scholarly journals Uncovering the basis of protein-protein interaction specificity with a combinatorially complete library

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Thuy-Lan V Lite ◽  
Robert A Grant ◽  
Isabel Nocedal ◽  
Megan L Littlehale ◽  
Monica S Guo ◽  
...  
Keyword(s):  
Molecular Recognition ◽  
Crystal Structures ◽  
Protein Interaction ◽  
Bacterial Toxin ◽  
General Nature ◽  
Primary Sequence ◽  
Wild Type ◽  
Mutational Robustness ◽  
Protein Protein Interaction ◽  
Interface Residues

Protein-protein interaction specificity is often encoded at the primary sequence level. However, the contributions of individual residues to specificity are usually poorly understood and often obscured by mutational robustness, sequence degeneracy, and epistasis. Using bacterial toxin-antitoxin systems as a model, we screened a combinatorially complete library of antitoxin variants at three key positions against two toxins. This library enabled us to measure the effect of individual substitutions on specificity in hundreds of genetic backgrounds. These distributions allow inferences about the general nature of interface residues in promoting specificity. We find that positive and negative contributions to specificity are neither inherently coupled nor mutually exclusive. Further, a wild-type antitoxin appears optimized for specificity as no substitutions improve discrimination between cognate and non-cognate partners. By comparing crystal structures of paralogous complexes, we provide a rationale for our observations. Collectively, this work provides a generalizable approach to understanding the logic of molecular recognition.

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

scholarly journals Proximal Protein Interaction Landscape of RAS Paralogs

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3326 ◽  
Author(s):  
Benoît Béganton ◽  
Etienne Coyaud ◽  
Estelle M. N. Laurent ◽  
Alain Mangé ◽  
Julien Jacquemetton ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Activities ◽  
Mammalian Species ◽  
Variable Region ◽  
Specific Protein ◽  
Protein Networks ◽  
Ras Proteins ◽  
Wild Type ◽  
Protein Protein Interaction

RAS proteins (KRAS, NRAS and HRAS) are frequently activated in different cancer types (e.g., non-small cell lung cancer, colorectal cancer, melanoma and bladder cancer). For many years, their activities were considered redundant due to their high degree of sequence homology (80% identity) and their shared upstream and downstream protein partners. However, the high conservation of the Hyper-Variable-Region across mammalian species, the preferential activation of different RAS proteins in specific tumor types and the specific post-translational modifications and plasma membrane-localization of each paralog suggest they could ensure discrete functions. To gain insights into RAS proteins specificities, we explored their proximal protein–protein interaction landscapes using the proximity-dependent biotin identification technology (BioID) in Flp-In T-REx 293 cell lines stably transfected and inducibly expressing wild type KRAS4B, NRAS or HRAS. We identified more than 800 high-confidence proximal interactors, allowing us to propose an unprecedented comparative analysis of wild type RAS paralogs protein networks. These data bring novel information on poorly characterized RAS functions, e.g., its putative involvement in metabolic pathways, and on shared as well as paralog-specific protein networks that could partially explain the complexity of RAS functions. These networks of protein interactions open numerous avenues to better understand RAS paralogs biological activities.

ConvsPPIS: Identifying Protein-protein Interaction Sites by an Ensemble Convolutional Neural Network with Feature Graph

2020 ◽  
Vol 15 (4) ◽  
pp. 368-378
Author(s):  
Huaixu Zhu ◽  
Xiuquan Du ◽  
Yu Yao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Protein Interaction ◽  
Learning Ability ◽  
Superior Performance ◽  
Model Learning ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Protein Interaction Sites ◽  
Interface Residues

Background/Objective: Protein-protein interactions are essentials for most cellular processes and thus, unveiling how proteins interact with is a crucial question that can be better understood by recognizing which residues participate in the interaction. Although many computational approaches have been proposed to predict interface residues, their feature perspective and model learning ability are not enough to achieve ideal results. So, our objective is to improve the predictive performance under considering feature perspective and new learning algorithm. Method: In this study, we proposed an ensemble deep convolutional neural network, which explores the context and positional context of consecutive residues within a protein sub-sequence. Specifically, unlike the feature view of previous methods, ConvsPPIS uses evolutionary, physicochemical, and structural protein characteristics to construct their own feature graph respectively. After that, three independent deep convolutional neural networks are trained on each type of feature graph for learning the underlying pattern in sub-sequence. Lastly, we integrated those three deep networks into an ensemble predictor with leveraging complementary information of those features to predict potential interface residues. Results: Some comparative experiments have conducted through 10-fold cross-validation. The results indicated that ConvsPPIS achieved superior performance on DBv5-Sel dataset with an accuracy of 88%. Additional experiments on CAPRI-Alone dataset demonstrated ConvsPPIS has also better prediction performance. Conclusion: The ConvsPPIS method provided a new perspective to capture protein feature expression for identifying protein-protein interaction sites. The results proved the superiority of this method.

scholarly journals PIPENN: Protein Interface Prediction with an Ensemble of Neural Nets

2021 ◽  
Author(s):  
Bas Stringer ◽  
Hans De Ferrante ◽  
Sanne Abeln ◽  
Jaap Heringa ◽  
K. Anton A. Feenstra ◽  
...  
Keyword(s):  
Small Molecules ◽  
Small Molecule ◽  
Protein Interaction ◽  
Receptor Activation ◽  
Neural Nets ◽  
Data Sets ◽  
Protein Interface ◽  
Protein Protein Interaction ◽  
Independent Test ◽  
Interface Residues

Motivation: Protein interactions play an essential role in many biological and cellular processes, such as protein—protein interaction (PPI) in signaling pathways, binding to DNA in transcription, and binding to small molecules in receptor activation or enzymatic activity. Experimental identification of protein binding interface residues is a time-consuming, costly, and challenging task. Several machine learning and other computational approaches exist which predict such interface residues. Here we explore if Deep Learning (DL) can be used effectively for this prediction task, and which learning strategies and architectures may be most efficient. We introduce seven DL architectures that are applied to eleven independent test sets, focused on the residues involved in PPI interfaces and in binding RNA/DNA and small molecule ligands. Results: We constructed a large data set dubbed BioDL, comprising protein-protein interaction data from the PDB and protein-ligand interactions (DNA, RNA and small molecules) from the BioLip database. Additionally, we reused our existing curated homo- and heteromeric PPI data sets. We performed several experiments to assess the impact of different data features, spatial forms, encoding schemes, network initializations, loss functions, regularization mechanisms, and activation functions on the performance of the predictors. Benchmarking the resulting DL models with an independent test set (ZK448) shows no single DL architecture performs best on all instances, but that an ensemble of DL architectures consistently achieves peak prediction performance. Our PIPENN's ensemble predictor outperforms current state-of-the-art sequence-based protein interface predictors on all interaction types, achieving AUCs of 0.718 (protein—protein), 0.823 (protein—nucleotide) and 0.842 (protein—small molecule) respectively. Availability: Source code and data sets at https://github.com/ibivu/

scholarly journals Iron–Sulfur Cluster Biogenesis Chaperones: Evidence for Emergence of Mutational Robustness of a Highly Specific Protein–Protein Interaction

2015 ◽  
Vol 33 (3) ◽  
pp. 643-656 ◽  
Author(s):  
Wojciech Delewski ◽  
Bogumiła Paterkiewicz ◽  
Mateusz Manicki ◽  
Brenda Schilke ◽  
Bartłomiej Tomiczek ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Specific Protein ◽  
Mutational Robustness ◽  
Iron Sulfur Cluster ◽  
Sulfur Cluster ◽  
Protein Protein Interaction ◽  
Iron Sulfur

scholarly journals Interaction between Leukotoxin and Cu,Zn Superoxide Dismutase in Aggregatibacter actinomycetemcomitans

2007 ◽  
Vol 75 (9) ◽  
pp. 4490-4497 ◽  
Author(s):  
Nataliya V. Balashova ◽  
Diane H. Park ◽  
Jigna K. Patel ◽  
David H. Figurski ◽  
Scott C. Kachlany
Keyword(s):  
Superoxide Dismutase ◽  
Protein Interaction ◽  
Inflammatory Cells ◽  
Aggressive Periodontitis ◽  
Etiologic Agent ◽  
Bacterial Toxins ◽  
Wild Type ◽  
Gram Negative ◽  
Protein Protein Interaction ◽  
Systemic Infections

ABSTRACT Aggregatibacter (Actinobacillus) actinomycetemcomitans is a gram-negative oral pathogen that is the etiologic agent of localized aggressive periodontitis and systemic infections. A. actinomycetemcomitans produces leukotoxin (LtxA), which is a member of the RTX (repeats in toxin) family of secreted bacterial toxins and is known to target human leukocytes and erythrocytes. To better understand how LtxA functions as a virulence factor, we sought to detect and study potential A. actinomycetemcomitans proteins that interact with LtxA. We found that Cu,Zn superoxide dismutase (SOD) interacts specifically with LtxA. Cu,Zn SOD was purified from A. actinomycetemcomitans to homogeneity and remained enzymatically active. Purified Cu,Zn SOD allowed us to isolate highly specific anti-Cu,Zn SOD antibody and this antibody was used to further confirm protein interaction. Cu,Zn SOD-deficient mutants displayed decreased survival in the presence of reactive oxygen and nitrogen species and could be complemented with wild-type Cu,Zn SOD in trans. We suggest that A. actinomycetemcomitans Cu,Zn SOD may protect both bacteria and LtxA from reactive species produced by host inflammatory cells during disease. This is the first example of a protein-protein interaction involving a bacterial Cu,Zn SOD.

Interallelic Complementation at the suppressor of forked Locus of Drosophila Reveals Complementation Between Suppressor of forked Proteins Mutated in Different Regions

Genetics ◽  
1996 ◽  
Vol 142 (4) ◽  
pp. 1225-1235
Author(s):  
Martine Simonelig ◽  
Kate Elliott ◽  
Andrew Mitchelson ◽  
Kevin O'Hare
Keyword(s):  
Drosophila Melanogaster ◽  
Gene Product ◽  
Protein Interaction ◽  
Wild Type ◽  
Type Function ◽  
Protein Protein Interaction ◽  
Molecular Complexity ◽  
Interallelic Complementation ◽  
Cleavage Stimulation Factor ◽  
Different Parts

Abstract The Su(f) protein of Drosophila melanogaster shares extensive homologies with proteins from yeast (RNA14) and man (77 kD subunit of cleavage stimulation factor) that are required for 3′ end processing of mRNA. These homologies suggest that su(f) is involved in mRNA 3′ end formation and that some aspects of this process are conserved throughout eukaryotes. We have investigated the genetic and molecular complexity of the su(f) locus. The su(f) gene is transcribed to produce three RNAs and could encode two proteins. Using constructs that contain different parts of the locus, we show that only the larger predicted gene product of 84 kD is required for the wild-type function of su(f). Some lethal alleles of su(f) complement to produce viable combinations. The structures of complementing and noncomplementing su(f) alleles indicate that 84-kD Su(f) proteins mutated in different domains can act in combination for partial su(f) function. Our results suggest protein-protein interaction between or within wild-type Su(f) molecules.

scholarly journals Machine Learning Approaches for Protein–Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2535 ◽  
Author(s):  
Siyu Liu ◽  
Chuyao Liu ◽  
Lei Deng
Keyword(s):  
Machine Learning ◽  
Protein Interaction ◽  
Hot Spots ◽  
Binding Free Energy ◽  
Hot Spot ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Alanine Scanning Mutagenesis ◽  
Protein Protein Interaction ◽  
Interface Residues

Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein–protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein–protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

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