Modeling buffer capacity and pH in acid and acidified foods.

Standard ionic equilibria equations may be used for calculating pH of weak acid and base solutions. These calculations are difficult or impossible to solve analytically for foods that include many unknown buffering components, making pH prediction in these systems impractical. We combined buffer capacity (BC) models with a pH prediction algorithm to allow pH prediction in complex food matrices from BC data. Numerical models were developed using Matlab software to estimate the pH and buffering components for mixtures of weak acid and base solutions. The pH model was validated with laboratory solutions of acetic or citric acids with ammonia, in combinations with varying salts using Latin hypercube designs. Linear regressions of observed versus predicted pH values based on the concentration and pK values of the solution components resulted in estimated slopes between 0.96 and 1.01 with and without added salts. BC models were generated from titration curves for 0.6 M acetic acid or 12.4 mM citric acid resulting in acid concentration and pK estimates. Predicted pH values from these estimates were within 0.11 pH units of the measured pH. Acetic acid concentration measurements based on the model were within 6% accuracy compared to high-performance liquid chromatography measurements for concentrations less than 400 mM, although they were underestimated above that. The models may have application for use in determining the BC of food ingredients with unknown buffering components. Predicting pH changes for food ingredients using these models may be useful for regulatory purposes with acid or acidified foods and for product development. PRACTICAL APPLICATION: Buffer capacity models may benefit regulatory agencies and manufacturers of acid and acidified foods to determine pH stability (below pH 4.6) and how low-acid food ingredients may affect the safety of these foods. Predicting pH for solutions with known or unknown buffering components was based on titration data and models that use only monoprotic weak acids and bases. These models may be useful for product development and food safety by estimating pH and buffering capacity.

Identification of protein-coding sequences using the hybridization of 18S rRNA and mRNA during translation.

We introduce a new approach in this article to distinguish protein-coding sequences from non-coding sequences utilizing a period-3, free energy signal that arises from the interactions of the 3'-terminal nucleotides of the 18S rRNA with mRNA. We extracted the special features of the amplitude and the phase of the period-3 signal in protein-coding regions, which is not found in non-coding regions, and used them to distinguish protein-coding sequences from non-coding sequences. We tested on all the experimental genes from Saccharomyces cerevisiae and Schizosaccharomyces pombe. The identification was consistent with the corresponding information from GenBank, and produced better performance compared to existing methods that use a period-3 signal. The primary tests on some fly, mouse and human genes suggests that our method is applicable to higher eukaryotic genes. The tests on pseudogenes indicated that most pseudogenes have no period-3 signal. Some exploration of the 3'-tail of 18S rRNA and pattern analysis of protein-coding sequences supported further our assumption that the 3'-tail of 18S rRNA has a role of synchronization throughout translation elongation process. This, in turn, can be utilized for the identification of protein-coding sequences.

Analysis of free energy signals arising from nucleotide hybridization between rRNA and mRNA sequences during translation in eubacteria.

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, 3'-terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species (G + C) content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.

Coding theory based models for protein translation initiation in prokaryotic organisms.

Our research explores the feasibility of using communication theory, error control (EC) coding theory specifically, for quantitatively modeling the protein translation initiation mechanism. The messenger RNA (mRNA) of Escherichia coli K-12 is modeled as a noisy (errored), encoded signal and the ribosome as a minimum Hamming distance decoder, where the 16S ribosomal RNA (rRNA) serves as a template for generating a set of valid codewords (the codebook). We tested the E. coli based coding models on 5' untranslated leader sequences of prokaryotic organisms of varying taxonomical relation to E. coli including: Salmonella typhimurium LT2, Bacillus subtilis, and Staphylococcus aureus Mu50. The model identified regions on the 5' untranslated leader where the minimum Hamming distance values of translated mRNA sub-sequences and non-translated genomic sequences differ the most. These regions correspond to the Shine-Dalgarno domain and the non-random domain. Applying the EC coding-based models to B. subtilis, and S. aureus Mu50 yielded results similar to those for E. coli K-12. Contrary to our expectations, the behavior of S. typhimurium LT2, the more taxonomically related to E. coli, resembled that of the non-translated sequence group.