FIGURE 2.

Strategies for rational peptide design. (A). The structure of target protein and its interacting partners is first identified, and interface features are extracted from the complex. (B) Machine learning models can use these interface features to perform constrained peptide design for that target. (C) Alternatively, using these interface features, bioinformatics tools can significantly narrow down the sequence search space. (D) Peptide docking tools or AI tools can then be used to predict binding in the now reduced set of candidate sequences. Competitive binding study or high throughput binding affinity measurement can rank order the selected sequences from the docking step. (E) In some cases, orthogonal modeling capable of predicting kinetic and/or thermodynamic properties can further narrow down the number of possible sequences selected for experimental determination (adapted from (Chang and Perez, 2022b)). (F) Finally, experimental characterization and validation can be carried out for the now manageable number of predicted peptide binders.