SPring-8 Structural Biology Beamlines / Automatic Beamline Operation at RIKEN Structural Genomics Beamlines

2007 ◽  
Author(s):  
Go Ueno ◽  
Kazuya Hasegawa ◽  
Nobuo Okazaki ◽  
Raita Hirose ◽  
Hisanobu Sakai ◽  
...  
Keyword(s):  
Structural Biology ◽  
Structural Genomics

scholarly journals NIGMS PSI:Biology Initiative ‐ Enabling High‐Throughput Structural Biology and Structural Genomics

2010 ◽  
Vol 24 (S1) ◽  
Author(s):  
Peter C. Preusch ◽  
Ravi Basavappa ◽  
Jean Chin ◽  
Charles Edmonds ◽  
Paula Flicker ◽  
...  
Keyword(s):  
Structural Biology ◽  
High Throughput ◽  
Structural Genomics

Focusing in on structural genomics: The University of Queensland structural biology pipeline

2006 ◽  
Vol 23 (6) ◽  
pp. 281-289 ◽  
Author(s):  
Munish Puri ◽  
Gautier Robin ◽  
Nathan Cowieson ◽  
Jade K. Forwood ◽  
Pawel Listwan ◽  
...  
Keyword(s):  
Structural Biology ◽  
Structural Genomics ◽  
The University

scholarly journals The Impact of Structural Genomics: Expectations and Outcomes

Science ◽  
2006 ◽  
Vol 311 (5759) ◽  
pp. 347-351 ◽  
Author(s):  
John-Marc Chandonia ◽  
Steven E. Brenner
Keyword(s):  
United States ◽  
Structural Biology ◽  
Structural Genomics ◽  
Structure Determination ◽  
The United States ◽  
Structural Knowledge ◽  
Biological Macromolecules ◽  
Current Impact ◽  
The Cost ◽  
The Impact

Structural genomics (SG) projects aim to expand our structural knowledge of biological macromolecules while lowering the average costs of structure determination. We quantitatively analyzed the novelty, cost, and impact of structures solved by SG centers, and we contrast these results with traditional structural biology. The first structure identified in a protein family enables inference of the fold and of ancient relationships to other proteins; in the year ending 31 January 2005, about half of such structures were solved at a SG center rather than in a traditional laboratory. Furthermore, the cost of solving a structure at the most efficient SG center in the United States has dropped to one-quarter of the estimated cost of solving a structure by traditional methods. However, the efficiency of the top structural biology laboratories—even though they work on very challenging structures—is comparable to that of SG centers; moreover, traditional structural biology papers are cited significantly more often, suggesting greater current impact.

STRUCTURAL BIOLOGY: Structural Genomics, Round 2

Science ◽  
2005 ◽  
Vol 307 (5715) ◽  
pp. 1554-1558 ◽  
Author(s):  
R. Service
Keyword(s):  
Structural Biology ◽  
Structural Genomics

scholarly journals Computational structural genomics unravels common folds and novel families in the secretome of fungal phytopathogen Magnaporthe oryzae

2021 ◽  
Author(s):  
Kyungyong Seong ◽  
Ksenia Krasileva
Keyword(s):  
Structural Biology ◽  
Structural Genomics ◽  
Magnaporthe Oryzae ◽  
Structure Prediction ◽  
Secreted Proteins ◽  
Homology Search ◽  
New Members ◽  
Pathogen Effectors ◽  
A Genome ◽  
Evolutionary Trajectories

Structural biology has the potential to illuminate the evolution of pathogen effectors and their commonalities that cannot be readily detected at the primary sequence level. Recent breakthroughs in protein structure modeling have demonstrated the feasibility to predict the protein folds without depending on homologous structures. These advances enabled a genome-wide computational structural biology approach to understand proteins based on their predicted folds. In this study, we employed structure prediction methods on the secretome of the destructive fungal pathogen Magnaporthe oryzae. Out of 1854 secreted proteins, we predicted the folds of 1295 (70%) proteins. We showed that template-free modeling by TrRosetta captured 514 folds missed by homology modeling, including many known effectors and virulence factors, and that TrRosetta generally produced higher quality models for secreted proteins. Along with sensitive homology search, we employed structure-based clustering, defining not only homologous groups with divergent members but also sequence-unrelated structural analogous groups. We demonstrate that this approach can reveal potential new members of structurally similar MAX effectors and novel analogous effector families present in M. oryzae and possibly in other phytopathogens. We also investigated the evolution of expanded putative ADP-ribose transferases with predicted structures. We suggest that the loss of catalytic activities of the enzymes might have led them to new evolutionary trajectories to be specialized as protein binders. Collectively, we propose that computational structural genomics approaches can be an integral part of studying effector biology and provide valuable resources that were inaccessible before the advent of machine learning-based structure prediction.

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