Predict protein-protein interactions from protein primary sequences: using wavelet transform combined with stacking algorithm

Data Set ◽

Virtual Technology ◽

Ppi Networks ◽

Protein Functions

Most biological processes within a cell are carried out by protein-protein interaction (PPI) networks, or so called interactomics. Therefore, identification of PPIs is crucial to elucidating protein functions and further understanding of various cellular biological processes. Currently, a series of high-throughput experimental technologies for detect PPIs have been presented. However, the time-consuming and labor-driven characteristics of these methods forced people to turn to virtual technology for PPIs prediction. Herein, we developed a new predictor which uses stacking algorithm with information extraction by wavelet transform. When applied on the Saccharomyces cerevisiae PPI dataset, the proposed method got a prediction accuracy of 83.35% with sensitivity of 92.95% at the specificity of 65.41%. An independent data set of 2726 Helicobacter pylori PPIs was also used to evaluate this prediction model, and the prediction accuracy is 80.39%, which is better than that of most existing methods.

Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interaction Network

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

In Silico Recognition of Protein-Protein Interaction

Advanced Data Mining Technologies in Bioinformatics ◽

10.4018/978-1-59140-863-5.ch013 ◽

2011 ◽

pp. 248-268

Author(s):

Byung-Hoon Park ◽

Phuongan Dam ◽

Chongle Pan ◽

Ying Xu ◽

Al Geist ◽

...

Keyword(s):

Protein Interactions ◽

Regulatory Networks ◽

Machine Learning Techniques ◽

Translation Regulation ◽

Future Research ◽

Protein Levels ◽

Protein Functions ◽

Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

Molecular Biology of Protein-Protein Interactions for Computer Scientists

Biological Data Mining in Protein Interaction Networks ◽

10.4018/978-1-60566-398-2.ch001 ◽

2009 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Christian Schönbach

Keyword(s):

Molecular Biology ◽

Protein Interactions ◽

Wnt Signaling Pathway ◽

Ppi Networks ◽

Detection Technology ◽

Challenges And Opportunities ◽

Computer Scientists ◽

Assay Methods

Advances in protein-protein interaction (PPI) detection technology and computational analysis methods have produced numerous PPI networks, whose completeness appears to depend on the extent of data derived from different PPI assay methods and the complexity of the studied organism. Despite the partial nature of human PPI networks, computational data integration and analyses helped to elucidate new interactions and disease pathways. The success of computational analyses considerably depends on PPI data understanding. Exploration of the data and verification of their quality requires basic knowledge of the molecular biology of PPIs and familiarity with the assay methods used to detect PPIs. Both topics are reviewed in this chapter. After introducing various types of PPIs the principles of selected PPI assays are explained and their limitations discussed. Case studies of the Wnt signaling pathway and splice regulation demonstrate some of the challenges and opportunities that arise from assaying and analyzing PPIs. The chapter is concluded with an extrapolation to human systems biology that offers a glimpse into the future of PPI networks.

New advances in extracting and learning from protein–protein interactions within unstructured biomedical text data

Emerging Topics in Life Sciences ◽

10.1042/etls20190003 ◽

2019 ◽

Vol 3 (4) ◽

pp. 357-369

Author(s):

J. Harry Caufield ◽

Peipei Ping

Keyword(s):

Protein Interactions ◽

Protein Function ◽

Historical Context ◽

Specific Protein ◽

Basic Unit ◽

Biomedical Text ◽

Text Data ◽

Ppi Networks ◽

Protein Functions

Abstract Protein–protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein–protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.

mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

Nucleic Acids Research ◽

10.1093/nar/gkz383 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W338-W344 ◽

Cited By ~ 51

Author(s):

Carlos H M Rodrigues ◽

Yoochan Myung ◽

Douglas E V Pires ◽

David B Ascher

Keyword(s):

Protein Interactions ◽

Interaction Network ◽

Evolutionary Information ◽

Biological Processes ◽

Missense Mutations ◽

Residue Interaction ◽

User Friendly ◽

Network Metrics

AbstractProtein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

MIPPIE: the mouse integrated protein–protein interaction reference

Database ◽

10.1093/database/baaa035 ◽

2020 ◽

Vol 2020 ◽

Cited By ~ 3

Author(s):

Gregorio Alanis-Lobato ◽

Jannik S Möllmann ◽

Martin H Schaefer ◽

Miguel A Andrade-Navarro

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Model Organism ◽

Web Interface ◽

Environmental Signals ◽

Ppi Networks ◽

User Friendly

Abstract Cells operate and react to environmental signals thanks to a complex network of protein–protein interactions (PPIs), the malfunction of which can severely disrupt cellular homeostasis. As a result, mapping and analyzing protein networks are key to advancing our understanding of biological processes and diseases. An invaluable part of these endeavors has been the house mouse (Mus musculus), the mammalian model organism par excellence, which has provided insights into human biology and disorders. The importance of investigating PPI networks in the context of mouse prompted us to develop the Mouse Integrated Protein–Protein Interaction rEference (MIPPIE). MIPPIE inherits a robust infrastructure from HIPPIE, its sister database of human PPIs, allowing for the assembly of reliable networks supported by different evidence sources and high-quality experimental techniques. MIPPIE networks can be further refined with tissue, directionality and effect information through a user-friendly web interface. Moreover, all MIPPIE data and meta-data can be accessed via a REST web service or downloaded as text files, thus facilitating the integration of mouse PPIs into follow-up bioinformatics pipelines.

ProCbA: Protein Function Prediction based on Clique Analysis

10.1101/2020.11.24.396432 ◽

2020 ◽

Author(s):

A. Khanteymoori ◽

M. B. Ghajehlo ◽

S. Behrouzinia ◽

M. H. Olyaee

Keyword(s):

Protein Interactions ◽

Protein Function ◽

Protein Function Prediction ◽

Function Prediction ◽

Experimental Result ◽

Ppi Networks ◽

Protein Functions ◽

Important Challenge ◽

Result Analysis

AbstractProtein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the Post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for un-annotated proteins. The aim of this study is to increase the accuracy of the protein function prediction using two proposed methods.To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which is not including new algorithm; we try to address the essence of incomplete and noisy data of PPI era in order to achieving a network with complete functional aggregation. The experimental results on MIPS data and the 17 different explained datasets validate the encouraging performance and the strength of both ProCbA and ProNC-FA on function prediction. Experimental result analysis as can be seen in Section IV, the both ProCbA and ProNC-FA are generally able to outperform all the other methods.

msiDBN: A Method of Identifying Critical Proteins in Dynamic PPI Networks

BioMed Research International ◽

10.1155/2014/138410 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Yuan Zhang ◽

Nan Du ◽

Kang Li ◽

Jinchao Feng ◽

Kebin Jia ◽

...

Keyword(s):

Yeast Cell ◽

Protein Interactions ◽

Structural Changes ◽

Biological Processes ◽

Cell Cycles ◽

Construction Methods ◽

Ppi Networks ◽

Time Courses ◽

Traditional Construction

Dynamics of protein-protein interactions (PPIs) reveals the recondite principles of biological processes inside a cell. Shown in a wealth of study, just a small group of proteins, rather than the majority, play more essential roles at crucial points of biological processes. This present work focuses on identifying these critical proteins exhibiting dramatic structural changes in dynamic PPI networks. First, a comprehensive way of modeling the dynamic PPIs is presented which simultaneously analyzes the activity of proteins and assembles the dynamic coregulation correlation between proteins at each time point. Second, a novel method is proposed, named msiDBN, which models a common representation of multiple PPI networks using a deep belief network framework and analyzes the reconstruction errors and the variabilities across the time courses in the biological process. Experiments were implemented on data of yeast cell cycles. We evaluated our network construction method by comparing the functional representations of the derived networks with two other traditional construction methods. The ranking results of critical proteins in msiDBN were compared with the results from the baseline methods. The results of comparison showed that msiDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.

CellMap visualizes protein-protein interactions and subcellular localization

F1000Research ◽

10.12688/f1000research.12707.2 ◽

2018 ◽

Vol 6 ◽

pp. 1824 ◽

Cited By ~ 2

Author(s):

Christian Dallago ◽

Tatyana Goldberg ◽

Miguel Angel Andrade-Navarro ◽

Gregorio Alanis-Lobato ◽

Burkhard Rost

Keyword(s):

Subcellular Localization ◽

Protein Interaction ◽

Protein Interactions ◽

Cellular Component ◽

Ppi Networks ◽

A Cell ◽

Areas Of Interest

Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers.