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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. 

Immunotherapy, particularly targeting the PD-1/PD-L1 pathway, holds promise in cancer treatment by regulating the immune response and preventing cancer cells from evading immune destruction. Nonetheless, this approach poses a risk of unwanted immune system activation against healthy cells. To minimize this risk, our study proposes a strategy based on selective targeting of the PD-L1 pathway within the acidic microenvironment of tumors. We employed in silico methods, such as virtual screening, molecular mechanics, and molecular dynamics simulations, analyzing approximately 10,000 natural compounds from the MolPort database to find potential hits with the desired properties. The simulations were conducted under two pH conditions (pH = 7.4 and 5.5) to mimic the environments of healthy and cancerous cells. The compound MolPort-001-742-690 emerged as a promising pH-selective inhibitor, showing a significant affinity for PD-L1 in acidic conditions and lower toxicity compared to known inhibitors like BMS-202 and LP23. A detailed 1000 ns molecular dynamics simulation confirmed the stability of the inhibitor-PD-L1 complex under acidic conditions. This research highlights the potential of using in silico techniques to discover novel pH-selective inhibitors, which, after experimental validation, may enhance the precision and reduce the toxicity of immunotherapies, offering a transformative approach to cancer treatment. 

Abstract BackgroundV0v spinal interneurons are highly conserved, glutamatergic, commissural neurons that function in locomotor circuits. We have previously shown that Evx1 and Evx2 are required to specify the neurotransmitter phenotype of these cells. However, we still know very little about the gene regulatory networks that act downstream of these transcription factors in V0v cells. MethodsTo identify candidate members of V0v gene regulatory networks, we FAC-sorted wild-type andevx1;evx2double mutant zebrafish V0v spinal interneurons and expression-profiled them using microarrays and single cell RNA-seq. We also used in situ hybridization to compare expression of a subset of candidate genes inevx1;evx2double mutants and wild-type siblings. ResultsOur data reveal two molecularly distinct subtypes of zebrafish V0v spinal interneurons at 48 h and suggest that, by this stage of development,evx1;evx2double mutant cells transfate into either inhibitory spinal interneurons, or motoneurons. Our results also identify 25 transcriptional regulator genes that require Evx1/2 for their expression in V0v interneurons, plus a further 11 transcriptional regulator genes that are repressed in V0v interneurons by Evx1/2. Two of the latter genes arehmx2andhmx3a. Intriguingly, we show that Hmx2/3a, repress dI2 interneuron expression ofskor1aandnefma, two genes that require Evx1/2 for their expression in V0v interneurons. This suggests that Evx1/2 might regulateskor1aandnefmaexpression in V0v interneurons by repressing Hmx2/3a expression. ConclusionsThis study identifies two molecularly distinct subsets of zebrafish V0v spinal interneurons, as well as multiple transcriptional regulators that are strong candidates for acting downstream of Evx1/2 to specify the essential functional characteristics of these cells. Our data further suggest that in the absence of both Evx1 and Evx2, V0v spinal interneurons initially change their neurotransmitter phenotypes from excitatory to inhibitory and then, later, start to express markers of distinct types of inhibitory spinal interneurons, or motoneurons. Taken together, our findings significantly increase our knowledge of V0v and spinal development and move us closer towards the essential goal of identifying the complete gene regulatory networks that specify this crucial cell type. 

Abstract BackgroundThe spinal cord is a crucial part of the vertebrate CNS, controlling movements and receiving and processing sensory information from the trunk and limbs. However, there is much we do not know about how this essential organ develops. Here, we describe expression of 21 transcription factors and one transcriptional regulator in zebrafish spinal cord. ResultsWe analyzed the expression ofaurkb,foxb1a,foxb1b,her8a,homeza,ivns1abpb,mybl2b,myt1a,nr2f1b,onecut1,sall1a,sall3a,sall3b,sall4,sox2,sox19b,sp8b,tsc22d1,wdhd1,zfhx3b,znf804a, andznf1032in wild‐type andMIB E3 ubiquitin protein ligase 1zebrafish embryos. While all of these genes are broadly expressed in spinal cord, they have distinct expression patterns from one another. Some are predominantly expressed in progenitor domains, and others in subsets of post‐mitotic cells. Given the conservation of spinal cord development, and the transcription factors and transcriptional regulators that orchestrate it, we expect that these genes will have similar spinal cord expression patterns in other vertebrates, including mammals and humans. ConclusionsOur data identify 22 different transcriptional regulators that are strong candidates for playing different roles in spinal cord development. For several of these genes, this is the first published description of their spinal cord expression.