High-Throughput and Rapid Screening of Novel ACE Inhibitory Peptides from Sericin Source and Inhibition Mechanism by Using in Silico and in Vitro Prescriptions

Abstract

Several novel peptides with high ACE-I inhibitory activity were successfully screened from sericin hydrolysate (SH) by coupling in silico and in vitro approaches for the first time. Most screening processes for ACE-I inhibitory peptides were achieved through high-throughput in silico simulation followed by in vitro verification. QSAR model based predicted results indicated that the ACE-I inhibitory activity of these SH peptides and six chosen peptides exhibited moderate high ACE-I inhibitory activities (log IC values: 1.63-2.34). Moreover, two tripeptides among the chosen six peptides were selected for ACE-I inhibition mechanism analysis which based on Lineweaver-Burk plots indicated that they behave as competitive ACE-I inhibitors. The C-terminal residues of short-chain peptides that contain more H-bond acceptor groups could easily form hydrogen bonds with ACE-I and have higher ACE-I inhibitory activity. Overall, sericin protein as a strong ACE-I inhibition source could be deemed a promising agent for antihypertension applications.

Two novel potent ACEI peptides isolated from Pinctada fucata meat hydrolysates using in silico analysis: identification, screening and inhibitory mechanisms

“… 43 Some peptides were reported to bind with these amino acid residues in active pocket. For example, VVSLSIPR bound with His353, Tyr523, and Glu384; 44 SSR bound with Tyr523, Glu384, and His387; 45 FHAPWK bound with Glu384 and His353. 46 However, these peptides belong to competitive inhibitors.…”

Research progress in the screening and evaluation of umami peptides

“…Because of their palatability, complicated taste, and multiple biological functions (Chen, Pan, et al., 2021; Hao et al., 2020; Shen et al., 2021; Zhang, Pan, et al., 2021), the discovery and identification of new umami peptides from foodborne proteins have attracted increasing attention (Li et al., 2020). The classical method of using column chromatography to separate and purify umami peptides, however, is usually time‐consuming and expensive (Sun et al., 2017), which has limited the development of industrialization of umami peptides. Recently, computer technology, such as molecular docking (Yu et al., 2021; Zhao et al., 2021; Zhu et al., 2021) and machine learning (Charoenkwan, Yana, Nantasenamat, et al., 2020), has been applied to the screening and identification of umami peptides to promote their development.…”

Novel Angiotensin‐Converting Enzyme Inhibitory Peptides Derived from Oncorhynchus mykiss Nebulin: Virtual Screening and In Silico Molecular Docking Study

“…The online and offline computational tools could help to predict and analyze the peptides on the basis of their structure and the interaction between the peptides and ACE. With these advanced prediction tools, the in silico method is simpler, cost‐effective, and faster to respond (Sun et al., ; Zhou et al., ).…”