Secondary Structures of MERS-CoV, SARS-CoV, and SARS-CoV-2 Spike Proteins Revealed by Infrared Vibrational Spectroscopy.

All coronaviruses are characterized by spike glycoproteins whose S1 subunits contain the receptor binding domain (RBD). The RBD anchors the virus to the host cellular membrane to regulate the virus transmissibility and infectious process. Although the protein/receptor interaction mainly depends on the spike's conformation, particularly on its S1 unit, their secondary structures are poorly known. In this paper, the S1 conformation was investigated for MERS-CoV, SARS-CoV, and SARS-CoV-2 at serological pH by measuring their Amide I infrared absorption bands. The SARS-CoV-2 S1 secondary structure revealed a strong difference compared to those of MERS-CoV and SARS-CoV, with a significant presence of extended ß-sheets. Furthermore, the conformation of the SARS-CoV-2 S1 showed a significant change by moving from serological pH to mild acidic and alkaline pH conditions. Both results suggest the capability of infrared spectroscopy to follow the secondary structure adaptation of the SARS-CoV-2 S1 to different environments.

The Study of Metal–Carbonyl Complexes by Means of Computational IR Spectra Analysis: A Remote Didactic Approach Based on Chemical Thinking

We describe a remote pedagogical approach based on chemical thinking to study metal-carbonyl complexes by analyzing simulated IR spectra. The proposed approach, implemented due to the COVID-19 pandemic, can be employed in classrooms that have very limited laboratory equipment for evaluating toxic metal-carbonyl compounds, as well as for synthesizing compounds that have not been reported . The method, consisting of a class lecture accompanied by a remote computational activity , aims to provide students with the ability to assemble concepts from different fields, such as organometallic chemistry and analytical chemistry, while taking advantage of computational methods to answer higher level questions. We evaluated whether analyzing the nature of M-CO bonding was appropriate for achieving these educational goals. Octahedral compounds of the M(CO)(6) and M(CO)(4)L-2 type, bearing a variety of metal centers (M = Cr, Mo, W, V, Mn and Fe) and ligands (L = phosphines and phosphites), as well as bimetallic Fe-2(CO)(9), were compared, showing how these modifications affect M-CO bonding. After the didactic session, attended by second-year and upper-division students of Facultad de Quimica at UNAM, an evaluation and survey showed that students improved their understanding of the subject when they obtained and visualized IR spectra, also exhibiting greater confidence and enthusiasm for addressing challenging topics. The combination of computational results, spectroscopic analysis, and organometallic theory represents an efficient and clear procedure for implementing chemical thinking, regardless of the difficulties posed by the COVID-19 pandemic.

Methods of optical spectroscopy in detection of virus in infected samples: A review.

Due to the recent COVID-19 pandemic that occurred worldwide since 2020, scientists and researchers have been studying methods to detect the presence of the virus causing COVID-19 disease, namely SARS-CoV-2. Optical spectroscopy is a method that employs the interaction of light in detecting virus on samples. It is a promising method that might help in detecting the presence of SARS-CoV-2 in samples. Four optical spectroscopy methods are discussed in this paper: ultraviolet (UV), infrared (IR), Raman spectroscopy and fluorescence spectroscopy. UV and IR spectroscopy differ in wavelength range (less than 400 nm for UV, more than 700 nm for IR). Raman spectroscopy involves shift in wavelength due to scattering of light. Fluorescence spectroscopy involves difference in wavelength between absorbed and emitted light due to vibrational relaxation. These four methods had been proven to differentiate healthy samples from virus-infected samples. UV spectroscopy is useful in determining presence of virus based on 260 nm/280 nm absorbance ratio. However, its usefulness is limited due to its destructive properties on virus at sufficiently high intensity. Meanwhile, IR spectroscopy has becoming popular in studies involving virus samples. Mid-infrared (MIR) spectroscopy is most commonly used among IR spectroscopy as it usually provides useful information directly from spectral data. Near infrared (NIR) spectroscopy is also used in studying virus samples, but additional methods such as principal component analysis (PCA) and partial least squares (PLS) are required to process raw spectral data and to identify molecules based on spectral peaks. On the other hand, Raman spectroscopy is useful because spectral data can be analyzed directly in identifying vibrational modes of specific molecules in virus samples. Fluorescence spectroscopy relies on interaction between viral particles and fluorescent tags for the detection of virus based on improvement or quenching of fluorescent signal. Due to non-invasive properties of virus samples, IR, Raman and fluorescence spectroscopy will be used more often in future studies involving virus detection in infected samples.

Evaluation of temperature resistance of non chemical crosslinked double-tailed hydrophobically associating polymer fracturing fluid

The global spread of COVID-19 continues, industrial raw material production is being tested, and the fracturing cost of oil and gas fields continues to rise, posing new challenges to polymer fracturing fluids. A new hydrophobic association polymer PDMA1 with a double tailed monomer structure was synthesized inside this study. Fourier infrared spectroscopy, Electron microscope scanning, Fluorescence spectroscopy, and polymer viscoelasticity were used to investigate the polymer's basic properties. Finally, using molecular dynamics simulation tools, the network structure of PDMA1 was discovered to be more temperature resistant than that of HPAM. PDMA1 has larger hydrodynamic dimensions than HPAM at the same temperature, its radius of gyration is more than HPAM, and its viscosity is greater than HPAM under the same conditions. This provides an additional avenue of investigation for temperature-resistant hydrophobically associating polymers.

Deep Learning Enabling Analysis of Exhaled Breath Using Fourier Transform Spectroscopy in the Mid-Infrared

Exhaled breath analysis is a promising noninvasive method for rapid diagnosis of diseases by detecting different types of volatile organic compounds (VOCs) that are used as biomarkers for early detection of various diseases such as lung cancer, diabetes, anemias, etc... and more recently COVID-19. Infrared spectroscopy seems to be a promising method for VOCs detection due to its ease of use, selectivity, and existence of compact low-cost devices. In this work, the use of Fourier transforms infrared (FTIR) spectrometer to analyze breath samples contained in a gas cell is investigated using deep learning and taking into account the practical performance limits of the spectrometer. Synthetic spectra are generated using infrared gas spectra databases to emulate real spectra resulted from a breath sample and train the neural network model (NNM). The dataset is generated in the spectral range of 2000 cm-1 to 6500 cm-1 and assuming a light-gas interaction length of 5 meters. The FTIR device performance is assumed with a signal-to-noise ratio (SNR) of 20,000:1 and a spectral resolution of 40 cm-1. The proposed NNM contains a locally connected and 4 fully connected layers. The concentrations of 9 biomarker gases in the exhaled breath are predicted with r2 score higher than 0.93, including carbon dioxide, water vapor, acetone, ethene, ammonia, methane, carbonyl sulfide, carbon monoxide and acetaldehyde demonstrating the possibility of detection. © 2021 IEEE.

The Influence of UV Light on Photodegradation of Acetylsalicylic Acid.

Photodegradation of the aqueous solutions of acetylsalicylic acid, in the absence (ASA) and the presence of excipients (ASE), is demonstrated by the photoluminescence (PL). A shift of the PL bands from 342 and 338 nm to 358 and 361-397 nm for ASA and ASE in solid state and as aqueous solutions was reported. By exposure of the solution of ASA 0.3 M to UV light, a decrease in the PL band intensity was highlighted. This behavior was revealed for ASA in the presence of phosphate buffer (PB) having the pH equal to 6.4, 7, and 8 or by the interaction with NaOH 0.3 M. A different behavior was reported in the case of ASE. In the presence of PB, an increase in the intensity of the PL band of ASE simultaneously with a change of the ratio between the intensities of the bands at 361-364 and 394-397 nm was highlighted. The differences between PL spectra of ASA and ASE have their origin in the presence of salicylic acid (SAL). The interaction of ASE with NaOH induces a shift of the PL band at 405-407 nm. Arguments for the reaction of ASA with NaOH are shown by Raman scattering and FTIR spectroscopy.

AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2.

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE2 and the spike glycoprotein (S protein) of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines, yet the dynamic nature of these interactions in fluctuating surroundings is very challenging to probe by those structure determination techniques requiring the structures of samples to be fixed. Here we demonstrate, by a proof-of-concept simulation of infrared (IR) spectra of S protein and hACE2, that time-resolved spectroscopy may monitor the real-time structural information of the protein-protein complexes of interest, with the help of machine learning. Our machine learning protocol is able to identify fine changes in IR spectra associated with variation of the secondary structures of S protein of the coronavirus. Further, it is three to four orders of magnitude faster than conventional quantum chemistry calculations. We expect our machine learning protocol would accelerate the development of real-time spectroscopy study of protein dynamics.