Improving structure-based function prediction using molecular dynamics.

The number of molecules with solved three-dimensional structure but unknown function is increasing rapidly. Particularly problematic are novel folds with little detectable similarity to molecules of known function. Experimental assays can determine the functions of such molecules, but are time-consuming and expensive.

The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications.

Structural genomics efforts contribute new protein structures that often lack significant sequence and fold similarity to known proteins. Traditional sequence and structure-based methods may not be sufficient to annotate the molecular functions of these structures. Techniques that combine structural and functional modeling can be valuable for functional annotation.

Combining molecular dynamics and machine learning to improve protein function recognition.

As structural genomics efforts succeed in solving protein structures with novel folds, the number of proteins with known structures but unknown functions increases. Although experimental assays can determine the functions of some of these molecules, they can be expensive and time consuming. Computational approaches can assist in identifying potential functions of these molecules.

A two-hybrid screen identifies cathepsins B and L as uncoating factors for adeno-associated virus 2 and 8.

Vectors based on different serotypes of adeno-associated virus hold great promise for human gene therapy, based on their unique tissue tropisms and distinct immunological profiles. A particularly interesting candidate is AAV8, which can efficiently and rapidly transduce a wide range of tissues in vivo. To further unravel the mechanisms behind AAV8 transduction, we used yeast two-hybrid analyses to screen a mouse liver complementary DNA library for cellular proteins capable of interacting with the viral capsid proteins.