scholarly journals Mouse Mutants Reveal that Putative Protein Interaction Sites in the p53 Proline-Rich Domain Are Dispensable for Tumor Suppression

2006 ◽  
Vol 27 (4) ◽  
pp. 1425-1432 ◽  
Author(s):  
Franck Toledo ◽  
Crystal J. Lee ◽  
Kurt A. Krummel ◽  
Luo-Wei Rodewald ◽  
Chung-Wen Liu ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Point Mutations ◽  
Tumor Suppressor P53 ◽  
Mutant Proteins ◽  
Interaction Sites ◽  
Rich Domain ◽  
Mouse Mutants ◽  
Induced Tumors ◽  
The Stability ◽  
Protein Interaction Sites

ABSTRACT The stability and activity of tumor suppressor p53 are tightly regulated and partially depend on the p53 proline-rich domain (PRD). We recently analyzed mice expressing p53 with a deletion of the PRD (p53ΔP). p53ΔP, a weak transactivator hypersensitive to Mdm2-mediated degradation, is unable to suppress oncogene-induced tumors. This phenotype could result from the loss of two motifs: Pin1 sites proposed to influence p53 stabilization and PXXP motifs proposed to mediate protein interactions. We investigated the importance of these motifs by generating mice encoding point mutations in the PRD. p53TTAA contains mutations suppressing all putative Pin1 sites in the PRD, while p53AXXA lacks PXXP motifs but retains one intact Pin1 site. Both mutant proteins accumulated in response to DNA damage, although the accumulation of p53TTAA was partially impaired. Importantly, p53TTAA and p53AXXA are efficient transactivators and potent suppressors of oncogene-induced tumors. Thus, Pin1 sites in the PRD may modulate p53 stability but do not significantly affect function. In addition, PXXP motifs are not essential, but structure dictated by the presence of prolines, PXXXXP motifs that may mediate protein interactions, and/or the length of this region appears to be functionally significant. These results may explain why the sequence of the p53 PRD is so variable in evolution.

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 Pathogenic D76N Variant of β2-Microglobulin: Synergy of Diverse Effects in Both the Native and Amyloid States

Biology ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1197
Author(s):  
Éva Bulyáki ◽  
Judit Kun ◽  
Tamás Molnár ◽  
Alexandra Papp ◽  
András Micsonai ◽  
...  
Keyword(s):  
Amyloid Fibrils ◽  
Conformational Stability ◽  
Point Mutations ◽  
Amyloid Formation ◽  
Mhc I ◽  
Mutant Proteins ◽  
Β2 Microglobulin ◽  
Partial Unfolding ◽  
The Stability

β2-microglobulin (β2m), the light chain of the MHC-I complex, is associated with dialysis-related amyloidosis (DRA). Recently, a hereditary systemic amyloidosis was discovered, caused by a naturally occurring D76N β2m variant, which showed a structure remarkably similar to the wild-type (WT) protein, albeit with decreased thermodynamic stability and increased amyloidogenicity. Here, we investigated the role of the D76N mutation in the amyloid formation of β2m by point mutations affecting the Asp76-Lys41 ion-pair of WT β2m and the charge cluster on Asp38. Using a variety of biophysical techniques, we investigated the conformational stability and partial unfolding of the native state of the variants, as well as their amyloidogenic propensity and the stability of amyloid fibrils under various conditions. Furthermore, we studied the intermolecular interactions of WT and mutant proteins with various binding partners that might have in vivo relevance. We found that, relative to WT β2m, the exceptional amyloidogenicity of the pathogenic D76N β2m variant is realized by the deleterious synergy of diverse effects of destabilized native structure, higher sensitivity to negatively charged amphiphilic molecules (e.g., lipids) and polyphosphate, more effective fibril nucleation, higher conformational stability of fibrils, and elevated affinity for extracellular components, including extracellular matrix proteins.

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.

scholarly journals Semi-supervised prediction of protein interaction sites from unlabeled sample information

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Ye Wang ◽  
Changqing Mei ◽  
Yuming Zhou ◽  
Yan Wang ◽  
Chunhou Zheng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Prediction Performance ◽  
Support Vector ◽  
Sequence Information ◽  
Biological Processes ◽  
Interaction Sites ◽  
Unlabeled Sample ◽  
Protein Interaction Sites

Abstract Background The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of prediction accuracy and generalization ability of predictors in protein interaction sites prediction. Therefore, it is necessary to effectively improve prediction performance of protein interaction sites using large amounts of unlabeled data together with small amounts of labeled data and background knowledge today. Results In this work, three semi-supervised support vector machine–based methods are proposed to improve the performance in the protein interaction sites prediction, in which the information of unlabeled protein sites can be involved. Herein, five features related with the evolutionary conservation of amino acids are extracted from HSSP database and Consurf Sever, i.e., residue spatial sequence spectrum, residue sequence information entropy and relative entropy, residue sequence conserved weight and residual Base evolution rate, to represent the residues within the protein sequence. Then three predictors are built for identifying the interface residues from protein surface using three types of semi-supervised support vector machine algorithms. Conclusion The experimental results demonstrated that the semi-supervised approaches can effectively improve prediction performance of protein interaction sites when unlabeled information is involved into the predictors and one of them can achieve the best prediction performance, i.e., the accuracy of 70.7%, the sensitivity of 62.67% and the specificity of 78.72%, respectively. With comparison to the existing studies, the semi-supervised models show the improvement of the predication performance.

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.

scholarly journals Pediatric Encephalopathy: Clinical, Biochemical and Cellular Insights into the Role of Gln52 of GNAO1 and GNAI1 for the Dominant Disease

Cells ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2749
Author(s):  
Gonzalo P. Solis ◽  
Tatyana V. Kozhanova ◽  
Alexey Koval ◽  
Svetlana S. Zhilina ◽  
Tatyana I. Mescheryakova ◽  
...  
Keyword(s):  
Developmental Delay ◽  
G Proteins ◽  
Protein Interactions ◽  
Movement Disorders ◽  
De Novo ◽  
Intracellular Localization ◽  
Point Mutations ◽  
Epileptic Seizures ◽  
Heterotrimeric G Proteins ◽  
Mutant Proteins

Heterotrimeric G proteins are immediate transducers of G protein-coupled receptors—the biggest receptor family in metazoans—and play innumerate functions in health and disease. A set of de novo point mutations in GNAO1 and GNAI1, the genes encoding the α-subunits (Gαo and Gαi1, respectively) of the heterotrimeric G proteins, have been described to cause pediatric encephalopathies represented by epileptic seizures, movement disorders, developmental delay, intellectual disability, and signs of neurodegeneration. Among such mutations, the Gln52Pro substitutions have been previously identified in GNAO1 and GNAI1. Here, we describe the case of an infant with another mutation in the same site, Gln52Arg. The patient manifested epileptic and movement disorders and a developmental delay, at the onset of 1.5 weeks after birth. We have analyzed biochemical and cellular properties of the three types of dominant pathogenic mutants in the Gln52 position described so far: Gαo[Gln52Pro], Gαi1[Gln52Pro], and the novel Gαo[Gln52Arg]. At the biochemical level, the three mutant proteins are deficient in binding and hydrolyzing GTP, which is the fundamental function of the healthy G proteins. At the cellular level, the mutants are defective in the interaction with partner proteins recognizing either the GDP-loaded or the GTP-loaded forms of Gαo. Further, of the two intracellular sites of Gαo localization, plasma membrane and Golgi, the former is strongly reduced for the mutant proteins. We conclude that the point mutations at Gln52 inactivate the Gαo and Gαi1 proteins leading to aberrant intracellular localization and partner protein interactions. These features likely lie at the core of the molecular etiology of pediatric encephalopathies associated with the codon 52 mutations in GNAO1/GNAI1.

Modeling the impact of point mutations on the stability of proteins

2020 ◽  
Vol 18 (04) ◽  
pp. 2050019
Author(s):  
K. G. Kulikov ◽  
T. V. Koshlan
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Point Mutations ◽  
Dimensional Structure ◽  
Amino Acid Residues ◽  
Three Dimensional Structure ◽  
The Stability ◽  
The Impact ◽  
Proven Theorem

A new method has been introduced which allows us to determine the stability of protein complexes with point changes of amino acid residues that also take into account the three-dimensional structure of the complex. This formulated and proven theorem is aimed at determining the criterion for the stability of protein molecules. The algorithm and software package were developed for analyzing protein interactions, taking into account their three-dimensional structure from the PDB database.

scholarly journals PBP5 Complementation of a PBP3 Deficiency in Enterococcus hirae

2006 ◽  
Vol 188 (17) ◽  
pp. 6298-6307 ◽  
Author(s):  
S. Leimanis ◽  
N. Hoyez ◽  
S. Hubert ◽  
M. Laschet ◽  
Eric Sauvage ◽  
...  
Keyword(s):  
Site Directed Mutagenesis ◽  
Penicillin Binding Protein ◽  
Enterococcus Hirae ◽  
Penicillin Binding Proteins ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Functional Potential ◽  
The Stability ◽  
Protein Interaction Sites ◽  
Made In

ABSTRACT The low susceptibility of enterococci to β-lactams is due to the activity of the low-affinity penicillin-binding protein 5 (PBP5). One important feature of PBP5 is its ability to substitute for most, if not all, penicillin-binding proteins when they are inhibited. That substitution activity was analyzed in Enterococcus hirae SL2, a mutant whose pbp5 gene was interrupted by the nisRK genes and whose PBP3 synthesis was submitted to nisin induction. Noninduced SL2 cells were unable to divide except when plasmid-borne pbp5 genes were present, provided that the PBP5 active site was functional. Potential protein-protein interaction sites of the PBP5 N-terminal module were mutagenized by site-directed mutagenesis. The T167-L184 region (designated site D) appeared to be an essential intramolecular site needed for the stability of the protein. Mutations made in the two globular domains present in the N-terminal module indicated that they were needed for the suppletive activity. The P197-N209 segment (site E) in one of these domains seemed to be particularly important, as single and double mutations reduced or almost completely abolished, respectively, the action of PBP5.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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