| SAbDab: the structural antibody database J Dunbar, K Krawczyk, J Leem, T Baker, A Fuchs, G Georges, J Shi, ... Nucleic acids research 42 (D1), D1140-D1146, 2014 | 453 | 2014 |
| ANARCI: antigen receptor numbering and receptor classification J Dunbar, CM Deane Bioinformatics 32 (2), 298-300, 2016 | 271 | 2016 |
| ABodyBuilder: Automated antibody structure prediction with data–driven accuracy estimation J Leem, J Dunbar, G Georges, J Shi, CM Deane MAbs 8 (7), 1259-1268, 2016 | 203 | 2016 |
| SAbPred: a structure-based antibody prediction server J Dunbar, K Krawczyk, J Leem, C Marks, J Nowak, C Regep, G Georges, ... Nucleic acids research 44 (W1), W474-W478, 2016 | 185 | 2016 |
| Observed antibody space: a resource for data mining next-generation sequencing of antibody repertoires A Kovaltsuk, J Leem, S Kelm, J Snowden, CM Deane, K Krawczyk The Journal of Immunology 201 (8), 2502-2509, 2018 | 176 | 2018 |
| Improving B-cell epitope prediction and its application to global antibody-antigen docking K Krawczyk, X Liu, T Baker, J Shi, CM Deane Bioinformatics 30 (16), 2288-2294, 2014 | 161 | 2014 |
| ABangle: characterising the VH–VL orientation in antibodies J Dunbar, A Fuchs, J Shi, CM Deane Protein Engineering, Design & Selection 26 (10), 611-620, 2013 | 123 | 2013 |
| Antibody i-Patch prediction of the antibody binding site improves rigid local antibody–antigen docking K Krawczyk, T Baker, J Shi, CM Deane Protein Engineering, Design & Selection 26 (10), 621-629, 2013 | 102 | 2013 |
| The H3 loop of antibodies shows unique structural characteristics C Regep, G Georges, J Shi, B Popovic, CM Deane Proteins: Structure, Function, and Bioinformatics 85 (7), 1311-1318, 2017 | 89 | 2017 |
| Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction C Marks, J Nowak, S Klostermann, G Georges, J Dunbar, J Shi, S Kelm, ... Bioinformatics 33 (9), 1346-1353, 2017 | 69 | 2017 |
| How B-cell receptor repertoire sequencing can be enriched with structural antibody data A Kovaltsuk, K Krawczyk, JD Galson, DF Kelly, CM Deane, J Trück Frontiers in immunology 8, 1753, 2017 | 64 | 2017 |
| Prediction of VH–VL domain orientation for antibody variable domain modeling A Bujotzek, J Dunbar, F Lipsmeier, W Schäfer, I Antes, CM Deane, ... Proteins: Structure, Function, and Bioinformatics 83 (4), 681-695, 2015 | 59 | 2015 |
| Length-independent structural similarities enrich the antibody CDR canonical class model J Nowak, T Baker, G Georges, S Kelm, S Klostermann, J Shi, S Sridharan, ... MAbs 8 (4), 751-760, 2016 | 50 | 2016 |
| Structurally mapping antibody repertoires K Krawczyk, S Kelm, A Kovaltsuk, JD Galson, D Kelly, J Trück, C Regep, ... Frontiers in immunology 9, 1698, 2018 | 40 | 2018 |
| Antibody side chain conformations are position‐dependent J Leem, G Georges, J Shi, CM Deane Proteins: Structure, Function, and Bioinformatics 86 (4), 383-392, 2018 | 27 | 2018 |
| SCALOP: sequence-based antibody canonical loop structure annotation WK Wong, G Georges, F Ros, S Kelm, AP Lewis, B Taddese, J Leem, ... Bioinformatics 35 (10), 1774-1776, 2019 | 25 | 2019 |
| Computational tools for aiding rational antibody design K Krawczyk, J Dunbar, CM Deane Computational protein design, 399-416, 2017 | 24 | 2017 |
| ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation. MAbs 8 (7): 1259–1268 J Leem, J Dunbar, G Georges, J Shi, CM Deane Cited on, 47, 2016 | 5 | 2016 |
| High-throughput antibody structure modeling and design using abodybuilder J Leem, CM Deane Computational Methods in Protein Evolution, 367-380, 2019 | 4 | 2019 |
| Describing protein structure geometry to aid in functional understanding CM Deane, J Dunbar, A Fuchs, KV Mardia, J Shi, HR Wilman, ... LASR2013 Proceedings—Statistical Models and Methods for non-Euclidean Data …, 2013 | 3 | 2013 |
