achieve satisfactory prediction accuracy on a given prediction task. In order to reach data completeness faster, it may be interesting to explore experimentally to what degree some parameters may be set constant, such as for example only working on the CDRH3 86 or with single-chain antibodies 207 or only with linear epitopes (or antigen immunizations with simple peptides). 208 , 209 Figure 4.…”
Section: Learnability Of Antibody–antigen Bindingmentioning
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of
in silico
on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an
in silico
mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven
in silico
mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
