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Protein distance constraints predicted by neural networks and probability density functions
Protein Engineering Design & Selection
We predict interatomic C α distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taking the context of the two residues into account. These two methods are used to predict whether distances in independent test sets were above or below given thresholds. We investigate which distancedoi:10.1093/protein/10.11.1241 pmid:9514112 fatcat:56cmbux3frf35enid2fnf4ha4y