It is often a challenge to atomically determine the structure of large macromolecular assemblies, even if successfully crystallized, due to their weak diffraction of X-rays. Refinement algorithms that work with low-resolution diffraction data are necessary for researchers to obtain a picture of the structure from limited experimental information. Relationship between the structure and function of proteins implies that a refinement approach delivering accurate structures could considerably facilitate further research on their function and other related applications such as drug design.
Here a refinement algorithm called the Deformable Complex Network is presented. Computation results revealed that, significant improvement was observed over the conventional refinement and DEN refinement, across a wide range of test systems from the Protein Data Bank, indicated by multiple criteria, including the free R value, the Ramachandran Statistics, the GDT (<1Å) score, TM-score as well as associated electron density map.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/71708 |
| Date | 24 July 2013 |
| Creators | Zhang, Chong |
| Contributors | Ma, Jianpeng |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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