COVID-19 has emerged as the most serious international pandemic in early 2020 and the lack of comprehensive knowledge in the recognition and transmission mechanisms of this virus hinders the development of suitable therapeutic strategies. The specific recognition during the binding of the spike glycoprotein (S protein) of coronavirus to the angiotensin-converting enzyme 2 (ACE2) in the host cell is widely considered the first step of infection. However, detailed insights on the underlying mechanism of dynamic recognition and binding of these two proteins remain unknown. In this work, molecular dynamics simulation and binding free energy calculation were carried out to systematically compare and analyze the receptor-binding domain (RBD) of six coronavirus’ S proteins. We found that affinity and stability of the RBD from SARS-CoV-2 under the binding state with ACE2 are stronger than those of other coronaviruses. The solvent-accessible surface area (SASA) and binding free energy of different RBD subunits indicate an “anchor-locker” recognition mechanism involved in the binding of the S protein to ACE2. Loop 2 (Y473-F490) acts as an anchor for ACE2 recognition, and Loop 3 (G496-V503) locks ACE2 at the other nonanchoring end. Then, the charged or long-chain residues in the β-sheet 1 (N450-F456) region reinforce this binding. The proposed binding mechanism was supported by umbrella sampling simulation of the dissociation process. The current computational study provides important theoretical insights for the development of new vaccines against SARS-CoV-2.

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Food‐derived angiotensin I‐converting enzyme (ACE) inhibitory peptides represent a potential source of new antihypertensive. However, their characteristics and binding mechanisms were not well understood. In this study, novel energy calculation and experimentation were combined to elucidate the characteristics and mechanisms of ACE inhibitory tripeptides. ACE inhibitory activity of all 8,000 tripeptides was investigated by in silico experiments. IC50 values of the five top‐rated tripeptides ranged from 5.86 to 21.84 μM. Five hundred top‐ranked tripeptides were chosen for detailed structure–activity analysis, and a significant preference for aromatic amino acids at both C‐ and N‐terminus was found. By binding free energy analysis of nine representative tripeptides via MM/GBSA, electrostatic energy was found to be the leading energy that contributed to the binding of ACE with its high affinity tripeptides. Besides, S355, V380, and V518, three residues positioned around the classical binding pockets of ACE, also played a key role in ACE's binding. Therefore, for tripeptides, their binding pockets in ACE were redefined. In conclusion, the characteristics of ACE inhibitory peptides were more deeply illustrated by the thorough analysis of all tripeptides. The energy analysis allows a better understanding of the binding mechanisms of ACE inhibitory peptides, which could be used to redesign the ACE inhibitors for stronger inhibitory activity.

Enzyme thermostability is an important parameter for estimating its industrial value. However, most naturally produced enzymes are incapable of meeting the industrial thermostability requirements. Software programs can be utilized to predict protein thermostability. Despite the fast-growing number of programs designed for this purpose; few provide reliable applicability because they do not account for thermodynamic weaknesses. Aspartic proteases are widely used in industrial processing; however, their thermostability is not able to meet the large-scale production requirements. In this study, through analyzing structural characteristics and modifying thermostability using prediction software programs, we improved the thermostability of pepsin, a representative aspartic protease. Based on the structural characteristics of pepsin and the experimental results of mutations predicted by several energy-based prediction software programs, it was found that the majority of pepsin’s thermodynamic weaknesses lie on its flexible regions on the surface. Using computational design, mutations were made based on the predicted sites of thermodynamic weakness. As a result, the half-lives of mutants D52N and S129A at 70°C were increased by 200.0 and 66.3%, respectively. Our work demonstrated that in the effort of improving protein thermostability, identification of structural weaknesses with the help of computational design, could efficiently improve the accuracy of protein rational design.