Stabillity of TDs from different bacteria.

<p>Stabillity of TDs from different bacteria.</p

SDS-PAGE of the nine purified TDs.

<p>Lane M, molecular mass marker; lane 1, CgCTD purified from BL21(DE3)/pET28a-<i>CgtdcB</i>; lane 2, EcCTD purified from BL21(DE3)/pET28a-<i>EctdcB</i>; lane 3, SaCTD purified from BL21(DE3)/pET28a-<i>SatdcB</i>; lane 4, CgBTD1 purified from BL21(DE3)/pET28a-<i>CgilvA</i>; lane 5, SaBTD1 purified from BL21(DE3)/pET28a-<i>SailvA</i>; lane 6, SgBTD1 purified from BL21(DE3)/pET28a-<i>SgilvA</i>; lane 7, BsBTD1 purified from BL21(DE3)/pET28a-<i>BsilvA</i>; lane 8, EcBTD2 purified from BL21(DE3)/pET28a-<i>EcilvA</i>; lane 9, PpBTD2 purified from BL21(DE3)/pET28a-<i>PpilvA</i>.</p

The effect of temperature on the activity of nine different TDs.

<p>Error bars indicate the standard deviations from three parallel samples.</p

The two different pathways BTD and CTD involved in bacteria.

<p>A. BTD catalyzes the first reaction in the biosynthesis of L-isoleucine in bacteria under aerobic conditions. BTD is feedback inhibited by L-isioleucine. B. CTD degrades L-threonine to propionate in bacteria under anaerobic conditions to generate ATP. CTD is activated by AMP and CMP.</p

Information of TDs from different bacteria.

<p>Information of TDs from different bacteria.</p

Bacterial strains and plasmids used in this work.

<p>Bacterial strains and plasmids used in this work.</p

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

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

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