Data_Sheet_1_A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.FASTA

IntroductionSeasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features.MethodsHere, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences.ResultsThis method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022.DiscussionInterestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.</p

Data_Sheet_4_A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.XLSX

IntroductionSeasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features.MethodsHere, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences.ResultsThis method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022.DiscussionInterestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.</p

Data_Sheet_3_A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.FASTA

IntroductionSeasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features.MethodsHere, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences.ResultsThis method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022.DiscussionInterestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.</p

Image_1_A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.PDF

IntroductionSeasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features.MethodsHere, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences.ResultsThis method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022.DiscussionInterestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.</p

Table_1_A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.PDF

IntroductionSeasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features.MethodsHere, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences.ResultsThis method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022.DiscussionInterestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.</p

Data_Sheet_5_A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.XLSX

IntroductionSeasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features.MethodsHere, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences.ResultsThis method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022.DiscussionInterestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.</p

Data_Sheet_2_A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.FASTA

IntroductionSeasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features.MethodsHere, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences.ResultsThis method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022.DiscussionInterestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.</p

Atomic-Scale Insight into Tautomeric Recognition, Separation, and Interconversion of Guanine Molecular Networks on Au(111)

Although tautomerization may directly affect the chemical or biological properties of molecules, real-space investigation on the tautomeric behaviors of organic molecules in a larger area of molecular networks has been scarcely reported. In this paper, we choose guanine (G) molecule as a model system. From the interplay of high-resolution scanning tunneling microscopy (STM) imaging and density functional theory (DFT) calculations, we have successfully achieved the tautomeric recognition, separation, and interconversion of G molecular networks (formed by two tautomeric forms G/9H and G/7H) with the aid of NaCl on the Au(111) surface in ultrahigh vacuum (UHV) conditions. Our results may serve as a prototypical system to provide important insights into tautomerization related issues, which should be intriguing to biochemistry, pharmaceutics, and other related fields

Atomic-Scale Investigation on the Facilitation and Inhibition of Guanine Tautomerization at Au(111) Surface

Nucleobase tautomerization might induce mismatch of base pairing. Metals, involved in many important biophysical processes, have been theoretically proven to be capable of affecting tautomeric equilibria and stabilities of different nucleobase tautomers. However, direct real-space evidence on demonstrating different nucleobase tautomers and further revealing the effect of metals on their tautomerization at surfaces has not been reported to date. From the interplay of high-resolution STM imaging and DFT calculations, we show for the first time that tautomerization of guanine from G/9H to G/7H is facilitated on Au(111) by heating, whereas such tautomerization process is effectively inhibited by introducing Ni atoms due to its preferential coordination at the N7 site of G/9H. These findings may help to elucidate possible influence of metals on nucleobase tautomerization and provide from a molecular level some theoretical basis on metal-based drug design

On-Surface Formation of One-Dimensional Polyphenylene through Bergman Cyclization

On-surface fabrication of covalently interlinked conjugated nanostructures has attracted significant attention, mainly because of the high stability and efficient electron transport ability of these structures. Here, from the interplay of scanning tunneling microscopy imaging and density functional theory calculations, we report for the first time on-surface formation of one-dimensional polyphenylene chains through Bergman cyclization followed by radical polymerization on Cu(110). The formed surface nanostructures were further corroborated by the results for the ex situ-synthesized molecular product after Bergman cyclization. These findings are of particular interest and importance for the construction of molecular electronic nanodevices on surfaces