Engineering of Bacillus thuringiensis Cry Proteins to Enhance the Activity against Western Corn Rootworm.

A novel Bacillus thuringiensis Cry protein, Cry8Hb, active against Diabrotica virgifera virgifera (Western corn rootworm, WCRW) was discovered. Unexpectedly, the anti-rootworm activity of the Cry8Hb toxin was enhanced significantly by fusing Escherichia coli maltose binding protein (MBP) to this Cry toxin. While the exact mechanism of the activity enhancement remains indefinite, it is probable that the enhancement is a result of increased solubility of the MBP-Cry8Hb fusion in the rootworm midgut. This hypothesis was examined using a synthetic Cry3 protein called IP3-1, which was not soluble at a neutral pH like Cry8Hb and marginally active to WCRW. When IP3-1 was fused to MBP, its anti-WCRW activity was enhanced 13-fold. To further test the hypothesis, DNA shuffling was performed on IP3-1 to increase the solubility without MBP. Screening of shuffled libraries found six new IP3 variants showing very high anti-WCRW activity without MBP. Sequence and 3D structure analysis of those highly active, shuffled IP3 variants revealed several charge-altering mutations such as Lys to Glu on the putative MBP-attaching side of the IP3 molecule. It is likely that those mutations make the protein acidic to substitute the functions of MBP including enhancing the solubility of IP3 at a neutral pH.

Structure-based inference of molecular functions of proteins of unknown function from Berkeley Structural Genomics Center.

Advances in sequence genomics have resulted in an accumulation of a huge number of protein sequences derived from genome sequences. However, the functions of a large portion of them cannot be inferred based on the current methods of sequence homology detection to proteins of known functions. Three-dimensional structure can have an important impact in providing inference of molecular function (physical and chemical function) of a protein of unknown function. Structural genomics centers worldwide have been determining many 3-D structures of the proteins of unknown functions, and possible molecular functions of them have been inferred based on their structures. Combined with bioinformatics and enzymatic assay tools, the successful acceleration of the process of protein structure determination through high throughput pipelines enables the rapid functional annotation of a large fraction of hypothetical proteins. We present a brief summary of the process we used at the Berkeley Structural Genomics Center to infer molecular functions of proteins of unknown function.

Structural genomics of minimal organisms and protein fold space.

The initial aim of the Berkeley Structural Genomics Center is to obtain a near-complete structural complement of two minimal organisms, closely related pathogens Mycoplasma genitalium and M. pneumoniae. The former has fewer than 500 genes and the latter fewer than 700 genes. To achieve this goal, the current protein targets have been selected starting with those predicted to be most tractable and likely to yield new structural and functional information. During the past 3 years, the semi-automated structural genomics pipeline has been set up from cloning, expression, purification, and ultimately to structural determination. The results from the pipeline substantially increased the coverage of the protein fold space of M. pneumoniae and M. genitalium. Furthermore, about 1/2 of the structures of 'unique' protein sequences revealed new and novel folds, and over 2/3 of the structures of previously annotated 'hypothetical proteins' inferred their molecular functions.

Global mapping of the protein structure space and application in structure-based inference of protein function.

We have constructed a map of the "protein structure space" by using the pairwise structural similarity scores calculated for all nonredundant protein structures determined experimentally. As expected, proteins with similar structures clustered together in the map and the overall distribution of structural classes of this map followed closely that of the map of the "protein fold space" we have reported previously. Consequently, proteins sharing similar molecular functions also were found to colocalize in the protein structure space map, pointing toward a previously undescribed scheme for structure-based functional inference for remote homologues based on the proximity in the map of the protein structure space. We found that this scheme consistently outperformed other predictions made by using either the raw scores or normalized Z-scores of pairwise DALI structure alignment.

A global representation of the protein fold space.

One of the principal goals of the structural genomics initiative is to identify the total repertoire of protein folds and obtain a global view of the "protein structure universe." Here, we present a 3D map of the protein fold space in which structurally related folds are represented by spatially adjacent points. Such a representation reveals a high-level organization of the fold space that is intuitively interpretable. The shape of the fold space and the overall distribution of the folds are defined by three dominant trends: secondary structure class, chain topology, and protein domain size. Random coil-like structures of small proteins and peptides are mapped to a region where the three trends converge, offering an interesting perspective on both the demography of fold space and the evolution of protein structures.

Fold prediction of helical proteins using torsion angle dynamics and predicted restraints.

We describe a procedure for predicting the tertiary folds of alpha-helical proteins from their primary sequences. The central component of the procedure is a method for predicting interhelical contacts that is based on a helix-packing model. Instead of predicting the individual contacts, our method attempts to identify the entire patch of contacts that involve residues regularly spaced in the sequences. We use this component to glue together two powerful existing methods: a secondary structure prediction program, whose output serves as the input to the contact prediction algorithm, and the tortion angle dynamics program, which uses the predicted tertiary contacts and secondary structural states to assemble three-dimensional structures. In the final step, the procedure uses the initial set of simulated structures to refine the predicted contacts for a new round of structure calculation. When tested against 24 small to medium-sized proteins representing a wide range of helical folds, the completely automated procedure is able to generate native-like models within a limited number of trials consistently.