Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

Abstract

Target evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. In addition, we showed that the entire protein regions or individual structural subsets can be profiled using our derived fi-score based on amino acid dihedral angle and B-factor distribution. We further described a method to assess the structural profile and extract information on sites of importance using machine learning Gaussian mixture models. In combination, these biophysical analytical methods could potentially help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites which would greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets.