Pharmacophore modeling and virtual screening in search of novel Bruton's tyrosine kinase inhibitors

Virtual screening and structure-activity relationship study of novel BTK inhibitors in Traditional Chinese Medicine for the treatment of rheumatoid arthritis

Bruton tyrosine kinase (BTK) is a known drug target for the treatment of autoimmune diseases, including rheumatoid arthritis (RA). In this study, a series of 1-amino-1H-imidazole-5-carboxamide derivatives with good inhibitory activity against BTK were selected to explore the structure-activity relationships of these BTK inhibitors (BTKIs). Furthermore, we concentrated on 182 prescriptions of Traditional Chinese Medicine with therapeutic effects on RA. 54 herbs with a frequency of ≥10 were counted to establish a database containing 4027 ingredients for virtual screening. Five compounds with relatively higher docking scores and better absorption, distribution, metabolism, elimination and toxicity (ADMET) parameters were then selected for higher precision docking. The results demonstrated that the potentially active molecules form hydrogen bond interactions with the hinge region residues Met477, Glu475, glycine-rich P-loop residue Val416, Lys430 and DFG motif Asp539. In particular, they also interact with the key residues Thr474 and Cys481 of BTK. The molecular dynamics (MD) results demonstrated that all five compounds above could bind with BTK stably as its cognate ligand in dynamic conditions. This work identified several potential BTKIs using a computer-aided drug design approach and may provide crucial information for developing novel BTKIs.Communicated by Ramaswamy H. Sarma.

Direct interaction between ABCA1 and HIV-1 Nef: Molecular modeling and virtual screening for inhibitors

HIV-1 infection impairs cellular cholesterol efflux by downmodulating the cholesterol transporter ABCA1, leading to metabolic co-morbidities like cardio-vascular disease. The main mechanism of this effect is impairment by the HIV-1 protein Nef of the ABCA1 interaction with the endoplasmic reticulum chaperone calnexin, which leads to a block in ABCA1 maturation followed by its degradation. However, ABCA1 is also downmodulated by Nef delivered with the extracellular vesicles, suggesting involvement of a direct Nef:ABCA1 interaction at the plasma membrane. Here, we present an optimized model of the Nef:ABCA1 interaction, which identifies interaction sites and provides an opportunity to perform a virtual screening for potential inhibitors. Interestingly, the predicted sites on Nef involved in the ABCA1 interaction overlap with those involved in the interaction with calnexin. The compounds previously shown to block Nef:calnexin interaction were among the top ranking ligands in docking simulations with ABCA1-interacting sites on Nef, suggesting the possibility that both interactions can be inhibited by the same chemical compounds. This study identifies a series of compounds for potential development as inhibitors of Nef-mediated co-morbidities of HIV infection.

Computational Approaches for the Design of (Mutant-)Selective Tyrosine Kinase Inhibitors: State-of-the-Art and Future Prospects

Kinases remain one of the major attractive therapeutic targets for a large number of indications such as cancer, rheumatoid arthritis, cardiac failure and many others. Design and development of kinase inhibitors (ATP-competitive, allosteric or covalent) is a clinically validated and successful strategy in the pharmaceutical industry. The perks come with limitations, particularly the development of resistance to highly potent and selective inhibitors. When this happens, the cycle needs to be repeated, i.e., the design and development of kinase inhibitors active against the mutated forms. The complexity of tumor milieu makes it awfully difficult for these molecularly-targeted therapies to work. Every year newer and better versions of these agents are introduced in the clinic. Several computational approaches such as structure-, ligand-based or hybrid ones continue to live up to their potential in discovering novel kinase inhibitors. New schools of thought in this area continue to emerge, e.g., development of dual-target kinase inhibitors. But there are fundamental issues with this approach. It is indeed difficult to selectively optimize binding at two entirely different or related kinases. In addition to the conventional strategies, modern technologies (machine learning, deep learning, artificial intelligence, etc.) started yielding the results and building success stories. Computational tools invariably played a critical role in catalysing the phenomenal progress in kinase drug discovery field. The present review summarized the progress in utilizing computational methods and tools for discovering (mutant-)selective tyrosine kinase inhibitor drugs in the last three years (2017-2019). Representative investigations have been discussed, while others are merely listed. The author believes that the enthusiastic reader will be inspired to dig out the cited literature extensively to appreciate the progress made so far and the future prospects of the field.