Detecting residues in targeting peptides

Knowledge of targeting signals is of immense importance for understanding the cellular processes by which proteins are sorted and transported. This paper presents a system of recurrent neural networks which demonstrate an ability to detect residues belonging to specific targeting peptides with greater accuracy than current feed forward models. The system can subsequently be used for determining sub-cellular localisation of proteins and for understanding the factors underlying translocation. The work can be seen as building upon the currently popular series of predictors SignalP and TargetP, by exploiting the inherent bias for sequential pattern recognition exhibited by recurrent networks