Cellular and molecular mechanisms involved in the neurotoxicity of opioid and psychostimulant drugs

Substance abuse and addiction are the most costly of all the neuropsychiatric disorders. In the last decades, much progress has been achieved in understanding the effects of the drugs of abuse in the brain. However, efficient treatments that prevent relapse have not been developed. Drug addiction is now considered a brain disease, because the abuse of drugs affects several brain functions. Neurological impairments observed in drug addicts may reflect drug-induced neuronal dysfunction and neurotoxicity. The drugs of abuse directly or indirectly affect neurotransmitter systems, particularly dopaminergic and glutamatergic neurons. This review explores the literature reporting cellular and molecular alterations reflecting the cytotoxicity induced by amphetamines, cocaine and opiates in neuronal systems. The neurotoxic effects of drugs of abuse are often associated with oxidative stress, mitochondrial dysfunction, apoptosis and inhibition of neurogenesis, among other mechanisms. Understanding the mechanisms that underlie brain dysfunction observed in drug-addicted individuals may contribute to improve the treatment of drug addiction, which may have social and economic consequences.http://www.sciencedirect.com/science/article/B6SYS-4S50K2J-1/1/7d11c902193bfa3f1f57030572f7034

Knowledge-guided docking of WW domain proteins and flexible ligands

10.1007/978-3-642-04031-3_16Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5780 LNBI175-18

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies