OPIG: Publications

Publications

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.

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Rxiv Preprints

Vost, L., Chenthamarakshan, V., Das, P. & Deane, C.M. (Rxiv) Improving Structural Plausibility in 3D Molecule Generation via Property-Conditioned Training with Distorted Molecules bioRxiv

Chinery, L., Jeliazkov, J.R. & Deane, C.M. (Rxiv) Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains bioRxiv

Ziv, Y., Marsden, B. & Deane, C. (Rxiv) MolSnapper: Conditioning Diffusion for Structure Based Drug Design bioRxiv

Chinery, L., Hummer, A.M., Mehta, B.B., Akbar, R., Rawat, P., Slabodkin, A., Quy, K.L., Lund-Johansen, F., Greiff, V., Jeliazkov, J.R. & Deane, C.M. (Rxiv) Baselining the Buzz Trastuzumab-HER2 Affinity, and Beyond bioRxiv

Ferla, M.P., Sanchez-Garcia, R., Skyner, R.E., Gahbauer, S., Taylor, J.C., von Delft, F., Marsden, B.D. & Deane, C.M. (Rxiv) Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology ChemRxiv

Olsen, T.H., Moal, I.H. & Deane, C. (Rxiv) Addressing the antibody germline bias and its effect on language models for improved antibody design bioRxiv

Jiang, Y., Rex, D.A.B., Schuster, D., Neely, B.A., Rosano, G.L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T.M., Egbert, S., Kreimer, S., Doud, E.H., Crook, O.M., Yadav, A.K., Vanuopadath, M., Mayta, M.L., Duboff, A.G., Riley, N.M., Moritz, R.L. & Meyer, J.G. (Rxiv) Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry Rxiv

Durant, G., Boyles, F., Birchall, K., Marsden, B. & Deane, C.M. (Rxiv) Robustly interrogating machine learning-based scoring functions: what are they learning? bioRxiv

Huhn, A., Nissley, D., Wilson, D.B., Kutuzov, M., Donat, R., Tan, T.K., Zhang, Y., Barton, M.I., Liu, C., Dejnirattisai, W., Supasa, P., Mongkolsapaya, J., Townsend, A., James, W., Screaton, G., Merwe, P.A.v.d., Deane, C.M., Isaacson, S.A. & Dushek, O. (Rxiv) The molecular reach of antibodies determines their SARS-CoV-2 neutralisation potency bioRxiv

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

Homberg, S., Janosch, M., Morris, G.M. & Koch, O. (Rxiv) Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches ChemRxiv

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

McMaster, B., Thorpe, C., Rossjohn, J., Deane, C. & Koohy, H. (Rxiv) Quantifying conformational changes in the TCR:pMHC-I binding interface bioRxiv

Wills, S., Sanchez-Garcia, R., Roughley, S.D., Merritt, A., Hubbard, R.E., von Delft, F. & Deane, C.M. (Rxiv) Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach bioRxiv

Turnbull, O.M., Oglic, D., Croasdale-Wood, R. & Deane, C.M. (Rxiv) p-IgGen: A Paired Antibody Generative Language Model bioRxiv

Klarner, L., Rudner, T.G., Morris, G.M., Deane, C.M. & Teh, Y.W. (Rxiv) Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design arXiv

Gordon, G.L., Greenshields-Watson, A., Agarwal, P., Wong, A., Boyles, F., Hummer, A.M., Lujan Hernandez, A.G. & Deane, C.M. (Rxiv) PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature bioRxiv

Warren, M., Valsson, I., Deane, C., Magarkar, A., Morris, G. & Biggin, P. (Rxiv) How to make machine learning scoring functions competitive with FEP ChemRxiv

Quast, N.P., Abanades, B., Guloglu, B., Karuppiah, V., Harper, S., Raybould, M.I.J. & Deane, C.M. (Rxiv) T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity bioRxiv

Crook, O.M., Gittens, N., Chung, C.w. & Deane, C.M. (Rxiv) Inferring residue level hydrogen deuterium exchange with ReX bioRxiv

2024

Riccabona, J.R., Spoendlin, F.C., Fischer, A.L.M., Loeffler, J.R., Quoika, P.K., Jenkins, T.P., Ferguson, J.A., Smorodina, E., Laustsen, A.H., Greiff, V., Forli, S., Ward, A.B., Deane, C.M. & Fernández-Quintero, M.L. (2024) Assessing AF2’s ability to predict structural ensembles of proteins Structure

Richardson, E., Bibi, S., McLean, F., Schimanski, L., Rijal, P., Ghraichy, M., Von Niederhäusern, V., Truück, J., Clutterbuck, E.A., O'Connor, D., Luhn, K., Townsend, A., Peters, B., Pollard, A., Deane, C.M. & Kelly, D. (2024) Computational mining of B cell receptor repertoires reveals antigen-specific and convergent responses to Ebola vaccination Frontiers in Immunology, 15:1383753

Outeiral, C. & Deane, C. (2024) Codon language embeddings provide strong signals for protein engineering Nature Machine Intelligence, 6:170-179

Greenshields-Watson, A., Abanades, B. & Deane, C.M. (2024) Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms Frontiers in Immunology, 15:1352703

Abanades, B., Olsen, T.H., Raybould, M.I.J., Aguilar-Sanjuan, B., Wong, W.K., Georges, G., Bujotzek, A. & Deane, C.M. (2024) The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures Nucleic Acids Research, 52(D1):D545-D551

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. (2024) Microfluidics-enabled fluorescence-activated cell sorting of single pathogen-specific antibody secreting cells for the rapid discovery of monoclonal antibodies Nature Biotechnology, ():10.1038/s41587-024-02346-5

Raybould, M.I.J., Greenshields-Watson, A., Agarwal, P., Aguilar-Sanjuan, B., Olsen, T.H., Turnbull, O.M., Quast, N.P. & Deane, C.M. (2024) The Observed T cell receptor Space database enables paired-chain repertoire mining, coherence analysis and language modelling Cell Reports, 43(9):114704

Gordon, G.L., Raybould, M.I.J., Wong, A. & Deane, C.M. (2024) Prospects for the computational humanization of antibodies and nanobodies Frontiers in Immunology, 15:1399438

McMaster, B., Thorpe, C., Ogg, G., Deane, C.M. & Koohy, H. (2024) Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity? Nature Methods, 21:766-776

Éliás, S., Wrzodek, C., Deane, C.M., Tissot, A.C., Klostermann, S. & Ros, F. (2024) Prediction of polyspecificity from antibody sequence data by machine learning Frontiers in Bioinformatics, 3:1286883

Jiang, Y., Deane, C.M., Morris, G.M. & O’Brien, E.P. (2024) It is theoretically possible to avoid misfolding into non-covalent lasso entanglements using small molecule drugs PLOS Computational Biology, 20(3):1-22

Carbery, A., Buttenschoen, M., Skyner, R., von Delft, F. & Deane, C.M. (2024) Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures Journal of Cheminformatics, 16(1):32

Theorell, J., Harrison, R., Williams, R., Raybould, M.I.J., Zhao, M., Fox, H., Fower, A., Miller, G., Wu, Z., Browne, E., Mgbachi, V., Sun, B., Mopuri, R., Li, Y., Waters, P., Deane, C.M., Handel, A., Makuch, M. & Irani, S.R. (2024) Ultrahigh frequencies of peripherally matured LGI1 & CASPR2-reactive B cells characterise encephalitis patient cerebrospinal fluid Proceedings of the National Academy of Sciences USA, 121(7):e2311049121

Raybould, M.I.J., Turnbull, O.M., Suter, A., Guloglu, B. & Deane, C.M. (2024) Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling Communications Biology, 7:62

2023

Villanueva, E., Smith, T., Pizzinga, M., Elzek, M., Queiroz, R.M.L., Harvey, R.F., Breckels, L.M., Crook, O.M., Monti, M., Dezi, V., Willis, A.E. & Lilley, K.S. (2023) System-wide analysis of RNA and protein subcellular localization dynamics Nature Methods, 21:60-71

Hoerschinger, V.J., Waibl, F., Pomarici, N.D., Loeffler, J.R., Deane, C.M., Georges, G., Kettenberger, H., Fernández-Quintero, M. & Liedl, K.R. (2023) PEP-Patch: Electrostatics in Protein–Protein Recognition, Specificity, and Antibody Developability Journal of Chemical Information and Modelling, 63(22):6964-6971

Gordon, G.L., Capel, H.L., Guloglu, B., Richardson, E., Stafford, R.L. & Deane, C.M. (2023) A comparison of the binding sites of antibodies and single-domain antibodies Frontiers in Immunology, 14:1231623

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, 63(11):3423-3437

Guloglu, B. & Deane, C.M. (2023) Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects Frontiers in Immunology, 14:1223802

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, 63(10):2960-2974

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

Crook, O.M., Chung, C.w. & Deane, C.M. (2023) A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry Journal of Proteome Research, 22(4):2959-2972

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, 5:386-394

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

Boby, M.L., Fearson, D., Ferla, M., Filep, M., Koekemoer, L., Robinson, M.C., Carbery A. & Morris G.M. & Smilova M.D. & Wild C.F. as part of Consortium, T.C.M., Chodera, J.D., Lee, A.A., London, N., Von Delft, A. & Von Delft, F. (2023) Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors Science, 382(6671):abo7201

Crook, O.M., Cucuringu, M., Hurst, T., Schönlieb, C.B., Thorpe, M. & Zygalakis, K. (2023) A linear transportation L^p distance for pattern recognition Pattern Recognition, 147(110080)

Hadfield, T.E., Scantlebury, J. & Deane, C.M. (2023) Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding Journal of Cheminformatics, 15:84

Spoendlin, F.C., Abanades, B., Raybould, M.I.J., Wong, W.K., Georges, G. & Deane, C.M. (2023) Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope Frontiers in Molecular Biosciences, 10:1237621

Vales, S., Kryukova, J., Chandra, S., Smagurauskaite, G., Payne, M., Clark, C.J., Hafner, K., Mburu, P., Denisov, S., Davies, G., Outeiral, C., Deane, C.M., Morris, G.M. & Bhattacharya, S. (2023) Discovery and pharmacophoric characterization of chemokine network inhibitors using phage-display, saturation mutagenesis and computational modelling Nature Communications, 14:5763

Buttenschoen, M., Morris, G.M. & Deane, C.M. (2023) PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences Chemical Science, 15(9):3130-3139

Olsen, T.H., Abanades, B., Moal, I.H. & Deane, C.M. (2023) KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies Scientific Reports, 13(1):11612

Klarner, L., Rudner, T.G.J., Reutlinger, M., Schindler, T., Morris, G.M., Deane, C.M. & Teh, Y.W. (2023) Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions Proceedings of the 40th International Conference on Machine Learning, PMLR, 202():17176-17191

2022

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, 2:103-111

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 mAbs, 14(1):2020082

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, 39:btac732

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, 65(16):11404-11413

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, 13:847092

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, 27(7):752-768

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

2021

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

2020

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

2019

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

2018

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

2017

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

2016

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

2015

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

2014

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

2013

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

2012

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

2011

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

2010

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

2009

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

2008

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

2007

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)

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

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