{'fraction_non_alphanumeric': 0.015160022459292532, 'fraction_numerical': 0.0, 'mean_word_length': 5.933852140077821, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Stepwise Strategy to Identify Thrombin as a Hydrolytic Substrate for Nattokinase.

Nattokinase (NK) is a serine protease with a potent thrombolytic activity that possesses multiple cardiovascular disease (CVD) preventative and treatment activities. In light of its advanced beneficial cardiovascular effects and its nature as a serine protease, characterizing its biological substrates is essential for informing and ultimately delineating the molecular mechanism of its thrombolytic and anticoagulant activities that will unlock the powerful strategic design of effective therapies for CVDs. Given the efficacy of NK to break the vicious loop between inflammation, oxidative stress, and thrombosis, and the extensive role of thrombin in the loop, a stepwise computational strategy was developed to investigate the cleavage events of NK, including both a protein-protein complex model for protein substrate recognition and a protease-peptide complex model for the cleavage site identification, whereby their contact region was sited to allow for the prediction of the corresponding cleavage site that was successfully verified by both mass spectrometry (MS)-based N-terminal sequencing and various functional assays. Collectively, thrombin was predicted and identified to be a novel biological substrate of NK, which expanded the comprehensive antithrombus mechanism of NK via breaking the vicious loop between inflammation, oxidative stress, and thrombosis. This study not only provided insight into the interaction characteristics between NK and its hydrolytic substrate for a better understanding toward its catalytic mechanism but also developed a comprehensive computational strategy to elucidate the proteolytic targets of NK for the breakthrough of feature drug development.