Binding Activity Prediction of Cyclin-Dependent Inhibitors

Discovery of Biphenyl-Sulfonamides as Novel β-N-Acetyl-d-Hexosaminidase Inhibitors via Structure-Based Virtual Screening

Novel insecticidal targets are always in demand due to the development of resistance. OfHex1, a β-N-acetyl-d-hexosaminidase identified in Ostrinia furnacalis (Asian corn borer), is involved in insect chitin catabolism and has proven an ideal target for insecticide development. In this study, structure-based virtual screening, structure simplification, and biological evaluation are used to show that compounds with a biphenyl-sulfonamide skeleton have great potential as OfHex1 inhibitors. Specifically, compounds 10k, 10u, and 10v have K_i values of 4.30, 3.72, and 4.56 μM, respectively, and thus, they are more potent than some reported nonglycosyl-based inhibitors such as phlegmacin B₁ (K_i = 26 μM), berberine (K_i = 12 μM), 2 (K_i = 11.2 μM), and 3 (K_i = 28.9 μM). Furthermore, inhibitory kinetic assessments reveal that the target compounds are competitive inhibitors with respect substrate, and based on toxicity predictions, most of them have potent drug properties. The obtained results indicate that the biphenyl-sulfonamide skeleton characterized by simple chemical structure, synthetic tractability, potent activity, and low toxicity has potential for further development in pest management targeting OfHex1.

Recent advances on CDK inhibitors: An insight by means of in silico methods

The cyclin dependent kinases (CDKs) are a small family of serine/threonine protein kinases that can act as a potential therapeutic target in several proliferative diseases, including cancer. This short review is a survey on the more recent research progresses in the field achieved by using in silico methods. All the "armamentarium" available to the medicinal chemists (docking protocols and molecular dynamics, fragment-based, de novo design, virtual screening, and QSAR) has been employed to the discovery of new, potent, and selective inhibitors of cyclin dependent kinases. The results cited herein can be useful to understand the nature of the inhibitor-target interactions, and furnish an insight on the structural/molecular requirements necessary to achieve the required selectivity against cyclin dependent kinases over other types of kinases.

In Silico Exploration for Novel Type-I Inhibitors of Tie-2/TEK: The Performance of Different Selection Strategy in Selecting Virtual Screening Candidates

The receptor tyrosine kinase Tie-2 is involved in vessel remodeling and maturation, and has been regarded as a potential target for the treatment of various solid tumors. The absence of novel, potent and selective inhibitors severely hampers the understanding of the therapeutic potential of Tie-2. In the present work, we describe the discovery of novel type-I inhibitors of Tie-2 by structure-based virtual screening. Preliminary SAR was also performed based on one active compound, and several novel inhibitors with low micro-molar affinity were discovered. To directly compare the efficiency between different filtering strategies in selecting VS candidates, two methods were separately carried out to screen the same chemical library, and the selected VS candidates were then experimentally assessed by in vitro enzymatic assays. The results demonstrate that the hit rate is improved when stricter drug-likeness criteria and less number of molecules for clustering analysis are used, and meanwhile, the molecular diversity of the compounds still maintains. As a case study of TIE-2, the information presented in this work underscores the importance of selecting an appropriate selection strategy in VS campaign, and the novel inhibitors identified and the detailed binding modes of action provide a starting point for further hit-to-lead optimization process.

Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach

Hemoglobin is an oxygen-binding protein widely present in all kingdoms of life from prokaryotic to eukaryotic, but well established in the vertebrate system. An attempt was made to determine the Vertebrate hemoglobin (VerHb) protein on their animal classifications, based on general pseudo amino acid composition (PseAAC)'s evolutionary profiles and hybrid approach. The support vector machine (SVM) has been applied to develop all models, the prediction results further compared according to their animal classification. The performance of the approaches estimated using five-fold cross-validation techniques. The prediction performance was further investigated by receiver operating characteristic (ROC) and prediction score graphs. The prediction accuracy (ACC), sensitivity (SN) and specificity (SP) were examined to find the accurate predictions on the threshold level. Based on the approach, a web-tool has been developed for identifying the VerHb proteins.