Functional characterization of the YmcB and YqeV tRNA methylthiotransferases of Bacillus subtilis.

Methylthiotransferases (MTTases) are a closely related family of proteins that perform both radical-S-adenosylmethionine (SAM) mediated sulfur insertion and SAM-dependent methylation to modify nucleic acid or protein targets with a methyl thioether group (-SCH(3)). Members of two of the four known subgroups of MTTases have been characterized, typified by MiaB, which modifies N(6)-isopentenyladenosine (i(6)A) to 2-methylthio-N(6)-isopentenyladenosine (ms(2)i(6)A) in tRNA, and RimO, which modifies a specific aspartate residue in ribosomal protein S12. In this work, we have characterized the two MTTases encoded by Bacillus subtilis 168 and find that, consistent with bioinformatic predictions, ymcB is required for ms(2)i(6)A formation (MiaB activity), and yqeV is required for modification of N(6)-threonylcarbamoyladenosine (t(6)A) to 2-methylthio-N(6)-threonylcarbamoyladenosine (ms(2)t(6)A) in tRNA. The enzyme responsible for the latter activity belongs to a third MTTase subgroup, no member of which has previously been characterized. We performed domain-swapping experiments between YmcB and YqeV to narrow down the protein domain(s) responsible for distinguishing i(6)A from t(6)A and found that the C-terminal TRAM domain, putatively involved with RNA binding, is likely not involved with this discrimination. Finally, we performed a computational analysis to identify candidate residues outside the TRAM domain that may be involved with substrate recognition. These residues represent interesting targets for further analysis.

An energetic representation of protein architecture that is independent of primary and secondary structure.

Protein fold classification often assumes that similarity in primary, secondary, or tertiary structure signifies a common evolutionary origin. However, when similarity is not obvious, it is sometimes difficult to conclude that particular proteins are completely unrelated. Clearly, a set of organizing principles that is independent of traditional classification could be valuable in linking different structural motifs and identifying common ancestry from seemingly disparate folds. Here, a four-dimensional ensemble-based energetic space spanned by a diverse set of proteins was defined and its characteristics were contrasted with those of Cartesian coordinate space. Eigenvector decomposition of this energetic space revealed the dominant physical processes contributing to the more or less stable regions of a protein. Unexpectedly, those processes were identical for proteins with different secondary structure content and were also identical among different amino-acid types. The implications of these results are twofold. First, it indicates that excited conformational states comprising the protein native state ensemble, largely invisible upon inspection of the high-resolution structure, are the major determinant of the energetic space. Second, it suggests that folds dissimilar in sequence or structure could nonetheless be energetically similar if their respective excited conformational states are considered, one example of which was observed in the N-terminal region of the Arc repressor switch mutant. Taken together, these results provide a surface area-based framework for understanding folds in energetic terms, a framework that may eventually yield a means of identifying common ancestry among structurally dissimilar proteins.

Energetic profiling of protein folds.

Current protein classification methods treat high-resolution structures as static entities. However, experiments have well documented the dynamic nature of proteins. With knowledge that thermodynamic fluctuations around the high-resolution structure contribute to a more physically accurate and biologically meaningful picture of a protein, the concept of a protein's energetic profile is introduced. It is demonstrated on a large scale that energetic profiles are both diagnostic of a protein fold and evolutionarily relevant. Development of Structural Thermodynamic Ensemble-based Protein Homology (STEPH), an algorithm that searches for local similarities between energetic profiles, constitutes a first step towards a long-term goal of our laboratory to integrate thermodynamic information into protein-fold classification approaches.

COREX/BEST server: a web browser-based program that calculates regional stability variations within protein structures.

SUMMARY: Utilizing the user-supplied coordinates of a protein structure, the COREX/BEST Server generates a structural thermodynamic ensemble. This conformational ensemble can then be used to calculate the regional variations in stability of a protein structure, and the stabilities are presented in units of energy (kcal/mol). The regional stabilities, which are calculated at the resolution of individual residues, can be mapped onto the protein structure for visual representation and downloaded from the site in the form of tab delimited text. The site provides an easy to follow summary of the theoretical and algorithmic approaches and provides links to references for more detailed descriptions. AVAILABILITY: The COREX/BEST Server may be accessed through a typical web browser by visiting http://best.utmb.edu/BEST/.