Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

2021 ◽  
Author(s):  
Derek M. Mason ◽  
Simon Friedensohn ◽  
Cédric R. Weber ◽  
Christian Jordi ◽  
Bastian Wagner ◽  
...  
Keyword(s):  
Deep Learning ◽  
Therapeutic Antibodies ◽  
Antigen Specificity ◽  
Antibody Sequence

scholarly journals AbDiver - A tool to explore the natural antibody landscape to aid therapeutic design

2021 ◽  
Author(s):  
Jakub Mlokosiewicz ◽  
Piotr Deszynski ◽  
Wiktoria Wilman ◽  
Igor Jaszczyszyn ◽  
Rajkumar Ganesan ◽  
...  
Keyword(s):  
Rational Design ◽  
Therapeutic Antibody ◽  
Human Antibody ◽  
Use Case ◽  
Therapeutic Antibodies ◽  
Road Map ◽  
Antibody Sequence ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

Motivation: Rational design of therapeutic antibodies can be improved by harnessing the natural sequence diversity of these molecules. Our understanding of the diversity of antibodies has recently been greatly facilitated through the deposition of hundreds of millions of human antibody sequences in next-generation sequencing (NGS) repositories. Contrasting a query therapeutic antibody sequence to naturally observed diversity in similar antibody sequences from NGS can provide a mutational road-map for antibody engineers designing biotherapeutics. Because of the sheer scale of the antibody NGS datasets, performing queries across them is computationally challenging. Results: To facilitate harnessing antibody NGS data, we developed AbDiver (http://naturalantibody.com/abdiver), a free portal allowing users to compare their query sequences to those observed in the natural repertoires. AbDiver offers three antibody-specific use-cases: 1) compare a query antibody to positional variability statistics precomputed from multiple independent studies 2) retrieve close full variable sequence matches to a query antibody and 3) retrieve CDR3 or clonotype matches to a query antibody. We applied our system to a set of 742 therapeutic antibodies, demonstrating that for each use-case our system can retrieve relevant results for most sequences. AbDiver facilitates the navigation of vast antibody mutation space for the purpose of rational therapeutic antibody de-sign and engineering. Availability: AbDiver is freely accessible at http://naturalantibody.com/abdiver

scholarly journals Deep learning enables therapeutic antibody optimization in mammalian cells by deciphering high-dimensional protein sequence space

2019 ◽  
Author(s):  
Derek M Mason ◽  
Simon Friedensohn ◽  
Cédric R Weber ◽  
Christian Jordi ◽  
Bastian Wagner ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sequence Space ◽  
Protein Sequence ◽  
In Silico ◽  
Mammalian Cells ◽  
Therapeutic Antibody ◽  
Quality Data ◽  
High Dimensional ◽  
Antigen Specificity ◽  
Protein Sequence Space

ABSTRACTTherapeutic antibody optimization is time and resource intensive, largely because it requires low-throughput screening (103 variants) of full-length IgG in mammalian cells, typically resulting in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-specificity from a massively diverse sequence space to identify globally optimized antibody variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small libraries (104) produced high quality data capable of training deep neural networks that accurately predict antigen-binding based on antibody sequence. Deep learning is then used to predict millions of antigen binders from an in silico library of ~108 variants, where experimental testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set of in silico predicted binders is then subjected to multiple developability filters, resulting in thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate high-dimensional protein sequence space, deep learning offers great potential for antibody engineering and optimization.

Applications of Machine and Deep Learning in Adaptive Immunity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Margarita Pertseva ◽  
Beichen Gao ◽  
Daniel Neumeier ◽  
Alexander Yermanos ◽  
Sai T. Reddy
Keyword(s):  
Deep Learning ◽  
Adaptive Immunity ◽  
Large Scale ◽  
Sequence Data ◽  
Annual Review ◽  
Publication Date ◽  
Biological Sequence ◽  
Antigen Specificity ◽  
Adaptive Immune ◽  
Receptor Repertoire

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide–MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Sequence and functional characterization of a public HIV-specific antibody clonotype

2021 ◽  
Author(s):  
Nagarajan Raju ◽  
Juliana S Qin ◽  
Emilee Friedman Fechter ◽  
Ian Setliff ◽  
Ivelin S Georgiev ◽  
...  
Keyword(s):  
Specific Antibody ◽  
Vaccine Development ◽  
Functional Characterization ◽  
Population Level ◽  
Light Chains ◽  
Antigen Specificity ◽  
The Public ◽  
Sequence Identity ◽  
Antibody Sequence

Public antibody clonotypes shared among multiple individuals have been identified for several pathogens. However, little is known about the limits of what constitutes a public clonotype. Here, we characterize the sequence and functional properties of antibodies from a public HIV-specific clonotype comprising sequences from 3 individuals. Our results showed that antigen specificity for the public antibodies was modulated by the VH, but not VL, germline gene. Non-native pairing of public heavy and light chains from different donors resulted in antibodies with consistent antigen specificity, suggesting functional complementation of sequences within the public antibody clonotype. The strength of antigen recognition appeared to be dependent on the specific antibody light chain used, but not on other sequence features such as germline or native-antibody sequence identity. Understanding the determinants of antibody clonotype "publicness" can provide insights into the fundamental rules of host-pathogen interactions at the population level, with implications for clonotype-specific vaccine development.

scholarly journals Antibody structure prediction using interpretable deep learning

2021 ◽  
Author(s):  
Jeffrey A Ruffolo ◽  
Jeremias Sulam ◽  
Jeffrey J. Gray
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Rational Design ◽  
Clinical Stage ◽  
Structural Diversity ◽  
Therapeutic Antibodies ◽  
Learning Methods ◽  
Black Boxes ◽  
Bonding Interaction ◽  
General Protein

Therapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design of antibodies is hindered by reliance on experimental methods for determining antibody structures. In recent years, deep learning methods have driven significant advances in general protein structure prediction. Here, we present DeepAb, a deep learning method for predicting accurate antibody FV structures from sequence. We evaluate DeepAb on two benchmark sets - one balanced for structural diversity and the other composed of clinical-stage therapeutic antibodies - and find that our method consistently outperforms the leading alternatives. Previous deep learning methods have operated as “black boxes” and offered few insights into their predictions. By introducing a directly interpretable attention mechanism, we show that our network attends to physically important residue pairs. For example, in prediction of one CDR H3 residue conformation, the network attends to proximal aromatics and a key hydrogen bonding interaction that constrain the loop conformation. Finally, we present a novel mutant scoring metric derived from network confidence and show that for a particular antibody, all ten of the top-ranked mutations improve binding affinity. These results suggest that this model will be useful for a broad range of antibody prediction and design tasks.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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