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    Distinguishing Turkish pine honey from multi-floral honey through MALDI-MS-based N-glycomics and machine learning
    (Springer, 2024) Masri, Saad; Aksoy, Sena; Duman, Hatice; Karav, Sercan; Kayili, Haci Mehmet; Salih, Bekir
    Honey, a multifaceted blend of sugars, amino acids, vitamins, proteins, and minerals, exhibits compositional variability dependent upon the floral source. While previous studies have attempted to categorize honey, the use of glycomic profiles for honey classification remains an unexplored avenue. This investigation seeks to establish a methodology for distinguishing honey types, specifically multi-floral and pine honey, employing mass spectrometry-based glycomic analysis in tandem with machine learning. In this search, seven samples of pine honey and eight samples of multi-floral honey were obtained from diverse regions of Turkey. Subsequently, the proteins within these honey samples were extracted, and glycans were enzymatically released. The released glycans were labeled with 2-aminobenzoic acid (2-AA) and subjected to analysis via matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). The glycan profiles of pine and multi-floral honey were determined through these analytical procedures, revealing a total of 76 distinct N-glycan structures. Among these, 13 N-glycan profiles consistently established at high levels across experimental replicates and were incorporated in subsequent analyses. Following the quantification of individual glycan abundances, statistically significant differences in glycan profiles were determined. Notably, N-glycans Hex5HexNAc2, Hex4HexNAc3, and Hex5HexNAc3 displayed considerable differences. Using the 13 N-glycan profiles, an accuracy rate of 93.5% was obtained from machine learning analysis, which increased to 100% when incorporating the identified significantly changed glycans. The most productive models were identified as subspace and fine k-nearest neighbors (KNN). The findings underscore the potential of mass spectrometry-based glycomics in conjunction with machine learning as a robust tool for precise honey type classification and its prospective utility in quality control and honey product authentication.
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    Öğe
    Distinguishing Turkish pine honey from multi-foral honey through MALDI-MS-based N-glycomics and machine learning (vol 18, pg 5673, 2024)
    (Springer, 2024) Masri, Saad; Aksoy, Sena; Duman, Hatice; Karav, Sercan; Kayili, Haci Mehmet; Salih, Bekir
    [Anstract Not Available]
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    Identification and comparison of N-glycome profiles from common dietary protein sources
    (Elsevier, 2025) Bolino, Matthew; Avci, Izzet; Kayili, Haci Mehmet; Duman, Hatice; Salih, Bekir; Karav, Sercan; Frese, Steven A.
    The N-glycomes of bovine whey, egg white, pea, and soy protein isolates are described here. N-glycans from four protein isolates were analyzed by HILIC high performance liquid chromatography and quadrupole time-of-flight tandem mass spectrometry (HILIC-FLD-QTOF-MS/MS). In total, 33 N-glycans from bovine whey and egg white and 10 N-glycans from soy and pea glycoproteins were identified. The type of N-glycans per glycoprotein source were attributable to differences in biosynthetic glycosylation pathways. Animal glycoprotein sources favored a combination of complex and hybrid glycan configurations, while the plant proteins were dominated by oligomannosidic N-glycans. Bovine whey glycoprotein isolate contained the most diverse N-glycans by monosaccharide composition as well as structure, while plant sources such as pea and soy glycoprotein isolates contained an overlap of oligomannosidic N-glycans. The results suggest N-glycan structure and composition is dependent on the host organism which are driven by the differences in N-glycan biosynthetic pathways.
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    Immobilization of a Bifidobacterial Endo-ss-N-Acetylglucosaminidase to Generate Bioactive Compounds for Food Industry
    (Frontiers Media Sa, 2022) Pekdemir, Burcu; Duman, Hatice; Arslan, Aysenur; Kaplan, Merve; Karyelioglu, Melda; ozer, Tolgahan; Kayili, Haci Mehmet
    Conjugated N-glycans are considered next-generation bioactive prebiotic compounds due to their selective stimulation of beneficial microbes. These compounds are glycosidically attached to proteins through N-acetylglucosamines via specific asparagine residue (AsN-X-Ser/Thr). Certain bacteria such as Bifidobacterium longum subspecies infantis (B. infantis) have been shown to be capable of utilizing conjugated N-glycans, owing to their specialized genomic abilities. B. infantis possess a unique enzyme, Endo-ss-N-acetylglucosaminidase (EndoBI-1), which cleaves all types of conjugated N-glycans from glycoproteins. In this study, recombinantly cloned EndoBI-1 enzyme activity was investigated using various immobilization methods: 1) adsorption, 2) entrapment-based alginate immobilization, 3) SulfoLink-, and 4) AminoLink-based covalent bonding immobilization techniques were compared to develop the optimum application of EndoBI-1 to food processes. The yield of enzyme immobilization and the activity of each immobilized enzyme by different approaches were investigated. The N-glycans released from lactoperoxidase (LPO) using different immobilized enzyme forms were characterized using MALDI-TOF mass spectrometry (MS). As expected, regardless of the techniques, the enzyme activity decreased after the immobilization methods. The enzyme activity of adsorption and entrapment-based alginate immobilization was found to be 71.55% +/- 0.6 and 20.32% +/- 3.18, respectively, whereas the activity of AminoLink- and SulfoLink-based covalent bonding immobilization was found to be 58.05 +/- 1.98 and 47.49% +/- 0.30 compared to the free form of the enzyme, respectively. However, extended incubation time recovery achieved activity similar to that of the free form. More importantly, each immobilization method resulted in the same glycan profile containing 11 different N-glycan structures from a model glycoprotein LPO based on MALDI-TOF MS analysis. The glycan data analysis suggests that immobilization of EndoBI-1 is not affecting the enzyme specificity, which enables full glycan release without a limitation. Hence, different immobilization methods investigated in this study can be chosen for effective enzyme immobilization to obtain bioactive glycans. These findings highlight that further optimization of these methods can be a promising approach for future processing scale-up and commercialization of EndoBI-1 and similar enzymes.