Dr Darren Flower

Research Theme

Chronic and Communicable Conditions

Research Centre

Aston Research Centre for Healthy Ageing (ARCHA) 

Dr Darren Flower
Dr Darren Flower
I am a truly interdisciplinary scientist. I am an innovative specialist in bioinformatics, computational chemistry, and cheminformatics, with unique experience of the pre-clinical research environment in both academia and the pharmaceutical industry.

I am a world leader in the area of Immunoinformatics, the application of informatics techniques to immunological biomacromolecules and immune systems.

My past experience includes a PhD in Structural Biology, 7 years doing drug design in the pharmaceutical industry, and 10 years in preclinical vaccine design and discovery.

I am a Fellow of the Royal Society of Chemistry and I chair its Molecular Modelling Group.

I am the author of ~140 papers, including 1 citation classic (400+ citations) and 12 mini-classics (100+ citations); I am a member of 11 Editorial Boards.

I am the editor of 5 books. I am one of BMCs HOT 100 authors 2006 and 2007. I wrote the monograph: Bioinformatics for Vaccinology.
PH2504: X-ray crystallography, drug design methodology. 
PH2507: Scientific communication.
PH3605: Drug design methodology, computational chemistry, immunoinformatics and vaccine discovery.
PH3608: Scientific communication, research methodology.
PH4704: Research methodology.
  • BSc, Chemistry and Biochemistry, Imperial College of Science and Technology, University of London, 1988.
  • ARCS, Imperial College of Science and Technology, University of London, 1988. 
  • Wellcome Prize PhD in Molecular Biophysics. Thesis Title: Structural Studies of the Lipocalin Protein Family. University of Leeds, 1992.
  • Chartered Chemist, Royal Society of Chemistry, 1995.
  • Fellow of the Royal Society of Chemistry, 2005.
I joined Aston as a Reader in the School of Life and Health Sciences in January 2010 after 4 years as a Jenner Fellow and Principal Investigator at the University of Oxford, following 6 years as a Senior Group Leader at the Edward Jenner Institute for Vaccine Research, and 7 years as a Principal Research Chemist with Astra Pharmaceuticals. 
I will be teaching on at least two courses: the MPharm and the MSC in Drug Design.

Archilochos (Ἀρχίλοχος) (c680-645 BC), the ancient greek poet famously said: “The fox knows many things, but the hedgehog knows one big thing.” Though not remotely vulpine, I am nonetheless a Fox by this definition. I have many research interests, many more than my track-record can attest. I see Aston as the place to develop these interests and discover many more. 

More specifically, my initial research objectives lie at the intersection of drug design and immunology. My greatest accomplishments lie in the field of Immunoinformatics, and thus many of my goals lie in that area: in silico, in vitro, and in vivo discovery of novel small-molecule adjuvants; epitope identification leveraging a range of techniques; and also the discovery of novel whole protein antigens as candidate vaccines. 

At the same time, I also anticipate developing research in other including bioinformatics, cheminformatics, and the molecular dynamic simulation of semi-stochastic systems. 

  • BBSRC Grant: India Partnering Award 1713: Anglo-Indian Immunoinformatics: delivering in silico vaccinology. £24.5K over 3 years. 

  • Wellcome Trust Grant: Ref 079287. Identification of CD8 epitopes in cattle pathogens by systematic synergistic analysis of peptide specificity and 3D structures of cattle MHC class I. ~£280K over 3 years. 

  • BBSRC Grant: Ref: BB/D004020/1. Robust experimental definition & computational prediction of peptide binding specificity of the Major Histocompatability Complex allele HLA-Cw*0102. ~£231K over 3 years. 

  • EPSRC Grant: Ref: EP/D501377/1. A Synergistic Integration of Natural and Artificial Immunology for the Prediction of Hierarchical Protein Functions. ~£430K over 2.5 years. 

  • Royal Society Grant: International Incoming Short Visits. ~4K for 2 months 2006.
Keen to supervise as the opportunity arises.

Fellow of the Royal Society of Chemistry

Editorial Positions

  • I am Editor-in-Chief for Immunology and Immunogenetics Insights
  • I am Editor-for-Europe for Current Computer-Aided Drug Design
  • I am an Associate Editor for BioInformation
  • I am a Section Editor for Immunomics Research
  • I am a member of the Editorial Advisory Panel of the Biochemical Journal

I am also a member of following Editorial Boards:

  • The Open Vaccine Journal
  • The Open Proteomics Journal
  • The Open Organic Chemistry Journal
  • The Open Natural Products Journal
  • Evolutionary Bioinformatics
  • Current Drug Discovery Technologies
I was formerly a member of the Editorial Board of Applied Bioinformatics (now defunct).

I am one of Biomed Central’s HOT 100 2006 and 2007. 


Bioinformatics for Vaccinology. Wiley, Author: Flower, DR. WileyBlackwell. 2008. ISBN-10: 0470027118. ISBN-13: 978-0470027110.

Edited books:

  • Bioinformatics for Immunomics (IIMS series). Springer. Editors: Davies MN, Ranganathan S, and Flower DR. Springer. 2009. ISBN-10: 1441905391. ISBN-13: 978-1441905390 

  • Immunoinformatics: predicting immunogenicity in silico. Editor: Flower, DR. Methods in Molecular Biology series, Humana Press. ISBN: 978-1-58829-699-3 (print). ISBN: 978-1-60327-118-9 (Online). 

  • In silico Immunology. Editors: Flower, DR and Timmis, J. Springer, 2006. ISBN-10: 0-387-39238-6 ISBN-13: 978-0-387-39238-7. 

  • Lipocalins. Editors: Flower, DR, Salier, JP, Akerstrom B, Borregarrd, N. Landes Press. 2006. ISBN: 1-58706-297-6. 

  • Drug Design: Cutting Edge Approaches. Editor: Flower, DR, The Royal Society of Chemistry, Cambridge, 1st edition, March 2002. Hardcover, 192pp. ISBN: 0854048162. ISBN-13: 978-0854048168

Published papers:

  • 133. Dimitrov I, Garnev P, Flower DR, Doytchinova I. Peptide binding to the HLA-DRB1 supertype: a proteochemometrics analysis. Eur J Med Chem. 2010 45:236-243.
  • 132: Walshe VA, Hattotuwagama CK, Doytchinova IA, Wong M, Macdonald IK, Mulder A, Claas FH, Pellegrino P, Turner J, Williams I, Turnbull EL, Borrow P, Flower DR. Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes. PLoS One. 2009 4:e8095.
  • 132: Davies MN, Bayry J, Tchilian EZ, Vani J, Shaila MS, Forbes EK, Draper SJ, Beverley PC, Tough DF, Flower DR. Toward the discovery of vaccine adjuvants:coupling in silico screening and in vitro analysis of antagonist binding to human and mouse CCR4 receptors. PLoS One 4:e8084
  • 131. Ansari HR, Flower DR, Raghava GPS. AntigenDB: an immunoinformatics database of pathogen antigens. Nucleic Acids Research 2010 Jan;38(Database issue):D847-853.
  • 130. Flower DR. Predicting and Manipulating the immunogenicity of biotherapeutics and vaccines. BioDrugs, 2009 23:231-240.
  • 129. Freitas, A.A., Davies, M.N., Clark, E. and Flower, D.R. (2009) Hierarchical Classification of G-Protein-Coupled-Receptors with Data-Driven Selection of Attributes and Classifiers. International Journal of Data Mining and Bioinformatics. Accepted.
  • 128. Secker A, Davies MN, Freitas AA, Timmis J, Clark E, Flower DR (2009). An Artificial Immune System for Clustering Amino Acids in the Context of Protein Function Classification. Journal of Mathematical Modelling and Algorithms (JMMA). 8:103-123.
  • 127. Halling-Brown M, Shaban R, Frampton D, Sansom CE, Davies MN, Flower DR, Duffield M, Titball RW, Brusic V, Moss DS. Proteins accessible to immune surveillance show significant T-cell epitope depletion: implications for vaccine design. Mol Immunology 2009 46:2699-2705.
  • 126. Davies MN, Secker A, Freitas AA, Timmis J, Clark E, Flower DR. Alignment-independent techniques for protein classification. Current Proteomics, 2008, 5, 217-223.
  • 125. O’Brien C, Flower DR, Flaherty, C. Class II peptide length and affinity. Immunome Res. 2008, 4, 6.
  • 124. Mizutori Y, Nagayama Y, Flower DR, Misharin A, Aliesky H, Rapoport B, McLachlan SM. Role of the Transgenic Human Thyrotropin Receptor A-Subunit in Thyroiditis Induced by A-Subunit Immunization and Regulatory T Cell Depletion. Clinical and Experimental Immunology, 2008, 154:305-315.
  • 123. Davies MN, Secker A, Halling-Brown M, Moss DS, Freitas AA, Timmis J, Clark E and Flower DR. GPCRTree: Online Hierarchical Classification of GPCR Function. BMC Review Notes 2008 1:67.
  • 122. Doytchinova IA and Flower DR. Bioinformatic Approach for Identifying Parasite and Fungal Candidate Subunit Vaccines. Open Vaccine Journal 2008 1:22-26.
  • 121. Davies, MN, Secker, A, Freitas, AA, Clark, E, Timmis, J, Flower, DR. Optimising amino acid groupings for GPCR Classification. Bioinformatics 2008 24:1980-1986.
  • 120. Doytchinova IA, Flower DR. QSAR and the Prediction of T-Cell Epitopes. Current proteomics, 2008 5:73-95.
  • 119. Bayry J, Tchilian EZ, Davies MN, Forbes EK, Draper SJ, Kaveri SV, Hill AV, Kazatchkine MD, Beverley PC, Flower DR, Tough DF. In silico identified CCR4 antagonists target regulatory T cells and exert adjuvant activity in vaccination. Proc Natl Acad Sci U S A. 2008 105:10221-10226.
  • 118. Vivona S, Gardy JL, Ramachandran S, Brinkman FS, Raghava GP, Flower DR, Filippini F. Computer-aided biotechnology: from immuno-informatics to reverse vaccinology. Trends Biotechnol. 2008 26:190-200.
  • 117. Wan S, Flower DR, Coveney PV. Toward an Atomistic Understanding of the Immune Synapse: Large-Scale Molecular Dynamics Simulation of a membrane-embedded TCR-pMHC-CD4 complex. Mol Immunology. 2008 45:1221-1230.
  • 116. Davies MN, Secker A, Freitas AA, Mendao M, Timmis J, Flower DR. On the hierarchical classification of G Protein Coupled Receptors. Bioinformatics 2007 23:3113-3118.
  • 115. Davies MN, Lamikanra A, Sansom C, Flower DR, Moss DS, Travers PJ. Identification of the HLA-DM/HLA-DR Interface. Mol Immunology 2008 45:1063-1070.
  • 114. Todman SJ, Halling-Brown MD, Davies MN, Flower DR, Kayikci M, Moss DS. Toward the atomistic simulation of T cell epitopes Automated construction of MHC: Peptide structures for free energy calculations. J Mol Graph Model. 2008 26:957-961.
  • 113. Davies MN, Gloriam DE, Secker A, Freitas AA, Mendao M, Timmis J, Flower DR. Proteomic applications of automated GPCR classification. Proteomics 2007 7:1-16.
  • 112. Davies MN, Flower DR. Harnessing bioinformatics to discover new vaccines. Drug Discov Today. 2007 12:389-395.
  • 111. Doytchinova IA, Flower DR. Predicting Class I MHC Binders Using Multivariate Statistics: Comparison of Discrimminant Analysis and Multiple Linear Regression. J. Chem. Inf. Mod. 2007 47:234-238.
  • 110. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007 8:4.
  • 109. Guan P, Davies MN, Blythe MJ, Salomon J, Walshe V, Doytchinova IA, Flower, DR. Using data mining and databases in vaccinology. Expert Opinion on Drug Discovery 2007 2:19-35.
  • 108. Doytchinova IA, Flower DR. Identifying candidate subunit vaccines by alignment-independent method based on principal amino acid properties. Vaccine 2007 25:856-866.
  • 107. Salomon J, Flower DR. Predicting Class II MHC-Peptide binding: A kernel based approach using similarity scores. BMC Bioinformatics 2006 7:501.
  • 106. Wan J, Liu W, Xu Q, Ren Y, Flower DR, Li T. SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics 2006 7:463.
  • 105. Taylor PD, Attwood TK, Flower DR. Combining algorithms to predict Bacterial protein sub-cellular location: Parallel versus Concurrent implementations. Bioinformation 2006 1:285-289.
  • 104. Taylor PD, Attwood TK, Flower DR. Toward bacterial protein sub-cellular location prediction: single-class discrimminant models for all Gram- and Gram+ compartments. Bioinformation 2006 1:276-280.
  • 103. Hattotuwagama CK, Flower DR. Empirical prediction of peptide octanol-water partition coefficients. Bioinformation 2006 1:258-260.
  • 102. Taylor PD, Attwood TK, Flower DR. Multi-class subcellular location prediction for bacterial proteins. Bioinformation 2006 1:261-265.
  • 101. Thompson SJ, Hattotuwagama CK, Holliday JD, Flower DR. On the hydrophobicity of peptides: Comparing empirical predictions of peptide Log P values. Bioinformation 2006 1:237-241.
  • 100. Taylor PD, Toseland CP, Attwood TK, Flower DR. Alpha Helix Transmembrane Proteins: Enhanced prediction using a Bayesian approach. Bioinformation 2006 1:234-236
  • 99. Taylor PD, Toseland CP, Attwood TK, Flower DR. Beta Barrel Transmembrane Proteins: Enhanced prediction using a Bayesian approach. Bioinformation 2006 1:231-233.
  • 98. Taylor PD, Toseland CP, Attwood TK, Flower DR. A Predictor of Membrane Class: Discriminating -helical and -barrel membrane proteins from non-membranous proteins. Bioinformation 2006 1:208-213.
  • 97. Taylor PD, Toseland CP, Attwood TK, Flower DR. TATPred: a Bayesian method for the identification of twin arginine translocation pathway signal sequences. Bioinformation 2006 1:184-187.
  • 96. Taylor PD, Toseland CP, Attwood TK, Flower DR. LIPPred: A web server for accurate prediction of lipoprotein signal sequences and cleavage sites. Bioinformation 2006 1:176-179.
  • 95. Davies MN, Toseland CP, Moss DS, Flower DR. Benchmarking pKa Prediction. BMC Biochemistry 2006 7:18.
  • 94. Hattotuwagama CK, Toseland CP, Guan P, Taylor DJ, Hemsley SL, Doytchinova IA, Flower DR. Class II Mouse Major Histocompatibility Complex Peptide Binding Affinity: In Silico bioinformatic prediction using robust multivariate statistics. J. Chem. Inf. Mod. 2006 46:1491-1502.
  • 93. Hattotuwagama CK, Davies NM, Flower DR. Receptor-Ligand Binding Sites and Virtual Screening, Curr Med Chem, 2006 13:1283-1304.
  • 92. Liu W, Meng X, Xu Q, Flower DR, Li T. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics 2006 7:182.
  • 91. Davies MN, Hattotuwagama CK, Moss DS, Drew MGB, Flower DR. Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity. BMC Structural Biology 2006 6:5.
  • 90. Doytchinova IA, Flower DR. Modeling the Peptide-T cell receptor interaction by the comparative molecular similarity indices analysis-soft independent modeling of class analogy technique. J Med Chem. 2006 49:2193-2199.
  • 89. Guan P, Hattotuwagama CK, Doytchinova IA, Flower DR. MHCPred 2.0, an updated quantitative T cell epitope prediction server. Applied Bioinformatics 2006 5:55-61.
  • 88. Doytchinova IA, Guan P, Flower DR. EpiJen: multistep T cell epitope prediction. BMC Bioinformatics 2006 7:131. on-line.
  • 87. Doytchinova IA, Flower DR. Class I T cell epitope prediction: improvements using a combination of Proteasome cleavage, TAP affinity, and MHC binding. Molecular Immunology 2006 43:2037-2044.
  • 86. Toseland CP, McSparron HM, Davies MN, Flower DR. PPD v1.0 – An integrated, web-accessible database of experimentally-determined protein pKa values. Nucleic Acid Research, 2006 34 (Database issue):D199-203.
  • 85. Toseland CP, McSparron HM, Flower DR. DSD – An integrated, web-accessible database of Dehydrogenase Enzyme Stereospecificities. BMC Bioinformatics 2005 6:283.
  • 84. Guan P, Doytchinova IA, Walshe VA, Borrow P, Flower DR. Analysis of peptide-protein binding using amino acid descriptors: prediction & experimental verification for human Histocompatibility Complex HLA-A*0201. J. Med. Chem. 2005 48:7418-7425.
  • 83. Toseland CP, DJ Clayton, McSparron H, Hemsley SL, Blythe MJ, Paine K, Doytchinova IA, Guan P. Hattotuwagama CK, & Flower DR. AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Research, 1:4.
  • 82. Hattotuwagama CK, Doytchinova IA, Flower DR. In Silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: A Comparative Molecular Similarity Index Analysis (CoMSIA) study. J. Chem. Inf. Mod. 2005 45:1415-1423.
  • 81. Guan P, Davies M, Taylor DJ, Wan S, McSparron H, Hemsley SL, Toseland C, Blythe MJ, Taylor PD, Walshe V, Hattotuwagama CK, Doytchinova IA, Coveney PV, Borrow P, Flower DR. Computational Chemistry, Informatics, and the Discovery of Vaccines. Current Computer-Aided Drug Design 2005 1:377-398.
  • 80. Wan S, Coveney PV, Flower DR. Peptide recognition by the T cell receptor: Comparison of binding free energies from thermodynamic integration, Poisson-Boltzmann and linear interaction energy approximations. Philos Transact A Math Phys Eng Sci. 2005 363:2037-2053.
  • 79. Wan S, Coveney PV, Flower DR. Molecular Basis of Peptide Recognition by the T-Cell Receptor: Affinity differences calculated using large scale computing. J Immunol. 2005 175:1715-1723.
  • 78. Schonbach C, Koh JL, Flower DR, Brusic V. An Update on the Functional Molecular Immunology (FIMM) Database. Appl Bioinformatics. 2005 4:25-31.
  • 77. Doytchinova IA, Walshe V, Borrow P, Flower, DR. Towards the chemometric dissection of peptide-HLA-A*0201 binding affinity: comparison of local and global QSAR models. J. Comput.-Aid. Mol. Des. 2005 19:203-212.
  • 76. Brown RJ, Juttla VS, Tarr AW, Finnis R, Irving WL, Hemsley S, Flower DR, Borrow P, Ball JK. Evolutionary dynamics of hepatitis C virus envelope genes during chronic infection. J Gen Virol. 2005 86:1931-1942.
  • 75. Doytchinova IA, Flower DR. In Silico Identification of Supertypes for Class II MHCs. J Immunol. 2005 174:7085-7095.
  • 74. Blythe MJ, Flower DR. Benchmarking B cell epitope prediction: underperformance of existing methods. Protein Science 2005 14:246-248.
  • 73. Doytchinova IA, Hemsley S, Flower DR. TAP pre-selection of peptides binding to the major histocompatibility complex: a bioinformatics evaluation. J Immunol. 2004 173:6813-6819.
  • 72. Jones NA, Wei X, Flower DR, Wong M-L, Michor F, Saag MS, Hahn BH, Nowak MA, Shaw GM, Borrow P. Determinants of human immunodeficiency virus type 1 escape from the primary CD8+ cytotoxic T lymphocyte response. J Exp Med. 2004 200:1243-1256.
  • 71. Hattotuwagama CK, Guan P, Doytchinova IA, & Flower DR. New Horizons In Mouse Immunoinformatics: Reliable In Silico Prediction Of Mouse Class I Histocompatibility Major Complex Peptide Binding Affinity. Org Biomol Chem. 2004 2:3274-3283.
  • 70. Doytchinova IA, Guan P., Flower DR. Quantitative Structure Activity Relationships and the prediction of MHC supermotifs. Methods. 2004 34:444-453.
  • 69. Attwood TK, Flower DR. The Importance of Integrating Bioinformatic Approaches to the genomic identification of G Protein-coupled Receptors. Seminars in Cell and Developmental Biology, Semin Cell Dev Biol. 2004 15:693-701.
  • 68. Wan S, Coveney PV, Flower DR. Large scale molecular dynamics simulations of HLA-A*0201 complexed with a tumour-specific antigenic peptide: Can the 3 and 2m domains be neglected? Journal of Computational Chemistry, J Comput Chem. 2004 25:1803-1810.
  • 67. Doytchinova IA, Walshe VA, Jones NA, Gloster SE, Borrow P, Flower DR. Coupling In Silico and In  Vitro Analysis of Peptide-MHC Binding: A Bioinformatic Approach Enabling Prediction of Superbinding Peptides and Anchorless Epitopes. J Immunol.  2004 172:7495-7502.
  • 66. Doytchinova IA, Guan P, Flower DR. Identifiying Human Major Histocompatibility Complex Supertypes Using Bioinformatic Methods, J Immunol. 2004 172:4314-4323.
  • 65. Brusic V, Flower DR. Bioinformatics tools for identifying T-cell epitopes. Drug Discovery Today:BioSilico 2004 2:18-23.
  • 64. Hattotuwagama CK, Guan P, Doytchinova IA, Zygouri C, Flower DR. Quantitative online prediction of peptide binding to the major histocompatibility complex. J Mol Graph Model 2004 22:195-207.
  • 63. Flower DR, McSparron H, Blythe MJ, Zygouri C, Taylor D, Guan P, Wan S, Coveney PV, Walshe V, Borrow P, Doytchinova IA. Computational vaccinology: quantitative approaches. Novartis Found Symp. 2003 254:102-120.
  • 62. Doytchinova IA, Taylor P, Flower DR. Proteomics in Vaccinology and Immunobiology: An Informatics Perspective of the Immunome. J Biomed Biotechnol. 2003 3:267-290.
  • 61. Flower DR. Towards in silico prediction of immunogenic epitopes. Trends Immunol. 2003 24:667-674.
  • 60. Doytchinova IA, Flower DR. Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction. Bioinformatics. 2003, 19, 2263-2270.
  • 59. Guan P, Doytchinova IA, Zygouri C, and Flower DR. MHCPred: bringing a quantitative dimension to online prediction of MHC Binding. Applied Bioinformatics 2003, 2, 63-66.
  • 58. Doytchinova IA, Flower DR. The HLA-A2 supermotif: A QSAR definition. Org. Biomol. Chem. 2003, 1, 2648-2654.
  • 57. Taylor PD, Attwood TK, Flower DR. BPROMPT: a consensus server for membrane protein prediction. Nucleic Acids Res. 2003 31 3698-3700.
  • 56. Guan P, Doytchinova IA, Zygouri C, Flower DR. MHCPred: a server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res. 2003 31 3621-3624.
  • 55. Lopes AR, Jaye A, Dorrell L, Sabally S, Alabi A, Jones NA, Flower DR, De Groot A, Newton P, Lascar RM, Williams I, Whittle H, Bertoletti A, Borrow P, Maini MK. Greater CD8(+) TCR Heterogeneity and Functional Flexibility in HIV-2 Compared to HIV-1 Infection. J Immunol. 2003 171 307-316
  • 54. McSparron H, Blythe MJ, Zygouri C, Doytchinova IA, and Flower DR. JenPep: A Novel Computational Information Resource for Immunobiology and Vaccinology. J Chem Inf Comput Sci 2003 43 1276-1287
  • 53. Flower DR. Databases and Data Mining for Computational Vaccinology. Curr Opin Drug Discov Devel 2003 6 396-400
  • 52. Guan P, Doytchinova IA, and Flower DR. A comparative molecular similarity indices (CoMSIA) study of peptide binding to the HLA-A3 superfamily. Bioorganic & Medicinal Chemistry 2003 11 2307-2311
  • 51. Guan P, Doytchinova IA, and Flower DR. HLA-A3 supermotif defined by quantitative structure-activity relationship analysis. Protein Eng 2003 16 11-18
  • 50. McCormick J, Flower DR, Strobel S, Wallace DL, Beverley PC, Tchilian EZ. Novel perforin mutation in a patient with hemophagocytic lymphohistiocytosis and CD45 abnormal splicing. Am J Med Genet 2003 117A 255-260
  • 49. Attwood TK, Bradley P, Flower DR, Gaulton A, Maudling N, Mitchell AL, Moulton G, Nordle A, Paine K, Taylor P, Uddin A, Zygouri C. PRINTS and its automatic supplement, prePRINTS. Nucleic Acids Res 2003 31 400-402.
  • 48. Flower DR and Doytchinova IA. Immunoinformatics and the prediction of Immunogenicity. Applied Bioinformatics 2002 1 167-176
  • 46. Doytchinov IA, Flower DR A Comparative Molecular Similarity Index Analysis (CoMSIA) study identifies an HLA-A2 binding supermotif. J Computer-Aided Molecular Design 2002 16 535-544
  • 45. Doytchinova IA, Flower DR. Physicochemical explanation of peptide binding to HLA-A*0201 major histocompatibility complex: A three-dimensional quantitative structure-activity relationship study. Proteins 2002 48 505-518
  • 44. Doytchinova IA, Flower DR. Quantitative approaches to computational vaccinology. Immunol Cell Biol 2002 80 270-279
  • 43. Paine K, Flower DR. Bacterial Bioinformatics: Pathogenesis and the Genome. J Mol Microb Biotechnol 2002 4 357-365
  • 42. Doytchinova IA, Blythe MJ, Flower DR. Additive Method for the Prediction of Protein-Peptide Binding Affinity. Application to the MHC Class I Molecule HLA-A*0201. J Proteome Research 2002 1 263-272
  • 41. Blythe MJ, Doytchinova IA, Flower DR. JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics 2002 18 434-439
  • 40. Walker J, Flower DR, Rigley K. Microarrays in hematology. Curr Opin Hematol 2002 9 23-29
  • 39. Schonbach C, Koh JL, Flower DR, Wong L, Brusic V. FIMM, a database of functional molecular immunology: update 2002. Nucleic Acids Res 2002 30 226-229
  • 38. Attwood TK, Blythe MJ, Flower DR, Gaulton A, Mabey JE, Maudling N, McGregor L, Mitchell AL, Moulton G, Paine K, Scordis P. PRINTS and PRINTS-S shed light on protein ancestry. Nucleic Acids Res 2002 30 239-241
  • 37. Doytchinova IA, Flower DR.Toward the Quantitative Prediction of T-Cell Epitopes: CoMFA and CoMSIA Studies of Peptides with Affinity for the Class I MHC Molecule HLA-A*0201. J Med Chem 2001 44 3572-3581
  • 36. Tchilian EZ, Wallace DL, Wells RS, Flower DR, Morgan G, Beverley PC. A deletion in the gene encoding the CD45 antigen in a patient with SCID. J Immunol 2001 166 1308-1313
  • 35. Paine K, Flower DR. The lipocalin website. Biochim Biophys Acta 2000 1482 351-352
  • 34. Flower DR. Beyond the superfamily: the lipocalin receptors. Biochim Biophys Acta 2000 1482 327-336
  • 33. Flower DR. Experimentally determined lipocalin structures. Biochim Biophys Acta 2000 1482 46-56
  • 32. Flower DR, North AC, Sansom CE. The lipocalin protein family: structural and sequence overview. Biochim Biophys Acta 2000 1482 9-24
  • 31. Akerstrom B, Flower DR, Salier JP. Lipocalins: unity in diversity. Biochim Biophys Acta 2000 1482 1-8
  • 30. Attwood TK, Croning MD, Flower DR, Lewis AP, Mabey JE, Scordis P, Selley JN, Wright W. PRINTS-S: the database formerly known as PRINTS. Nucleic Acids Res 2000 28 225-227
  • 29. Flower DR. Rotational superposition: a review of methods. J Mol Graph Model 1999 17 238-244
  • 28. Scordis P, Flower DR, Attwood TK. FingerPRINTScan: intelligent searching of the PRINTS motif database. Bioinformatics 1999 15 799-806
  • 27. Flower DR. Modelling G-protein-coupled receptors for drug design. Biochim Biophys Acta 1999 1422 207-234
  • 26. Marriott DP, Dougall IG, Meghani P, Liu YJ, Flower DR. Lead generation using pharmacophore mapping and three-dimensional database searching: application to muscarinic M(3) receptor antagonists. J Med Chem 1999 42 3210-3216
  • 25. Attwood TK, Flower DR, Lewis AP, Mabey JE, Morgan SR, Scordis P, Selley JN, Wright W. PRINTS prepares for the new millennium. Nucleic Acids Res 1999 27 220-225
  • 24. Flower DR. DISSIM: a program for the analysis of chemical diversity. J Mol Graph Model 1998 16 239-253
  • 23. Flower DR. On the properties of bit string-based measures of chemical similarity. J Chem Inf Comp Sci 1998 38 379-386 
  • 22. Flower DR. A topological nomenclature for protein structure. Protein Eng 1998 11 723-727
  • 21. Attwood TK, Beck ME, Flower DR, Scordis P, Selley JN. The PRINTS protein fingerprint database in its fifth year. Nucleic Acids Res 1998 26 304-308
  • 20. Flower DR. SERF: a program for accessible surface area calculations. J Mol Graph Model 1997 15 238-244
  • 19. Flower DR. ALTER: eclectic management of molecular structure data. J Mol Graph Model 1997 15 161-169
  • 18. Clementi M, Clementi S, Cruciani G, Pastor M, Davis AM, Flower DR. Robust multivariate statistics and the prediction of protein secondary structure content. Protein Eng 1997 10 747-749
  • 17. Attwood TK, Avison H, Beck ME, Bewley M, Bleasby AJ, Brewster F, Cooper P, Degtyarenko K, Geddes AJ, Flower DR, Kelly MP, Lott S, Measures KM, Parry-Smith DJ, Perkins DN, Scordis P, Scott D, Worledge C. The PRINTS database of protein fingerprints: a novel information resource for computational molecular biology. J Chem Inf Comput Sci 1997 37 417-424
  • 16. Brownlow S, Morais Cabral JH, Cooper R, Flower DR, Yewdall SJ, Polikarpov I, North AC, Sawyer L. Bovine -lactoglobulin at 1.8Å resolution-still an enigmatic lipocalin. Structure 1997 5:481-495
  • 15. Flower DR. The lipocalin protein family: structure and function. Biochem J 1996 318:1-14
  • 14. Tomkinson NP, Marriott DP, Cage PA, Cox D, Davis AM, Flower DR, Gensmantel NP, Humphries RG, Ingall AH, Kindon ND. P2T purinoceptor antagonists. A QSAR study of some 2-substituted ATP analogues. J Pharm Pharmacol 1996 48 206-209
  • 13. Flower DR. FOLD: integrated analysis and display of protein secondary structure. J Mol Graph 1995 13 377-384
  • 12. Flower DR. A Structural Signature Characteristic of the Calycin Protein Superfamily. Protein and Peptide Letters 1995 2 341-346
  • 11. Flower DR, Sansom CE, Beck ME, Attwood TK. The first prokaryotic lipocalins. Trends Biochem Sci 1995 20 498-499
  • 10. Flower DR. Multiple molecular recognition properties of the lipocalin protein family.J Mol Recognit 1995 8 185-195
  • 9. Flower DR. Automating the identification and analysis of protein -barrels. Protein Eng 1994 7 1305-1310
  • 8. Flower DR. The lipocalin protein family: a role in cell regulation. FEBS Lett 1994 354 7-11
  • 7. Flower DR. sheet topology. A new system of nomenclature. FEBS Lett 1994 344 247-250
  • 6. Flower DR. Structural relationship of streptavidin to the calycin protein superfamily. FEBS Lett 1993 333 99-102
  • 5. Flower DR, North AC, Attwood TK. Structure and sequence relationships in the lipocalins and related proteins. Protein Sci 1993 2 753-761
  • 4. Bocskei Z, Groom CR, Flower DR, Wright CE, Phillips SE, Cavaggioni A, Findlay JB, North AC. Pheromone binding to two rodent urinary proteins revealed by X-ray crystallography. Nature 1992 360 186-188
  • 3. Wright CE, Rafferty JB, Flower DR, Groom CR, Findlay JB, North AC, Phillips SE, Zagalsky PF. Crystallization and initial X-ray analysis of the C2-subunit of crustacyanin. J Mol Biol 1992 224 283-284
  • 2. Flower DR. Improved ribbon-drawing programs. J Mol Graph 1991 9 257-258
  • 1. Flower DR, North AC, Attwood TK. Mouse oncogene protein 24p3 is a member of the lipocalin protein family. Biochem Biophys Res Commun 1991 180 69-74

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