Computational Design and Optimization of Peptide Inhibitors for SIRT2.

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Chemistry

Background:

• Sirtuin 2 (SIRT2) is an NAD+-dependent deacetylase involved in aging and disease.
• Abnormal SIRT2 activity links to Parkinson's disease, cancer, and metabolic disorders.
• Peptide inhibitors offer advantages over small molecules for targeting SIRT2.

Purpose of the Study:

• To convert a cyclic SIRT2 inhibitor (S2iL5) into a non-cyclic peptide.
• To enhance peptide flexibility and binding affinity for SIRT2.
• To identify novel peptide-based SIRT2 inhibitors using computational methods.

Main Methods:

• Peptide modeling and conformational analysis.
• Molecular docking and MM/GBSA calculations for binding energy assessment.
• Machine learning-based Quantitative Structure-Activity Relationship (QSAR) screening of mutant peptides.
• Molecular dynamics (MD) simulations for stability and flexibility analysis.

Main Results:

• The non-cyclic peptide showed improved binding free energy (-50.66 kcal/mol) compared to cyclic S2iL5 (-49.44 kcal/mol).
• QSAR screening identified three promising peptide candidates.
• MD analyses indicated potential for Peptide 1 and Peptide 2 as SIRT2 inhibitors, with binding energies of -59.07 kcal/mol and -46.01 kcal/mol, respectively.

Conclusions:

• Optimized peptide inhibitors can effectively interact with SIRT2, enhancing binding affinity and flexibility.
• Computational strategies can identify novel peptide-based SIRT2 inhibitors.
• Further experimental validation is required to confirm the therapeutic potential of the identified peptides.