Khan, WaqasuddinWaqasuddinKhanDuffy, Fergal J.Fergal J.DuffyPollastri, GianlucaGianlucaPollastriShields, Denis C.Denis C.ShieldsMooney, CatherineCatherineMooney2021-08-112021-08-112013 the A2013-05http://hdl.handle.net/10197/12406Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short regions involved in binding. We set out to determine if docking peptides to peptide binding domains would assist in these predictions. First, we investigated the docking of known short peptides to their native and non-native peptide binding domains. We then investigated the docking of overlapping peptides adjacent to the native peptide. We found only weak discrimination of docking scores between native peptide and adjacent peptides in this context with similar results for both ordered and disordered regions. Finally, we trained a bidirectional recurrent neural network using as input the peptide sequence, predicted secondary structure, Vina docking score and Pepsite score.We conclude that docking has only modest power to define the location of a peptide within a larger protein region known to contain it. However, this information can be used in training machine learning methods which may allow for the identification of peptide binding regions within a protein sequence.enProtein-peptide interactionsProtein classificationComputational dockingRoot-mean-square deviation (RMSD)Technical Report2021-07-2708/IN.1/B1864https://creativecommons.org/licenses/by-nc-nd/3.0/ie/