Another Brick in the Wall. Validation of the σ1 Receptor 3D Model by Computer-Assisted Design, Synthesis and Activity of New o1 Ligands
Originally considered an enigmatic polypeptide, the σ1 receptor has recently been identiﬁed as a unique ligand regulated protein. Many studies have shown the potential of σ1 receptor ligands for the treatment of various diseases of the central nervous system (CNS); nevertheless, almost no information about the 3D structure of the receptor and/or the possible modes of interaction of the σ1 protein with its ligands have been unveiled so far.
Identification of Peptides with ELAV-like mRNA-Stabilizing Effect
Two peptides has been identified as valuable starting point for designing the first class of small molecules endowed with ELAV-like stabilizing effects, which could represent a highly innovative therapeutic tool. modeFRONTIER has been integrated with AMBER 11 to automate and optimize the complex workflows typically related to molecular dynamics simulations.
Degradable Self-Assembling Dendrons for Gene Delivery Experimental and Theoretical Insights into the Barriers to Cellular Uptake
In this study, multilevel modeling was used to demonstrate that complete dendron degradation would be necessary for effective DNA release. The choosen integrated experimental and theoretical approach has provided a unique insight into the way in which gene delivery vectors can approach cellular barriers to gene delivery. The entire MD simulation and data analysis procedure was optimized by integrating molecular modeling tools in modeFRONTIER.
Homology Model and Docking-Based Virtual Screening for Ligands of the σ1 Receptor
This study allowed for the definition of a computational strategy to analyze the structure of the σ1 Receptor based on a combination of 3D pharmacophore-based docking and MM/PBSA free energy of binding scoring. modeFRONTIER demostrated to be a powerful tool to automate and optimize the coupling between docking and Molecular Dynamics codes, helping in providing evidence that these in silico models can be useful for virtual screening of new σ1 ligands.