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In Journal of biomolecular structure & dynamics

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 the dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. Our derived Fi-score can be used to classify protein regions or individual structural subsets of interest and the described scoring system could be integrated into other discovery pipelines, such as protein classification databases, or applied to investigate new targets. We further demonstrated how our method can be integrated into machine learning Gaussian mixture models to predict different structural elements. Fi-score, in combination with other biophysical analytical methods depending on the research goals, could help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites. The described methodology could greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets. HIGHLIGHTS Description and derivation of a first-of-its-kind in silico protein fingerprinting method using B-factors and dihedral angles. Derived Fi-score allows to characterise the whole protein or selected regions of interest. Demonstration how machine learning using Gaussian mixture models on Fi-scores captures and allows to predict functional protein topology elements. Fi-score is a novel method to help evaluate therapeutic targets and engineer effective biologics. Communicated by Ramaswamy H. Sarma.

KanapeckaitÄ— AustÄ—, Beaurivage Claudia, Hancock Matthew, Verschueren Erik


B-factor, Drug discovery, Gaussian mixture models, conformation distal information, dihedral angles, machine learning, protein site characterisation