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Nanozymes with intrinsic enzyme-like properties and excellent stability are promising alternatives to natural enzymes. Yet, their low density of active sites and unclear crystal structure have been the major obstacles that impede their progress. Single-atom nanozymes (SAzymes) have emerged as a unique system to mitigate these issues, due to maximal atomic utilization, well-defined electronic and geometric structures, and outstanding catalytic activity distinct from their nanosized counterparts. Furthermore, the homogeneously dispersed active sites and well-defined coordination structures provide rare pathways to shed light on the catalytic mechanisms. In this review, we summarize the latest progress in the rational design and engineering of SAzymes and their applications in biomedicine and biosensing. We then conclude the review with highlights of the remaining challenges and perspectives of this emerging technology. 

Computational modeling of protein–DNA complex structures has important implications in biomedical applications such as structure‐based, computer aided drug design. A key step in developing methods for accurate modeling of protein–DNA complexes is similarity assessment between models and their reference complex structures. Existing methods primarily rely on distance‐based metrics and generally do not consider important functional features of the complexes, such as interface hydrogen bonds that are critical to specific protein–DNA interactions. Here, we present a new scoring function, ComparePD, which takes interface hydrogen bond energy and strength into account besides the distance‐based metrics for accurate similarity measure of protein–DNA complexes. ComparePD was tested on two datasets of computational models of protein–DNA complexes generated using docking (classified as easy, intermediate, and difficult cases) and homology modeling methods. The results were compared with PDDockQ, a modified version of DockQ tailored for protein–DNA complexes, as well as the metrics employed by the community‐wide experiment CAPRI (Critical Assessment of PRedicted Interactions). We demonstrated that ComparePD provides an improved similarity measure over PDDockQ and the CAPRI classification method by considering both conformational similarity and functional importance of the complex interface. ComparePD identified more meaningful models as compared to PDDockQ for all the cases having different top models between ComparePD and PDDockQ except for one intermediate docking case.

 

While it has been scientifically proven that COVID-19 vaccine is a safe and effective measure to reduce the severity of infection and curbing the spread of the SARS-CoV-2 virus, skepticism remains widespread, and in many countries vaccine mandates have been met with strong opposition. In this study, we applied machine learning-based analyses of the U.S.-based tweets covering the periods leading toward and after the Biden Administration’s announcement of federal vaccine mandates, supplemented by a qualitative content analysis of a random sample of relevant tweets. The objective was to examine the beliefs held among twitter users toward vaccine mandates, as well as the evidence that they used to support their positions. The results show that while approximately 30% of the twitter users included in the dataset supported the measure, more users expressed differing opinions. Concerns raised included questioning on the political motive, infringement of personal liberties, and ineffectiveness in preventing infection. 

Abstract Insertions and deletions (Indels) represent one of the major variation types in the human genome and have been implicated in diseases including cancer. To study the features of somatic indels in different cancer genomes, we investigated the indels from two large samples of cancer types: invasive breast carcinoma (BRCA) and lung adenocarcinoma (LUAD). Besides mapping somatic indels in both coding and untranslated regions (UTRs) from the cancer whole exome sequences, we investigated the overlap between these indels and transcription factor binding sites (TFBSs), the key elements for regulation of gene expression that have been found in both coding and non-coding sequences. Compared to the germline indels in healthy genomes, somatic indels contain more coding indels with higher than expected frame-shift (FS) indels in cancer genomes. LUAD has a higher ratio of deletions and higher coding and FS indel rates than BRCA. More importantly, these somatic indels in cancer genomes tend to locate in sequences with important functions, which can affect the core secondary structures of proteins and have a bigger overlap with predicted TFBSs in coding regions than the germline indels. The somatic CDS indels are also enriched in highly conserved nucleotides when compared with germline CDS indels. 

It has long been known that exons can serve ascis‐regulatory sequences, such as enhancers. However, the prevalence of such dual‐use of exons and how they evolve remain elusive. Based on our recently predicted, highly accurate large sets ofcis‐regulatory module candidates (CRMCs) and non‐CRMCs in the human genome, we find that exonic transcription factor binding sites (TFBSs) occupy at least a third of the total exon lengths, and 96.7% of genes have exonic TFBSs. Both A/T and C/G in exonic TFBSs are more likely under evolutionary constraints than those in non‐CRMC exons. Exonic TFBSs in codons tend to encode loops rather than more critical helices and strands in protein structures, while exonic TFBSs in untranslated regions (UTRs) tend to avoid positions where known UTR‐related functions are located. Moreover, active exonic TFBSs tend to be in close physical proximity to distal promoters whose genes have elevated transcription levels. These results suggest that exonic TFBSs might be more prevalent than originally thought and likely in dual‐use. We proposed a parsimonious model that well explains the observed evolutionary behaviors of exonic TFBS as well as how a stretch of codons evolve into a TFBS.

There are more exonic regulatory sequences in the human genome than originally thought.

Exonic transcription factor binding sites are more likely under negative selection or positive selection than counterpart nonregulatory sequences.

Exonic transcription factor binding sites tend to be located in genome sequences that encode less critical loops in protein structures, or in less critical parts in 5′ and 3′ untranslated regions.