Comparing the conformational behavior of a series of diastereomeric cyclic urea HIV-1 inhibitors using the low mode:monte carlo conformational search method

Isomannide-based peptidomimetics as inhibitors for human tissue kallikreins 5 and 7

Human kallikrein 5 (KLK5) and 7 (KLK7) are potential targets for the treatment of skin inflammation and cancer. Previously, we identified isomannide derivatives as potent and competitive KLK7 inhibitors. The introduction of N-protected amino acids into the isomannide-based scaffold was studied. Some KLK5 inhibitors with submicromolar affinity (K i values of 0.3-0.7 μM) were identified, and they were 6- to 13-fold more potent than our previous hits. Enzyme kinetics studies and the determination of the mechanism of inhibition confirmed that the new isomannide-based derivatives are competitive inhibitors of both KLK5 and KLK7. Molecular docking and MD simulations of selected inhibitors into the KLK5 binding site provide insight into the molecular mechanism by which these compounds interact with the enzyme. The promising results obtained in this study open new prospects on the design and synthesis of highly specific KLK5 and KLK7 inhibitors.

Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination method

OBJECTIVE

Accurate prediction of major histocompatibility complex (MHC) class II binding peptides helps reducing the experimental cost for identifying helper T cell epitopes, which has been a challenging problem partly because of the variable length of the binding peptides. This work is to develop an accurate model for predicting MHC-binding peptides using machine learning methods.

METHODS

In this work, a machine learning method, continuous kernel discrimination (CKD), was used for predicting MHC class II binders of variable lengths. The composition transition and distribution features were used for encoding peptide sequence and the Metropolis Monte Carlo simulated annealing approach was used for feature selection.

RESULTS

Feature selection was found to significantly improve the performance of the model. For benchmark dataset Dataset-1, the number of features is reduced from 147 to 24 and the area under the receiver operating characteristic curve (AUC) is improved from 0.8088 to 0.9034, while for benchmark dataset Dataset-2, the number of features is reduced from 147 to 44 and the AUC is improved from 0.7349 to 0.8499. An optimal CKD model was derived from the feature selection and bandwidth optimization using 10-fold cross-validation. Its AUC values are between 0.831 and 0.980 evaluated on benchmark datasets BM-Set1 and are between 0.806 and 0.949 on benchmark datasets BM-Set2 for MHC class II alleles. These results indicate a significantly better performance for our CKD model over other earlier models based on the training and testing of the same datasets.

CONCLUSIONS

Our study suggested that the CKD method outperforms other machine learning methods proposed earlier in the prediction of MHC class II biding peptides. Moreover, the choice of the cut-off for CKD classifier is crucial for its performance.

DFT-GIAO study of aryltetralin lignan lactones: conformational analyses and chemical shifts calculations

The conformational properties of polygamain and morelensin, two aryltetralin lignan lactones, have been investigated in both the gas-phase and chloroform solution using DFT calculations at the B3LYP/6-311G(d,p) level. Results indicate that the conformation of polygamain is very rigid. Thus, the conformational flexibility of its five-membered rings is considerably restricted as reflects the pseudorotational parameters of the corresponding envelope conformations. On the other hand, morelensin shows a notable conformational flexibility, which is mainly due to its two methoxy groups. Accordingly, 16 minimum energy conformations with relative energies smaller than 2.4 kcal/mol were detected. Furthermore, chemical shifts for 13C nuclei have been calculated using the GIAO method, results being compared with experimental data. A good agreement was found for both polygamain and morelensin.