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Even though COVID-19 is no longer the primary focus of the global scientific community, its high mutation rate (nearly 30 substitutions per year) poses a threat of a potential comeback. Effective vaccines have been developed and administered to the population, ending the pandemic. Nonetheless, reinfection by newly emerging subvariants, particularly the latest JN.1 strain, remains common. The rapid mutation of this virus demands a fast response from the scientific community in case of an emergency. While the immune escape of earlier variants was extensively investigated, one still needs a comprehensive understanding of how specific mutations, especially in the newest subvariants, influence the antigenic escape of the pathogen. Here, we tested comprehensive in silico approaches to identify methods for fast and accurate prediction of antibody neutralization by various mutants. As a benchmark, we modeled the complexes of the murine antibody 2B04, which neutralizes infection by preventing the SARS-CoV-2 spike glycoprotein’s association with angiotensin-converting enzyme (ACE2). Complexes with the wild-type, B.1.1.7 Alpha, and B.1.427/429 Epsilon SARS-CoV-2 variants were used as positive controls, while complexes with the B.1.351 Beta, P.1 Gamma, B.1.617.2 Delta, B.1.617.1 Kappa, BA.1 Omicron, and the newest JN.1 Omicron variants were used as decoys. Three essentially different algorithms were employed: forced placement based on a template, followed by two steps of extended molecular dynamics simulations; protein–protein docking utilizing PIPER (an FFT-based method extended for use with pairwise interaction potentials); and the AlphaFold 3.0 model for complex structure prediction. Homology modeling was used to assess the 3D structure of the newly emerged JN.1 Omicron subvariant, whose crystallographic structure is not yet available in the Protein Database. After a careful comparison of these three approaches, we were able to identify the pros and cons of each method. Protein–protein docking yielded two false-positive results, while manual placement reinforced by molecular dynamics produced one false positive and one false negative. In contrast, AlphaFold resulted in only one doubtful result and a higher overall accuracy-to-time ratio. The reasons for inaccuracies and potential pitfalls of various approaches are carefully explained. In addition to a comparative analysis of methods, some mechanisms of immune escape are elucidated herein. This provides a critical foundation for improving the predictive accuracy of vaccine efficacy against new viral subvariants, introducing accurate methodologies, and pinpointing potential challenges. 

Clean and renewable energy development is becoming frontier research for future energy resources, as renewable energy offers sustainable and environmentally friendly alternatives to non-renewable sources such as fossil fuels. Among various renewable energy sources, tremendous progress has been made in converting solar energy to electric energy by developing efficient organic photovoltaics. Organic photovoltaic materials comprising conjugated polymers (CP) with narrow optical energy gaps are promising candidates for developing sustainable sources due to their potentially lower manufacturing costs. Organic semiconductor materials with a high electron affinity are required for many optoelectronic applications. We have designed a series of organic semiconductors comprised of cyclopentadithiophene as a donor and thiadiazoloquinoxaline (TQ) as an acceptor, varying the π-conjugation and TQ-derivatives. We have employed density functional theory (DFT) and time-dependent DFT (TDDFT) to evaluate the designed CP’s optoelectronic properties, such as optical energy gap, dipole moment, and absorption spectra. Our DFT/TDDFT result shows that the energy gap of CPs is lowered and redshifted in the absorption spectra if there is no insertion of conjugation units such as thiophene and selenophene between donor and acceptor. In addition, selenophene shows relatively better redshift behavior compared to thiophene. Our work also provides rational insight into designing donor/acceptor-based CPs for organic solar cells. 

NIR dyes have become popular for many applications, including biosensing and imaging. For this reason, the molecular switch mechanism of the xanthene dyes makes them useful for in vivo detection and imaging of bioanalytes. Our group has been designing NIR xanthene-based dyes by the donor-acceptor-donor approach; however, the equilibrium between their opened and closed forms varies depending on the donors and spacer. We synthesized donor-acceptor-donor NIR xanthene-based dyes with an alkyne spacer via the Sonogashira coupling reaction to investigate the effects of the alkyne spacer and the donors on the maximum absorption wavelength and the molecular switching (ring opening) process of the dyes. We evaluated the strength and nature of the donors and the presence and absence of the alkyne spacer on the properties of the dyes. It was shown that the alkyne spacer extended the conjugation of the dyes, leading to absorption wavelengths of longer values compared with the dyes without the alkyne group. In addition, strong charge transfer donors shifted the absorption wavelength towards the NIR region, while donors with strong π-donation resulted in xanthene dyes with a smaller equilibrium constant. DFT/TDDFT calculations corroborated the experimental data in most of the cases. Dye 2 containing the N,N-dimethylaniline group gave contrary results and is being further investigated.