MOTIVATION: Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery. RESULTS: We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

BACKGROUND AND SIGNIFICANCE: Intellectual disability is a condition characterized by significant limitations in cognitive abilities and social/behavioral adaptive skills and is an important reason for pediatric, neurologic, and genetic referrals. Approximately 10% of protein-encoding genes on the X chromosome are implicated in intellectual disability, and the corresponding intellectual disability is termed X-linked ID (XLID). Although few mutations and a small number of families have been identified and XLID is rare, collectively the impact of XLID is significant because patients usually are unable to fully participate in society. OBJECTIVE: To reveal the molecular mechanisms of various intellectual disabilities and to suggest small molecules which by binding to the malfunctioning protein can reduce unwanted effects. METHODS: Using various in silico methods we reveal the molecular mechanism of XLID in cases involving proteins with known 3D structure. The 3D structures were used to predict the effect of disease-causing missense mutations on the folding free energy, conformational dynamics, hydrogen bond network and, if appropriate, protein-protein binding free energy. RESULTS: It is shown that the vast majority of XLID mutation sites are outside the active pocket and are accessible from the water phase, thus providing the opportunity to alter their effect by binding appropriate small molecules in the vicinity of the mutation site. CONCLUSIONS: This observation is used to demonstrate, computationally and experimentally, that a particular condition, Snyder-Robinson syndrome caused by the G56S spermine synthase mutation, might be ameliorated by small molecule binding.