A reactive center loop-based prediction platform to enhance the design of therapeutic SERPINs.

Serine proteases are essential for many physiological processes and require tight regulation by serine protease inhibitors (SERPINs). A disturbed SERPIN–protease balance may result in disease. The reactive center loop (RCL) contains an enzymatic cleavage site between the P1 through P1’ residues that controls SERPIN specificity. This RCL can be modified to improve SERPIN function; however, a lack of insight into sequence–function relationships limits SERPIN development. This is complicated by more than 25 billion mutants needed to screen the entire P4 to P4’ region. Here, we developed a platform to predict the effects of RCL mutagenesis by using α1-antitrypsin as a model SERPIN. We generated variants for each of the residues in P4 to P4’ region, mutating them into each of the 20 naturally occurring amino acids. Subsequently, we profiled the reactivity of the resulting 160 variants against seven proteases involved in coagulation. These profiles formed the basis of an in silico prediction platform for SERPIN inhibitory behavior with combined P4 to P4’ RCL mutations, which were validated experimentally. This prediction platform accurately predicted SERPIN behavior against five out of the seven screened proteases, one of which was activated protein C (APC). Using these findings, a next-generation APC-inhibiting α1-antitrypsin variant was designed (KMPR/RIRA; / indicates the cleavage site). This variant attenuates blood loss in an in vivo hemophilia A model at a lower dosage than the previously developed variant AIKR/KIPP because of improved potency and specificity. We propose that this SERPIN-based RCL mutagenesis approach improves our understanding of SERPIN behavior and will facilitate the design of therapeutic SERPINs.