SARS-CoV-2 has been spreading around the world for the past year. Recently, several variants such as B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma), which share a key mutation N501Y on the receptor-binding domain (RBD), appear to be more infectious to humans. To understand the underlying mechanism, we used a cell surface-binding assay, a kinetics study, a single-molecule technique, and a computational method to investigate the interaction between these RBD (mutations) and ACE2. Remarkably, RBD with the N501Y mutation exhibited a considerably stronger interaction, with a faster association rate and a slower dissociation rate. Atomic force microscopy (AFM)-based single-molecule force microscopy (SMFS) consistently quantified the interaction strength of RBD with the mutation as having increased binding probability and requiring increased unbinding force. Molecular dynamics simulations of RBD–ACE2 complexes indicated that the N501Y mutation introduced additional π-π and π-cation interactions that could explain the changes observed by force microscopy. Taken together, these results suggest that the reinforced RBD–ACE2 interaction that results from the N501Y mutation in the RBD should play an essential role in the higher rate of transmission of SARS-CoV-2 variants, and that future mutations in the RBD of the virus should be under surveillance.
The recent development of chemical and bio-conjugation techniques allows for the engineering of various protein polymers. However, most of the polymerization process is difficult to control. To meet this challenge, we develop an enzymatic procedure to build polyprotein using the combination of a strict protein ligase OaAEP1 ( Oldenlandia affinis asparaginyl endopeptidases 1) and a protease TEV (tobacco etch virus). We firstly demonstrate the use of OaAEP1-alone to build a sequence-uncontrolled ubiquitin polyprotein and covalently immobilize the coupled protein on the surface. Then, we construct a poly-metalloprotein, rubredoxin, from the purified monomer. Lastly, we show the feasibility of synthesizing protein polymers with rationally-controlled sequences by the synergy of the ligase and protease, which are verified by protein unfolding using atomic force microscopy-based single-molecule force spectroscopy (AFM-SMFS). Thus, this study provides a strategy for polyprotein engineering and immobilization.
Coronavirus disease-19 (COVID-19) is spreading around the world for the past year. Enormous efforts have been taken to understand its mechanism of transmission. It is well established now that the SARS-CoV-2 receptor-binding domain (RBD) of the spike protein binds to the human angiotensin-converting enzyme 2 (ACE2) as its first step of entry. Being a single-stranded RNA virus, SARS-CoV-2 is evolving rapidly. Recently, two variants, B.1.1.7 and B.1.351, both with a key mutation N501Y on the RBD, appear to be more infectious to humans. To understand its mechanism, we combined kinetics assay, single-molecule technique, and computational method to compare the interaction between these RBD (mutations) and ACE2. Remarkably, RBD with the N501Y mutation exhibited a considerably stronger inter-action characterized from all these methodologies, while the other two mutations from B.1.351 contributed to a less effect. Surface plasmon resonance and fluorescence-activated cell scan (FACS) assays found that both RBD mutations are of higher binding affinity to ACE2 than the wild type. In addition, atomic force microscopy-based single-molecule force microscopy quantify their strength on living cells, showing a higher binding probability and unbinding force for both mutations. Finally, Steered Molecular Dynamics (SMD) simulations on the dissociation of RBD-ACE2 complexes revealed the possible structural details for the higher force/interaction. Taking together, we suggested that the stronger inter-action from N501Y mutation in RBD should play an essential role in the higher transmission of COVID-19 variants.
Protein immobilization is an essential method for both basic and applied research for protein, and covalent, site-specific attachment is the most desirable strategy. Classic methods typically rely on a heterobifunctional cross-linker, such as N-hydroxysuccinimide (NHS)-linker-maleimide, or a similar two-step process. It utilizes the amino-reactive NHS and the thiolreactive maleimide to conjugate protein to the solid support. However, NHS as a chemical is susceptible to hydrolysis during storage and handling, and maleimide reacts nonspecifically with all cysteines available in the protein, leading to an inconsistent result. To solve these problems, we have developed a method by combining a strain-promoted azide-alkyne cycloaddition (SPAAC) click reaction and an OaAEP1 (C247A)-based enzymatic ligation. The method was demonstrated by the successful immobilization of enhanced green fluorescent protein (eGFP), which was visualized by fluorescent imaging. Moreover, the correct folding and stability of the immobilized protein were verified by atomic force microscopy-based single-molecule force spectroscopy (AFM-SMFS) measurement with a high success rate (89%). Finally, the strength of the 1,2,3-triazole linkage from the azide-dibenzocyclooctyne (DBCO)-based SPAAC reaction was quantified with an ultrahigh rupture force >1.7 nN. Thus, this stable, efficient, and site-specific immobilization method can be used for many challenging systems, especially SMFS studies.
Code review is the process of manual inspection on the revision of the source code in order to find out whether the revised source code eventually meets the revision requirements. However, manual code review is time-consuming, and automating such the code review process will alleviate the burden of code reviewers and speed up the software maintenance process. To construct the model for automatic code review, the characteristics of the revisions of source code (i.e., the difference between the two pieces of source code) should be properly captured and modeled. Unfortunately, most of the existing techniques can easily model the overall correlation between two pieces of source code, but not for the “difference” between two pieces of source code. In this paper, we propose a novel deep model named DACE for automatic code review. Such a model is able to learn revision features by contrasting the revised hunks from the original and revised source code with respect to the code context containing the hunks. Experimental results on six open source software projects indicate by learning the revision features, DACE can outperform the competing approaches in automatic code review.
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