Computational Methods for Prediction of Protein-Protein Interactions

Cellular Processes ◽

Large Numbers ◽

Transient Interactions ◽

The key signaling pathways in cellular processes involve protein-protein interactions (PPIs). A perturbation in the balance of PPIs occurs in various pathophysiological processes. There are a large numbers of experimental methods for detection of PPIs. However, experimental PPI determination is time consuming, expensive, error prone and does not effectively cover transient interactions. Therefore, overlaying and integration of predictive methods with experimental results provides statistical robustness and biological significance to the PPI data. In this chapter, the authors describe PPIs in terms of types, importance, and biological consequences. This chapter also provides a comprehensive description on various computational approaches for PPI prediction. Prediction of PPI can be done through: 1) Genomic information based methods 2) Structure based methods 3) Network topology based methods: 4) Literature and data mining based methods 5) Machine learning methods. For ease of use and convenience, a summary of various databases and software for PPI prediction has been provided.

Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

Journal of Applied Mathematics ◽

10.1155/2012/897289 ◽

2012 ◽

Vol 2012 ◽

pp. 1-23

Author(s):

J. M. Urquiza ◽

I. Rojas ◽

H. Pomares ◽

J. Herrera ◽

J. P. Florido ◽

...

Keyword(s):

Support Vector Machine ◽

Hierarchical Clustering ◽

Protein Interactions ◽

Support Vector Machine Model ◽

Support Vector ◽

Machine Model ◽

Cellular Processes ◽

Parallel Feature ◽

Protein-protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets.

In Vivo Methods to Study Protein–Protein Interactions as Key Players in Mycobacterium Tuberculosis Virulence

Pathogens ◽

10.3390/pathogens8040173 ◽

2019 ◽

Vol 8 (4) ◽

pp. 173 ◽

Cited By ~ 2

Author(s):

Veyron-Churlet ◽

Locht

Keyword(s):

Mycobacterium Tuberculosis ◽

Protein Interactions ◽

Cellular Processes ◽

Unknown Protein ◽

Protein Functions ◽

Transient Interactions ◽

Protein Complementation

Studies on protein–protein interactions (PPI) can be helpful for the annotation of unknown protein functions and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher tuberculosis (TB) pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

In Vivo Methods to Study Protein-Protein Interactions as Key Players in Mycobacterium tuberculosis Virulence

10.20944/preprints201908.0309.v1 ◽

2019 ◽

Author(s):

Romain Veyron-Churlet ◽

Camille Locht

Keyword(s):

Mycobacterium Tuberculosis ◽

Protein Interactions ◽

Protein Function ◽

Cellular Processes ◽

Unknown Protein ◽

Transient Interactions ◽

Protein Complementation

Studies on Protein-Protein interactions (PPI) can be helpful for the annotation of unknown protein function and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher TB pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

Current Medicinal Chemistry ◽

10.2174/0929867325666180214113704 ◽

2019 ◽

Vol 26 (21) ◽

pp. 3890-3910 ◽

Cited By ~ 6

Author(s):

Branislava Gemovic ◽

Neven Sumonja ◽

Radoslav Davidovic ◽

Vladimir Perovic ◽

Nevena Veljkovic

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Protein Complexes ◽

Experimental Studies ◽

Web Based ◽

Prediction Tools ◽

Physiological Processes ◽

Modern Drug ◽

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

Current Medicinal Chemistry ◽

10.2174/0929867324666170426095015 ◽

2018 ◽

Vol 25 (1) ◽

pp. 5-21 ◽

Cited By ~ 25

Author(s):

Ylenia Cau ◽

Daniela Valensin ◽

Mattia Mori ◽

Sara Draghi ◽

Maurizio Botta

Keyword(s):

Protein Interactions ◽

Molecular Mechanisms ◽

Physiological Role ◽

Specific Protein ◽

Cellular Processes ◽

Protein Partners ◽

High Sequence Homology ◽

Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

Visual detection of binary, ternary, and quaternary protein interactions in fission yeast by Pil1 co-tethering assay

Journal of Cell Science ◽

10.1242/jcs.258774 ◽

2021 ◽

Author(s):

Zhong-Qiu Yu ◽

Xiao-Man Liu ◽

Dan Zhao ◽

Dan-Dan Xu ◽

Li-Lin Du

Keyword(s):

Fission Yeast ◽

Protein Interactions ◽

Visual Detection ◽

Phosphatidylinositol 3 Kinase ◽

Basic Form ◽

Bait Protein ◽

Cellular Processes ◽

Prey Protein ◽

Versatile Tool

Protein-protein interactions are vital for executing nearly all cellular processes. To facilitate the detection of protein-protein interactions in living cells of the fission yeast Schizosaccharomyces pombe, here we present an efficient and convenient method termed the Pil1 co-tethering assay. In its basic form, we tether a bait protein to mCherry-tagged Pil1, which forms cortical filamentary structures, and examine whether a GFP-tagged prey protein colocalizes with the bait. We demonstrate that this assay is capable of detecting pairwise protein-protein interactions of cytosolic proteins and nuclear proteins. Furthermore, we show that this assay can be used for detecting not only binary protein-protein interactions, but also ternary and quaternary protein-protein interactions. Using this assay, we systematically characterized the protein-protein interactions in the Atg1 complex and in the phosphatidylinositol 3-kinase (PtdIns3K) complexes and found that Atg38 is incorporated into the PtdIns3K complex I via an Atg38-Vps34 interaction. Our data show that this assay is a useful and versatile tool and should be added to the routine toolbox of fission yeast researchers.

Protein–protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics

Beilstein Journal of Organic Chemistry ◽

10.3762/bjoc.14.267 ◽

2018 ◽

Vol 14 ◽

pp. 2881-2896 ◽

Cited By ~ 7

Author(s):

Laura Carro

Keyword(s):

Protein Interactions ◽

Bacterial Infections ◽

Multidrug Resistant ◽

Resistant Bacteria ◽

Lead Discovery ◽

Antibiotic Development ◽

Cellular Processes ◽

Ppi Networks ◽

New Antibiotics

Antibiotics are potent pharmacological weapons against bacterial infections; however, the growing antibiotic resistance of microorganisms is compromising the efficacy of the currently available pharmacotherapies. Even though antimicrobial resistance is not a new problem, antibiotic development has failed to match the growth of resistant pathogens and hence, it is highly critical to discover new anti-infective drugs with novel mechanisms of action which will help reducing the burden of multidrug-resistant microorganisms. Protein–protein interactions (PPIs) are involved in a myriad of vital cellular processes and have become an attractive target to treat diseases. Therefore, targeting PPI networks in bacteria may offer a new and unconventional point of intervention to develop novel anti-infective drugs which can combat the ever-increasing rate of multidrug-resistant bacteria. This review describes the progress achieved towards the discovery of molecules that disrupt PPI systems in bacteria for which inhibitors have been identified and whose targets could represent an alternative lead discovery strategy to obtain new anti-infective molecules.

A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2014.2359441 ◽

2015 ◽

Vol 12 (3) ◽

pp. 538-550 ◽

Cited By ~ 5

Author(s):

Adriana Birlutiu ◽

Florence d'Alche-Buc ◽

Tom Heskes

Keyword(s):

Network Topology ◽

Protein Interactions ◽

Bayesian Framework ◽

Topology Information

Multimodal deep representation learning for protein interaction identification and protein family classification

BMC Bioinformatics ◽

10.1186/s12859-019-3084-y ◽

2019 ◽

Vol 20 (S16) ◽

Cited By ~ 4

Author(s):

Da Zhang ◽

Mansur Kabuka

Keyword(s):

Protein Interactions ◽

Protein Sequence ◽

Representation Learning ◽

Superior Performance ◽

Sequence Information ◽

Learning Framework ◽

Topological Features ◽

Ppi Networks ◽

Abstract Background Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge. Results In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods. Conclusion To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.