The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure‐based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen‐binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein–protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high‐throughput computer design of antibody mimetics and experimental realization, offering a forward‐looking perspective into the field and the promises it holds to biotechnology.

Protein–protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to ex vivo biotechnology, such as the development of therapeutic monoclonal antibodies, the engineering of enzymes for industrial biocatalysis, the development of biosensors for disease detection, and the assembly of artificial protein complexes for drug screening. Therefore, predicting the strength of their association allows for understanding the molecular mechanisms and ultimately controlling them. We devised a machine learning ensemble model that uses Rosetta-based quantities to predict binding free energies of protein–protein complexes with accuracy rivaling both computationally demanding methods and currently available ML/DL tools. The method was encoded into an application Python pipeline named PBEE, which stands for Protein Binding Energy Estimator, allowing a rapid calculation of the absolute binding free energies of protein complexes from their PDB coordinates.

The main-protease (M^pro) catalyzes a crucial step for the SARS-CoV-2 life cycle. The recent SARS-CoV-2 presents the main protease (M^CoV2_pro) with 12 mutations compared to SARS-CoV (M^CoV1_pro). Recent studies point out that these subtle differences lead to mobility variances at the active site loops with functional implications. We use metadynamics simulations and a sort of computational analysis to probe the dynamic, pharmacophoric and catalytic environment differences between the monomers of both enzymes. So, we verify how much intrinsic distinctions are preserved in the functional dimer of M^CoV2_pro, as well as its implications for ligand accessibility and optimized drug screening. We find a significantly higher accessibility to open binding conformers in the M^CoV2_pro monomer compared to M^CoV1_pro. A higher hydration propensity for the M^CoV2_pro S2 loop with the A46S substitution seems to exercise a key role. Quantum calculations suggest that the wider conformations for M^CoV2_pro are less catalytically active in the monomer. However, the statistics for contacts involving the N-finger suggest higher maintenance of this activity at the dimer. Docking analyses suggest that the ability to vary the active site width can be important to improve the access of the ligand to the active site in different ways. So, we carry out a multiconformational virtual screening with different ligand bases. The results point to the importance of taking into account the protein conformational multiplicity for new promissors anti M^CoV2_pro ligands. We hope these results will be useful in prospecting, repurposing and/or designing new anti SARS-CoV-2 drugs. Communicated by Ramaswamy H. Sarma

Arboviruses represent a contemporary global challenge, prompting coordinated efforts from health organizations and governments worldwide. Dengue, chikungunya, and Zika viruses have become endemic in the tropics, resulting in the so-called “triple arbovirus epidemic”. These viruses are transmitted typically through the bites of infected mosquitoes, especially A. aegypti and A. albopictus. These mosquito species are distributed across all continents and exhibit a high adaptive capacity in diverse environments. When combined with unplanned urbanization, uncontrolled population growth, and international travel—the so-called “triad of the modern world”—the maintenance and spread of these pathogens to new areas are favored. This review provides updated information on vaccine candidates targeting dengue, chikungunya, and Zika viruses. Additionally, we discuss the challenges, perspectives, and issues associated with their successful production, testing, and deployment within the context of public health.