1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
14-3-3 sigma: new peptidic inhibitors by fragment-based drug design
Paper ID : 1274-ICB10
Authors:
Yasin Fahimi1, سید احمد عبادی *2, دارا دستان3
1دانشکده داروسازی، دانشگاه علوم پزشکی همدان، همدان، ایران
2دپارتمان شیمی دارویی، دانشکده داروسازی همدان، مرکز تحقیقات گیاهان دارویی و ،فراورده های طبیعی، همدان، ایران
3دپارتمان فارماکوگنوزی، دانشکده داروسازی همدان، مرکز تحقیقات گیاهان دارویی و ،فراورده های طبیعی، همدان، ایران
Abstract:
Modulating disease-relevant protein-protein interactions (PPIs) is a reliable and evolving approach. Studies have revealed almost 700 PPIs of the 14-3-3 family. Regulating a wide variety of signaling pathways, 14-3-3s are potential targets for drug discovery. 14-3-3 Sigma is a unique isoform which is most associated with the occurrence and development of malignancies. Overexpression of SFN (14-3-3 sigma gen) is related to gallbladder, liver, and breast cancer. Inhibition of 14-3-3 sigma interactions can both lead malignant cells to apoptosis and prevent the patient from chemotherapy resistance. Due to the structure of 14-3-3sigma in protein interactions, formation and occupation, the U-shaped groove between the both monomers of 14-3-3 sigma, plays the most important role in establishing complexes that lead to cell cycle regulation [1]. The design of anti-cancer peptides (ACPs) is a promising method for inhibiting protein interactions. So, in this study, we designed ACPs to inhibit the sigma isoform using a fragment-based drug design (FBDD) strategy [2]. After binding the potential fragments, a peptide library consisting of 1059049 peptides were designed. We performed a supervised machine learning in such a way that dataset were divided into 70% to 30% for training and testing, respectively [3]. Classification and logistic regression model were used for analysis in which 100 high-scored peptides were docked at the amphipathic groove of 14-3-3sigma. In the next step, 10 selected peptides were examined by molecular dynamic simulation. Finally, p11 (NKWRRF, Mw: 840.48 gr/mol) and p15 (NRWRRF, Mw: 937.54 gr/mol) hexapeptides with the least free energy were synthesized by the solid-phase peptide synthesis (SPPS) and then characterized by LC–MS and RP-HPLC.
Keywords:
PPIs, peptidic inhibitor, 14-3-3 sigma, FBDD, Machine Learning
Status : Paper Accepted (Poster Presentation)