Peptideâ??protein interactions are corner-stones of living functions involved in essential\nmechanisms, such as cell signaling. Given the difficulty of obtaining direct experimental structural\nbiology data, prediction of those interactions is of crucial interest for the rational development of\nnew drugs, notably to fight diseases, such as cancer or Alzheimerâ??s disease. Because of the high\nflexibility of natural unconstrained linear peptides, prediction of their binding mode in a protein\ncavity remains challenging. Several theoretical approaches have been developed in the last decade to\naddress this issue. Nevertheless, improvements are needed, such as the conformation prediction of\npeptide side-chains, which are dependent on peptide length and flexibility. Here, we present a novel\nin silico method, Iterative Residue Docking and Linking (IRDL), to efficiently predict peptideâ??protein\ninteractions. In order to reduce the conformational space, this innovative method splits peptides\ninto several short segments. Then, it uses the performance of intramolecular covalent docking to\nrebuild, sequentially, the complete peptide in the active site of its protein target. Once the peptide is\nconstructed, a rescoring step is applied in order to correctly rank all IRDL solutions. Applied on a set\nof 11 crystallized peptideâ??protein complexes, the IRDL method shows promising results, since it is\nable to retrieve experimental binding conformations with a Root Mean Square Deviation (RMSD) \nbelow 2 Angstrom in the top five ranked solutions. For some complexes, IRDL method outperforms two\nother docking protocols evaluated in this study. Hence, IRDL is a new tool that could be used in drug\ndesign projects to predict peptide-protein interactions.
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