Bioinformatics molecular dynamics and docking pipeline analysis for high-throughput genome analysis and drug discovery oriented to personalized pain therapy in non-responsive patients.

Some functions of the nervous system are pain transduction and pain perception. These functions are directly involved in many chronic diseases due to the pain condition to which they are associated. Peripheral and central nervous system are the main targets of pain therapy. Pain therapy uses variety of drugs in relationship of severity of the illness and the degree of the patient's response. The individual variability in response to drugs depends on different variables such as pathological (timing and severity), physiological (age, gender and weight), genetic and environmental aspects involved in pharmacokinetics and pharmacodynamics. Although patients can be classified as poor, intermediate, normal or extensive responders, 30% of patients do not respond to pain treatments. Our aim is to collect and analyse DNA samples in terminal patients to define their drug response phenotype. A bioinformatics analysis of the GWA DNA samples will be performed to identify mutations in candidate genes. A molecular dynamic study will be used to investigate the misfolding structures and the molecular docking will be applied for testing the binding activity of the drugs in use. Variants with low binding affinity will be submitted to virtual screening to identify all the potential leads by using the ZINC database of UCSF with over 21 million ligands. We have developed the molecular dynamics and docking pipeline analysis based on the High-Performance and Distributed Computing (eg. GPU and GRID clusters) to perform large scale of analysis. Data will be collected in a portal infrastructure organised in three layers: a Portal layer, an Application layer and a Data layer. The Application layer is a variety of java portlets, each of which allow the end user, depending on its permissions, to add and retrieve records stored in the Data layer. The Data layer will be subdivided into three layers: the first is a database that interacts with the Portal layer, the second contains all patients clinical data, the third and last contains personal data of the patients. The infrastructure will use a unique retrieval system that randomly codes patients information to prevent users to obtain a correlation between pathologies and personal data of the patients. This procedure will be used to support high-throughput genome analysis and drug discovery oriented to personalized pain therapy in non-responsive patients.