GlyNet: a multi-task neural network for predicting protein–glycan interactions

Carpenter, Eric J.; Seth, Shaurya; Yue, Noel; Greiner, Russell; Derda, Ratmir

doi:10.1039/d1sc05681f

Cited by 9 publications

(10 citation statements)

References 65 publications

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“…We suggest the use of DeepFRI (53), a deep learning model that predicts protein function, to first determine if the protein is a carbohydrate binding protein. If the protein is a carbohydrate binding protein, then LectinOracle(41) or GlyNet (42) can be used to predict which carbohydrates bind the protein. CAPSIF can then predict binding locations, either from an experimental structure or AF2 generated structures, and then GlycanDock(24) can predict a docked protein-carbohydrate structure.…”

Section: Discussionmentioning

confidence: 99%

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Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Canner

Shanker

Gray

2023

Preprint

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Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate binding sites on any given protein. Here, we present two deep learning models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predict carbohydrate binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2 predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

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Section: Discussionmentioning

confidence: 99%

“…Understanding the physiological response and determining a glycomimetic drug to neutralize the infection requires residue-level knowledge (40). Currently, DL algorithms LectinOracle (41) and GlyNet (42) predict lectin-carbohydrate binding on a protein level; however, pharmaceutical development requires residue-level information.…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Canner

Shanker

Gray

2023

Preprint

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“…Other approaches have used glycan-binding data from glycan arrays, where lectins are probed for their binding to immobilized glycans on a glass slide . One example for this would be GlyNet, a neural network-based approach for using the motifs that occur in a glycan to predict its binding to the lectins that have historically been assayed on the CFG platform. Combining interpretable, rule-based machine learning with expert annotation has also recently resulted in the detailed elucidation of the binding motifs of a large set of commonly used lectins .…”

Section: Next-generation Machine Learningmentioning

confidence: 99%

Glycoinformatics in the Artificial Intelligence Era

Bojar

Lisacek

2022

Chem. Rev.

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Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

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“…7 . It is observed, there are 13 studies including ( Chien et al, 2020 ; Taherzadeh et al, 2019 ; Liu et al, 2019 ; Li et al, 2019 ; Thomès, Burkholz & Bojar, 2021 ; Pitti et al, 2019 ; Adolf-Bryfogle et al, 2021 ; Park et al, 2019 ; He, Wei & Zou, 2019 ; Audagnotto & Dal Peraro, 2017 ; Mondragon-Shem et al, 2020 ; Dimeglio et al, 2020 ; Ruiz-Blanco et al, 2017 ) which have developed the tool specific to the N-Linked site identifications, few studied developed tool for glycosylation sites identification irrespective of the specific type including ( Bojar et al, 2021b ; Carpenter et al, 2022 ; Lundstrøm et al, 2022 ; Burkholz, Quackenbush & Bojar, 2021 ; Coff et al, 2020 ; Shek, Kotidis & Betenbaugh, 2021 ) and some authors ( Le, Sandag & Ou, 2018 ; Liu et al, 2021 ; Yang et al, 2019 ; Campbell, 2017 ) develop tool without mentioning the type of PTM. These all tools have list down in the Table 9 .…”

Section: Assessment Of Q2mentioning

confidence: 99%

“…It is important to specify for which kind of species these tools will be operating, therefore to achieve this purpose the information is also extracted from the selected studies. Some authors ( He, Wei & Zou, 2019 ; Audagnotto & Dal Peraro, 2017 ; Shek, Kotidis & Betenbaugh, 2021 ; Carpenter et al, 2022 ) did not mention the organism type while other mentioned it and it is observed most of them use human data for site identification as mention in Table 9 .…”

Section: Assessment Of Q2mentioning

confidence: 99%

An analytical study on the identification of N-linked glycosylation sites using machine learning model

Akmal

Hassan

Shoaib

et al. 2022

PeerJ Computer Science

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N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

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GlyNet: a multi-task neural network for predicting protein–glycan interactions

Abstract: Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative...

Cited by 9 publications

References 65 publications

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Glycoinformatics in the Artificial Intelligence Era

An analytical study on the identification of N-linked glycosylation sites using machine learning model

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