Computer-Assisted Design and In Silico Screening of Brain-Targeting Peptide Prodrugs




Prokai, Laszlo
De La Cruz, Daniel
Prokai-Tatrai, Katalin
Nguyen, Vien


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Purpose: Prodrug approaches promise overcoming obstacles for the brain delivery of therapeutically useful small neuropeptides. However, their rational design and large-scale computational screening have been elusive. Recently, we have introduced a method that not only enhances a prodrug's access to the brain through lipoamino acid residues (LAAs), but also ensures site-specific bioactivation by prolyl oligopepdidase (POP) selectively expressed in neuronal milieu. Using thyrotropin-releasing hormone (TRH) as a model, we show a computer-aided strategy to assist the design and optimization of brain-targeting prodrugs for small neuropeptides. Methods: Using SCIGRESS molecular modeling and AutoDock Vina docking, lipophilicity (indicated by calculated logarithm of octanol/water partition coefficient, clogP) and binding affinity to POP (expressed as free energy change, ΔG) were assessed in silico for a virtual library of prodrugs. A computational model of human TRH receptor (hTRHr-1) was also adapted to provide proof of concept for the prodrug principle. Results: Prodrugs of a virtual library docked computationally to POP's active site displayed ΔG values comparable to that of a co-crystallized POP ligand, but with subtle differences based on the configuration of the LAAs and the POP-sensitive linker. Expectedly, they showed no affinity for binding to hTRHr-1's active site. Co-optimization based on clogP has allowed for the selection of top TRH prodrug candidates for further in vitro and in vivo evaluations. Conclusion: We have created a comprehensive in silico workflow to aid the rational design and large-scale virtual screening of brain-targeting neuropeptide prodrugs.