Computational design of a cyclic peptide that inhibits the CTLA4 immune checkpoint
Ravindra Thakkar1, Deepa Upreti1, Susumu Ishiguro1
1Department of Anatomy and Physiology, Kansas State University 1620 Denison Avenue Manhattan Kansas USA jeffcomer@ksu.edu +1 785 532 6311.
Abstract:
Proteins involved in immune checkpoint pathways, such as CTLA4, PD1, and PD-L1, have become important targets for cancer immunotherapy; however, development of small molecule drugs targeting these pathways has proven difficult due to the nature of their protein-protein interfaces. Here, using a hierarchy of computational techniques, we design a cyclic peptide that binds CTLA4 and follow this with experimental verification of binding and biological activity, using bio-layer interferometry, cell culture, and a mouse tumor model. Beginning from a template excised from the X-ray structure of the CTLA4:B7-2 complex, we generate several peptide sequences using flexible docking and modeling steps. These peptides are cyclized head-to-tail to improve structural and proteolytic stability and screened using molecular dynamics simulation and MM-GBSA calculation. The standard binding free energies for shortlisted peptides are then calculated in explicit-solvent simulation using a rigorous multistep technique. The most promising peptide, cyc(EIDTVLTPTGWVAKRYS), yields the standard free energy -6.6 ± 3.5 kcal mol-1, which corresponds to a dissociation constant of ∼15 μmol L-1. The binding affinity of this peptide for CTLA4 is measured experimentally (31 ± 4 μmol L-1) using bio-layer interferometry. Treatment with this peptide inhibited tumor growth in a co-culture of Lewis lung carcinoma (LLC) cells and antigen primed T cells, as well as in mice with an orthotropic Lewis lung carcinoma allograft model.
Insights
Researchers designed a cyclic peptide targeting CTLA4, a key immune checkpoint protein, for cancer immunotherapy. This novel peptide demonstrated significant binding affinity and inhibited tumor growth in preclinical models.
Area of Science:
- Immunology
- Computational Biology
- Drug Discovery
Background:
- Immune checkpoints like CTLA4, PD1, and PD-L1 are crucial targets in cancer immunotherapy.
- Developing small molecule drugs for these targets is challenging due to their protein-protein interaction interfaces.
Purpose of the Study:
- To computationally design a cyclic peptide that binds to CTLA4.
- To experimentally validate the binding affinity and biological activity of the designed peptide.
Main Methods:
- Utilized a hierarchy of computational techniques, including flexible docking, molecular dynamics simulations, and MM-GBSA calculations.
- Generated and cyclized peptide sequences for enhanced stability.
- Experimentally verified binding using bio-layer interferometry and assessed biological activity in cell culture and a mouse tumor model.
Main Results:
- A cyclic peptide, cyc(EIDTVLTPTGWVAKRYS), was designed with a calculated binding free energy of -6.6 ± 3.5 kcal mol⁻¹.
- Experimental bio-layer interferometry confirmed a binding affinity of 31 ± 4 μmol L⁻¹ for CTLA4.
- The peptide demonstrated efficacy in inhibiting tumor growth in Lewis lung carcinoma models.
Conclusions:
- The study successfully designed and validated a cyclic peptide targeting CTLA4, offering a promising new avenue for cancer immunotherapy.
- Computational design combined with experimental validation is an effective strategy for developing peptide therapeutics against challenging protein targets.
