In-silico Prediction of the Sweetness of Aspartame Analogues from QSPR Analysis

The extensive utilization of the low-energy dipeptide sweetener aspartame in foods leads to various studies on searching for new sweeteners in series. However, the real mechanistic cause of their sweetness power is still not completely known owing to their complex interactions with human sweet receptor, which may be different from that of other sweeteners to some extent. In this contribution, predictive quantitative structure-property relationship(QSPR) models have been developed for diverse aspartame analogues using Materials Studio 5.0 software. The optimal QSPR model(r2 = 0.913, r2 CV = 0.881 and r2 pred = 0.730) constructed by the genetic function approximation method has been validated by the tests of cross validation, randomization, external prediction and other statistical criteria, which shows that their sweetness power is mainly governed by their electrotopological-state indices(SssCH and SsNH), spatial descriptors(Shadow length: LX, ellipsoidal volume and Connolly surface occupied volume) and topological descriptors(Chi(3): cluster and Chi(0)(valence modified)), which partially supports both multipoint attachment theory proposed by Nofre and Tinti et al. and B-X theory proposed by Kier et al.. Present exploited results provide the key structural features for the sweetness power of aspartame analogues, supplement the mechanistic understanding of the sweet perception, and would be also helpful for the design of potent sweetener analogs prior to their synthesis.

2D-QSAR Studies on Anthranilic Acid Derivatives: A Novel Class of Allosteric Inhibitors of Hepatitis C NS5B Polymerase

Quantitative structure activity relationship （QSAR） studies were performed on 45 anthranilic acid derivatives for their potent allosteric inhibition activities of HCV NSSB polymerase. Genetic algorithm based genetic function approximation （GFA） method of variable selection was used to generate the model. Highly statistically significant model with r^2 = 0.966 and r^2cv = 0.951 was obtained when the number of descriptors in the equation was set to 5. High r^2pred value of 0.884 indicates the good predictive power of the best model. Spatial descriptors of radius of gyration （RadOfGration）, molecular volume （Vm）, length of molecule in the z dimension （Shadow-Zlength）, thermodynamic descriptors of the octanol/water partition coefficient （LogP） and molecular refractivity index （MR） showed enormous contributions to HCV NS5B polymerase inhibition. The validation of the model was done by leave-one-out （LOO） test, randomization tests and external test set prediction. The model gives insight on indispensable structural requirements for the activity and can be used to design more potent analogs against HCV NSSB polymerase.