In silico drug discovery for a complex immunotherapeutic target- human c-Rel protein

Target evaluation and rational drug design rely on identifying and characterising small-molecule binding sites on therapeutically relevant target proteins. Immunotherapeutics development is especially challenging because of complex disease etiology and heterogenous nature of targets. c-Rel protein, a promising target in many human inflammatory and cancer pathologies, was selected as a case study for an effective in silico screening platform development since this transcription factor currently has no successful therapeutic inhibitors or modulators. This study introduces a novel in silico screening approach to probe binding sites using structural validation sets, molecular modelling and describes a method of a computer-aided drug design when a crystal structure is not available for the target of interest. In addition, we showed that binding sites can be analysed with the machine learning as well as molecular simulation approaches to help assess and systematically analyse how drug candidates can exert their mode of action. Finally, this cutting-edge approach was subjected to a high through-put virtual screen of selected 34 M drug-like compounds filtered from a library of 659 M compounds by identifying the most promising structures and proposing potential action mechanisms for the future development of highly selective human c-Rel inhibitors and/or modulators.

Automated summary

Target evaluation and rational drug design rely on identifying and characterizing small-molecule binding sites on therapeutically relevant target proteins. Immunotherapeutics development is especially challenging due to complex disease etiology and heterogeneous nature of targets. This study introduces a novel in silico screening approach to probe binding sites using structural validation sets, molecular modelling, and describes a method of computer-aided drug design when a crystal structure is not available for the target of interest. Additionally, machine learning and molecular simulation approaches are used to analyze binding sites to systematically assess how drug candidates can exert their mode of action. The study concludes with a high throughput virtual screen of selected drug-like compounds to identify promising structures for the future development of highly selective human c-Rel inhibitors and/or modulators.