Structural genomics seeks to rapidly expand the database of protein structures to permit the extract8on of much more information from the genomic sequence databases than is currently possible. This requires methods for the rapid determination of macromolecular structures using X-ray crystallography. At present the need for continual manual intervention makes rapid, minimally biased structure solution impossible. Therefore, it is imperative that crystallographic structure determination be automated in the near future. Unfortunately the crystallographic packages currently available, although possessing many powerful features, are collectively too diverse, out-data and lacking in the required functionality to be used as a solution to the problem. Major bottlenecks remain, especially during the process of model building and refinement. Therefore we will build a new system for crystallographic computing that will allow, amongst other things, the creation of new software that combines the powerful automation features of the SOLVE system, the computational flexibility of the Crystallography & NMR System and the automated map interpretation algorithms of the TEXTAL program. This system, named PHENIX, will permit tasks required for the computation of phases, automated map interpretation, model building and refinement, to be integrated with newly developed procedures for automated decision-making. Our ultimate goal is the development of a system that will take x-ray diffraction data and rapidly procedure biased atomic coordinates with little or no human intervention. The advantages of such a system extend beyond the realm of structural, genomics, allowing crystallographers to focus on more challenging biological problems.
| Kryshtafovych, Andriy; Monastyrskyy, Bohdan; Adams, Paul D et al. (2018) Distribution of evaluation scores for the models submitted to the second cryo-EM model challenge. Data Brief 20:1629-1638 |
| Moriarty, Nigel W; Liebschner, Dorothee; Klei, Herbert E et al. (2018) Interactive comparison and remediation of collections of macromolecular structures. Protein Sci 27:182-194 |
| Kryshtafovych, Andriy; Adams, Paul D; Lawson, Catherine L et al. (2018) Evaluation system and web infrastructure for the second cryo-EM model challenge. J Struct Biol 204:96-108 |
| Terwilliger, Thomas C; Adams, Paul D; Afonine, Pavel V et al. (2018) Map segmentation, automated model-building and their application to the Cryo-EM Model Challenge. J Struct Biol 204:338-343 |
| Williams, Christopher J; Headd, Jeffrey J; Moriarty, Nigel W et al. (2018) MolProbity: More and better reference data for improved all-atom structure validation. Protein Sci 27:293-315 |
| Terwilliger, Thomas C; Adams, Paul D; Afonine, Pavel V et al. (2018) A fully automatic method yielding initial models from high-resolution cryo-electron microscopy maps. Nat Methods 15:905-908 |
| Richardson, Jane S; Williams, Christopher J; Videau, Lizbeth L et al. (2018) Assessment of detailed conformations suggests strategies for improving cryoEM models: Helix at lower resolution, ensembles, pre-refinement fixups, and validation at multi-residue length scale. J Struct Biol 204:301-312 |
| Richardson, Jane S; Williams, Christopher J; Hintze, Bradley J et al. (2018) Model validation: local diagnosis, correction and when to quit. Acta Crystallogr D Struct Biol 74:132-142 |
| Herzik Jr, Mark A; Fraser, James S; Lander, Gabriel C (2018) A Multi-model Approach to Assessing Local and Global Cryo-EM Map Quality. Structure : |
| Hintze, Bradley J; Richardson, Jane S; Richardson, David C (2017) Mismodeled purines: implicit alternates and hidden Hoogsteens. Acta Crystallogr D Struct Biol 73:852-859 |
Showing the most recent 10 out of 136 publications
