OPIG logo
Oxford logo


A full list of the group's publications can be found below. Click on the relevant years to see the corresponding papers. You can also download this information as a bibtex file here.

Open all Close all
Hummer, A.M., Schneider, C., Chinery, L. & Deane, C.M. (Rxiv) Investigating the Volume and Diversity of Data Needed for Generalizable Antibody-Antigen ∆∆G Prediction bioRxiv
Guloglu, B. & Deane, C.M. (Rxiv) Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects bioRxiv
Homberg, S., Janosch, M., Morris, G.M. & Koch, O. (Rxiv) Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches ChemRxiv
Hadfield, T.E., Scantlebury, J. & Deane, C.M. (Rxiv) Exploring The Ability Of Machine Learning-Based Virtual Screening Models To Identify The Functional Groups Responsible For Binding bioRxiv
Fischer, K., Lulla, A., So, T., Pereyra-Gerber, P., Raybould, M.I.J., Kohler, T.N., Kaminski, T.S., Yam-Puc, J.C., Hughes, R., Leiss-Maier, F., Brear, P., Matheson, N.J., Deane, C.M., Hyvonen, M., Thaventhiran, J. & Hollfelder, F. (Rxiv) Microfluidics-enabled fluorescence-activated cell sorting of single pathogen-specific antibody secreting cells for the rapid discovery of monoclonal antibodies bioRxiv
Outeiral, C. & Deane, C. (Rxiv) Codon language embeddings provide strong signals for protein engineering bioRxiv
Olsen, T.H., Abanades, B., Moal, I.H. & Deane, C.M. (Rxiv) KA-Search: Rapid and exhaustive sequence identity search of known antibodies bioRxiv
Crook, O.M., Chung, C.w. & Deane, C.M. (Rxiv) A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry bioRxiv
Moesser, M.A., Klein, D., Boyles, F., Deane, C.M., Baxter, A. & Morris, G.M. (Rxiv) Protein-Ligand Interaction Graphs: Learning from Ligand-Shaped 3D Interaction Graphs to Improve Binding Affinity Prediction bioRxiv
Consortium, T.C.M., Achdout, H., Aimon, A., Bar-David, E., Barr, H., Ben-Shmuel, A., Bennett, J., Bobby, M.L., Brun, J., Sarma, B., Calmiano, M., Carbery, A., Cattermole, E., Chodera, J.D., Clyde, A., Coffland, J.E., Cohen, G., Cole, J., Contini, A., Cox, L., Cvitkovic, M., Dias, A., Douangamath, A., Duberstein, S., Dudgeon, T., Dunnett, L., Eastman, P.K., Erez, N., Fairhead, M., Fearon, D., Fedorov, O., Ferla, M., Foster, H., Foster, R., Gabizon, R., Gehrtz, P., Gileadi, C., Giroud, C., Glass, W.G., Glen, R., Glinert, I., Gorichko, M., Gorrie-Stone, T., Griffen, E.J., Heer, J., Hill, M., Horrell, S., Hurley, M.F., Israely, T., Jajack, A., Jnoff, E., John, T., Kantsadi, A.L., Kenny, P.W., Kiappes, J.L., Koekemoer, L., Kovar, B., Krojer, T., Lee, A.A., Lefker, B.A., Levy, H., London, N., Lukacik, P., Macdonald, H.B., MacLean, B., Malla, T.R., Matviiuk, T., McCorkindale, W., Melamed, S., Michurin, O., Mikolajek, H., Morris, A., Morris, G.M., Morwitzer, M.J., Moustakas, D., Neto, J.B., Oleinikovas, V., Overheul, G.J., Owen, D., Pai, R., Pan, J., Paran, N., Perry, B., Pingle, M., Pinjari, J., Politi, B., Powell, A., Psenak, V., Puni, R., Rangel, V.L., Reddi, R.N., Reid, S.P., Resnick, E., Robinson, M.C., Robinson, R.P., Rufa, D., Schofield, C., Shaikh, A., Shi, J., Shurrush, K., Sittner, A., Skyner, R., Smalley, A., Smilova, M.D., Spencer, J., Strain-Damerell, C., Swamy, V., Tamir, H., Tennant, R., Thompson, A., Thompson, W., Tomasio, S., Tumber, A., Vakonakis, I., van Rij, R.P., Varghese, F.S., Vaschetto, M., Vitner, E.B., Voelz, V., von Delft, A., von Delft, F., Walsh, M., Ward, W., Weatherall, C., Weiss, S., Wild, C.F., Wittmann, M., Wright, N., Yahalom-Ronen, Y., Zaidmann, D., Zidane, H. & Zitzmann, N. (Rxiv) COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning bioRxiv
Abanades, B., Wong, W.K., Boyles, F., Georges, G., Bujotzek, A. & Deane, C.M. (2023) ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins Communications Biology, 6:575
Wills, S., Sanchez-Garcia, R., Dudgeon, T., Roughley, S.D., Merritt, A., Hubbard, R.E., Davidson, J., von Delft, F. & Deane, C.M. (2023) Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search Journal of Chemical Information and Modeling
Scantlebury, J., Vost, L., Carbery, A., Hadfield, T.E., Turnbull, O.M., Brown, N., Chenthamarakshan, V., Das, P., Grosjean, H., von Delft, F. & Deane, C.M. (2023) A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening Journal of Chemical Information and Modeling
Dablander, M., Hanser, T., Lambiotte, R. & Morris, G.M. (2023) Exploring QSAR Models for Activity-Cliff Prediction Journal of Cheminformatics, 15:47
Raybould, M.I.J., Nissley, D.A., Kumar, S. & Deane, C.M. (2023) Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies Frontiers in Immunology, 13:1080596
Mokaya, M., Imrie, F., van Hoorn, W.P., Kalisz, A., Bradley, A.R. & Deane, C.M. (2023) Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning Nature Machine Intelligence
Richardson, E., Binter, S., Kosmac, M., Ghraichy, M., von Niederhäusern, V., Kovaltsuk, A., Galson, J.D., Trück, J., Kelly, D.F., Deane, C.M., Kellam, P. & Watson, S. (2023) Characterisation of the immune repertoire of a humanised transgenic mouse through immunophenotyping and high-throughput sequencing eLife, 12:e81629
Pardo-Diaz, J., Poole, P.S., Beguerisse-Díaz, M., Deane, C.M. & Reinert, G. (2022) Generating weighted and thresholded gene coexpression networks using signed distance correlation Network Science, 10(2):131-145
Baddock, H.T., Brolih, S., Yosaatmadja, Y., Ratnaweera, M., Bielinski, M., Swift, L., Cruz-Migoni, A., Fan, H., Keown, J.R., Walker, A.P., Morris, G., Grimes, J., Fodor, E., Schofield, C., Gileadi, O. & McHugh, P. (2022) Characterization of the SARS-CoV-2 ExoN (nsp14ExoN–nsp10) complex: implications for its role in viral genome stability and inhibitor identification Nucleic Acids Research, 50(3):1484-1500
Crook, O.M., Chung, C.w. & Deane, C.M. (2022) Challenges and Opportunities for Bayesian Statistics in Proteomics Journal of Proteome Research, 21(4):849-864
Hadfield, T.E., Imrie, F., Merritt, A., Birchall, K. & Deane, C.M. (2022) Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration Journal of Chemical Information and Modeling, 62(10):2280-2292
Lomize, A.L., Schnitzer, K.A., Todd, S.C., Cherepanov, S., Outeiral, C., Deane, C.M. & Pogozheva, I.D. (2022) Membranome 3.0: Database of single-pass membrane proteins with AlphaFold models Protein Science, 31(5):e4318
Hummer, A.M., Abanades, B. & Deane, C.M. (2022) Advances in computational structure-based antibody design Current Opinion in Structural Biology, 74:102379
Sanchez-Garcia, R., Havasi, D., Takács, G., Robinson, M.C., Lee, A., von Delft, F. & Deane, C.M. (2022) CoPriNet: Deep learning compound price prediction for use in de novo molecule generation and prioritization Digital Discovery
Outeiral, C., Nissley, D.A. & Deane, C.M. (2022) Current structure predictors are not learning the physics of protein folding Bioinformatics, 38(7):1881-1887
Abanades, B., Georges, G., Bujotzek, A. & Deane, C.M. (2022) ABlooper: Fast accurate antibody CDR loop structure prediction with accuracy estimation Bioinformatics, 38(7):1887-1880
Hadfield, T.E. & Deane, C.M. (2022) AI in 3D compound design Current Opinion in Structural Biology, 73:102326
Olsen, T.H., Moal, I.H. & Deane, C.M. (2022) AbLang: An antibody language model for completing antibody sequences Bioinformatics Advances, ():vbac046
Khetan, R., Curtis, R., Deane, C.M., Hadsung, J.T., Kar, U., Krawczyk, K., Kuroda, D., Robinson, S.A., Sormanni, P., Tsumoto, K., Warwicker, J. & Martin, A.C.R. (2022) Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics
Ko, K.T., Lennartz, F., Mekhaiel, D., Guloglu, B., Marini, A., Deuker, D.J., Long, C.A., Jore, M.M., Miura, K., Biswas, S. & Higgins, M.K. (2022) Structure of the malaria vaccine candidate Pfs48/45 and its recognition by transmission blocking antibodies Nature Communications, 13(1):5603
Schneider, C., Raybould, M.I.J. & Deane, C.M. (2022) SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker Nucleic Acids Research, 50(D1):D1368-D1372
Chinery, L., Wahome, N., Moal, I. & Deane, C.M. (2022) Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors Bioinformatics
Carbery, A., Skyner, R., von Delft, F. & Deane, C.M. (2022) Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries Journal of Medicinal Chemistry
Goto, A., Rodriguez-Esteban, R., Scharf, S.H. & Morris, G.M. (2022) Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature Scientific Reports, 12:14476
Wang, Y., Tsitsiklis, A., Devoe, S., Gao, W., Chu, H.H., Zhang, Y., Li, W., Wong, W.K., Deane, C.M., Neau, D., Slansky, J.E., Thomas, P.G., Robey, E.A. & Dai, S. (2022) Peptide Centric Vβ Specific Germline Contacts Shape a Specialist T Cell Response Frontiers in Immunology
Pardo-Diaz, J., Beguerisse-Diaz, M., Poole, P.S., Deane, C.M. & Reinert, G. (2022) Extracting Information from Gene Coexpression Networks of Rhizobium leguminosarum Journal of Computational Biology
Meli, R., Morris, G.M. & Biggin, P.C. (2022) Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review Frontiers in Bioinformatics, 2:885983
Crook, O.M., Chung, C.w. & Deane, C.M. (2022) Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry Communications Biology, 5:588
Raybould, M.I.J., Rees, A.R. & Deane, C.M. (2021) Current strategies for detecting functional convergence across B-cell receptor repertoires MAbs, 13(1):1996732
Outeiral, C., Morris, G.M., Shi, J., Strahm, M., Benjamin, S.C. & Deane, C.M. (2021) Investigating the potential for a limited quantum speedup on protein lattice problems New Journal of Physics, 23(10):103030
Marks, C., Hummer, A.M., Chin, M. & Deane, C.M. (2021) Humanization of antibodies using a machine learning approach on large-scale repertoire data Bioinformatics, 37(22):4041-4047
Robinson, S.A., Raybould, M.I.J., Schneider, C., Wong, W.K., Marks, C. & Deane, C.M. (2021) Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies PLoS Computational Biology, 17(2):e1009675
Chan, L., Morris, G.M. & Hutchison, G.R. (2021) Understanding Conformational Entropy in Small Molecules Journal of Chemical Theory and Computation, 17(4):2099-2106
Macpherson, A., Laabei, M.L., Ahdash, Z.A., Graewert, M., Birtley, J.R., Schulze, S., Crennell, S., Robinson, S.A., Holmes, B., Oleinikovas, V., Nilsson, P.H., Snowden, J., Ellis, V., Mollnes, T.E., Deane, C.M., Svergun, D., Lawson, A.D.G. & van den Elsen, J. (2021) The allosteric modulation of Complement C5 by knob domain peptides eLife, 10:e63586
Imrie, F., Bradley, A.R. & Deane, C. (2021) Generating Property-Matched Decoy Molecules Using Deep Learning Bioinformatics, 37(15):2134-2141
Nissley, D.A., Carbery, A., Chonofsky, M. & Deane, C.M. (2021) Ribosome occupancy profiles are conserved between structurally and evolutionarily related yeast domains Bioinformatics, 37(13):1853-1859
Chan, L., Hutchison, G. & Morris, G.M. (2021) Understanding Ring Puckering in Small Molecules and Cyclic Peptides Journal of Chemical Information and Modeling, 61(2):743-755
Pardo-Diaz, J., Bozhilova, L.V., Mariano, B.D., Poole, P.S., Deane, C.M. & Reinert, G. (2021) Robust gene coexpression networks using signed distance correlation Bioinformatics, 37(14):1982-1989
Richardson, E., Galson, J.D., Kellam, P., Kelly, D.F., Smith, S.E., Palser, A., Watson, S. & Deane, C.M. (2021) A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-Pertussis toxoid antibodies MAbs, 13(1):1869406
Raybould, M.I.J., Kovaltsuk, A., Marks, C. & Deane, C.M. (2021) CoV-AbDab: the Coronavirus Antibody Database Bioinformatics, 37(5):734-735
Imrie, F., Hadfield, T.E., Bradley, A.R. & Deane, C.M. (2021) Deep generative design with 3D pharmacophoric constraints Chemical Science, 12:14577-14589
Klimm, F., Deane, C.M. & Reinert, G. (2021) Hypergraphs for predicting essential genes using multiprotein complex data Journal of Complex Networks, 9(2):cnaa028
Wong, W.K., Robinson, S.A., Bujotzek, A., Georges, G., Lewis, A.P., Shi, J., Snowden, J., Taddese, B. & Deane, C.M. (2021) Ab-Ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope MAbs, 13(1):1873478
Raybould, M.I.J., Marks, C., Kovaltsuk, A., Lewis, A.P., Shi, J. & Deane, C.M. (2021) Public Baseline and Shared Response Structures Support the Theory of Antibody Repertoire Functional Commonality PLoS Computational Biology, 17(3):e1008781
Olsen, T.H., Boyles, F. & Deane, C.M. (2021) OAS: A diverse database of cleaned, annotated and translated unpaired and paired antibody sequences Protein Science
Schneider, C., Buchanan, A., Taddese, B. & Deane, C.M. (2021) DLAB—Deep learning methods for structure-based virtual screening of antibodies Bioinformatics, 38(2):377-383
Chan, H.T.H., Moesser, M.A., Walters, R.K., Malla, T.R., Twidale, R.M., John, T., Deeks, H.M., Johnston-Wood, T., Mikhailov, V., Sessions, R.B., Dawson, W., Saleh, E., Lukacik, P., Strain-Damerell, C., Owen, C.D., Nakajima, T., Swiderek, K., Lodola, A., Moliner, V., Glowacki, D.R., Spencer, J., Walsh, M.A.A., Schofield, C.J., Genovese, L., Shoemark, D.K., Mulholland, A.J., Duarte, F. & Morris, G.M. (2021) Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding Chemical Science, 12:13686-13703
Boyles, F., Deane, C.M. & Morris, G.M. (2021) Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses Journal of Chemical Information and Modeling
Schwarz, D., Georges, G., Kelm, S., Shi, J., Vangone, A. & Deane, C.M. (2021) Co-evolutionary distance predictions contain flexibility information Bioinformatics, 38(1):65-72
Meli, R., Anighoro, A., Bodkin, M.J., Morris, G.M. & Biggin, P.C. (2021) Learning protein-ligand binding affinity with atomic environment vectors Journal of Cheminformatics, 13:59
Ghraichy, M., von Niederhäusern, V., Kovaltsuk, A., Galson, J.D., Deane, C.M. & Trück, J. (2021) Different B cell subpopulations show distinct patterns in their IgH repertoire metrics eLife, 10:e73111
Bozhilova, L.V., Pardo-Diaz, J., Reinert, G. & Deane, C.M. (2020) COGENT: evaluating the consistency of gene co-expression networks Bioinformatics, ():btaa787
Galson, J.D., Schaetzle, S., Bashford-Rogers, R.J.M., Raybould, M.I.J., Kovaltsuk, A., Kilpatrick, G.J., Minter, R., Finch, D.K., Dias, J., James, L., Thomas, G., Lee, W.Y.J., Betley, J., Cavlan, O., Leech, A., Deane, C.M., Seoane, J., Caldas, C., Pennington, D., Pfeffer, P. & Osbourn, J. (2020) Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures Frontiers in Immunology, 11:605170
Klimm, F., Toledo, E.M., Monfeuga, T., Zhang, F., Deane, C.M. & Reinert, G. (2020) Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks. BMC Bioinformatics, 21:756
Ghraichy, M., Galson, J.D., Kovaltsuk, A., von Niederhäusern, V., Schmid, J.M., Miho, E., Kelly, D.F., Deane, C.M. & Trück, J. (2020) Maturation of Naïve and Antigen-experienced B-cell Receptor Repertoires with Age Frontiers in Immunology, 11:1734
Outeiral, C., Strahm, M., Shi, J., Morris, G.M., Benjamin, S.C. & Deane, C.M. (2020) The prospects of quantum computing in computational molecular biology WIRES, 11(1):e1481
Marks, C. & Deane, C.M. (2020) How repertoire data is changing antibody science Journal of Biological Chemistry, 295:9823-9837
Wong, W.K., Marks, C., Leem, J., Lewis, A.P., Shi, J. & Deane, C.M. (2020) TCRBuilder: Multi-state T-cell receptor structure prediction Bioinformatics, 36(11):3580-3581
Scantlebury, J., Brown, N., Von Delft, F. & Deane, C.M. (2020) Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions. Journal of Chemical Information Modeling, 60(8):3722-3730
Chan, L., Hutchison, G.R. & Morris, G.M. (2020) BOKEI: Bayesian optimization using knowledge of correlated torsions and expected improvement for conformer generation Physical Chemistry Chemical Physics, 22(9):5211-5219
Imrie, F., Bradley, A.R., van der Schaar, M. & Deane, C.M. (2020) Deep Generative Models for 3D Linker Design Journal of Chemical Information Modeling, 60(4):1983-1995
Kovaltsuk, A., Raybould, M.I.J., Wong, W.K., Marks, C., Kelm, S., Snowden, J., Trück, J. & Deane, C.M. (2020) Structural Diversity of B-cell Receptor Repertoires along the B-cell Differentiation Axis in Humans and Mice PLoS Computational Biology, 16(2):e1007636
Raybould, M.I.J., Marks, C., Lewis, A.P., Shi, J., Bujotzek, A., Taddese, B. & Deane, C.M. (2020) Thera-SAbDab: the Therapeutic Structural Antibody Database Nucleic Acids Research, 48(D1):D383-D388
Knapp, B., van der Merwe, P.A., Dushek, O. & Deane, C.M. (2019) MHC binding affects the dynamics of different T-cell receptors in different ways PLoS Computational Biology, 15:1-17
Wong, W.K., Leem, J. & Deane, C.M. (2019) Comparative analysis of the CDR loops of antigen receptors Frontiers in Immunology, 10:2454
Ebejer, J.P., Finn, P.W., Wong, W.K., Deane, C.M. & Morris, G.M. (2019) Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening Journal of Chemical Information and Modeling, 59(6):2600-2616
Chan, L., Hutchison, G.R. & Morris, G.M. (2019) Bayesian Optimization for Conformer Generation Journal of Cheminformatics, 11:32
Raybould, M.I.J., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A.P., Bujotzek, A., Shi, J. & Deane, C.M. (2019) Five Computational Developability Guidelines for Therapeutic Antibody Profiling Proceedings of the National Academy of Sciences USA, 116(10):4025-4030
Chonofsky, M., de Oliveira, S.H.P., Krawczyk, K. & Deane, C.M. (2019) The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing Bioinformatics, ():btz816
West, C.E., de Oliveira, S.H.P. & Deane, C.M. (2019) RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold PLoS One, 14(10):1-16
Bozhilova, L.V., Whitmore, A.V., Wray, J., Reinert, G. & Deane, C.M. (2019) Measuring rank robustness in scored protein interaction networks BMC Bioinformatics, 20:446
Boyles, F., Deane, C.M. & Morris, G.M. (2019) Learning From The Ligand: Using Ligand-Based Features To Improve Binding Affinity Prediction Bioinformatics, 36(3):758-764
Schwarz, D., Merget, B., Deane, C.M. & Fulle, S. (2019) Modeling conformational flexibility of kinases in inactive states Proteins, 87(11):943-951
Krawczyk, K., Raybould, M.I.J., Kovaltsuk, A. & Deane, C.M. (2019) Looking for Therapeutic Antibodies in Next Generation Sequencing Repositories MAbs, 11(7):1197-1205
Raybould, M.I.J., Wong, W.K. & Deane, C.M. (2019) Antibody-antigen Complex Modelling in the Era of Immunoglobulin Repertoire Sequencing Molecular Systems Design & Engineering, 4:679-688
Demharter, S., Knapp, B., Deane, C.M. & Minary, P. (2019) HLA-DM stabilises the empty MHCII binding groove: A model using customised Natural Move Monte Carlo Journal of Chemical Information and Modeling, 59(6):2894-2899
Marks, C. & Deane, C.M. (2018) Increasing the accuracy of protein loop structure prediction with evolutionary constraints Bioinformatics, ():bty996
Kovaltsuk, A., Krawczyk, K., Kelm, S., Snowden, J. & Deane, C.M. (2018) Filtering Next-Generation Sequencing of the Ig Gene Repertoire Data Using Antibody Structural Information Journal of Immunology, 201(12):3694-3704
Leem, J., Georges, G., Shi, J. & Deane, C.M. (2018) Antibody side chain conformations are position-dependent Proteins: Structure, Function, and Bioinformatics, 86(4):383-392
Knapp, B., Alcala, M., Zhang, H., West, C.E., van der Merwe, P.A. & Deane, C.M. (2018) pyHVis3D: Visualising Molecular Simulation deduced H-bond networks in 3D: Application to T-cell receptor interactions Bioinformatics, ():btx842
Wegner, A.E., Ospina-Forero, L., Gaunt, R.E., Deane, C.M. & Reinert, G. (2018) Identifying networks with common organizational principles Journal of Complex Networks, ():cny003
Marks, C., Shi, J. & Deane, C.M. (2018) Predicting loop conformational ensembles Bioinformatics, 34(6):949-956
Knapp, B., Ospina, L. & Deane, C.M. (2018) Avoiding false positive conclusions in molecular simulation: the importance of replicas Journal of Chemical Theory and Computation, 14(12):6127-6138
Wong, W.K., Georges, G., Ros, F., Kelm, S., Lewis, A.P., Taddese, B., Leem, J. & Deane, C.M. (2018) SCALOP: sequence-based antibody canonical loop structure annotation Bioinformatics, 35(10):1774-1776
Imrie, F., Bradley, A.R., van der Schaar, M. & Deane, C.M. (2018) Protein Family-specific Models using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data Journal of Chemical Information and Modeling, 58(11):2319-2330
Ospina-Forero, L., Deane, C.M. & Reinert, G. (2018) Assessment of model fit via network comparison methods based on subgraph counts Journal of Complex Networks, ():cny017
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C.M. & Krawczyk, K. (2018) Observed Antibody Space: a resource for data mining next generation sequencing of antibody repertoires Journal of Immunology, 201(7):2502-2509
Krawczyk, K., Kelm, S., Kovaltsuk, A., Galson, J.D., Kelly, D., Trück, J., Regep, C., Leem, J., Wong, W.K., Nowak, J., Snowden, J., Wright, M., Starkie, L., Scott-Turner, A., Shi, J. & Deane, C.M. (2018) Structurally Mapping Antibody Repertoires Frontiers in Immunology, 9:1698
de Oliveira, S.H.P. & Deane, C.M. (2018) Combining co-evolution and secondary structure prediction to improve fragment library generation Bioinformatics, ():bty084
Mardia, K.V., Sriram, K. & Deane, C.M. (2018) A statistical model for helices with applications Biometrics, 74(3):845-854
Kovaltsuk, A., Krawczyk, K., Galson, J.D., Kelly, D.F., Deane, C.M. & Trück, J. (2017) How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data Frontiers in Immunology, 8:1753
de Oliveira, S.H.P., Law, E.C., Shi, J. & Deane, C.M. (2017) Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction Bioinformatics, 10
Knapp, B., Dunbar, J., Alcala, M. & Deane, C.M. (2017) Variable Regions of Antibodies and T-Cell Receptors May Not Be Sufficient in Molecular Simulations Investigating Binding Journal of Chemical Theory and Computation, 13(7):3097-3105
Pearce, N.M., Bradley, A.R., Krojer, T., Marsden, B.D., Deane, C.M. & von Delft, F. (2017) Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites Structural Dynamics, 4(3):32104
de Oliveira, S. & Deane, C. (2017) Co-evolution techniques are reshaping the way we do structural bioinformatics F1000Research, 6:1224
Marks, C., Nowak, J., Klostermann, S., Georges, G., Dunbar, J., Shi, J., Kelm, S. & Deane, C.M. (2017) Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction Bioinformatics, 33(9):1346-1353
Marks, C. & Deane, C.M. (2017) Antibody H3 Structure Prediction Computional and Structural Biotechnology Journal, 15:222-231
Chen, J.W.C., Chen, Z.A., Rogala, K.B., Metz, J., Deane, C.M., Rappsilber, J. & Wakefield, J.G. (2017) Cross-linking mass spectrometry identifies new interfaces of Augmin required to localise the gamma-tubulin ring complex to the mitotic spindle. Biology open, 6(5):654-663
Deane, C.M. & Vásquez, M. (2017) Developability of Biotherapeutics: Computational Approaches . Edited by Sandeep Kumar and Satish K. Singh MAbs, 9(1):12-14
Demharter, S., Pearce, N., Beattie, K., Frost, I., Leem, J., Martin, A., Oppenheimer, R., Regep, C., Rukat, T., Skates, A., Trendel, N., Gavaghan, D.J., Deane, C.M. & Knapp, B. (2017) Ten simple rules for surviving an interdisciplinary PhD PLOS Computational Biology, 13(5):e1005512
Krawczyk, K., Demharter, S., Knapp, B., Deane, C.M. & Minary, P. (2017) In silico structural modeling of multiple epigenetic marks on DNA Bioinformatics
Luecken, M.D., Page, M.J.T., Crosby, A.J., Mason, S., Reinert, G. & Deane, C.M. (2017) CommWalker: Correctly Evaluating Modules in Molecular Networks in Light of Annotation Bias Bioinformatics
Leem, J., de Oliveira, S., Krawczyk, K. & Deane, C. (2017) STCRDab: the structural T-cell receptor database Nucleic Acids Research
Pearce, N.M., Krojer, T., Bradley, A.R., Collins, P., Nowak, R.P., Talon, R., Marsden, B.D., Kelm, S., Shi, J., Deane, C.M. & von Delft, F. (2017) A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density Nature Communications, 8:15123
Parks, T., Mirabel, M.M., Kado, J., Auckland, K., Nowak, J., Rautanen, A., Mentzer, A.J., Marijon, E., Jouven, X., Perman, M.L., Cua, T., Kauwe, J.K., Allen, J.B., Taylor, H., Robson, K.J., Deane, C.M., Steer, A.C. & Hill, A.V.S. (2017) Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania Nature Communications, 8(14946)
Krawczyk, K., Dunbar, J. & Deane, C.M. (2017) Computational Tools for Aiding Rational Antibody Design Methods in Molecular Biology, 1529:399-416
de Oliveira, S.H.P., Shi, J. & Deane, C.M. (2017) Comparing co-evolution methods and their application to template-free protein structure prediction Bioinformatics, 33(3):373-381
Regep, C., Georges, G., Shi, J., Popovic, B. & Deane, C.M. (2017) The H3 loop of antibodies shows unique structural characteristics Proteins: Structure, Function, and Bioinformatics, 85(7):1311-1318
Dunbar, J., Krawczyk, K., Leem, J., Marks, C., Nowak, J., Regep, C., Georges, G., Kelm, S., Popovic, B. & Deane, C.M. (2016) SAbPred: a structure-based antibody prediction server. Nucleic Acids Research, 44(W1):W474-W478
Demharter, S., Knapp, B., Deane, C.M. & Minary, P. (2016) Modeling Functional Motions of Biological Systems by Customized Natural Moves Biophys. J., 111(4):710-721
Nowak, J., Baker, T., Georges, G., Kelm, S., Klostermann, S., Shi, J., Sridharan, S. & Deane, C.M. (2016) Length-independent structural similarities enrich the antibody CDR canonical class model. MAbs, 8(4):751-760
Ali, W., Wegner, A.E., Gaunt, R.E., Deane, C.M. & Reinert, G. (2016) Comparison of large networks with sub-sampling strategies Sci. Rep., 6:28955
Leem, J., Dunbar, J., Georges, G., Shi, J. & Deane, C.M. (2016) ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation MAbs, 8(7):1259-1268
Krawczyk, K., Sim, A.Y.L., Knapp, B., Deane, C.M. & Minary, P. (2016) Tertiary Element Interaction in HIV-1 TAR J. Chem. Inf. Model., 56(9):1746-1754
Law, E.C., Wilman, H.R., Kelm, S., Shi, J. & Deane, C.M. (2016) Examining the Conservation of Kinks in Alpha Helices PLoS One, 11(6):e0157553
Zhang, H., Lim, H.S., Knapp, B., Deane, C.M., Aleksic, M., Dushek, O. & van der Merwe, P.A. (2016) The contribution of major histocompatibility complex contacts to the affinity and kinetics of T cell receptor binding Sci. Rep., 6:35326
Wan, S., Knapp, B., Wright, D.W., Deane, C.M. & Coveney, P.V. (2015) Rapid, Precise, and Reproducible Prediction of Peptide.MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment J. Chem. Theory Comput., 11(7):3346-3356
de Oliveira, S.H.P., Shi, J. & Deane, C.M. (2015) Building a Better Fragment Library for De Novo Protein Structure Prediction PLoS One, 10(4):e0123998
Alexander, L.T., Möbitz, H., Drueckes, P., Savitsky, P., Fedorov, O., Elkins, J.M., Deane, C.M., Cowan-Jacob, S.W. & Knapp, S. (2015) Type II Inhibitors Targeting CDK2. ACS Chem. Biol., 10(9):2116-2125
Knapp, B. & Deane, C.M. (2015) T-cell Receptor Binding Affects the Dynamics of the Peptide/MHC-I Complex. J. Chem. Inf. Model.
Knapp, B., Demharter, S., Deane, C.M. & Minary, P. (2015) Exploring peptide/MHC detachment processes using Hierarchical Natural Move Monte Carlo Bioinformatics, pages btv502
Dunbar, J. & Deane, C.M. (2015) ANARCI: Antigen receptor numbering and receptor classification Bioinformatics, pages btv552
Edwards, H. & Deane, C.M. (2015) Structural Bridges through Fold Space. PLoS Comput. Biol., 11(9):e1004466
Bujotzek, A., Dunbar, J., Lipsmeier, F., Schäfer, W., Antes, I., Deane, C.M. & Georges, G. (2015) Prediction of VH-VL domain orientation for antibody variable domain modeling. Proteins, 83(4):681-695
Knapp, B., Demharter, S., Esmaielbeiki, R. & Deane, C.M. (2015) Current status and future challenges in T-cell receptor/peptide/MHC molecular dynamics simulations Brief. Bioinform.
Knapp, B., Bardenet, R., Bernabeu, M.O., Bordas, R., Bruna, M., Calderhead, B., Cooper, J., Fletcher, A.G., Groen, D., Kuijper, B., Lewis, J., McInerny, G., Minssen, T., Osborne, J., Paulitschke, V., Pitt-Francis, J., Todoric, J., Yates, C.A., Gavaghan, D. & Deane, C.M. (2015) Ten Simple Rules for a Successful Cross-Disciplinary Collaboration. PLoS Comput. Biol., 11(4):e1004214
Esmaielbeiki, R., Krawczyk, K., Knapp, B., Nebel, J.C. & Deane, C.M. (2015) Progress and challenges in predicting protein interfaces Brief. Bioinform.
Palumbo, V., Pellacani, C., Heesom, K.J., Rogala, K.B., Deane, C.M., Mottier-Pavie, V., Gatti, M., Bonaccorsi, S. & Wakefield, J.G. (2015) Misato Controls Mitotic Microtubule Generation by Stabilizing the Tubulin Chaperone Protein-1 Complex. Curr. Biol.
Bradley, A.R., Wall, I.D., von Delft, F., Green, D.V.S., Deane, C.M. & Marsden, B.D. (2015) WONKA: objective novel complex analysis for ensembles of protein-ligand structures. J. Comput. Aided. Mol. Des.
Rogala, K.B., Dynes, N.J., Hatzopoulos, G.N., Yan, J., Pong, S.K., Robinson, C.V., Deane, C.M., Gönczy, P. & Vakonakis, I. (2015) The Caenorhabditis elegans protein SAS-5 forms large oligomeric assemblies critical for centriole formation Elife, 4:e07410
Dunbar, J., Knapp, B., Fuchs, A., Shi, J. & Deane, C.M. (2014) Examining Variable Domain Orientations in Antigen Receptors Gives Insight into TCR-Like Antibody Design PLoS Comput Biol, 10:e1003852
Dien, H., Deane, C.M. & Knapp, B. (2014) Gro2mat: A package to efficiently read gromacs output in MATLAB J. Comput. Chem., 35(20):1528-1531
Knapp, B., Dunbar, J. & Deane, C.M. (2014) Large Scale Characterization of the LC13 TCR and HLA-B8 Structural Landscape in Reaction to 172 Altered Peptide Ligands: A Molecular Dynamics Simulation Study PLoS Comput Biol(8)pages e1003748
Ali, W., Rito, T., Reinert, G., Sun, F. & Deane, C.M. (2014) Alignment-free protein interaction network comparison Bioinformatics, 30(17):i430-i437
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J. & Deane, C.M. (2014) SAbDab: the structural antibody database Nucleic Acids Res., 42(Database issue):D1140--D1146
Wilman, H.R., Ebejer, J.P., Shi, J., Deane, C.M. & Knapp, B. (2014) Crowdsourcing Yields a New Standard for Kinks in Protein Helices. J. Chem. Inf. Model., 54(9):2585-2593
Krawczyk, K., Liu, X., Baker, T., Shi, J. & Deane, C.M. (2014) Improving B-cell epitope prediction and its application to global antibody-antigen docking Bioinformatics, 30(16):2288-2294
Wilman, H.R., Shi, J. & Deane, C.M. (2014) Helix kinks are equally prevalent in soluble and membrane proteins. Proteins, 82(9):1960-1970
Kelm, S., Vangone, A., Choi, Y., Ebejer, J.P., Shi, J. & Deane, C.M. (2014) Fragment-based modeling of membrane protein loops: successes, failures, and prospects for the future Proteins, 82(2):175-186
Osborne, J.M., Bernabeu, M.O., Bruna, M., Calderhead, B., Cooper, J., Dalchau, N., Dunn, S.J., Fletcher, A.G., Freeman, R., Groen, D., Knapp, B., McInerny, G.J., Mirams, G.R., Pitt-Francis, J., Sengupta, B., Wright, D.W., Yates, C.A., Gavaghan, D.J., Emmott, S. & Deane, C.M. (2014) Ten Simple Rules for Effective Computational Research PLoS Comput. Biol., 10(3):e1003506
Bradley, A.R., Wall, I.D., Green, D.V.S., Deane, C.M. & Marsden, B.D. (2014) OOMMPPAA: A Tool To Aid Directed Synthesis by the Combined Analysis of Activity and Structural Data J. Chem. Inf. Model., 54(10):2636-2646
Zheng, S., Moehlenbrink, J., Lu, Y.C., Zalmas, L.P., Sagum, C.A., Carr, S., McGouran, J.F., Alexander, L.T., Fedorov, O., Munro, S., Kessler, B., Bedford, M.T., Yu, Q. & La Thangue, N.B. (2013) Arginine methylation-dependent reader-writer interplay governs growth control by E2F-1 Mol. Cell, 52(1):37-51
Hischenhuber, B., Havlicek, H., Todoric, J., Höllrigl-Binder, S., Schreiner, W. & Knapp, B. (2013) Differential Geometric Analysis of Alterations in MH \alpha-Helices J. Comput. Chem., 34(21):1862-1879
Choi, Y., Agarwal, S. & Deane, C.M. (2013) How long is a piece of loop? PeerJ, 1
Ebejer, J.P., Fulle, S., Morris, G.M. & Finn, P.W. (2013) The emerging role of cloud computing in molecular modelling J. Mol. Graph. Model., 44:177-187
Lori, C., Lantella, A., Pasquo, A., Alexander, L.T., Knapp, S., Chiaraluce, R. & Consalvi, V. (2013) Effect of Single Amino Acid Substitution Observed in Cancer on Pim-1 Kinase Thermodynamic Stability and Structure PLoS One, 8(6):e64824
Kelm, S., Choi, Y. & Deane, C.M.; Kasabov, N. (eds). (2013) Protein Modelling and Structural Prediction In Springer Handb. Bio-/Neuro-informatics
Hill, J.R. & Deane, C.M. (2013) MP-T: improving membrane protein alignment for structure prediction Bioinformatics, 29(1):54-61
Dunbar, J., Fuchs, A., Shi, J. & Deane, C.M. (2013) ABangle: characterising the VH–VL orientation in antibodies Protein Design Selection & Engineering, 26(10):611-620
Ebejer, J.P., Hill, J.R., Kelm, S., Shi, J. & Deane, C.M. (2013) Memoir: template-based structure prediction for membrane proteins Nucleic Acids Res., 41(Web Server issue):W379--W383
Edwards, H., Abeln, S. & Deane, C.M. (2013) Exploring Fold Space Preferences of New-born and Ancient Protein Superfamilies PLoS Comput Biol, 9(11):e1003325
Harrington, L., Cheley, S., Alexander, L.T., Knapp, S. & Bayley, H. (2013) Stochastic detection of Pim protein kinases reveals electrostatically enhanced association of a peptide substrate Proc. Natl. Acad. Sci., 110(47):E4417--E4426
Krawczyk, K., Baker, T., Shi, J. & Deane, C.M. (2013) Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking Protein Eng. Des. Sel., 26(10):621-629
Luo, Q., Hamer, R., Reinert, G. & Deane, C.M. (2013) Local Network Patterns in Protein-Protein Interfaces PLoS One, 8(3):e57031
Knapp, B., Dorffner, G. & Schreiner, W. (2013) Early Relaxation Dynamics in the LC 13 T Cell Receptor in Reaction to 172 Altered Peptide Ligands: A Molecular Dynamics Simulation Study PLoS One, 8(6):e64464
Kashir, J., Konstantinidis, M., Jones, C., Lemmon, B., Lee, H.C., Hamer, R., Heindryckz, B., Deane, C.M., De Sutter, P., Fissore, R.A., Parrington, J., Wells, D. & Coward, K. (2012) A maternally inherited autosomal point mutation in human phospholipase C zeta (PLCζ) leads to male infertility. Hum. Reprod., 27(1):222-231
Mann, M., Saunders, R., Smith, C., Backofen, R. & Deane, C.M. (2012) Producing High-Accuracy Lattice Models from Protein Atomic Coordinates Including Side Chains Adv. Bioinformatics, 2012:e148045
Withers-Martinez, C., Suarez, C., Fulle, S., Kher, S., Penzo, M., Ebejer, J.P., Koussis, K., Hackett, F., Jirgensons, A., Finn, P. & Blackman, M.J. (2012) Plasmodium subtilisin-like protease 1 (SUB1): Insights into the active-site structure, specificity and function of a pan-malaria drug target Int. J. Parasitol., 42(6):597-612
Ebejer, J.P., Morris, G.M. & Deane, C.M. (2012) Freely Available Conformer Generation Methods: How Good Are They? J. Chem. Inf. Model., 52(5):1146-1158
Lewis, A.C.F., Jones, N.S., Porter, M.A. & Deane, C.M. (2012) What Evidence Is There for the Homology of Protein-Protein Interactions? PLoS Comput Biol, 8(9):e1002645
Rito, T., Deane, C.M. & Reinert, G. (2012) The importance of age and high degree, in protein-protein interaction networks J. Comput. Biol. A J. Comput. Mol. Cell Biol., 19(6):785-795
Pawelczyk, S., Scott, K.A., Hamer, R., Blades, G., Deane, C.M. & Wadhams, G.H. (2012) Predicting Inter-Species Cross-Talk in Two-Component Signalling Systems PLoS One, 7(5):e37737
Gomes, M., Hamer, R., Reinert, G. & Deane, C.M. (2012) Mutual information and variants for protein domain-domain contact prediction BMC Res. Notes, 5(1):472
Hill, J.R., Kelm, S., Shi, J. & Deane, C.M. (2011) Environment specific substitution tables improve membrane protein alignment. Bioinformatics, 27(13):i15-23
Choi, Y. & Deane, C.M. (2011) Predicting antibody complementarity determining region structures without classification Mol. Biosyst., 7(12):3327-3334
Saunders, R., Mann, M. & Deane, C.M. (2011) Signatures of co-translational folding Biotechnol. J., 6(6):742-751
Ali, W., M., D.C. & Reinert, G.; Stumpf, M., Balding, D.J. & Girolami, M. (eds). (2011) Protein interaction networks and their statistical analysis In Handb. Statistical Systems Biology
Deane, C.M. & Saunders, R. (2011) The imprint of codons on protein structure Biotechnol. J., 6(6):641-649
Rito, T., Wang, Z., Deane, C.M. & Reinert, G. (2010) How threshold behaviour affects the use of subgraphs for network comparison Bioinformatics, 26(18):i611--i617
Lance, B.K., Deane, C.M. & Wood, G.R. (2010) Exploring the potential of template-based modelling Bioinformatics, 26(15):1849-1856
Choi, Y. & Deane, C.M. (2010) FREAD revisited: Accurate loop structure prediction using a database search algorithm Proteins, 78(6):1431-1440
Saunders, R. & Deane, C.M. (2010) Synonymous codon usage influences the local protein structure observed Nucleic Acids Res., 38(19):6719-6728
Lewis, A.C.F., Saeed, R. & Deane, C.M. (2010) Predicting protein-protein interactions in the context of protein evolution Mol. Biosyst., 6(1):55-64
Agarwal, S., Deane, C.M., Porter, M.A. & Jones, N.S. (2010) Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks PLoS Comput. Biol., 6(6):e1000817
Kelm, S., Shi, J. & Deane, C.M. (2010) MEDELLER: homology-based coordinate generation for membrane proteins Bioinformatics, 26(22):2833-2840
Ellis, J.J., Huard, F.P.E., Deane, C.M., Srivastava, S. & Wood, G.R. (2010) Directionality in protein fold prediction. BMC Bioinformatics, 11(1):172
Hamer, R., Luo, Q., Armitage, J.P., Reinert, G. & Deane, C.M. (2010) i-Patch: interprotein contact prediction using local network information Proteins, 78(13):2781-2797
Saunders, R. & Deane, C.M. (2010) Protein structure prediction begins well but ends badly Proteins, 78(5):1282-1290
Lewis, A.C.F., Jones, N.S., Porter, M.A. & Deane, C.M. (2010) The function of communities in protein interaction networks at multiple scales BMC Syst. Biol., 4(1):100
Hamer, R., Chen, P.Y., Armitage, J.P., Reinert, G. & Deane, C.M. (2010) Deciphering chemotaxis pathways using cross species comparisons BMC Syst. Biol., 4:3
Ali, W. & Deane, C.M. (2010) Evolutionary analysis reveals low coverage as the major challenge for protein interaction network alignment Mol. Biosyst., 6(11):2296-2304
Ali, W. & Deane, C.M. (2009) Functionally guided alignment of protein interaction networks for module detection Bioinformatics, 25(23):3166-3173
Kelm, S., Shi, J. & Deane, C.M. (2009) iMembrane: homology-based membrane-insertion of proteins. Bioinformatics, 25(8):1086-1088
Patel, P.C., Fisher, K.H., Yang, E.C.C., Deane, C.M. & Harrison, R.E. (2009) Proteomic Analysis of Microtubule-associated Proteins during Macrophage Activation Mol. Cell. Proteomics, 8(11):2500-2514
Heytens, E., Parrington, J., Coward, K., Young, C., Lambrecht, S., Yoon, S.Y., Fissore, R.A., Hamer, R., Deane, C.M., Ruas, M., Grasa, P., Soleimani, R., Cuvelier, C.A., Gerris, J., Dhont, M., Deforce, D., Leybaert, L. & De Sutter, P. (2009) Reduced amounts and abnormal forms of phospholipase C zeta (PLCzeta) in spermatozoa from infertile men Hum. Reprod., 24(10):2417-2428
Chen, P.Y., Deane, C.M. & Reinert, G. (2008) Predicting and validating protein interactions using network structure PLoS Comput. Biol., 4(7):e1000118
Mann, M., Maticzka, D., Saunders, R. & Backofen, R. (2008) Classifying proteinlike sequences in arbitrary lattice protein models using LatPack HFSP J., 2(6):396-404
Hughes, J.R., Meireles, A.M., Fisher, K.H., Garcia, A., Antrobus, P.R., Wainman, A., Zitzmann, N., Deane, C., Ohkura, H. & Wakefield, J.G. (2008) A Microtubule Interactome: Complexes with Roles in Cell Cycle and Mitosis PLoS Biol., 6(4)
Saeed, R. & Deane, C. (2008) An assessment of the uses of homologous interactions Bioinformatics, 24(5):689-695
Valeyev, N.V., Downing, A.K., Sondek, J. & Deane, C. (2008) Electrostatic and functional analysis of the seven-bladed WD beta-propellers Evol. Bioinform. Online, 4:203-216
Fisher, K.H., Deane, C.M. & Wakefield, J.G. (2008) The functional domain grouping of microtubule associated proteins Commun Integr Biol, 1(1):47-50
Deane, C.M., Dong, M., Huard, F.P.E., Lance, B.K. & Wood, G.R. (2007) Cotranslational protein folding–fact or fiction? Bioinformatics, 23(13):i142-8
Abeln, S. & Deane, C. (2007) Linking evolution of protein structures through fragments BMC Syst. Biol., 1(Suppl 1):S12
Chen, P.Y., Deane, C.M. & Reinert, G. (2007) A statistical approach using network structure in the prediction of protein characteristics Bioinformatics, 23(17):2314-2321
Abeln, S., Teubner, C. & Deane, C.M. (2007) Using Phylogeny to Improve Genome-Wide Distant Homology Recognition PLoS Comput. Biol., 3(1)
Saeed, R. & Deane, C.M. (2006) Protein protein interactions, evolutionary rate, abundance and age BMC Bioinformatics, 7(1):128
Huard, F.P.E., Deane, C.M. & Wood, G.R. (2006) Modelling sequential protein folding under kinetic control In Bioinformatics, 22(14)
Winstanley, H.F., Abeln, S. & Deane, C.M. (2005) How old is your fold? Bioinformatics, 21 Suppl 1:i449--458
Abeln, S. & Deane, C.M. (2005) Fold usage on genomes and protein fold evolution Proteins, 60(4):690-700
O'Leary, J.M., Hamilton, J.M., Deane, C.M., Valeyev, N.V., Sandell, L.J. & Downing, A.K. (2004) Solution structure and dynamics of a prototypical chordin-like cysteine-rich repeat (von Willebrand Factor type C module) from collagen IIA J. Biol. Chem., 279(51):53857-53866