Raina Rhoades

Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure–function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental Bos taurus structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures.

Severe Acute respiratory syndrome coronavirus (SARS-CoV-1) attaches to the host cell surface to initiate the interaction between the receptor-binding domain (RBD) of its spike glycoprotein (S) and the human Angiotensin-converting enzyme (hACE2) receptor. SARS-CoV-1 mutates frequently because of its RNA genome, which challenges the antiviral development. Here, we performed computational saturation mutagenesis of the S protein of SARS-CoV-1 to identify the residues crucial for its functions. We used the structure-based energy calculations to analyze the effects of the missense mutations on the SARS-CoV-1 S stability and the binding affinity with hACE2. The sequence and structure alignment showed similarities between the S proteins of SARS-CoV-1 and SARS-CoV-2. Interestingly, we found that target mutations of S protein amino acids generate similar effects on their stabilities between SARS-CoV-1 and SARS-CoV-2. For example, G839W of SARS-CoV-1 corresponds to G857W of SARS-CoV-2, which decrease the stability of their S glycoproteins. The viral mutation analysis of the two different SARS-CoV-1 isolates showed that mutations, T487S and L472P, weakened the S-hACE2 binding of the 2003-2004 SARS-CoV-1 isolate. In addition, the mutations of L472P and F360S destabilized the 2003-2004 viral isolate. We further predicted that many mutations on N-linked glycosylation sites would increase the stability of the S glycoprotein. Our results can be of therapeutic importance in the design of antivirals or vaccines against SARS-CoV-1 and SARS-CoV-2. Author Summary Severe acute respiratory syndrome coronavirus (SARS-CoV-1) is an RNA virus that undergoes frequent mutations, which may result in more virulent SARS-CoV-1 variants. To prevent another pandemic in the future, scientists must understand the mechanisms of viral mutations and predict if any variants could become a dominant. The infection of SARS-CoV-1 in cells is largely depending on the interactions of the viral Spike (S) and human angiotensin-converting enzyme 2 (hACE2). We applied a computational method to predict S missense mutations that will make SARS-CoV-1 more virulent. We are interested in the variants that can change SARS-CoV-1 spike protein stability and/or change the virus-receptor interactions. We mutated each residue of SARS-CoV-1 spike to all possible amino acids; we calculated the differences between the folding energy and binding energy of each variant and the wildtype and identified the target S mutations with significant effects on protein stability and protein-protein interaction. We found some viral mutations could destabilize S and weaken S-hACE2 binding of SARS-CoV-1 isolate. Our results show that the computational saturation mutagenesis is a reliable approach in the analysis and prediction of missense mutations.

The words “National Institutes of Health/ National Institute of Allergy and Infectious Diseases [1SC1AI112785-05, R01DE028583-01]” were included in the funding section of this paper by mistake. These should have been removed, so that the funding section reads: “Howard University startup funds [U100193]; National Science Foundation [DBI 2000296, IIS 1924092]; National Institute on Minority Health and Health Disparities of the National Institutes of Health [2U54MD007597].” This has been corrected online.

The spike (S) glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the binding to the permissive cells. The receptor-binding domain (RBD) of SARS-CoV-2 S protein directly interacts with the human angiotensin-converting enzyme 2 (ACE2) on the host cell membrane. In this study, we used computational saturation mutagenesis approaches, including structure-based energy calculations and sequence-based pathogenicity predictions, to quantify the systemic effects of missense mutations on SARS-CoV-2 S protein structure and function. A total of 18 354 mutations in S protein were analyzed, and we discovered that most of these mutations could destabilize the entire S protein and its RBD. Specifically, residues G431 and S514 in SARS-CoV-2 RBD are important for S protein stability. We analyzed 384 experimentally verified S missense variations and revealed that the dominant pandemic form, D614G, can stabilize the entire S protein. Moreover, many mutations in N-linked glycosylation sites can increase the stability of the S protein. In addition, we investigated 3705 mutations in SARS-CoV-2 RBD and 11 324 mutations in human ACE2 and found that SARS-CoV-2 neighbor residues G496 and F497 and ACE2 residues D355 and Y41 are critical for the RBD–ACE2 interaction. The findings comprehensively provide potential target sites in the development of drugs and vaccines against COVID-19.