A theoretical study of supramolecular aggregation of polydopamine tetramer subunits in aqueous solution.

Conformational search for the most stable geometry connection of 16 sets of polydopamine (PDA) tetramer subunits has been systematically investigated using density functional theory (DFT) calculations. Our results indicated that the more planar subunits are, the more stable they are. This finding is in good agreement with recent experimental observations, which have suggested that PDA are composed of the nearly planar subunits that appear to be stacked together via the π-π interactions to form graphite-like layered aggregates associated with the balance of the intramolecular hydrogen bonds and steric effects from the indole and catechol moieties. Molecular dynamics (MD) simulations of 16 spherical clusters of the tetramer subunits of PDA in the gas and aqueous phase were performed at 298 K and confirmed the stability of supramolecular tetramer aggregates. The complex formation and binding energy of all 16 clusters are very strong although the shapes of the clusters in aqueous solution are not spherical and are very much different from those in the gas phase. The aggregations of all 16 clusters in aqueous solution were also confirmed from the profiles of the Kratky plot and the radius of gyration of all clusters. Our MD results in both gas phase and aqueous solution pointed out that there are high possibilities of aggregations of the 16 kinds of tetramer subunits although the conformations of each tetramer subunit are not flat. In summary, this work brings an insight into the controversial structure of PDA tetramer units and explains some of the important structural features found in the aqueous phase in comparison to the gas phase.

iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides.

Umami or the taste of monosodium glutamate represents one of the major attractive taste modalities in humans. Therefore, knowledge about biophysical and biochemical properties of the umami taste is important for both scientific research and the food industry. Experimental approaches for predicting umami peptides are labor intensive, time consuming, and expensive. To date, computational models for the prediction and analysis of umami peptides as a function of sequence information have not been developed yet. In this study, we have proposed the first sequence-based predictor named iUmami-SCM using primary sequence information for the identification and characterization of umami peptides. iUmami-SCM utilized a newly developed scoring card method (SCM) in conjunction with the propensity scores of amino acids and dipeptide. Our predictor demonstrated excellent prediction performance ability for predicting umami peptides as well as outperforming other commonly used machine learning classifiers. Particularly, iUmami-SCM afforded the highest accuracy and Matthews correlation coefficient of 0.865 and 0.679, respectively, on an independent data set. Furthermore, the analysis of SCM-derived propensity scores was performed so as to provide a more in-depth understanding and knowledge of biophysical and biochemical properties of umami intensities of peptides. To develop a convenient bioinformatics tool, the best model is deployed as a web server that is made publicly available at http://camt.pythonanywhere.com/iUmami-SCM. The iUmami-SCM, as presented herein, serves as a powerful computational technique for large-scale umami peptide identification as well as facilitating the interpretation of umami peptides.

iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides.

In general, hydrolyzed proteins, plant-derived alkaloids and toxins displays unpleasant bitter taste. Thus, the perception of bitter taste plays a crucial role in protecting animals from poisonous plants and environmental toxins. Therapeutic peptides have attracted great attention as a new drug class. The successful identification and characterization of bitter peptides are essential for drug development and nutritional research. Owing to the large volume of peptides generated in the post-genomic era, there is an urgent need to develop computational methods for rapidly and effectively discriminating bitter peptides from non-bitter peptides. To the best of our knowledge, there is yet no computational model for predicting and analyzing bitter peptides using sequence information. In this study, we present for the first time a computational model called the iBitter-SCM that can predict the bitterness of peptides directly from their amino acid sequence without any dependence on their functional domain or structural information. iBitter-SCM is a simple and effective method that was built using the scoring card method (SCM) with estimated propensity scores of amino acids and dipeptides. Our benchmarking results demonstrated that iBitter-SCM achieved an accuracy and Matthews coefficient correlation of 84.38% and 0.688, respectively, on the independent dataset. Rigorous independent test indicated that iBitter-SCM was superior to those of other widely used machine-learning classifiers (e.g. k-nearest neighbor, naive Bayes, decision tree and random forest) owing to its simplicity, interpretability and implementation. Furthermore, the analysis of estimated propensity scores of amino acids and dipeptides were performed to provide a better understanding of the biophysical and biochemical properties of bitter peptides. For the convenience of experimental scientists, a web server is provided publicly at http://camt.pythonanywhere.com/iBitter-SCM. It is anticipated that iBitter-SCM can serve as an important tool to facilitate the high-throughput prediction and de novo design of bitter peptides.

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method.

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.