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Antibody design is an essential yet challenging task in various domains like therapeutics and biology.
On information and sufficiency
Kullback, S. and Leibler, R. A · 1951
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Abbreviations and symbols for the description of the conformation of polypeptide chains
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A solution for the best rotation to relate two sets of vectors
Kabsch, W · 1976
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Antibody framework residues affecting the conformation of the hypervariable loops
Foote, J. and Winter, G · 1992
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Amino acid substitution matrices from protein blocks
Henikoff, S. and Henikoff, J. G · 1992
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Robust estimation of a location parameter
Huber, P. J · 1992
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Automated docking of flexible ligands: applications of autodock
Goodsell, D. S., Morris, G. M., and Olson, A. J · 1996
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Principles of protein-protein interactions
Jones, S. and Thornton, J. M · 1996
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Antibody-antigen interactions: contact analysis and binding site topography
MacCallum, R. M., Martin, A. C., and Thornton, J. M · 1996
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Charmm: the energy function and its parameterization
MacKerell Jr, A. D., Brooks, B., Brooks III, C. L., Nilsson, L., Roux, B., Won, Y., and Karplus, M · 2002
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Imgt unique numbering for immunoglobulin and t cell receptor variable domains and ig superfamily v-like domains
Lefranc, M.-P., Pommié, C., Ruiz, M., Giudicelli, V., Foulquier, E., Truong, L., Thouvenin-Contet, V., and Lefranc, G · 2003
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Scoring function for automated assessment of protein structure template quality
Zhang, Y. and Skolnick, J · 2004
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The foldx web server: an online force field
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., and Serrano, L · 2005
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Potent antibody therapeutics by design
Carter, P. J · 2006
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How significant is a protein structure similarity with tm-score= 0.5?
Xu, J. and Zhang, Y · 2010
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Rosetta3: an object-oriented software suite for the simulation and design of macromolecules
Leaver-Fay, A., Tyka, M., Lewis, S. M., Lange, O. F., Thompson, J., Jacak, R., Kaufman, K. W., Renfrew, P. D., Smith, C. A., Sheffler, W., et al · 2011
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Computer-aided antibody design
Kuroda, D., Shirai, H., Jacobson, M. P., and Nakamura, H · 2012
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Antigen–antibody interface properties: Composition, residue interactions, and features of 53 non-redundant structures
Ramaraj, T., Angel, T., Dratz, E. A., Jesaitis, A. J., and Mumey, B · 2012
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Somatic mutations of the immunoglobulin framework are generally required for broad and potent hiv-1 neutralization
Klein, F., Diskin, R., Scheid, J. F., Gaebler, C., Mouquet, H., Georgiev, I. S., Pancera, M., Zhou, T., Incesu, R.-B., Fu, B. Z., et al · 2013
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lddt: a local superposition-free score for comparing protein structures and models using distance difference tests
Mariani, V., Biasini, M., Barbato, A., and Schwede, T · 2013
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Sabdab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., and Deane, C. M · 2014
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Optmaven–a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes
Li, T., Pantazes, R. J., and Maranas, C. D · 2014
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Advances in antibody design
Tiller, K. E. and Tessier, P. M · 2015
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Dockq: a quality measure for protein-protein docking models
Basu, S. and Wallner, B · 2016
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Is it reliable to use common molecular docking methods for comparing the binding affinities of enantiomer pairs for their protein target?
Ramírez, D. and Caballero, J · 2016
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The rosetta all-atom energy function for macromolecular modeling and design
A highly conserved cryptic epitope in the receptor binding domains of sars-cov-2 and sars-cov
Yuan, M., Wu, N. C., Zhu, X., Lee, C.-C. D., So, R. T., Lv, H., Mok, C. K., and Wilson, I. A · 2020
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A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding
Akbar, R., Robert, P. A., Pavlović, M., Jeliazkov, J. R., Snapkov, I., Slabodkin, A., Weber, C. R., Scheffer, L., Miho, E., Haff, I. H., et al · 2021
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Independent se (3)-equivariant models for end-to-end rigid protein docking
Ganea, O.-E., Huang, X., Bunne, C., Bian, Y., Barzilay, R., Jaakkola, T., and Krause, A · 2021
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Iterative refinement graph neural network for antibody sequence-structure co-design
Jin, W., Wohlwend, J., Barzilay, R., and Jaakkola, T · 2021
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Highly accurate protein structure prediction with alphafold
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Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al · 2017
Cited alongside, same era.
Openmm 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al · 2017
Cited alongside, same era.
The cluspro web server for protein–protein docking
Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., and Vajda, S · 2017
Cited alongside, same era.
Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M. and Söding, J · 2017
Cited alongside, same era.
Rosettaantibodydesign (rabd): A general framework for computational antibody design
Adolf-Bryfogle, J., Kalyuzhniy, O., Kubitz, M., Weitzner, B. D., Hu, X., Adachi, Y., Schief, W. R., and Dunbrack Jr, R. L · 2018
Cited alongside, same era.
Progress and challenges in the design and clinical development of antibodies for cancer therapy
Almagro, J. C., Daniels-Wells, T. R., Perez-Tapia, S. M., and Penichet, M. L · 2018
Cited alongside, same era.
Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data?
Ramírez, D. and Caballero, J · 2018
Cited alongside, same era.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al · 2021
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Deciphering antibody affinity maturation with language models and weakly supervised learning
Ruffolo, J. A., Gray, J. J., and Sulam, J · 2021
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Antibody design using lstm based deep generative model from phage display library for affinity maturation
Saka, K., Kakuzaki, T., Metsugi, S., Kashiwagi, D., Yoshida, K., Wada, M., Tsunoda, H., and Teramoto, R · 2021
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E (n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
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In silico proof of principle of machine learning-based antibody design at unconstrained scale
Akbar, R., Robert, P. A., Weber, C. R., Widrich, M., Frank, R., Pavlović, M., Scheffer, L., Chernigovskaya, M., Snapkov, I., Slabodkin, A., et al · 2022
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Protein complex prediction with alphafold-multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al · 2022
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Equivariant graph mechanics networks with constraints
Huang, W., Han, J., Rong, Y., Xu, T., Sun, F., and Huang, J · 2022
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Antibody-antigen docking and design via hierarchical structure refinement
Jin, W., Barzilay, R., and Jaakkola, T · 2022
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Conditional antibody design as 3d equivariant graph translation
Kong, X., Huang, W., and Liu, Y · 2022
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Antigen-specific antibody design and optimization with diffusion-based generative models
Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., and Ma, J · 2022
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Proximal exploration for model-guided protein sequence design
Ren, Z., Li, J., Ding, F., Zhou, Y., Ma, J., and Peng, J · 2022
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Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Ruffolo, J. A. and Gray, J. J · 2022
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Deep learning guided optimization of human antibody against sars-cov-2 variants with broad neutralization
Shan, S., Luo, S., Yang, Z., Hong, J., Su, Y., Ding, F., Fu, L., Li, C., Chen, P., Ma, J., et al · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J · 2022
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al · 2023
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