Abstract

Protein–protein interactions are a fundamental component of most biological processes in living organisms. Therefore, enhancing these molecular interactions is of major importance in the life sciences field. In this paper we present the redesign of a protein–protein complex using single-elitist and multi-objective evolutionary algorithms. We focus on a structural complex composed of a bacteriophage protein interacting with a bacterial protein, emphasizing the interaction zone, which is defined by the closest distance between residues from proteins and comprises thirty-eight positions. Our approach fuses physics-based energy calculations with data-driven models to maximize the effectiveness of the search process. By simultaneously minimizing interaction energy and maximizing the log-likelihood ratio, the proposed algorithms show a balance between thermodynamic stability and biological sequence plausibility. This hybrid strategy guides the mutation of specific residues, enabling the identification of optimal solutions that are both physically robust and evolutionarily relevant. Results demonstrate that the evolved Pareto optimal set exhibits a significant improvement in binding affinity, with mean interface energy decreasing from +32 to −60 REU (Rosetta Energy Units). Furthermore, the analysis identifies key conserved residue positions, validating the capability of the framework to produce energetically favorable and biologically consistent protein designs.

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