Computational structural biology: developments mathematical algorithms and software tools for structural biology.
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Research in our group evolves around development of mathematical, computational and statistical methods and their implementation in software tools that are used by the structural biology community worldwide. Examples of software developed in and distributed by our group include the maximum likelihood program for macromolecular crystallographic (MX) refinement and fitting into cryo-EM maps– REFMAC, automatic structure solution pipeline – BALBES, conformation-independent protein 3D structure comparison package – ProSMART, and accurate ligand chemistry description program - AceDRG. According to the web of science one of the papers describing one of the methods has now been cited more than 11000 times.
Due to rapid advances in and power of MX and single particle cryoEM, they are now being applied to more and more challenging structural biological problems. As a result, in many cases, produced data are noisy, limited and often incomplete with varying signal to noise ratio. Potential PhD students will be involved in development and implementation of state-of-the-art techniques to extract biologically relevant information from such noisy and limited data. Projects will include development and use of techniques from a wide range of fields, such as: 1) Image processing: computational super-resolution and optimal feature extraction; 2) Bayesian statistics: organisation and use of chemical and structural prior knowledge; 3) Regularisation of ill-posed inverse problems such as image de-blurring; 4) Bioinformatics: Analysis of protein conformation space: extraction and use of information invariant to all and/or classes of macromolecules; 5) Applied statistics: automatic atomic model building and refinement using single particle cryo-EM maps;
One of the attractive aspects of research in such an interdisciplinary field is that the knowledge and skills gained are transferable and applicable to many areas of applied computational mathematics.
Fitzpartrick, AWP, Falcon B, He S, Murzin AG, Murshudov GN, Garringer HJ, Crowther RA, Ghetti BF, Goedert M, Scheres SHW (2017)
Cryo-EM structures of tau filaments from Alzheimer's disease.
Nature, 547, 185-190
Long F., Nicholls R.A., Emsley P., Grazulis S., Merkys A., Vatikus A. and Murshudov G.N. (2017)
ACEDRG: A stereo-chemical description generator for ligands.
Acta Cryst D73. 112-122
Brown, A., Amunts, A., Bai, X. C., Sugimoto, Y., Edwards, P. C., Murshudov, G., Scheres, S. H. & Ramakrishnan, V. (2014)
Structure of the large ribosomal subunit from human mitochondria.
Science 346, 718-722
Chen S, McMullan, G, Faruqi, AR, Murshudov, GN, Short, JM, Scheres, SHW, Henderson, R. (2013)
High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy.
Murshudov GN, Skubak P, Lebedev AA, Pannu NS, Steiner RA, Nicholls RA, Winn MD, Long F, Vagin AA. (2011)
REFMAC5 for the Refinement of Macromolecular Crystal Structures.
Acta Cryst., D67;355-367
Murshudov GN, Vagin AA, Dodson EJ. (1997)
Refinement of Macromolecular structures by Maximum likelihood method
Acta Cryst., D53:240-255