Overlapping Roles of Yeast Transporters Aqr1, Qdr2, and Qdr3 in Amino Acid Excretion and Cross-Feeding of Lactic Acid Bacteria

Microbial species occupying the same ecological niche or codeveloping during a fermentation process can exchange metabolites and mutualistically influence each other’s metabolic states. For instance, yeast can excrete amino acids, thereby cross-feeding lactic acid bacteria unable to grow without an external amino acid supply. The yeast membrane transporters involved in amino acid excretion remain poorly known. Using a yeast mutant overproducing and excreting threonine (Thr) and its precursor homoserine (Hom), we show that excretion of both amino acids involves the Aqr1, Qdr2, and Qdr3 proteins of the Drug H+-Antiporter Family (DHA1) family. We further investigated Aqr1 as a representative of these closely related amino acid exporters. In particular, structural modeling and molecular docking coupled to mutagenesis experiments and excretion assays enabled us to identify residues in the Aqr1 substrate-binding pocket that are crucial for Thr and/or Hom export. We then co-cultivated yeast and Lactobacillus fermentum in an amino-acid-free medium and found a yeast mutant lacking Aqr1, Qdr2, and Qdr3 to display a reduced ability to sustain the growth of this lactic acid bacterium, a phenotype not observed with strains lacking only one of these transporters. This study highlights the importance of yeast DHA1 transporters in amino acid excretion and mutualistic interaction with lactic acid bacteria.

INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC=0.81, external validation data set AUC=0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called “neoantigens.” Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC=0.78). Further, an easy-to-use web-based tool “INeo-Epp” was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

FluPhenotype—a one-stop platform for early warnings of the influenza A virus

Motivation Newly emerging influenza viruses keep challenging global public health. To evaluate the potential risk of the viruses, it is critical to rapidly determine the phenotypes of the viruses, including the antigenicity, host, virulence and drug resistance. Results Here, we built FluPhenotype, a one-stop platform to rapidly determinate the phenotypes of the influenza A viruses. The input of FluPhenotype is the complete or partial genomic/protein sequences of the influenza A viruses. The output presents five types of information about the viruses: (i) sequence annotation including the gene and protein names as well as the open reading frames, (ii) potential hosts and human-adaptation-associated amino acid markers, (iii) antigenic and genetic relationships with the vaccine strains of different HA subtypes, (iv) mammalian virulence-related amino acid markers and (v) drug resistance-related amino acid markers. FluPhenotype will be a useful bioinformatic tool for surveillance and early warnings of the newly emerging influenza A viruses. Availability and implementation It is publicly available from: http://www.computationalbiology.cn : 18888/IVEW. Supplementary information Supplementary data are available at Bioinformatics online.

INeo-Epp: A novel T-cell HLA class-I immunogenicity or neoantigenic epitope prediction method based on sequence related amino acid features

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g. proteasomal cleavage, transporter associated with antigen processing (TAP) and major histocompatibility complex (MHC) combination. To date, however, the mechanism for immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including amino acid physicochemical property of epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score and frequency score for immunogenic peptide. Subsequently, a random forest classifier for T cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC=0.81, external validation data set AUC=0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called ‘neoantigens’. Based on mutation information and sequence related amino acid characteristics, a prediction model of neoantigen was established as well (the optimal AUC=0.78). Further, an easy-to-use web-based tool ‘INeo-Epp’ was developed (available at http://www.biostatistics.online/INeo-Epp/neoantigen.php)for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

SEPPA 3.0—enhanced spatial epitope prediction enabling glycoprotein antigens

B-cell epitope information is critical to immune therapy and vaccine design. Protein epitopes can be significantly affected by glycosylation, while no methods have considered this till now. Based on previous versions of Spatial Epitope Prediction of Protein Antigens (SEPPA), we here present an enhanced tool SEPPA 3.0, enabling glycoprotein antigens. Parameters were updated based on the latest and largest dataset. Then, additional micro-environmental features of glycosylation triangles and glycosylation-related amino acid indexes were added as important classifiers, coupled with final calibration based on neighboring antigenicity. Logistic regression model was retained as SEPPA 2.0. The AUC value of 0.794 was obtained through 10-fold cross-validation on internal validation. Independent testing on general protein antigens resulted in AUC of 0.740 with BA (balanced accuracy) of 0.657 as baseline of SEPPA 3.0. Most importantly, when tested on independent glycoprotein antigens only, SEPPA 3.0 gave an AUC of 0.749 and BA of 0.665, leading the top performance among peers. As the first server enabling accurate epitope prediction for glycoproteins, SEPPA 3.0 shows significant advantages over popular peers on both general protein and glycoprotein antigens. It can be accessed at http://bidd2.nus.edu.sg/SEPPA3/ or at http://www.badd-cao.net/seppa3/index.html. Batch query is supported.

Development and clinical application of a LC-MS/MS method for simultaneous determination of one-carbon related amino acid metabolites in NTD tissues

Simultaneous quantification of 9 pivotal amino acid metabolites in neural tube defect tissues using a LC-MS/MS method.

Redetermined crystal structure of α-DL-methionine at 340 K

Two forms, α and β, are known for the racemic amino acid DL-methionine, C5H11NO2S. The phase transition between them, taking place around 326 K, is associated with sliding at the central interfaces of the hydrophobic regions in the crystal, leaving the hydrogen-bonding pattern unperturbed. For the high-temperature α phase, only a structure of rather low quality has been available [Rfactor = 0.118, no H-atom coordinates; Taniguchiet al.(1980).Bull. Chem. Soc. Jpn,53, 803–804]. We here present accurate structural data for this polymorph [R(F) = 0.049], which are compared with other related amino acid structures with similar properties. We report for the first time that the side chain of this phase has a minor disorder component [occupancy 0.0491 (18)] with agauche+ rather than agauche− conformation for the N—C—C—C group. In the crystal of the title compound, N—H...O hydrogen bonds link the molecules into (100) sheets.