Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

Carcass and cut yields and meat qualitative traits of broilers fed diets containing probiotics and prebiotics

This study evaluated the use of different probiotics, prebiotics and symbiotics on the quality of carcasses and meat of broiler chickens. One hundred and eight day-old Cobb male broilers were used (n=108) in a completely randomized design according to a 3x3 factorial, with 3 probiotics in the diet (no probiotics, probiotics 1, probiotics 2) and 3 prebiotics in the diet (no prebiotics, prebiotics 1, prebiotics 2). There were nine treatments with 4 replicates and 3 birds per replicate. The results showed that the carcass and cut yields, color (L* - lightness, a* - redness, and b* - yellowness), pH, cooking losses, shearing force and sensory analysis were not affected by the use of different growth promoters at 42 days of age. It was concluded that growth promoters supplemented to the diet did not affect the studied quantitative and qualitative parameters of the carcass and breast meat of broiler chickens