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ABSTRACT Phage display remains a powerful high‐throughput selection platform for the in vitro evolution of biomolecules. Random peptide libraries have been successfully utilized to discover a diverse range of peptides with diagnostic and therapeutic applications. Nevertheless, intrinsic limitations—such as sparse sampling of the vast theoretical sequence space, compositional bias in the naïve library, and the unintended enrichment of nonspecific binders during biopanning—can skew selection outcomes. In the current review, we examine the dynamics of phage display selection through the conceptual frameworks of sequence space and fitness landscapes. Unlike some classical directed evolution approaches, the fitness landscape in phage display is not only shaped by the binding affinity of displayed peptides for the target but also immensely influenced by the biological nature of the bacteriophage itself. These properties can distort the landscape during iterative cycles of selection and amplification. Technical refinements in library design and construction, utilizing next‐generation sequencing (NGS) to identify enriched sequence clusters or recurring motifs in biopanning outputs, building smart, motif‐guided secondary libraries to narrow the search toward high‐fitness regions of peptide space, avoiding repetitious selection rounds, and applying sophisticated computational tools to decode large NGS datasets can significantly enhance the statistical chance of uncovering rare, high‐affinity, target‐specific peptides. Integrating these strategies into the phage display workflow enables researchers to more effectively explore the functional regions of sequence space and facilitates a more efficient, targeted navigation of the fitness landscape, reorienting phage display selection from a blind, largely random search into a guided, more informed journey.
Bakhshinejad et al. (Wed,) studied this question.