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Home > Bayesian tools for experimental structural biology
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Garib Murshudov

Bayesian tools for experimental structural biology

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Research in our group evolves around the 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 single particle cryo-EM maps– REFMAC, Bayesian statistics based software for cryoEM map manipulation, fitting and validation - EMDA, 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 13000 times.

Due to rapid advances in MX and single particle cryoEM, they now allow elucidation of macromolecular structures in unprecedented atomic details. To extract maximum possible biologically relevant information from a vast amount of experimental data produced by these techniques it is necessary to develop and use state-of-the-art statistical methods accounting for chemistry of macromolecules and physics of the image formation. Potential projects will address several long-standing problems in this field: 1) knowledge as well as physics based modelling and use of electron scattering factors of atoms accounting for their environment and charges; 2) development of Bayesian statistical tools for modelling, fitting and validation of cryoEM maps in the presence of structural flexibility; 3) extraction, design and use of prior stereochemical knowledge from small (e.g. Crystallography Open Database) as well as large molecule databases (e.g. Protein Data Bank) for building reliable atomic models using noisy experimental data.

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. Skills learned will include Bayesian statistics, machine learning techniques, computational mathematics and computational structural biology.


References:

Yamashita, K., Palmer, C., Burnley, T. and Murshudov, G.N. (2021)
Cryo-EM single particle structure refinement and map calculation using Servalcat.
BioRxiv. doi: 10.1101/2021.05.04.442493 and accepted in Acta Cryst: D77

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

Murshudov, G.N. (2016)
Refinement of atomic structures against CryoEM maps.
in Methods in Enzymology, 579:277-305.

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

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

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