A statistical framework for analysing structural change and its representation in data (time and/or state)

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This proposal integrates across a number of approaches in a molecule-centric vision rather than the traditional discipline-centric approach. This reframing is made possible by the complementary expertise and skills of the collaborating research teams. Recognising current limitations, multi data - multi model scenarios are a major focus, and are tackled with novel and transformational approaches. These will allow better understanding of the small changes in data and in models that reflect the dynamic function of macromolecules. In this way, macromolecular crystallography (MX) will move beyond the static view of the classic one-dataset one-structure approach, addressing the challenge of using unmerged data and joint refinement techniques. The availability of novel AI based methods, in particular for RNA, and the fast development in electron diffraction are each exploited to complete the portfolio.

This proposal comprises four connected work packages
WP1 A statistical framework for analysing the significance of change in data as a function of ligand, time, dose or other state, with a view to giving live feedback to influence data collection and insight to use in structure refinement in WP2
WP2 Joint refinement of related structures; transformation of models into a common coordinate frame, refinement against unmerged intensities, modelling posttranslational modifications and ligands, and Bayesian decomposition of mixtures of states
WP3 Methods for electron diffraction data for the refinement of macromolecular structures, using machine learning approaches to filter out dynamical scattering, and procedures for taking crystal defects and inelastic scattering terms into account
WP4 Exploiting Deep Learning-based structural bioinformatics: use of covariance-based distance and contact predictions to validate protein and nucleic acid structures; development of rational editing of RNA models for molecular replacement