Protein-Protein Interactions and Prediction: A Comprehensive Overview

Sowmya, Gopichandran; Ranganathan, Shoba

doi:10.2174/09298665113209990056

Cited by 21 publications

(14 citation statements)

References 133 publications

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“…Protein–protein interaction (PPI) is critical for molecular functions in living systems. PPIs are associated with catalysis, regulation, signaling, immunity, and inhibition, thereby playing a critical role in functional genomics . Extensive studies on the existing PPI complexes are the key to understanding cellular machinery as reviewed elsewhere .…”

Section: Introductionmentioning

confidence: 99%

“…PPIs are associated with catalysis, regulation, signaling, immunity, and inhibition, thereby playing a critical role in functional genomics. 1 Extensive studies on the existing PPI complexes are the key to understanding cellular machinery as reviewed elsewhere. [1][2][3][4][5] With modern experimental techniques such as two hybrid systems, protein fragment complementation, tandem affinity purification methods and protein arrays, several interacting protein pairs have been detected in largescale studies, 6 although their biological role may not be well characterized or known.…”

Section: Introductionmentioning

confidence: 99%

“…1 Extensive studies on the existing PPI complexes are the key to understanding cellular machinery as reviewed elsewhere. [1][2][3][4][5] With modern experimental techniques such as two hybrid systems, protein fragment complementation, tandem affinity purification methods and protein arrays, several interacting protein pairs have been detected in largescale studies, 6 although their biological role may not be well characterized or known. Comprehensive homology modeling techniques of known interacting proteins combined with docking studies and PPI data helps in understanding structural assembly for functional preferences as shown for integrin avb6 heterodimer (two different protein subunits) complex.…”

Section: Introductionmentioning

confidence: 99%

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Linking structural features of protein complexes and biological function

2015

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Protein-protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure-function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r 5 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Linking structural features of protein complexes and biological function

2015

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“…Effects of mutations on Kv11.1a protein function can be interpreted on the basis of how they impact secondary (alpha helix, random coil, beta sheets), tertiary (protein and subdomain folding) and quaternary (protein/protein associations) factors. Myriad algorithms have been introduced to predict protein folding tendencies for forming canonical secondary structures, identify protein folds based on sequence similarities to other protein structures deposited in the Protein Databank, and predict potential protein-protein interaction interfaces [82,[89][90][91]. Nonetheless, a gold standard for surmising structure/function relationships in proteins and how they might be perturbed by missense mutations is interpreting atomistic-scale structural data of intact proteins, when available.…”

Section: Kcnh2 Variants Of Uncertain Significance (Vus)mentioning

confidence: 99%

Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2 KCNH2 (hERG) Mutations and Identifying New Patients

et al. 2020

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Significant advances in our understanding of the molecular mechanisms that cause congenital long QT syndrome (LQTS) have been made. A wide variety of experimental approaches, including heterologous expression of mutant ion channel proteins and the use of inducible pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) from LQTS patients offer insights into etiology and new therapeutic strategies. This review briefly discusses the major molecular mechanisms underlying LQTS type 2 (LQT2), which is caused by loss-of-function (LOF) mutations in the KCNH2 gene (also known as the human ether-à-go-go-related gene or hERG). Almost half of suspected LQT2-causing mutations are missense mutations, and functional studies suggest that about 90% of these mutations disrupt the intracellular transport, or trafficking, of the KCNH2-encoded Kv11.1 channel protein to the cell surface membrane. In this review, we discuss emerging strategies that improve the trafficking and functional expression of trafficking-deficient LQT2 Kv11.1 channel proteins to the cell surface membrane and how new insights into the structure of the Kv11.1 channel protein will lead to computational approaches that identify which KCNH2 missense variants confer a high-risk for LQT2.

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“…Currently, machine learning approaches dominate most of the computational methods for the prediction of PPIs [ 7 ]. Building a meaningful feature set and choosing corresponding machine learning algorithms are two key steps for successful predictions in traditional machine learning.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

Gong

et al. 2018

Molecules

109

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Machine learning based predictions of protein–protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios. Here, we present a deep neural network framework (DNN-PPI) for predicting PPIs using features learned automatically only from protein primary sequences. Within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (CNN), and long short-term memory (LSTM) neural network layers. Then, a concatenated vector of the two outputs from the previous layer is wired as the input of the fully connected neural network. Finally, the Adam optimizer is applied to learn the network weights in a back-propagation fashion. The different types of features, including semantic associations between amino acids, position-related sequence segments (motif), and their long- and short-term dependencies, are captured in the embedding, CNN and LSTM layers, respectively. When the model was trained on Pan’s human PPI dataset, it achieved a prediction accuracy of 98.78% at the Matthew’s correlation coefficient (MCC) of 97.57%. The prediction accuracies for six external datasets ranged from 92.80% to 97.89%, making them superior to those achieved with previous methods. When performed on Escherichia coli, Drosophila, and Caenorhabditis elegans datasets, DNN-PPI obtained prediction accuracies of 95.949%, 98.389%, and 98.669%, respectively. The performances in cross-species testing among the four species above coincided in their evolutionary distances. However, when testing Mus Musculus using the models from those species, they all obtained prediction accuracies of over 92.43%, which is difficult to achieve and worthy of note for further study. These results suggest that DNN-PPI has remarkable generalization and is a promising tool for identifying protein interactions.

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Protein-Protein Interactions and Prediction: A Comprehensive Overview

Cited by 21 publications

References 133 publications

Linking structural features of protein complexes and biological function

Linking structural features of protein complexes and biological function

Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2 KCNH2 (hERG) Mutations and Identifying New Patients

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

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