۱۴-۳-۳ sigma: new peptidic inhibitors by fragment-based drug design

Modulating disease-relevant protein-protein interactions (PPIs) is a reliable and evolving approach. Studieshave revealed almost ۷۰۰ PPIs of the ۱۴-۳-۳ family. Regulating a wide variety of signaling pathways, ۱۴-۳-۳s are potential targets for drug discovery. ۱۴-۳-۳ Sigma is a unique isoform which is most associated withthe occurrence and development of malignancies. Overexpression of SFN (۱۴-۳-۳ sigma gen) is related togallbladder, liver, and breast cancer. Inhibition of ۱۴-۳-۳ sigma interactions can both lead malignant cells toapoptosis and prevent the patient from chemotherapy resistance. Due to the structure of ۱۴-۳-۳sigma inprotein interactions, formation and occupation, the U-shaped groove between the both monomers of ۱۴-۳-۳sigma, plays the most important role in establishing complexes that lead to cell cycle regulation. The designof anti-cancer peptides (ACPs) is a promising method for inhibiting protein interactions. So, in this study, wedesigned ACPs to inhibit the sigma isoform using a fragment-based drug design (FBDD) strategy. Afterbinding the potential fragments, a peptide library consisting of ۱۰۵۹۰۴۹ peptides were designed. Weperformed a supervised machine learning in such a way that dataset were divided into ۷۰% to ۳۰% for trainingand testing, respectively. Classification and logistic regression model were used for analysis in which ۱۰۰high-scored peptides were docked at the amphipathic groove of ۱۴-۳-۳sigma. In the next step, ۱۰ selectedpeptides were examined by molecular dynamic simulation. Finally, p۱۱ (NKWRRF, Mw: ۸۴۰.۴۸ gr/mol)and p۱۵ (NRWRRF, Mw: ۹۳۷.۵۴ gr/mol) hexapeptides with the least free energy were synthesized by thesolid-phase peptide synthesis (SPPS) and then characterized by LC–MS and RP-HPLC.

Keywords:

PPIs , peptidic inhibitor , ۱۴-۳-۳ sigma , FBDD , Machine Learning