Bioinformatics and Computer Simulation approaches to the discovery and analysis of Bioactive Peptides

The traditional process of separating and purifying bioactive peptides is laborious and time-consuming. Using a traditional process to identify is difficult, and there is a lack of fast and accurate activity evaluation methods. How to extract bioactive peptides quickly and efficiently is still the focus of bioactive peptides research. In order to improve the present situation of the research, bioinformatics techniques and peptidome methods are widely used in this field. At the same time, bioactive peptides have their own specific pharmacokinetic characteristics, so computer simulation methods have incomparable advantages in studying the pharmacokinetics and pharmacokinetic-pharmacodynamic correlation models of bioactive peptides. The purpose of this review is to summarize the combined applications of bioinformatics and computer simulation methods in the study of bioactive peptides, with focuses on the role of bioinformatics in simulating the selection of enzymatic hydrolysis and precursor proteins, activity prediction, molecular docking, physicochemical properties, and molecular dynamics. Our review shows that new bioactive peptide molecular sequences with high activity can be obtained by computer-aided design. The significance of the pharmacokinetic-pharmacodynamic correlation model in the study of bioactive peptides is emphasized. Finally, some problems and future development potential of bioactive peptides binding new technologies are prospected.

Simplified, interpretable graph convolutional neural networks for small molecule activity prediction

We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN QSAR architecture, and we observe that such a model can yield performance improvements over both standard gCNN and RF methods on difficult-to-classify test sets. Additionally, we discuss how reductions in convolutional layer dimensions potentially speak to the “anatomical” needs of gCNNs with respect to radial coarse graining of molecular substructure. We augment this simplified architecture with saliency map technology that highlights molecular substructures relevant to activity, and we perform saliency analysis on nearly 100 data-rich protein targets. We show that resultant substructural clusters are useful visualization tools for understanding substructure-activity relationships. We go on to highlight connections between our models’ saliency predictions and observations made in the medicinal chemistry literature, focusing on four case studies of past lead finding and lead optimization campaigns.

SKRINING SENYAWA INHIBITOR H2 DARI KAYU MANIS (Cinnamomum verum J.Presl)

Digestive tract disorders, especially gastric disorders, are often experienced by people. One drug to treat this disorder has a mechanism of blocking the H2 receptor. This research was conducted to find compounds from C.verum which have the stability of bind to H2 receptors. The method used is protein modeling with swiss-model, docking with PLANTS (CHEMPLP) and activity prediction. The test results obtained that the docking score was ?- amorphene (-65,79), ?-bergamotene (-65,48), ?-copaene (-66,62), ?-cubebene (-66,46), Cadinene (-64 , 79), Camphor (-52.15), Caryophyllene (-62.61), Cinnamaldehyde (-68.17), Epicatechin (-80.43), Ergosterol (-85.24), Eugenol (-67.35), Hydrocinnamaldehyde (-65,53), Quercetin (-74,38), Protocatechuic acid (-71,49), Stigmasterol (-88,88), 4- (2,3-dihydro-3- (hydroxymethyl) - 5- (3-hydroxypropyl) -7- (methoxy) benzofuranyl] -2-methoxyphenyl (-85,29). Combined with the probability activity of compounds that have the potential to be further developed are Epicatechin and urolignoside.

Water Activity Prediction in Sugar and Polyol Systems Using Theoretical Molecular Descriptors

Water activity is a key factor in the development of pharmaceutical, cosmetic, and food products. In aqueous solutions of nonelectrolytes, the Norrish model provides a simple and effective way to evaluate this quantity. However, it contains a parameter, known as the Norrish constant, that must be estimated from experimental data. In this study, a new strategy is proposed for the prediction of water activity in the absence of experimental information, based on the use of theoretical molecular descriptors for characterizing the effects of a solute. This approach was applied to the evaluation of water activity in the presence of sugars (glucose, fructose, xylose, sucrose) and polyols (sorbitol, xylitol, glycerol, erythritol). The use of two descriptors related to the constitutional and connectivity properties of the solutes was first investigated. Subsequently, a new theoretical descriptor, named the global information index (G), was developed. By using this index, the water activity curves in the binary systems were reconstructed. The positive results obtained support the proposed strategy, as well as the possibility of including, in a single information index, the main molecular features of a solute that determine its effects on water activity.