Sunday, December 22, 2024
12/22/2024
Project Summary/Abstract The aim of this project is to continue to invent, refine and apply generalizable computational tools for neutron protein crystallography. Our primary focus will be to provide innovative, but practical solutions, that can be rapidly deployed to the target user community to address the computational bottleneck in neutron protein crystallography and to determine the neutron structures of a series of proteins, many with high biomedical importance, that span a spectrum of size and complexity. We will continue our current work adapting these computational tools for incorporation into PHENIX (Python- based Hierarchical Environment for Integrated Xtallography) for automated crystallography as extensible C++ and Python modules. The software will use and contribute towards the basic programming tools for crystallography in the Computational Crystallographic Toolbox (cctbx). Our vision is to contribute to a computational workbench that structural biologist, with a range of crystallographic experience, can use alternately for neutron, X-ray, or global neutron/X-ray/energy crystallography. Our research so far has demonstrated that the computational methods for analysis of neutron diffraction data can be improved, with better density maps, better models, increased automation and more reliable structure refinement. We have also shown that these methods can be successfully applied to challenging biological systems. However, as new experimental facilities come on line it is clear that new classes of biological systems will be studied. Neutron methods will need to be further advanced for success in these cases. In our interactions with the growing neutron crystallography user community it is becoming increasingly clear that there are remaining challenges that limit progress in this field and that will require the innovative solutions proposed in this project. The computational tools developed in this project will be used to shift current research to more complex structures that are larger in size, complexity and biomedical relevance. Our new developments will lead to improved models from all classes of neutron diffraction data, while simultaneously making it possible to study new challenging biological systems. 1
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