Publications

38. Hussain, A., Shaw, P.E. & Hirst, J.D., Molecular dynamics simulations and in silico peptide ligand screening of the Elk-1 ETS domain. J. Cheminf., 3, 49 (2011).
DOI: http://dx.doi.org/10.1186/1758-2946-3-49
37. Pu, M., Garrahan, J.P. & Hirst, J.D., Influence of solvent model on protein dynamics. Chem. Phys. Lett., 515, 283–289 (2011).
DOI: http://dx.doi.org/10.1016/j.cplett.2011.09.026
36. Chen, P., Evans, C.-L., Hirst, J.D., Searle, M.S., Structural insights into the two sequential folding transition states of the PB1 domain of NBR1 from Φ value analysis and biased molecular dynamics simulations. Biochemistry, 50, 125–135 (2011).
DOI: http://dx.doi.org/10.1021/bi1016793
35. Turpin, E.R., Bonev, B.B., Hirst, J.D., Stereoselective disulfide formation stabilizes the local peptide conformation in Nisin mimics. Biochemistry, 49, 9594–9603 (2010).
DOI: http://dx.doi.org/10.1021/bi101214t
34. Kountouris, P. & Hirst, J.D., Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures. BMC Bioinformatics, 11, 407 (2010).
DOI: http://dx.doi.org/10.1186/1471-2105-11-407
33. Jain, P. & Hirst, J.D., Automatic structure classification of small proteins using random forest. BMC Bioinformatics, 11, 364 (2010).
DOI: http://dx.doi.org/10.1186/1471-2105-11-364
32. Bromley, E.H.C., Channon, K.J., King, P.J.S., Mahmoud, Z.N., Banwell, E.F., Butler, M.F., Crump, M.P., Dafforn, T.R., Hicks, M.R., Hirst, J.D., Rodger, A. & Woolfson, D.N., The assembly pathway of a designed α-helical protein fiber. Biophys. J., 98, 1668–1676 (2010).
DOI: http://dx.doi.org/10.1016/j.bpj.2009.12.4309
31. Smith, R.E., Liang, M., Bacardit, J., Stout, M., Krasnogor, N. & Hirst, J.D., A Learning Classifier System with Mutual-Information-Based Fitness. Evolutionary Intelligence, 3, 31–50 (2010).
DOI: http://dx.doi.org/10.1007/s12065-010-0037-9
30. Kountouris, P. & Hirst, J.D., Prediction of backbone dihedral angles and protein secondary structure using support vector machines. BMC Bioinformatics, 10, 437 (2009).
DOI: http://dx.doi.org/10.1186/1471-2105-10-437
29. Jain, P. & Hirst, J.D., Exploring protein structural dissimilarity to facilitate structure classification. BMC Structural Biology, 9, 60 (2009).
DOI: http://dx.doi.org/10.1186/1472-6807-9-60
28. Jain, P., Garibaldi, J.M. & Hirst, J.D., Supervised machine learning algorithms for protein structure classification. Comp. Biol. Chem., 33, 216–223 (2009).
DOI: http://dx.doi.org/10.1016/j.compbiolchem.2009.04.004
27. Bulheller, B.M. & Hirst, J.D., DichroCalc–circular and linear dichroism online. Bioinformatics, 25, 539–540 (2009).
DOI: http://dx.doi.org/10.1093/bioinformatics/btp016
26. Bacardit, J., Stout, M., Hirst, J.D., Valencia, A., Smith R.E., & Krasnogor, N., Automated alphabet reduction for protein datasets. BMC Bioinformatics, 10, 6 (2009).
DOI: http://dx.doi.org/10.1186/1471-2105-10-6
25. Stout, M., Bacardit, J., Hirst, J.D., Smith, R.E. & Krasnogor, N., Prediction of Topological Contacts in Proteins Using Learning Classifier Systems. Soft Comput., 13, 245–258 (2009).
DOI: http://dx.doi.org/10.1007/s00500-008-0318-8
24. Hamby, S.E. & Hirst, J.D., Prediction of Glycosylation Sites Using Random Forests. BMC Bioinformatics, 9, 500 (2008).
DOI: http://dx.doi.org/10.1186/1471-2105-9-500
23. Oakley, M.T., Barthel, D., Bykov, Y., Garibaldi, J.M., Burke, E.K., Krasnogor, N. & Hirst, J.D., Search Strategies in Structural Bioinformatics. Curr. Prot. Peptide. Sci., 9, 260–274 (2008).
DOI: http://dx.doi.org/10.2174/138920308784534032
22. Stout, M., Bacardit, J., Hirst, J.D. & Krasnogor, N., Prediction of Recursive Convex Hull Class Assignments for Protein Residues Using Learning Classifier Systems. Bioinformatics, 24, 916–923 (2008).
DOI: http://dx.doi.org/10.1093/bioinformatics/btn050
21. Evans, C.-L., Long, J.E., Gallagher, T.R.A., Hirst, J.D. & Searle, M.S., Conformation and dynamics of the three-helix bundle UBA domain of p62 from experiment and simulation. Proteins: Structure, Function & Bioinformatics, 71, 227–240 (2008).
DOI: http://dx.doi.org/10.1002/prot.21692
20. Barthel, D., Hirst, J.D., Blazewicz, J., Burke, E.K. & Krasnogor, N., ProCKSI: a decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information. BMC Bioinformatics, 8, 416 (2007).
DOI: http://dx.doi.org/10.1186/1471-2105-8-416
19. Bacardit, J., Stout, M., Hirst, J.D., Sastry, K. Llora, X. & Krasnogor, N., Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, London, England, (ISBN 978-1-59593-697-4), 346–353 (2007).
DOI: http://dx.doi.org/10.1145/1276958.1277033
18. Bacardit, J., Stout, M., Krasnogor, N., Hirst, J.D., & Blazewicz, J., Coordination number prediction using learning classifier systems: performance and interpretability. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (Seattle, Washington, USA, July 08 - 12, 2006). GECCO '06, ACM Press, New York, NY, 247–254 (2006).
DOI: http://dx.doi.org/10.1145/1143997.1144041
17. Stout, M., Bacardit, J., Hirst, J.D., Krasnogor, N., & Blazewicz, J., From HP Lattice Models to Real Proteins: coordination number prediction using Learning Classifier Systems. Lectures Notes in Computer Science, 3907, 208–220 (2006).
DOI: http://dx.doi.org/10.1007/11732242_19
16. Blackburne, B.P. & Hirst, J.D., Population Dynamics Simulations of Functional Model Proteins. J. Chem. Phys., 123, 154907/1–154907/9 (2005).
DOI: http://dx.doi.org/10.1063/1.2056545
15. Oakley, M.T., Garibaldi, J.M. & Hirst, J.D., Lattice models of peptide aggregation: Evaluation of conformational search algorithms. J. Comp. Chem., 26, 1638–1646 (2005).
DOI: http://dx.doi.org/10.1002/jcc.20306
14. Wood, M.J. & Hirst, J.D., Protein Secondary Structure Prediction with Dihedral Angles. Proteins: Structure, Function & Bioinformatics, 59, 476–481 (2005).
DOI: http://dx.doi.org/10.1002/prot.20435
13. Pelta, D.A., Krasnogor, N., Bousono-Calzon, C., Verdegay, J.L., Hirst, J.D., Burke, E.K., A Fuzzy Sets based Generalization of Contact Maps for the Overlap of Protein Structures. Fuzzy Sets and Systems, 152, 103–121 (2005).
DOI: http://dx.doi.org/10.1016/j.fss.2004.10.017
12. Wood, M.J. & Hirst, J.D., Predicting Protein Secondary Structure by Cascade- Correlating Neural Networks. Bioinformatics, 20, 419–420 (2004).
DOI: http://dx.doi.org/10.1093/bioinformatics/btg423
11. Blackburne, B.P. & Hirst, J.D., Three Dimensional Functional Model Proteins: Structure, Function and Evolution. J. Chem. Phys., 119, 3453–3460 (2003).
DOI: http://dx.doi.org/10.1063/1.1590310
10. Cox, K., Watson, T., Soultanas, P. & Hirst, J.D., Molecular Dynamics Simulations of a Helicase. Proteins: Structure, Function & Genetics, 52, 254–262 (2003).
DOI: http://dx.doi.org/10.1002/prot.10400
9. Krasnogor, N., Blackburne, B.P., Burke, E.K. & Hirst, J.D., Multimeme Algorithms for Protein Structure Prediction. Proceedings of the 7th International Conference on Parallel Problem Solving from Nature, Granada, Spain, Publishers: Springer, pp 769–778 (2002).
8. Carr, B., Hart, W.E., Hirst, J.D., Krasnogor, N., Burke, E.K & Smith, J., Alignment of Protein Structures with a Memetic Evolutionary Algorithm. Proceedings of the Genetic and Evolutionary Computation Conference 2002, NewYork, USA, Publishers: Morgan Kaufmann, pp 1027–1034 (2002).
7. Blackburne, B.P. & Hirst, J.D., Evolution of Functional Model Proteins. J. Chem. Phys., 115, 1935–1942 (2001).
DOI: http://dx.doi.org/10.1063/1.1383051
6. Hirst, J.D., The Evolutionary Landscapes of Functional Model Proteins. Protein Engineering, 12, 721–726 (1999).
5. Hirst, J.D., Dominy, B., Guo, Z., Vieth, M. & Brooks III, C.L., Conformational and Energetic Aspects of Receptor-Ligand Recognition. Am. Chem. Soc. Symp. Series, 719, 13485 (1999).
4. Hirst, J.D., Vieth, M., Skolnick, J. & Brooks III, C.L., Predicting Leucine Zipper Structures from Sequence. Protein Engineering, 9, 657–662 (1996).
DOI: http://dx.doi.org/10.1093/protein/9.8.657
3. Hirst, J.D. & Brooks III, C.L., Molecular Dynamics Simulations of Isolated Helices of Myoglobin. Biochemistry, 34, 7614–7621 (1995).
DOI: http://dx.doi.org/10.1021/bi00023a007
2. Hirst, J.D. & Sternberg, M.J.E., Prediction of Structural and Functional Features of Protein and Nucleic Acid Sequences by Artificial Neural Networks. Biochemistry, 31, 7211–7218 (1992).
DOI: http://dx.doi.org/10.1021/bi00147a001
1. Hirst, J.D. & Sternberg, M.J.E., Prediction of ATP-Binding Motifs: A Comparison of a Perceptron-Type Neural Network and a Consensus Sequence Method. Protein Engineering, 4, 615–623 (1991).
DOI: http://dx.doi.org/10.1093/protein/4.6.615