According to Dr. Kingwell, KT cell receptor therapeutics have reached the stage of immuno-oncology. nut. review. drag discob. https://www.nature.com/articles/d41573-022-00073-7 (2022).
Kaplon, H., Chenoweth, A., Crescioli, S., Reichert, JM Antibodies to watch in 2022. monoclonal antibody 142014296 (2022).
Lou, R.-M. et al. Development of therapeutic antibodies for the treatment of disease. J. Biomed. Science. 271–30 (2020).
Yang, EY & Shah, K. Nanobodies: the next generation of cancer diagnosis and therapy. front. Onkol. Ten1182 (2020).
Regep, C., Georges, G., Shi, J., Popovic, B. & Deane, CM The H3 loop of antibodies exhibits unique structural features. protein structure. Function, biometric information. 851311–1318 (2017).
Tsuchiya, Y. & Mizuguchi, K. H3 loop diversity determines the antigen-binding propensity of antibody CDR loops. protein science. twenty five815–825 (2016).
Wong, WK, Leem, J. & Deane, CM Comparative analysis of CDR loops of antigen receptors. front. immunol. Ten2454 (2019).
Mitchell, LS & Colwell, LJ Comparative analysis of sequence and structural data for nanobodies. Protein: structure. function. bioinform. 86697–706 (2018).
Kovaltsuk, A. et al. The Observed Antibody Space: A Resource for Data Mining Next Generation Sequencing of Antibody Repertoires. J. Immunol. 2012502–2509 (2018).
Olsen, TH, Boyles, F. & Deane, CM The Observed Antibody Space: A Diverse Database of Cleaned, Annotated and Translated Unpaired and Paired Antibody Sequences. protein science. 31141–146 (2022).
Dunbar, J. et al. SAbDab: Structural Antibody Database. Nucleic Acid Research Institute 42D1140–D1146 (2014).
Leem, J., de Oliveira, SHP, Krawczyk, K. & Deane, CM STCRDab: Structural T Cell Receptor Database. Nucleic Acid Research Institute 46D406–D412 (2018).
Schneider, C., Raybold, Mich., CM Dean SAbDab in the Biopharmaceutical Era: Updates Including the Nanobody Structure Tracker SAbDab-nano. Nucleic Acid Research Institute 50D1368–D1372 (2022).
Chiu, ML, Goulet, DR, Teplyakov, A. & Gilliland, GL Antibody structure and function: a basis for engineered therapeutics. antibody 855 (2019).
Robinson, SA et al. Epitope profiling using computer structural modeling was demonstrated in coronavirus-binding antibodies. PLoS computing. biol. 17e1009675 (2021).
Ambrosetti, F., Jiménez-García, B., Roel-Touris, J., Bonvin, AM Modeling antibody-antigen complexes by information-driven docking. structure 28119–129 (2020).
Schneider, C., Buchanan, A., Taddese, B. & Deane, CM DLAB: Deep learning methods for structure-based virtual screening of antibodies. bioinformatics 38377–383 (2021).
Slavinski, L. et al. Challenges to Protein Structure Determination – Lessons from Structural Genomics. protein science. 162472–2482 (2007).
Brown, AJ et al. Enhancing adaptive immunity: advances and challenges in the quantitative engineering and analysis of the adaptive immune receptor repertoire. Mole. system. death.engineering Four701–736 (2019).
Nielsen, SC & Boyd, SD Human adaptive immune receptor repertoire analysis – past, present and future. immunol.Pastor 2849–23 (2018).
Jumper, J. et al. Accurate protein structure prediction by AlphaFold. Nature 596583–589 (2021).
Evans, R. et al. Protein complex prediction by AlphaFold-Multimer. Bio Rxiv (2021).
Lin, Z. et al. A linguistic model of protein sequences on an evolutionary scale enables accurate structural predictions. Bio Rxiv (2022).
Baek, M. et al. Accurate prediction of protein structure and interactions using 3-track neural networks. chemistry 373871–876 (2021).
Ruffolo, JA, Chu, L.-S., Mahajan, SP & Gray, JJ Fast and accurate antibody structure prediction from deep learning on a large set of natural antibodies. nut. common. 142389 (2023).
Wong, WK et al. TCRBuilder: Prediction of her T cell receptor structures in multiple states. bioinformatics 363580–3581 (2020).
Ruffolo, JA, Sulam, J. & Gray, JJ Antibody structure prediction using interpretable deep learning. pattern 3100406 (2022).
Ruffolo, JA, Guerra, C., Mahajan, SP, Sulam, J. & Gray, JJ Deep Learning Geometric Potential Improves Prediction of CDR H3 Loop Structures. bioinformatics 36i268–i275 (2020).
Jan, J. and others. Improved protein structure prediction using predicted inter-residue orientations. Procedure National Academy. Science. 1171496–1503 (2020).
Cohen, T., Halfon, M., Schneidman-Duhovny, D. Nanonet: Rapid and Accurate End-to-End Nanobody Modeling with Deep Learning. front. immunol. 13958584 (2022).
Lee, JH et al. Equifold: Protein Structure Prediction with New Coarse-grained Structural Representation. Bio Rxiv (2022).
Leem, J., Dunbar, J., Georges, G., Shi, J. & Deane, CM ABodyBuilder: Automated Antibody Structure Prediction with Data-Driven Accuracy Estimation. MAbs 81259–1268 (2016).
Abanades, B., Georges, G., Bujotzek, A. & Deane, CM ABlooper: Fast and Accurate Antibody CDR Loop Structure Prediction with Accurate Estimation. bioinformatics 381877–1880 (2022).
Lefranc, M.-P. et al. His IMGT-specific numbering for immunoglobulin and T-cell receptor variable domains and Ig superfamily V-like domains. fat. complete. immunol. 2755–77 (2003).
Eyal, E., Gerzon, S., Potapov, V., Edelman, M. & Sobolev, V. Accuracy limits of protein modeling: Effects of crystal packing on protein structure. J. Mol. biol. 351431–442 (2005).
Schritt, D. et al. Repertoire builder: high-throughput structural modeling of b- and t-cell receptors. Mole. system. death.engineering Four761–768 (2019).
Maier, JK & Labute, P. Evaluation of a fully automated antibody homology modeling protocol in a molecular engineering environment. Proteins: Structural, Functional Bioinformatics 821599–1610 (2014).
Dunbar, J., Fuchs, A., Shi, J. & Deane, CM ABangle: Characterization of the VH-VL orientation of antibodies. Protein Engineering, Ph.D. select. 26611–620 (2013).
Leem, J., Georges, G., Shi, J. & Deane, CM The side chain conformation of antibodies is position dependent. Proteins: structure, function, biological information. 86383–392 (2018).
Tien, MZ, Meyer, AG, Sydykova, DK, Spielman, SJ & Wilke, CO Maximum solvent accessibility of residues in proteins. pro swan 8e80635 (2013).
Eastman, P. et al. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLoS computing. biol. 13e1005659 (2017).
Alford, RF et al. Rosetta total atomic energy function for macromolecular modeling and design. J. Chem. theoretical calculation. 133031–3048 (2017).
Steinegger, M. & Söding, J. MMseqs2 enables highly sensitive protein sequence searching for analysis of large datasets. nut. biotechnology. 351026–1028 (2017).
Mildita, M. et al. ColabFold: Making protein folding accessible to everyone. nut.method 19679–682 (2022).
Berman, HM et al. protein data bank. Nucleic Acid Research Institute 28235–242 (2000).
Liu, L. et al. On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265 (2019).
Meyer, JA et al. ff14SB: ff99SB improved the accuracy of protein side-chain and backbone parameters. J. Chem. theoretical calculation. 113696–3713 (2015).
Schreiner, E., Trabuco, LG, Freddolino, PL & Schulten, K. Stereochemical errors and their effects on molecular dynamics simulations. BMC bioinform. 121–9 (2011).
Dunbar, J. & Deane, CM ANARCI: Antigen Receptor Numbering and Receptor Classification. bioinformatics 32298–300 (2016).
