Introduction: The regulation of T-cell activity is critically dependent on immune checkpoints, among which Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) plays a pivotal inhibitory role. CTLA-4 competes with the costimulatory receptor CD28 for binding to B7 ligands (CD80/CD86) on antigen-presenting cells, thereby transmitting inhibitory signals that downregulate T-cell activation and proliferation. While this mechanism maintains immune homeostasis and prevents autoimmunity, it also facilitates tumor immune evasion. Therapeutic antibodies, such as Ipilimumab, target CTLA-4 to restore T-cell activity, resulting in enhanced antitumor responses. Ipilimumab, a fully human IgG1 monoclonal antibody, exerts its effects through both direct blockade of B7 interactions and Fc-mediated depletion of intratumoral regulatory T cells. Despite its clinical success in treating advanced melanoma, further improvements in binding affinity are desirable to maximize efficacy, reduce dosage, and minimize immune-related adverse events.Advances in antibody engineering now allow precise modifications of amino acids within antigen-binding regions to optimize interactions. Site-directed mutagenesis, guided by computational modeling and molecular docking, enables rational design of antibody variants with improved binding energetics. This in silico approach identifies critical residues, particularly within complementarity-determining regions, that govern antigen recognition and allows systematic prediction of substitutions likely to enhance affinity. Combining site-directed mutation with energy-based assessment tools provides a rapid and efficient framework to generate high-affinity antibody variants. Such strategies not only facilitate the development of next-generation therapeutic antibodies but also provide insights into structure-function relationships that govern immune checkpoint interactions. In this study, we leverage computational rational design to optimize the binding of Ipilimumab to CTLA-4, demonstrating the potential of site-directed mutagenesis for creating superior immunotherapeutic agents.
Methods: In this study, the three-dimensional structure of CTLA-4 in complex with the Ipilimumab antibody was retrieved from the Protein Data Bank (PDB ID: 5XJ3). The sequence of Ipilimumab’s complementarity-determining regions (CDRs) was identified using the SAbDab server. PyMOL was then employed to locate the relevant regions within the heavy chain CDRs and their corresponding antigen-binding sites. Subsequent structural analysis using PyMOL allowed the identification of potential mutations aimed at improving the binding affinity of Ipilimumab to CTLA-4 and provided insight into their three-dimensional conformations. The validity of the proposed mutations was assessed using the Swiss-Model server to ensure structural feasibility. Protein–protein interactions between the mutated antibody and CTLA-4 were evaluated through molecular docking using the HADDOCK server. Finally, the binding free energy (ΔG) of the antibody–antigen complex was calculated using the PRODIGY server, while the impact of the mutations on antibody stability was analyzed with the I-Mutant server. This integrative computational approach enabled the rational design and in silico evaluation of Ipilimumab variants with potentially enhanced affinity and stability.
Results: The results obtained from PyMOL analyses indicate that mutating the Asparagine (N) residue at position 57 in the CDR2 of Ipilimumab’s heavy chain to Arginine (R) enhances the interaction between the antibody and the CTLA-4 antigen. Specifically, this mutation reduces the bond distance between the antibody and antigen from 3.5 Å to 2.2 Å, indicating a stronger and more stable interaction. To validate these findings, the structural and binding effects of the mutation were further assessed using Swiss-Model, HADDOCK, PRODIGY, and I-Mutant servers, confirming the improved binding affinity and stability of the mutated antibody.
Conclusion: In this study, computational site-directed mutagenesis of Ipilimumab was successfully applied to enhance its binding affinity to CTLA-4. The Asn57→Arg mutation in the CDR2 of the heavy chain reduced the antibody–antigen bond distance from 3.5 Å to 2.2 Å, strengthening the interaction. Structural validation using Swiss-Model confirmed the feasibility of the mutation, while molecular docking via HADDOCK demonstrated improved binding orientation. Binding free energy analysis with PRODIGY and stability prediction with I-Mutant further supported the enhanced affinity and structural robustness of the modified antibody. These findings highlight the utility of in silico approaches for rational antibody optimization and provide a framework for designing next-generation immunotherapeutics with superior specificity and efficacy against CTLA-4-mediated immune checkpoints.