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Final Activity Report Summary - DBN FOLD RECOGNITION (Structure based fold recognition using Dynamic Bayesian Networks)

The prediction of protein structure from sequence is without doubt one of the most important open problems in computational biology, and biology in general. Currently, the most successful prediction methods use a divide-and-conquer approach to attack the problem. Typically, protein-like conformations are generated and evaluated using very approximate energy functions. After the generation of a large number of candidates, the putative native-like conformations is somehow selected, most often by clustering and the use of an expensive all-atom energy function. One of the main bottlenecks in the process is the generation of protein-like conformations, or, in other words, the exploration of the conformational landscape. Currently, this is typically done by tying together discrete fragments from existing structures. However, this approach has many disadvantages: it discretizes the continuous conformational space, is prone to data sparseness and cannot be used in a probabilistically sound way.

We developed a probabilistic model of the local structure of proteins, based on dynamic Bayesian networks (DBNs) and directional statistics that solves these problems. The model allows sampling of protein-like conformations in continuous space, and is computationally efficient, mathematically rigorous, probabilistic and conceptually elegant. We expect this model will lead to important breakthroughs in de novo structure prediction, fold recognition, protein design and experimental determination of protein structure.

The method was published in the journal PLoS Computational Biology, and featured on the cover of the September 2006 issue. In addition, we solved an important subproblem that arises in the use of the model of local structure for the above mentioned applications (loop closure in C-alpha space).

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Universitetsparken, 15
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