Quantum chemical studies on the binding domain of SARS-CoV-2 S-protein: human ACE2 interface complex

A two-layer ONIOM(B3LYP/6-31G*:PM7) method is used to model the binding of several drug/drug-like molecules (L) at the SARS-CoV-2 S-protein: human ACE2 protein interface cavity. The selected molecules include a set of thirty-five ligands from the study of Smith and Smith which showed a high docking score in the range of -7.0 to -7.7 kcal/mol and another set of seven repurposing drugs, viz. favipiravir, remdesivir, EIDD, galidesivir, triazavirin, ruxolitinib, and baricitinib. The ONIOM model of the cavity (M) showed a highly polarized electron distribution along its top-to-bottom direction while Ls with lengths in the range 1.0 - 1.5 nm fitted well inside the cavity in a head-to-tail fashion to yield ML complexes. The ligands showed a large variation in the ONIOM-level binding energy (Eb), in the range -2.7 to -85.4 kcal/mol. The Eb of ML complexes better than -40.0 kcal/mol is observed for myricetin, fidarestat, protirelin, m-digallic acid, glucogallin, benserazide hydrochlorideseradie, remdesivir, tazobactum, sapropterin, nitrofurantoin, quinonoid, pyruvic acid calcium isoniazid, and aspartame, and among them the highest Eb -85.4 kcal/mol is observed for myricetin. A hydroxy substitution is suggested for the phenyl ring of aspartame to improve its binding behavior at the cavity, and the resulting ligand 43 showed the best Eb -84.5 kcal/mol. The ONIOM-level study is found to be effective for the interpretation of the noncovalent interactions resulting from residues such as arginine, histidine, tyrosine, lysine, carboxylate, and amide moieties in the active site and suggests rational design strategies for COVID-19 drug development.Communicated by Ramaswamy H. Sarma.

Chiral Detection of Glucose: An Amino Acid-Assisted Surface-Enhanced Raman Scattering Strategy Showing Opposite Enantiomeric Effects on SERS Signals

It is of considerable concern to establish chiral detection methods for revealing enantioselective interactions among chiral molecules. Surface-enhanced Raman scattering (SERS) spectroscopy is sensitive to molecular interaction due to bond variations. However, its application in chiral detection is underexplored. Inspired by the chiral selectivity toward glucose and amino acids in life, we herein propose a SERS strategy based on molecular interactions for the discrimination of d- and l-glucose (Glu) using chiral phenylalanine (Phe) decorated on gold nanoparticles as a chirality selector and Raman reporter. Interestingly, the SERS signal of l-Phe is enhanced by d-Glu but suppressed by l-Glu. In contrast, the SERS signal of d-Phe is increased by l-Glu but decreased by d-Glu. According to the above-observed intensity change (ΔI) of the SERS signal of Phe induced by Glu, it is easy to determine the chiral configurations (judged by the positive or negative sign of ΔI), enantiomeric excess (ee) values, and concentrations (estimated by the magnitude of ΔI) of Glu. Taking advantage of the high SERS enhancement and opposite enantiomeric effects on SERS signals, the proposed strategy enables enantiomeric discrimination at a low Glu concentration (10^-6 mol/L) and is further exerted for the noninvasive detection of d-/l-Glu in saliva samples. In contrast, the common chiroptical analysis tool of circular dichroism (CD) spectroscopy failed to directly detect Glu enantiomers.

Early assessment of chemotherapeutic response in hepatocellular carcinoma based on serum surface-enhanced Raman spectroscopy

In clinical practice, the transcatheter arterial chemoembolization (TACE) has been widely accepted as the first option for non-surgical hepatocellular carcinoma (HCC) treatment. However, patients with HCC often suffer from poor response to TACE therapy. This can be prevented if the chemotherapeutic response can be early and accurately assessed, which is essential to guide timely and rational management. In this study, the serum SERS technique was for the first time investigated as a potential prognostic tool for early assessment of HCC chemotherapeutic response. According to the SERS spectral analysis results, it is newly found that not only the absolute circulating nucleic acids and collagen levels in pre-therapeutic serum but also the changes in circulating nucleic acids and amino acids between pre-therapeutic and post-therapeutic serum are expected to be potential serum markers for HCC prognosis. By further applying chemometrics methods to establish prognostic models, excellent prognostic accuracies were achieved within only 3 days after TACE therapy. Thus, the proposed method is expected to provide guidance on timely and rational management of HCC to improve its survival rate.

A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

Insight into prognostics, diagnostics, and management strategies for SARS CoV-2

The foremost challenge in countering infectious diseases is the shortage of effective therapeutics. The emergence of coronavirus disease (COVID-19) outbreak has posed a great menace to the public health system globally, prompting unprecedented endeavors to contain the virus. Many countries have organized research programs for therapeutics and management development. However, the longstanding process has forced authorities to implement widespread infrastructures for detailed prognostic and diagnostics study of severe acute respiratory syndrome (SARS CoV-2). This review discussed nearly all the globally developed diagnostic methodologies reported for SARS CoV-2 detection. We have highlighted in detail the approaches for evaluating COVID-19 biomarkers along with the most employed nucleic acid- and protein-based detection methodologies and the causes of their severe downfall and rejection. As the variable variants of SARS CoV-2 came into the picture, we captured the breadth of newly integrated digital sensing prototypes comprised of plasmonic and field-effect transistor-based sensors along with commercially available food and drug administration (FDA) approved detection kits. However, more efforts are required to exploit the available resources to manufacture cheap and robust diagnostic methodologies. Likewise, the visualization and characterization tools along with the current challenges associated with waste-water surveillance, food security, contact tracing, and their role during this intense period of the pandemic have also been discussed. We expect that the integrated data will be supportive and aid in the evaluation of sensing technologies not only in current but also future pandemics.