Researchers generate first complete 3-D structures of bacterial chromosome

A team of researchers at the University of Massachusetts Medical School, Harvard Medical School, Stanford University and the Prince Felipe Research Centre in Spain have deciphered the complete three-dimensional structure of the bacterium Caulobacter cresc ...

Source: University of Massachusetts Medical School 

Gamers succeed where scientists fail

"Gamers have solved the structure of a retrovirus enzyme whose configuration had stumped scientists for more than a decade. The gamers achieved their discovery by playing Foldit, an online game that allows players to collaborate and compete in predicting the structure of protein molecules."

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Several baffling puzzles in protein molecular structure solved with new method

The structures of many protein molecules remain unsolved even after experts apply an extensive array of approaches. An international collaboration has led to a new, high-performance method that rapidly determined the structure of protein molecules in several cases where previous methods had failed.

The usefulness of the new method is reported May 1 inNature advanced online publication. The lead authors are Dr. Frank DiMaio of the University of Washington (UW) in Seattle and Dr. Thomas C. Terwilliger of Los Alamos National Laboratory in New Mexico. The senior author is Dr. David Baker, of the UW Department of Biochemistry.

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Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure

Abstract (provisional)

Background

The present knowledge of protein structures at atomic level derives from some 60,000 molecules. Yet the exponential ever growing set of hypothetical protein sequences comprises some 10 million chains and this makes the problem of protein structure prediction one of the challenging goals of bioinformatics. In this context, the protein representation with contact maps is an intermediate step of fold recognition and constitutes the input of contact map predictors. However contact map representations require fast and reliable methods to reconstruct the specific folding of the protein backbone.

Methods

In this paper, by adopting a GRID technology, our algorithm for 3D reconstruction FT-COMAR is benchmarked on a huge set of non redundant proteins (1716) taking random noise into consideration and this makes our computation the largest ever performed for the task at hand.

Results

We can observe the effects of introducing random noise on 3D reconstruction and derive some considerations useful for future implementations. The dimension of the protein set allows also statistical considerations after grouping per SCOP structural classes.

Conclusions

All together our data indicate that the quality of 3D reconstruction is unaffected by deleting up to an average 75% of the real contacts while only few percentage of randomly generated contacts in place of non-contacts are sufficient to hamper 3D reconstruction.

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