Computed optical spectra of SARS-CoV-2 proteins

Ligand fishing approach to explore Amaryllidaceae alkaloids as potential antiviral candidates targeting SARS-CoV-2 Nsp4

Ligand fishing, also described as affinity-based assay, represents a convenient and efficient approach to separate potential ligands from complex matrixes or chemical libraries. This approach contributes to the identification of lead compounds that can bind to a specific target. In the context of COVID-19, the search for novel therapeutic agents is crucial. Small molecule-based antiviral drugs, such as Amaryllidaceae alkaloids, have been described as potential candidates because they can inhibit RNA viruses. Among various SARS-CoV-2 proteins, Nsp3, Nsp4, and Nsp6 play a crucial role in the pathogenicity of the virus and are attractive targets for developing COVID-19 treatments. These proteins are responsible for the replication/transcription complex (RTC) within double-membrane vesicles (DMVs), and their inhibition disrupts the virus's infectious cycle. Herein, we have successfully expressed and immobilized the SARS-CoV-2 Nsp4 protein on magnetic beads (Nsp4-MBs) and employed a ligand fishing assay to screen a collection of ten Amaryllidaceae-based alkaloids and applied to Hippeastrum aulicum extract. Remarkably, four out of ten alkaloids, namely 2-α-7-dimethoxyhomolycorine (6), haemanthamine (5), albomaculine (8), and tazettine (9), exhibited selective affinities for Nsp4. Albomaculine (8) and haemanthamine (5) were also identified from extract by the affinity assay. These findings highlight the potential of these alkaloids as model compounds for future drug discovery studies aimed at developing therapeutic interventions against SARS-CoV-2 infections.

Conformational characterization of the mammalian-expressed SARS-CoV-2 recombinant receptor binding domain, a COVID-19 vaccine

The COVID-19 pandemic has caused a large number of diseases worldwide. There are few vaccines to constrain this disease and the value of them is high. In this sense, the antigens of the vaccine platform Soberana, the receptor binding domain from SARS-CoV-2 Spike protein, both the monomeric (mRBD) and dimeric (dRBD) forms, have been developed. This study encompassed several analyses by different techniques like circular dichroism (CD), fluorescence spectroscopy (FS) and Gel Filtration- High Performance Liquid ChLC of mRBD and dRBD. Monomer and dimer exhibited similar far-UV CD spectral characteristics with 54% of β-sheet content. Similar conformational features according to near-UV CD and FS studies were observed in both RBD. Stress stability studies by far-UV CD, FS, biological activity and GF-HPLC at 37 °C showed that mRBD is very stable. On the other hand, dRBD fluorescent emission showed a shift towards higher wavelengths as the incubation time increases, suggesting exposition of tryptophan residues, unlike what happens with mRBD. Biological activity outcome confirms these results. GF-HPLC profiles showed that in mRBD, the product of molecular stress are dimers and does not increase over time. However, dRBD showed dimer fragmentation as the main degradation species. This study reveals the usefulness of CD techniques for the analysis of degradation of RBD molecules as well as showed the difference in stability of both RBD molecules. Besides, our work provides useful insights into the production of a key protein used in diagnosis and therapeutics to fight COVID-19 pandemia.

Using AlphaFold Predictions in Viral Research

Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.

Measuring the Radius of Gyration and Intrinsic Flexibility of Viral Proteins in Buffer Solution Using Small-Angle X-ray Scattering

Measuring structural features of proteins dispersed in buffer solution, in contrast to crystal form, is indispensable in understanding morphological characteristics of the biomolecule in a native environment. We report on the structure and apparent viscosity of unfolded α and β variants of SARS-CoV-2 spike proteins dispersed in buffer solutions. The radius of gyration of the β variant is found to be larger than that of the α variant, while the ab initio computation of one of the possible particle-like bodies is consistent with the small-angle X-ray scattering (SAXS) profiles resembling a conformation similar to the three-dimensional structure of the folded state of the corresponding α and β spike variant. However, a smaller radius of gyration with respect to the predicted folded state of 2.4 and 2.7 is observed for both α and β variants, respectively. Our work complements the structural characterization of spike proteins using cryo-electron microscopy techniques. The measurement/analysis discussed here might be useful for quick and cost-effective evaluation of several protein structures, let alone mutated viral proteins, which is useful for drug discovery/development applications.

Unfolding of the SARS-CoV-2 spike protein through infrared and ultraviolet-C radiation based disinfection

The spreading of coronavirus from contacting surfaces and aerosols created a pandemic around the world. To prevent the transmission of SARS-CoV-2 virus and other contagious microbes, disinfection of contacting surfaces is necessary. In this study, a disinfection box equipped with infrared (IR) radiation heating and ultraviolet-C (UV-C) radiation is designed and tested for its disinfection ability against pathogenic bacteria and SARS-CoV-2 spike protein. The killing of a Gram-positive, namely, S. aureus and a Gram-negative namely, S. typhi bacteria was studied followed by the inactivation of the spike protein. The experimental parameters were optimized using a statistical tool. For the broad-spectrum antibacterial activity, the optimum condition was holding at 65.61 °C for 13.54 min. The killing of the bacterial pathogen occurred via rupturing the cell walls as depicted by electron microscopy. Further, the unfolding of SARS-CoV-2 spike protein and RNase was studied under IR and UV-C irradiations at the aforesaid optimized condition. The unfolding of both the proteins was confirmed by changes in the secondary structure, particularly an increase in β-sheets and a decrease in α-helixes. Remarkably, the higher penetration depth of IR waves up to subcutaneous tissue resulted in lower optimum disinfection temperature, <70 °C in vogue. Thus, the combined UV-C and IR radiation is effective in killing the pathogenic bacteria and denaturing the glycoproteins.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

B.1.1.7 (Alpha) variant is the most antigenic compared to Wuhan strain, B.1.351, B.1.1.28/triple mutant and B.1.429 variants

The rapid spread of the SARS-CoV-2 virus and its variants has created a catastrophic impact worldwide. Several variants have emerged, including B.1.351 (Beta), B.1.1.28/triple mutant (P.1), B.1.1.7 (Alpha), and B.1.429 (Epsilon). We performed comparative and comprehensive antigenicity mapping of the total S-glycoprotein using the Wuhan strain and the other variants and identified 9-mer, 15-mer, and 20-mer CTL epitopes through in silico analysis. The study found that 9-mer CTL epitope regions in the B.1.1.7 variant had the highest antigenicity and an average of the three epitope types. Cluster analysis of the 9-mer CTL epitopes depicted one significant cluster at the 70% level with two nodes (KGFNCYFPL and EGFNCYFPL). The phage-displayed peptides showed mimic 9-mer CTL epitopes with three clusters. CD spectra analysis showed the same band pattern of S-glycoprotein of Wuhan strain and all variants other than B.1.429. The developed 3D model of the superantigen (SAg)-like regions found an interaction pattern with the human TCR, indicating that the SAg-like component might interact with the TCR beta chain. The present study identified another partial SAg-like region (ANQFNSAIGKI) from the S-glycoprotein. Future research should examine the molecular mechanism of antigen processing for CD8⁺ T cells, especially all the variants’ antigens of S-glycoprotein.

Knowledge-Based Atomic Polarizabilities Used to Model Circular Dichroism Spectra of Proteins

We present a model of circular dichroism for proteins, which is mainly based on both the classical theory of optical activity and a series of effective atomic polarizabilities. Such polarizabilities are extracted from the analysis of a set of synchrotron radiation circular dichroism spectra and their corresponding three-dimensional structures from the Protein Data Bank. Each modeled spectrum is obtained from the protein atomic coordinates and the identification of its secondary structure elements. The resulting spectra are in good agreement with additional experimental data and also with the predictions of some other models. Among them, only our approach is able to describe the effect of d-amino acids. Moreover, our model is also utilized to evaluate protein reconstructions as well as structural changes.

Design and Manufacturing Optoelectronic Sensors for the Measurement of Refractive Index Changes under Unknown Polarization State

This article proposes a new method for detecting slight refractive index changes under conditions of unknown polarization state. It is argued that an insignificant modification of the tilted fiber Bragg grating (TFBG) structure and selecting the appropriate spectral region allows us to accurately track changes in the refractive index. It has also been proven that the method can be easily made insensitive to temperature and that the sensitivity to changes in the polarization plane of the input light can be significantly reduced, which is crucial in later practical applications. Analytes in the form of an aqueous glucose solution were used to calibrate the sensor. The proposed method, based on perpendicular tilted fiber Bragg grating (P-TFBG), has a wide range of universality because its development and slight modification will enable the detection of glucose, pathogens, and viruses.

A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations

Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3-4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.