Food-derived linear vs. rationally designed cyclic peptides as potent TNF-alpha inhibitors: an integrative...

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• Tumor necrosis factor-alpha (TNF-alpha) is a critical mediator in chronic inflammatory diseases, including atherosclerosis.
• Targeting TNF-alpha offers a therapeutic strategy for cardiovascular disorders, but durable inhibition remains a challenge.
• Peptide therapeutics present an opportunity for specific and low-toxicity treatments.

Purpose of the Study:

• To identify and optimize food-derived peptides for potent and durable inhibition of TNF-alpha.
• To explore the potential of in silico strategies for peptide-based drug discovery.
• To develop next-generation TNF-alpha antagonists with improved drug-like properties.

Main Methods:

• Employed a dual in silico approach: virtual screening of food-derived peptides and rational design of a cyclized peptide.
• Utilized molecular docking, 200-ns molecular dynamics simulations, and MM/PBSA free-energy calculations.
• Performed ADMET profiling to assess metabolic stability, plasma clearance, and potential cardiotoxicity.

Main Results:

• Selected peptides demonstrated strong, persistent interactions with key TNF-alpha residues like Tyr119.
• The rationally designed cyclic peptide exhibited enhanced binding affinity and stability compared to linear sequences.
• ADMET profiling indicated superior metabolic stability, reduced clearance, and no predicted cardiotoxicity for the cyclic analogue.

Conclusions:

• Dietary peptides can serve as effective templates for TNF-alpha inhibitors.
• Interface-guided cyclization rationally improves peptide stability, binding affinity, and pharmacokinetic properties.
• This study provides a framework for developing novel, low-toxicity peptide therapeutics for chronic inflammation and cardiovascular diseases, aligning with UN SDGs 3 and 9.