A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding.
Rahmad Akbar,Philippe Robert,Milena Pavlović,Jeliazko R. Jeliazkov,Igor Snapkov,Andrei Slabodkin,Cédric R. Weber,Lonneke Scheffer,Enkelejda Miho,Ingrid Hobæk Haff,Dag Haug,Fridtjof Lund-Johansen,Yana Safonova,Geir Kjetil Sandve,Victor Greiff +14 more
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In this paper, the authors identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions, and show that this vocabulary enables the machine learnability of antibody-antigen binding using generative machine learning.About:
This article is published in Cell Reports.The article was published on 2021-03-16 and is currently open access. It has received 74 citations till now. The article focuses on the topics: Paratope & Epitope.read more
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Antibody structure prediction using interpretable deep learning
TL;DR: DeepAb as discussed by the authors uses a directly interpretable attention mechanism to attend to physically important residue pairs (e.g., proximal aromatics and key hydrogen bonding interactions) for predicting antibody FV structures.
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Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery.
Andreas Hougaard Laustsen,Victor Greiff,Aneesh Karatt-Vellatt,Serge Muyldermans,Timothy P. Jenkins +4 more
TL;DR: In this article, the authors argue that the number of animals used in immunization campaigns is dwarfed by the number sacrificed during preclinical studies, and that improving quality control of antibodies before entering in vivo studies will have a larger impact on animal consumption.
Journal ArticleDOI
Understanding the human antibody repertoire
TL;DR: This review evaluates the evolutionary aspects of the adaptive immune system, the calculations that lead to the large repertoire estimates, some of the experimental evidence pointing to a more restricted repertoire whose variation appears to derive from convergent ‘structure and specificity features’, and includes a theoretical model that seems to support it.
Journal ArticleDOI
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
Rahmad Akbar,Habib Bashour,Puneet Rawat,Philippe Robert,Eva Smorodina,Tudor-Stefan Cotet,Karine Flem-Karlsen,Robert Frank,Brij Bhushan Mehta,Mai Ha Vu,Talip Zengin,José F. Gutierrez-Marcos,Fridtjof Lund-Johansen,Jan Terje Andersen,Victor Greiff +14 more
TL;DR: It is argued that the main necessary machine learning 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 silica m Ab sequence synthesis.
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DLAB-Deep learning methods for structure-based virtual screening of antibodies.
TL;DR: In this article, a framework for structure-based deep learning for antibodies (DLAB) is introduced, which can virtually screen putative binding antibodies against antigen targets of interest, and can be used both to improve antibody-antigen docking and to perform a virtual screening of antibody drug candidates.
References
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Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
Paul Shannon,Andrew Markiel,Owen Ozier,Nitin S. Baliga,Jonathan T. Wang,Daniel Ramage,Nada Amin,Benno Schwikowski,Trey Ideker +8 more
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Book
ggplot2: Elegant Graphics for Data Analysis
TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
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