Generalized schemes for high-throughput manipulation of the Desulfovibrio vulgaris genome.

The ability to conduct advanced functional genomic studies of the thousands of sequenced bacteria has been hampered by the lack of available tools for making high-throughput chromosomal manipulations in a systematic manner that can be applied across diverse species. In this work, we highlight the use of synthetic biological tools to assemble custom suicide vectors with reusable and interchangeable DNA "parts" to facilitate chromosomal modification at designated loci. These constructs enable an array of downstream applications, including gene replacement and the creation of gene fusions with affinity purification or localization tags. We employed this approach to engineer chromosomal modifications in a bacterium that has previously proven difficult to manipulate genetically, Desulfovibrio vulgaris Hildenborough, to generate a library of over 700 strains. Furthermore, we demonstrate how these modifications can be used for examining metabolic pathways, protein-protein interactions, and protein localization. The ubiquity of suicide constructs in gene replacement throughout biology suggests that this approach can be applied to engineer a broad range of species for a diverse array of systems biological applications and is amenable to high-throughput implementation.

High-resolution autoreactive epitope mapping and structural modeling of the 65 kDa form of human glutamic acid decarboxylase.

The smaller isoform of the GABA-synthesizing enzyme, glutamic acid decarboxylase 65 (GAD65), is unusually susceptible to becoming a target of autoimmunity affecting its major sites of expression, GABA-ergic neurons and pancreatic beta-cells. In contrast, a highly homologous isoform, GAD67, is not an autoantigen. We used homolog-scanning mutagenesis to identify GAD65-specific amino acid residues which form autoreactive B-cell epitopes in this molecule. Detailed mapping of 13 conformational epitopes, recognized by human monoclonal antibodies derived from patients, together with two and three-dimensional structure prediction led to a model of the GAD65 dimer. GAD65 has structural similarities to ornithine decarboxylase in the pyridoxal-5'-phosphate-binding middle domain (residues 201-460) and to dialkylglycine decarboxylase in the C-terminal domain (residues 461-585). Six distinct conformational and one linear epitopes cluster on the hydrophilic face of three amphipathic alpha-helices in exons 14-16 in the C-terminal domain. Two of those epitopes also require amino acids in exon 4 in the N-terminal domain. Two distinct epitopes reside entirely in the N-terminal domain. In the middle domain, four distinct conformational epitopes cluster on a charged patch formed by amino acids from three alpha-helices away from the active site, and a fifth epitope resides at the back of the pyridoxal 5'-phosphate binding site and involves amino acid residues in exons 6 and 11-12. The epitopes localize to multiple hydrophilic patches, several of which also harbor DR*0401-restricted T-cell epitopes, and cover most of the surface of the protein. The results reveal a remarkable spectrum of human autoreactivity to GAD65, targeting almost the entire surface, and suggest that native folded GAD65 is the immunogen for autoreactive B-cells.

Neural networks for secondary structure and structural class predictions.

A pair of neural network-based algorithms is presented for predicting the tertiary structural class and the secondary structure of proteins. Each algorithm realizes improvements in accuracy based on information provided by the other. Structural class prediction of proteins nonhomologous to any in the training set is improved significantly, from 62.3% to 73.9%, and secondary structure prediction accuracy improves slightly, from 62.26% to 62.64%. A number of aspects of neural network optimization and testing are examined. They include network overtraining and an output filter based on a rolling average. Secondary structure prediction results vary greatly depending on the particular proteins chosen for the training and test sets; consequently, an appropriate measure of accuracy reflects the more unbiased approach of "jackknife" cross-validation (testing each protein in the data-base individually).

The importance of larger data sets for protein secondary structure prediction with neural networks.

A neural network algorithm is applied to secondary structure and structural class prediction for a database of 318 nonhomologous protein chains. Significant improvement in accuracy is obtained as compared with performance on smaller databases. A systematic study of the effects of network topology shows that, for the larger database, better results are obtained with more units in the hidden layer. In a 32-fold cross validated test, secondary structure prediction accuracy is 67.0%, relative to 62.6% obtained previously, without any evolutionary information on the sequence. Introduction of sequence profiles increases this value to 72.9%, suggesting that the two types of information are essentially independent. Tertiary structural class is predicted with 80.2% accuracy, relative to 73.9% obtained previously. The use of a larger database is facilitated by the introduction of a scaled conjugate gradient algorithm for optimizing the neural network. This algorithm is about 10-20 times as fast as the standard steepest descent algorithm.

Update on the pfam5000 strategy for selection of structural genomics targets.

Structural Genomics is an international effort to determine the three-dimensional shapes of all important biological macromolecules, with a primary focus on proteins. Target proteins should be selected according to a strategy that is medically and biologically relevant, of good financial value, and tractable. In 2003, we presented the "Pfam5000" strategy, which involves selecting the 5,000 most important families from the Pfam database as sources for targets. In this update, we show that although both the Pfam database and the number of sequenced genomes have increased in size, the expected benefits of the Pfam5000 strategy have not changed substantially. Solving the structures of proteins from the 5,000 largest Pfam families would allow accurate fold assignment for approximately 65% of all prokaryotic proteins (covering 54% of residues) and 63% of eukaryotic proteins (42% of residues). Fewer than 2,300 of the largest families on this list remain to be solved, making the project feasible in the next five years given the expected throughput to be achieved in the production phase of the Protein Structure Initiative.

New methods for accurate prediction of protein secondary structure.

A primary and a secondary neural network are applied to secondary structure and structural class prediction for a database of 681 non-homologous protein chains. A new method of decoding the outputs of the secondary structure prediction network is used to produce an estimate of the probability of finding each type of secondary structure at every position in the sequence. In addition to providing a reliable estimate of the accuracy of the predictions, this method gives a more accurate Q3 (74.6%) than the cutoff method which is commonly used. Use of these predictions in jury methods improves the Q3 to 74.8%, the best available at present. On a database of 126 proteins commonly used for comparison of prediction methods, the jury predictions are 76.6% accurate. An estimate of the overall Q3 for a given sequence is made by averaging the estimated accuracy of the prediction over all residues in the sequence. As an example, the analysis is applied to the target beta-cryptogein, which was a difficult target for ab initio predictions in the CASP2 study; it shows that the prediction made with the present method (62% of residues correct) is close to the expected accuracy (66%) for this protein. The larger database and use of a new network training protocol also improve structural class prediction accuracy to 86%, relative to 80% obtained previously. Secondary structure content is predicted with accuracy comparable to that obtained with spectroscopic methods, such as vibrational or electronic circular dichroism and Fourier transform infrared spectroscopy.