A purple-blue chromoprotein from Goniopora tenuidens belongs to the DsRed subfamily of GFP-like proteins.

A number of recently cloned chromoproteins homologous to the green fluorescent protein show a substantial bathochromic shift in absorption spectra. Compared with red fluorescent protein from Discosoma sp. (DsRed), mutants of these so-called far-red proteins exhibit a clear red shift in emission spectra as well. Here we report that a far-red chromoprotein from Goniopora tenuidens (gtCP) contains a chromophore of the same chemical structure as DsRed. Denaturation kinetics of both DsRed and gtCP under acidic conditions indicates that the red form of the chromophore (absorption maximum at 436 nm) converts to the GFP-like form (384 nm) by a one-stage reaction. Upon neutralization, the 436-nm form of gtCP, but not the 384-nm form, renaturates instantly, implying that the former includes a chromophore in its intact state. gtCP represents a single-chain protein and, upon harsh denaturing conditions, shows three major bands in SDS/PAGE, two of which apparently result from hydrolysis of an acylimine C=N bond. Instead of having absorption maxima at 384 nm and 450 nm, which are characteristic for a GFP-like chromophore, fragmented gtCP shows a different spectrum, which presumably corresponds to a 2-keto derivative of imidazolidinone. Mass spectra of the chromophore-containing peptide from gtCP reveal an additional loss of 2 Da relative to the GFP-like chromophore. Tandem mass spectrometry of the chromopeptide shows that an additional bond is dehydrogenated in gtCP at the same position as in DsRed. Altogether, these data suggest that gtCP belongs to the same subfamily as DsRed (in the classification of GFP-like proteins based on the chromophore structure type).

Database search post-processing by neural network: Advanced facilities for identification of components in protein mixtures using mass spectrometric peptide mapping.

Database search post-processing by neural network was employed in peptide mapping experiments. The database search was performed using both the known algorithms and score functions, such as Bayesian, MOWSE, Z-score, correlations between calculated and actual peptide length fractional abundance, and, in addition, the probability of protein digest pattern in peptide fingerprint, all embedded in locally developed program. The new signal-processing algorithm based on neural network improves signal-noise separation and is acceptable for automatic protein identification in mixtures. Its power was tested on Helicobacter pylori protein inventory after preceding protein separation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Increase in protein identification success rate was observed, and about 100 proteins were identified with no need of human participation in database search estimation.