Titlebook: Computer-Aided Antibody Design; Kouhei Tsumoto,Daisuke Kuroda Book 2023 Springer Science+Business Media, LLC, part of Springer Nature 2023

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Computer-Aided Antibody Design978-1-0716-2609-2Series ISSN 1064-3745 Series E-ISSN 1940-6029

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https://doi.org/10.1007/978-3-030-47223-8Complex and coordinated dynamics are closely connected with protein functions, including the binding of antibodies to antigens. Knowledge of such dynamics could improve the design of antibodies. Molecular dynamics (MD) simulations provide a “computational microscope” that can resolve atomic motions and inform antibody design efforts.

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https://doi.org/10.1007/978-1-0716-2609-2Antibodies; Drug discovery; Computational methods; Protein engineering; Biotechnology

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,Trade Unions and Employers’ Associations,MD simulation procedure for the antigen–antibody system but also an umbrella sampling method following a multistep targeted MD simulation (US/mTMD), which is useful for evaluating the free energy profile along the antigen–antibody dissociation coordinate.

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Book 2023 structures,  modeling antibody structures and dynamics, prediction and optimization of biological and biophysical properties of antibodies, prediction of antibody-antigen interactions, and computer-aided antibody affinity maturation and beyond. Written in the format of the highly successful .Method

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1064-3745 ation advice from the experts.This volume details state-of-the- art methods on computer-aided antibody design. Chapters guide readers through information on antibody sequences and structures,  modeling antibody structures and dynamics, prediction and optimization of biological and biophysical proper

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Anna Kicinger,Agnieszka Weinar,Agata GórnyHere, we describe an efficient structure-based computational method for epitope prediction, called MLCE. In this approach, all that is required is the 3D structure of the antigen of interest. MLCE can be applied to glycosylated proteins, facilitating the identification of immunoreactive versus immune-shielding carbohydrates.