scholarly journals 77Se-Enriched Selenoglycoside Enables Significant Enhancement in NMR Spectroscopic Monitoring of Glycan–Protein Interactions

Pharmaceutics ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 201
Author(s):  
István Timári ◽  
Sára Balla ◽  
Krisztina Fehér ◽  
Katalin E. Kövér ◽  
László Szilágyi
Keyword(s):  
Protein Interactions ◽  
Lectin Binding ◽  
Biological Processes ◽  
Spectroscopic Monitoring ◽  
Convenient Synthesis ◽  
Galectin 3 ◽  
Rapid Monitoring ◽  
Promising Perspective ◽  
77Se Nmr Spectroscopy ◽  
Sensitivity Gain

Detailed investigation of ligand–protein interactions is essential for better understanding of biological processes at the molecular level. Among these binding interactions, the recognition of glycans by lectins is of particular importance in several diseases, such as cancer; therefore, inhibition of glycan-lectin/galectin interactions represents a promising perspective towards developing therapeutics controlling cancer development. The recent introduction of 77Se NMR spectroscopy for monitoring the binding of a selenoglycoside to galectins prompted interest to optimize the sensitivity by increasing the 77Se content from the natural 7.63% abundance to 99%. Here, we report a convenient synthesis of 77Se-enriched selenodigalactoside (SeDG), which is a potent ligand of the medically relevant human galectin-3 protein, and proof of the expected sensitivity gain in 2D 1H, 77Se correlation NMR experiments. Our work opens perspectives for adding isotopically enriched selenoglycans for rapid monitoring of lectin-binding of selenated as well as non-selenated ligands and for ligand screening in competition experiments.

An Integrated Prediction Method for Identifying Protein-Protein Interactions

2020 ◽  
Vol 17 (4) ◽  
pp. 271-286
Author(s):  
Chang Xu ◽  
Limin Jiang ◽  
Zehua Zhang ◽  
Xuyao Yu ◽  
Renhai Chen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Feature Vector ◽  
Prediction Method ◽  
Feature Representation ◽  
Protein Interaction Networks ◽  
Learning Approach ◽  
Biological Processes ◽  
Integrated Learning ◽  
Protein Protein Interactions ◽  
Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

scholarly journals Cross-Linking Effects Dictate the Preference of Galectins to Bind LacNAc-Decorated HPMA Copolymers

2021 ◽  
Vol 22 (11) ◽  
pp. 6000
Author(s):  
Sara Bertuzzi ◽  
Ana Gimeno ◽  
Ane Martinez-Castillo ◽  
Marta G. Lete ◽  
Sandra Delgado ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Lectin Binding ◽  
Building Blocks ◽  
Cryo Electron Microscopy ◽  
Hpma Copolymers ◽  
Hydroxypropyl Methacrylamide ◽  
Galectin 3 ◽  
Nmr Methods ◽  
The Individual ◽  
Statistical Effects

The interaction of multi-LacNAc (Galβ1-4GlcNAc)-containing N-(2-hydroxypropyl) methacrylamide (HPMA) copolymers with human galectin-1 (Gal-1) and the carbohydrate recognition domain (CRD) of human galectin-3 (Gal-3) was analyzed using NMR methods in addition to cryo-electron-microscopy and dynamic light scattering (DLS) experiments. The interaction with individual LacNAc-containing components of the polymer was studied for comparison purposes. For Gal-3 CRD, the NMR data suggest a canonical interaction of the individual small-molecule bi- and trivalent ligands with the lectin binding site and better affinity for the trivalent arrangement due to statistical effects. For the glycopolymers, the interaction was stronger, although no evidence for forming a large supramolecule was obtained. In contrast, for Gal-1, the results indicate the formation of large cross-linked supramolecules in the presence of multivalent LacNAc entities for both the individual building blocks and the polymers. Interestingly, the bivalent and trivalent presentation of LacNAc in the polymer did not produce such an increase, indicating that the multivalency provided by the polymer is sufficient for triggering an efficient binding between the glycopolymer and Gal-1. This hypothesis was further demonstrated by electron microscopy and DLS methods.

scholarly journals Clicked and long spaced galactosyl- and lactosylcalix[4]arenes: new multivalent galectin-3 ligands

2014 ◽  
Vol 10 ◽  
pp. 1672-1680 ◽  
Author(s):  
Silvia Bernardi ◽  
Paola Fezzardi ◽  
Gabriele Rispoli ◽  
Stefania E Sestito ◽  
Francesco Peri ◽  
...  
Keyword(s):  
Surface Plasmon Resonance ◽  
Ethylene Glycol ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Lectin Binding ◽  
The Other ◽  
Isomeric Form ◽  
Synthetic Pathway ◽  
Cuaac Reaction ◽  
Galectin 3

Four novel calix[4]arene-based glycoclusters were synthesized by conjugating the saccharide units to the macrocyclic scaffold using the CuAAC reaction and using long and hydrophilic ethylene glycol spacers. Initially, two galactosylcalix[4]arenes were prepared starting from saccharide units and calixarene cores which differ in the relative dispositions of the alkyne and azido groups. Once the most convenient synthetic pathway was selected, two further lactosylcalix[4]arenes were obtained, one in the cone, the other one in the 1,3-alternate structure. Preliminary studies of the interactions of these novel glycocalixarenes with galectin-3 were carried out by using a lectin-functionalized chip and surface plasmon resonance. These studies indicate a higher affinity of lactosyl- over galactosylcalixarenes. Furthermore, we confirmed that in case of this specific lectin binding the presentation of lactose units on a cone calixarene is highly preferred with respect to its isomeric form in the 1,3-alternate structure.

scholarly journals NMR Experiments Shed New Light on Glycan Recognition by Human and Murine Norovirus Capsid Proteins

Viruses ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 416
Author(s):  
Robert Creutznacher ◽  
Thorben Maass ◽  
Patrick Ogrissek ◽  
Georg Wallmann ◽  
Clara Feldmann ◽  
...  
Keyword(s):  
Blood Group ◽  
Protein Interactions ◽  
Capsid Proteins ◽  
Blood Group Antigens ◽  
Biological Processes ◽  
Murine Norovirus ◽  
Human Norovirus ◽  
Head Groups ◽  
Significant Difference

Glycan–protein interactions are highly specific yet transient, rendering glycans ideal recognition signals in a variety of biological processes. In human norovirus (HuNoV) infection, histo-blood group antigens (HBGAs) play an essential but poorly understood role. For murine norovirus infection (MNV), sialylated glycolipids or glycoproteins appear to be important. It has also been suggested that HuNoV capsid proteins bind to sialylated ganglioside head groups. Here, we study the binding of HBGAs and sialoglycans to HuNoV and MNV capsid proteins using NMR experiments. Surprisingly, the experiments show that none of the norovirus P-domains bind to sialoglycans. Notably, MNV P-domains do not bind to any of the glycans studied, and MNV-1 infection of cells deficient in surface sialoglycans shows no significant difference compared to cells expressing respective glycans. These findings redefine glycan recognition by noroviruses, challenging present models of infection.

scholarly journals Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ying Li ◽  
Hang Sun ◽  
Shiyao Feng ◽  
Qi Zhang ◽  
Siyu Han ◽  
...  
Keyword(s):  
Protein Interactions ◽  
State Of The Art ◽  
Recognition Performance ◽  
Feature Learning ◽  
Biological Processes ◽  
Multimodal Features ◽  
Learning Architectures ◽  
Motif Information ◽  
Experimental Comparisons ◽  
Better Than

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.

scholarly journals Immunomodulatory action of jacalin from Artocarpus integrifolia and mannoprotein from Saccharomyces uvarum on the humoral immunity of laying hens

2020 ◽  
Vol 50 (4) ◽  
Author(s):  
Marco Aurélio Chiara Silva ◽  
Miriele Caroline da Silva ◽  
João Waine Pinheiro ◽  
Raul Jorge Hernan Castro-Goméz ◽  
Alice Eiko Murakami ◽  
...  
Keyword(s):  
Immune Response ◽  
Protein Interactions ◽  
Antibody Production ◽  
Laying Hens ◽  
Lectin Binding ◽  
Primary Immune Response ◽  
Immunomodulatory Effects ◽  
Saccharomyces Uvarum ◽  
Immunomodulatory Properties ◽  
Artocarpus Integrifolia

ABSTRACT: Advances in the fields of glycobiology and immunology have provided many insights into the role of carbohydrate-protein interactions in the immune system. Jacalin of Artocarpus integrifolia (JCA) and structural mannoprotein of Saccharomyces uvarum (MPS) are molecules with immunomodulatory properties. JCA is an IgA human lectin binding molecule that causes the mitogenic stimulation of immune cells, production of cytokines, chemotaxis, and activation of leukocytes. Studies on the immunomodulatory properties of JCA and MPS in mammals and fish suggest that they have an action on antibody production. The aim of this study was to investigate the possible action of JCA and MPS on the production of specific antibodies in laying hens. For this, laying hens were inoculated with an intra abdominal injection of sheep red blood cells (SRBC) with either JCA (0.075 µg, 0.75 µg, and 7.5 µg) or MPS (20 µg and 100 µg). Levels of anti-SRBC antibodies of the IgY, IgM, and IgA classes were evaluated by ELISA. Results showed that JCA and MPS have immunomodulatory effects on levels of anti-SRBC IgM, IgA, and IgY. An immunostimulatory effect of JCA was observed in primary immune response on anti-SRBC IgY, while an inhibitory effect of JCA and MPS was observed in secondary immune response on the production of IgM and IgA anti-SRBC. These results suggested that MPS and JCA have immunomodulatory effects on antibody production and could be used in future studies on humoral immune response in poultry.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

2019 ◽  
Vol 26 (8) ◽  
pp. 601-619 ◽  
Author(s):  
Amit Sagar ◽  
Bin Xue
Keyword(s):  
Machine Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Experimental Methods ◽  
Machine Learning Techniques ◽  
Computational Techniques ◽  
Biological Processes ◽  
Complete Spectrum ◽  
Future Developments ◽  
Learning Techniques

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

scholarly journals Databases and Bioinformatic Tools for Glycobiology and Glycoproteomics

2020 ◽  
Vol 21 (18) ◽  
pp. 6727 ◽  
Author(s):  
Xing Li ◽  
Zhijue Xu ◽  
Xiaokun Hong ◽  
Yan Zhang ◽  
Xia Zou
Keyword(s):  
Protein Interactions ◽  
Glycan Structure ◽  
Biological Processes ◽  
Protein Scaffolds ◽  
New Disease ◽  
The Public ◽  
Disease Biomarkers ◽  
Bioinformatic Tools ◽  
The Past ◽  
Glycosylation Sites

Glycosylation plays critical roles in various biological processes and is closely related to diseases. Deciphering the glycocode in diverse cells and tissues offers opportunities to develop new disease biomarkers and more effective recombinant therapeutics. In the past few decades, with the development of glycobiology, glycomics, and glycoproteomics technologies, a large amount of glycoscience data has been generated. Subsequently, a number of glycobiology databases covering glycan structure, the glycosylation sites, the protein scaffolds, and related glycogenes have been developed to store, analyze, and integrate these data. However, these databases and tools are not well known or widely used by the public, including clinicians and other researchers who are not in the field of glycobiology, but are interested in glycoproteins. In this study, the representative databases of glycan structure, glycoprotein, glycan–protein interactions, glycogenes, and the newly developed bioinformatic tools and integrated portal for glycoproteomics are reviewed. We hope this overview could assist readers in searching for information on glycoproteins of interest, and promote further clinical application of glycobiology.

scholarly journals mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

2019 ◽  
Vol 47 (W1) ◽  
pp. W338-W344 ◽  
Author(s):  
Carlos H M Rodrigues ◽  
Yoochan Myung ◽  
Douglas E V Pires ◽  
David B Ascher
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Evolutionary Information ◽  
Biological Processes ◽  
Missense Mutations ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Residue Interaction ◽  
User Friendly ◽  
Network Metrics

AbstractProtein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

Matrix-assisted nanoelectrospray mass spectrometry for soft ionization of metal(i)–protein complexes

The Analyst ◽  
2020 ◽  
Vol 145 (5) ◽  
pp. 1646-1656
Author(s):  
Jin Li ◽  
Yajun Zheng ◽  
Jia Zhao ◽  
Daniel E. Austin ◽  
Zhiping Zhang
Keyword(s):  
Mass Spectrometry ◽  
Metal Ions ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Biological Processes ◽  
Soft Ionization

Metal ions play significant roles in biological processes, and investigation of metal–protein interactions provides a basis to understand the functions of metal ions in such systems.

Sign in / Sign up

Export Citation Format

Share Document