Functional and computational analysis of amino acid patterns predictive of type III secretion system substrates in Pseudomonas syringae.

Bacterial type III secretion systems (T3SSs) deliver proteins called effectors into eukaryotic cells. Although N-terminal amino acid sequences are required for translocation, the mechanism of substrate recognition by the T3SS is unknown. Almost all actively deployed T3SS substrates in the plant pathogen Pseudomonas syringae pathovar tomato strain DC3000 possess characteristic patterns, including (i) greater than 10% serine within the first 50 amino acids, (ii) an aliphatic residue or proline at position 3 or 4, and (iii) a lack of acidic amino acids within the first 12 residues. Here, the functional significance of the P. syringae T3SS substrate compositional patterns was tested. A mutant AvrPto effector protein lacking all three patterns was secreted into culture and translocated into plant cells, suggesting that the compositional characteristics are not absolutely required for T3SS targeting and that other recognition mechanisms exist. To further analyze the unique properties of T3SS targeting signals, we developed a computational algorithm called TEREE (Type III Effector Relative Entropy Evaluation) that distinguishes DC3000 T3SS substrates from other proteins with a high sensitivity and specificity. Although TEREE did not efficiently identify T3SS substrates in Salmonella enterica, it was effective in another P. syringae strain and Ralstonia solanacearum. Thus, the TEREE algorithm may be a useful tool for identifying new effector genes in plant pathogens. The nature of T3SS targeting signals was additionally investigated by analyzing the N-terminus of FtsX, a putative membrane protein that was classified as a T3SS substrate by TEREE. Although the first 50 amino acids of FtsX were unable to target a reporter protein to the T3SS, an AvrPto protein substituted with the first 12 amino acids of FtsX was translocated into plant cells. These results show that the T3SS targeting signals are highly mutable and that secretion may be directed by multiple features of substrates.

GCAT-SEEKquence: genome consortium for active teaching of undergraduates through increased faculty access to next-generation sequencing data.

To transform undergraduate biology education, faculty need to provide opportunities for students to engage in the process of science. The rise of research approaches using next-generation (NextGen) sequencing has been impressive, but incorporation of such approaches into the undergraduate curriculum remains a major challenge. In this paper, we report proceedings of a National Science Foundation-funded workshop held July 11-14, 2011, at Juniata College. The purpose of the workshop was to develop a regional research coordination network for undergraduate biology education (RCN/UBE). The network is collaborating with a genome-sequencing core facility located at Pennsylvania State University (University Park) to enable undergraduate students and faculty at small colleges to access state-of-the-art sequencing technology. We aim to create a database of references, protocols, and raw data related to NextGen sequencing, and to find innovative ways to reduce costs related to sequencing and bioinformatics analysis. It was agreed that our regional network for NextGen sequencing could operate more effectively if it were partnered with the Genome Consortium for Active Teaching (GCAT) as a new arm of that consortium, entitled GCAT-SEEK(quence). This step would also permit the approach to be replicated elsewhere.

The effect of target vector selection on the invariance of classifier performance measures.

In this paper, the multiclass supervised training problem is considered when a discrete set of classes is assumed. Upon generating affine models for finite data sets, we have observed the invariance of certain measures of performance after a trained classifier has been presented with test data of unknown classification. Specifically, after constructing mappings between training vectors and their desired targets, the class membership and ranking of test data has been found to remain either invariant or close to invariant under a transformation of the set of target vectors. Therefore, we derive conditions explaining how this type of invariance can arise when the multiclass problem is phrased in the context of linear networks. A bioinformatics example is then presented in order to demonstrate various principles outlined in this work.

On the computation of molecular surface correlations for protein docking using fourier techniques.

The computation of surface correlations using a variety of molecular models has been applied to the unbound protein docking problem. Because of the computational complexity involved in examining all possible molecular orientations, the fast Fourier transform (FFT) (a fast numerical implementation of the discrete Fourier transform (DFT)) is generally applied to minimize the number of calculations. This approach is rooted in the convolution theorem which allows one to inverse transform the product of two DFTs in order to perform the correlation calculation. However, such a DFT calculation results in a cyclic or "circular" correlation which, in general, does not lead to the same result as the linear correlation desired for the docking problem. In this work, we provide computational bounds for constructing molecular models used in the molecular surface correlation problem. The derived bounds are then shown to be consistent with various intuitive guidelines previously reported in the protein docking literature. Finally, these bounds are applied to different molecular models in order to investigate their effect on the correlation calculation.