Absolute Configuration of Chiral Molecules by VCD
This week in the world of BioTools:
SSSCPreds: Deep Neural Network-Based Software for the Prediction of Conformational Variability and Application to SARS-CoV-2
Hiroshi Izumi*, Laurence A. Nafie, and Rina K. Dukor
ACS Omega 2020, 5, 47, 30556-30567 (Article)
Amino acid mutations that improve protein stability and rigidity can accompany increases in binding affinity. Therefore, conserved amino acids located on a protein surface may be successfully targeted by antibodies. The quantitative deep mutational scanning approach is an excellent technique to understand viral evolution, and the obtained data can be utilized to develop a vaccine. However, the application of the approach to all of the proteins in general is difficult in terms of cost. To address this need, we report the construction of a deep neural network-based program for sequence-based prediction of supersecondary structure codes (SSSCs), called SSSCPrediction (SSSCPred). Further, to predict conformational flexibility or rigidity in proteins, a comparison program called SSSCPreds that consists of three deep neural network-based prediction systems (SSSCPred, SSSCPred100, and SSSCPred200) has also been developed. Using our algorithms we calculated here shows the degree of flexibility for the receptor-binding motif of SARS-CoV-2 spike protein and the rigidity of the unique motif (SSSC: SSSHSSHHHH) at the S2 subunit and has a value independent of the X-ray and Cryo-EM structures. The fact that the sequence flexibility/rigidity map of SARS-CoV-2 RBD resembles the sequence-to-phenotype maps of ACE2-binding affinity and expression, which were experimentally obtained by deep mutational scanning, suggests that the identical SSSC sequences among the ones predicted by three deep neural network-based systems correlate well with the sequences with both lower ACE2-binding affinity and lower expression. The combined analysis of predicted and observed SSSCs with keyword-tagged datasets would be helpful in understanding the structural correlation to the examined system.
The minimum chemical modification that can be incorporated into an organic molecule is the replacement of a hydrogen atom for a deuterium atom. This change is not altering the pharmacological properties of a molecule, although it provides the possibility of making specific spectroscopic evaluations. Thus, in the present study, we explore how a stereogenic center is influenced by such an isotopic labeling. The studies were conducted on both enantiomers of flavanone (1 and 2) which is the parent molecule of a large group of pharmacologically active natural occurring secondary metabolites. Flavanone comprised 12 carbon atoms forming two benzene rings, a carbonyl group, an ethereal oxygen atom, a methylene group, and only one C–H stereogenic center, so it seems to be an ideal candidate for such studies. Density functional theory (DFT) calculations were used for the accurate prediction of vibrational circular dichroism (VCD) spectra of (R)‐(3) and (S)‐flavanone‐2‐d (4), of (R)‐(5) and (S)‐flavanone‐3,3‐d2 (6), and of (R)‐(7) and (S)‐flavanone‐2′,3′,4′,5′,6′‐d5 (8). To gain compounds that provide experimental VCD spectra for comparative purposes, the calculated spectra of both enantiomers of the corresponding flavanones, obtained after HPLC separation of the racemates by means of a chiral column, were contrasted, thereby revealing excellent agreements when using the CompareVOA software. In addition, the VCD spectra of both unlabeled enantiomeric flavanones (1 and 2) were also compared to the labeled molecules, revealing that the VCD spectra show significant variations induced by the deuterium incorporation.
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