scholarly journals Straightforward Protein-Protein Interaction Interface Mapping via Random Mutagenesis and Mammalian Protein Protein Interaction Trap (MAPPIT)

2019 ◽  
Vol 20 (9) ◽  
pp. 2058 ◽  
Author(s):  
Laurens Vyncke ◽  
Delphine Masschaele ◽  
Jan Tavernier ◽  
Frank Peelman
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Random Mutagenesis ◽  
Interaction Mechanism ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Multiple Partners ◽  
Interaction Trap ◽  
Mammalian Protein

The MAPPIT (mammalian protein protein interaction trap) method allows high-throughput detection of protein interactions by very simple co-transfection of three plasmids in HEK293T cells, followed by a luciferase readout. MAPPIT detects a large percentage of all protein interactions, including those requiring posttranslational modifications and endogenous or exogenous ligands. Here, we present a straightforward method that allows detailed mapping of interaction interfaces via MAPPIT. The method provides insight into the interaction mechanism and reveals how this is affected by disease-associated mutations. By combining error-prone polymerase chain reaction (PCR) for random mutagenesis, 96-well DNA prepping, Sanger sequencing, and MAPPIT via 384-well transfections, we test the effects of a large number of mutations of a selected protein on its protein interactions. The entire screen takes less than three months and interactions with multiple partners can be studied in parallel. The effect of mutations on the MAPPIT readout is mapped on the protein structure, allowing unbiased identification of all putative interaction sites. We have thus far analysed 6 proteins and mapped their interfaces for 16 different interaction partners. Our method is broadly applicable as the required tools are simple and widely available.

scholarly journals Mammalian Protein-Protein Interaction Trap (MAPPIT) Analysis of STAT5, CIS, and SOCS2 Interactions with the Growth Hormone Receptor

2007 ◽  
Vol 21 (11) ◽  
pp. 2821-2831 ◽  
Author(s):  
Isabel Uyttendaele ◽  
Irma Lemmens ◽  
Annick Verhee ◽  
Anne-Sophie De Smet ◽  
Joël Vandekerckhove ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Binding Sites ◽  
Growth Hormone Receptor ◽  
Functional Divergence ◽  
Critical Role ◽  
Sh2 Domain ◽  
Cytokine Signaling ◽  
Protein Protein Interaction ◽  
Interaction Trap ◽  
Mammalian Protein

Abstract Binding of GH to its receptor induces rapid phosphorylation of conserved tyrosine motifs that function as recruitment sites for downstream signaling molecules. Using mammalian protein-protein interaction trap (MAPPIT), a mammalian two-hybrid method, we mapped the binding sites in the GH receptor for signal transducer and activator of transcription 5 (STAT5) a and b and for the negative regulators of cytokine signaling cytokine-inducible Src-homology 2 (SH2)-containing protein (CIS) and suppressor of cytokine signaling 2 (SOCS2). Y534, Y566, and Y627 are the major recruitment sites for STAT5. A non-overlapping recruitment pattern is observed for SOCS2 and CIS with positions Y487 and Y595 as major binding sites, ruling out SOCS-mediated inhibition of STAT5 activation by competition for shared binding sites. More detailed analysis revealed that CIS binding to the Y595, but not to the Y487 motif, depends on both its SH2 domain and the C-terminal part of its SOCS box, with a critical role for the CIS Y253 residue. This functional divergence of the two CIS/SOCS2 recruitment sites is also observed upon substitution of the Y+1 residue by leucine, turning the Y487, but not the Y595 motif into a functional STAT5 recruitment site.

scholarly journals MAPPIT: a versatile tool to study cytokine receptor signalling

2008 ◽  
Vol 36 (6) ◽  
pp. 1448-1451 ◽  
Author(s):  
Irma Lemmens ◽  
Sam Lievens ◽  
Jan Tavernier
Keyword(s):  
Protein Interactions ◽  
Mammalian Cells ◽  
Cytokine Receptor ◽  
Bait Protein ◽  
Protein Protein Interaction ◽  
Receptor Signalling ◽  
Versatile Tool ◽  
Two Hybrid ◽  
Interaction Trap ◽  
Mammalian Protein

MAPPIT (mammalian protein–protein interaction trap) is a cytokine receptor-based two-hybrid method that operates in intact mammalian cells. A bait is fused C-terminally to a STAT (signal transducer and activator of transcription) recruitment-deficient receptor, whereas the prey is linked to functional STAT-binding sites. When bait and prey interact a ligand-dependent complementation of the STAT recruitment deficiency occurs, leading to activation of a STAT-responsive reporter. MAPPIT is very well suited to study protein interactions involving activated cytokine receptors as the technique allows modification of the bait protein in a physiologically optimal environment.

scholarly journals Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

2020 ◽  
Vol 21 (7) ◽  
pp. 2274 ◽  
Author(s):  
Aijun Deng ◽  
Huan Zhang ◽  
Wenyan Wang ◽  
Jun Zhang ◽  
Dingdong Fan ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Significance ◽  
Prediction Performance ◽  
Good Prediction ◽  
Feature Extraction Method ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Original Dataset ◽  
Protein Interaction Sites

The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.

scholarly journals Prediction of Protein–Protein Interaction Sites Based on Stratified Attentional Mechanisms

2021 ◽  
Vol 12 ◽  
Author(s):  
Minli Tang ◽  
Longxin Wu ◽  
Xinyu Yu ◽  
Zhaoqi Chu ◽  
Shuting Jin ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Human Life ◽  
Amino Acid Position ◽  
Biological Macromolecules ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Scoring Matrix ◽  
Protein Interaction Sites ◽  
Better Than

Proteins are the basic substances that undertake human life activities, and they often perform their biological functions through interactions with other biological macromolecules, such as cell transmission and signal transduction. Predicting the interaction sites between proteins can deepen the understanding of the principle of protein interactions, but traditional experimental methods are time-consuming and labor-intensive. In this study, a new hierarchical attention network structure, named HANPPIS, by adding six effective features of protein sequence, position-specific scoring matrix (PSSM), secondary structure, pre-training vector, hydrophilic, and amino acid position, is proposed to predict protein–protein interaction (PPI) sites. The experiment proved that our model has obtained very effective results, which was better than the existing advanced calculation methods. More importantly, we used the double-layer attention mechanism to improve the interpretability of the model and to a certain extent solved the problem of the “black box” of deep neural networks, which can be used as a reference for location positioning on the biological level.

Design and Use of a Mammalian Protein-Protein Interaction Trap (MAPPIT)

2002 ◽  
Vol 2002 (162) ◽  
pp. pl18-pl18 ◽  
Author(s):  
S. Eyckerman ◽  
I. Lemmens ◽  
S. Lievens ◽  
J. Van der Heyden ◽  
A. Verhee ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Protein Interaction ◽  
Interaction Trap ◽  
Mammalian Protein

scholarly journals A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites

2021 ◽  
Vol 12 ◽  
Author(s):  
Pan Wang ◽  
Guiyang Zhang ◽  
Zu-Guo Yu ◽  
Guohua Huang
Keyword(s):  
Deep Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Gradient Boosting ◽  
Protein Protein Interactions ◽  
Global Features ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Extreme Gradient Boosting ◽  
Protein Interaction Sites

Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.

Design of a Fluorescence-Activated Cell Sorting-Based Mammalian Protein–Protein Interaction Trap

2004 ◽  
pp. 293-310 ◽  
Author(s):  
Sam Lievens ◽  
José Van der Heyden ◽  
Els Vertenten ◽  
Jean Plum ◽  
Joël Vandekerckhove ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Cell Sorting ◽  
Fluorescence Activated Cell Sorting ◽  
Protein Protein Interaction ◽  
Interaction Trap ◽  
Mammalian Protein
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