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Frontiers in Glycome Informatics Resources Aiming to Reveal Glycan Function
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Frontiers in Glycome Informatics Resources Aiming to Reveal Glycan Function

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Chemical Biology".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 18269

Special Issue Editor

Prof. Dr. Kiyoko F. Aoki-Kinoshita

E-Mail Website
Guest Editor
Glycan & Life Systems Integration Center (GaLSIC), Faculty of Science and Engineering, Soka University, Hachioji 192-8577, Japan
Interests: glycoinformatics; databases; artificial intelligence; machine learning; Web programming

Special Issue Information

Dear Colleagues,

Glycans, or carbohydrate sugar chains, are known to be extremely important in many biological processes, and informatics resources for glycans have increased rapidly in recent years. Because of the high speed at which informatics technologies progress, the latest research in this field continues to change. Machine learning and artificial intelligence, as well as systems biology and synthetic biology, have slowly entered the glycobiology field in an attempt to better understand how glycans are recognized and utilized in biological systems. Moreover, as our understanding of glycans deepens, their complexity is becoming better understood. For example, it is now common knowledge that the function of glycans depends upon environmental factors, whether it be their core proteins or core lipids, their cellular localization, their spatial configuration, etc. Therefore, in this Special Issue, we aim to accumulate articles on the latest research in glycome informatics resources aiming to reveal glycan function. That is, this series aims to cover the newest databases, Web resources, and software tools that have been developed to better understand the biological roles of glycans and glycoconjugates. Review articles by experts in the field are particularly welcomed.

Prof. Dr. Kiyoko F. Aoki-Kinoshita
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

  • Glycoinformatics
  • Systems biology
  • Synthetic biology
  • Machine learning
  • Artificial intelligence

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Published Papers (5 papers)

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12 pages, 2805 KiB  
Article
Computational Modeling of O-Linked Glycan Biosynthesis in CHO Cells
by Thukaa Kouka, Sachiko Akase, Isami Sogabe, Chunsheng Jin, Niclas G. Karlsson and Kiyoko F. Aoki-Kinoshita
Molecules 2022, 27(6), 1766; https://doi.org/10.3390/molecules27061766 - 8 Mar 2022
Cited by 4 | Viewed by 4316
Abstract
Glycan biosynthesis simulation research has progressed remarkably since 1997, when the first mathematical model for N-glycan biosynthesis was proposed. An O-glycan model has also been developed to predict O-glycan biosynthesis pathways in both forward and reverse directions. In this work, [...] Read more.
Glycan biosynthesis simulation research has progressed remarkably since 1997, when the first mathematical model for N-glycan biosynthesis was proposed. An O-glycan model has also been developed to predict O-glycan biosynthesis pathways in both forward and reverse directions. In this work, we started with a set of O-glycan profiles of CHO cells transiently transfected with various combinations of glycosyltransferases. The aim was to develop a model that encapsulated all the enzymes in the CHO transfected cell lines. Due to computational power restrictions, we were forced to focus on a smaller set of glycan profiles, where we were able to propose an optimized set of kinetics parameters for each enzyme in the model. Using this optimized model we showed that the abundance of more processed glycans could be simulated compared to observed abundance, while predicting the abundance of glycans earlier in the pathway was less accurate. The data generated show that for the accurate prediction of O-linked glycosylation, additional factors need to be incorporated into the model to better reflect the experimental conditions. Full article
(This article belongs to the Special Issue Frontiers in Glycome Informatics Resources Aiming to Reveal Glycan Function)
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15 pages, 757 KiB  
Article
Dealing with the Ambiguity of Glycan Substructure Search
by Vincenzo Daponte, Catherine Hayes, Julien Mariethoz and Frederique Lisacek
Molecules 2022, 27(1), 65; https://doi.org/10.3390/molecules27010065 - 23 Dec 2021
Cited by 7 | Viewed by 2930
Abstract
The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also [...] Read more.
The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited for painless future expansion. Full article
(This article belongs to the Special Issue Frontiers in Glycome Informatics Resources Aiming to Reveal Glycan Function)
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19 pages, 5920 KiB  
Article
DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction
by Subash C. Pakhrin, Kiyoko F. Aoki-Kinoshita, Doina Caragea and Dukka B. KC
Molecules 2021, 26(23), 7314; https://doi.org/10.3390/molecules26237314 - 2 Dec 2021
Cited by 16 | Viewed by 5263
Abstract
Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that [...] Read more.
Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community. Full article
(This article belongs to the Special Issue Frontiers in Glycome Informatics Resources Aiming to Reveal Glycan Function)
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11 pages, 1454 KiB  
Article
The Need for Community Standards to Enable Accurate Comparison of Glycoproteomics Algorithm Performance
by William E. Hackett and Joseph Zaia
Molecules 2021, 26(16), 4757; https://doi.org/10.3390/molecules26164757 - 6 Aug 2021
Cited by 8 | Viewed by 2485
Abstract
Protein glycosylation that mediates interactions among viral proteins, host receptors, and immune molecules is an important consideration for predicting viral antigenicity. Viral spike proteins, the proteins responsible for host cell invasion, are especially important to be examined. However, there is a lack of [...] Read more.
Protein glycosylation that mediates interactions among viral proteins, host receptors, and immune molecules is an important consideration for predicting viral antigenicity. Viral spike proteins, the proteins responsible for host cell invasion, are especially important to be examined. However, there is a lack of consensus within the field of glycoproteomics regarding identification strategy and false discovery rate (FDR) calculation that impedes our examinations. As a case study in the overlap between software, here as a case study, we examine recently published SARS-CoV-2 glycoprotein datasets with four glycoproteomics identification software with their recommended protocols: GlycReSoft, Byonic, pGlyco2, and MSFragger-Glyco. These software use different Target-Decoy Analysis (TDA) forms to estimate FDR and have different database-oriented search methods with varying degrees of quantification capabilities. Instead of an ideal overlap between software, we observed different sets of identifications with the intersection. When clustering by glycopeptide identifications, we see higher degrees of relatedness within software than within glycosites. Taking the consensus between results yields a conservative and non-informative conclusion as we lose identifications in the desire for caution; these non-consensus identifications are often lower abundance and, therefore, more susceptible to nuanced changes. We conclude that present glycoproteomics softwares are not directly comparable, and that methods are needed to assess their overall results and FDR estimation performance. Once such tools are developed, it will be possible to improve FDR methods and quantify complex glycoproteomes with acceptable confidence, rather than potentially misleading broad strokes. Full article
(This article belongs to the Special Issue Frontiers in Glycome Informatics Resources Aiming to Reveal Glycan Function)
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10 pages, 2627 KiB  
Technical Note
SugarDrawer: A Web-Based Database Search Tool with Editing Glycan Structures
by Shinichiro Tsuchiya, Masaaki Matsubara, Kiyoko F. Aoki-Kinoshita and Issaku Yamada
Molecules 2021, 26(23), 7149; https://doi.org/10.3390/molecules26237149 - 25 Nov 2021
Cited by 5 | Viewed by 2037
Abstract
In life science fields, database integration is progressing and contributing to collaboration between different research fields, including the glycosciences. The integration of glycan databases has greatly progressed collaboration worldwide with the development of the international glycan structure repository, GlyTouCan. This trend has increased [...] Read more.
In life science fields, database integration is progressing and contributing to collaboration between different research fields, including the glycosciences. The integration of glycan databases has greatly progressed collaboration worldwide with the development of the international glycan structure repository, GlyTouCan. This trend has increased the need for a tool by which researchers in various fields can easily search glycan structures from integrated databases. We have developed a web-based glycan structure search tool, SugarDrawer, which supports the depiction of glycans including ambiguity, such as glycan fragments which contain underdetermined linkages, and a database search for glycans drawn on the canvas. This tool provides an easy editing feature for various glycan structures in just a few steps using template structures and pop-up windows which allow users to select specific information for each structure element. This tool has a unique feature for selecting possible attachment sites, which is defined in the Symbol Nomenclature for Glycans (SNFG). In addition, this tool can input and output glycans in WURCS and GlycoCT formats, which are the most commonly-used text formats for glycan structures. Full article
(This article belongs to the Special Issue Frontiers in Glycome Informatics Resources Aiming to Reveal Glycan Function)
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