scholarly journals Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mayumi Kamada ◽  
Yusuke Sakuma ◽  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interactions ◽  
Interaction Strength ◽  
Feature Space ◽  
Relevance Vector Machine ◽  
Protein Domain ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Living Organisms ◽  
Domain Information

Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.

INFERRING PROTEIN-PROTEIN INTERACTIONS FROM MESSENGER RNA EXPRESSION PROFILES WITH SVM

2005 ◽  
Vol 13 (03) ◽  
pp. 287-298 ◽  
Author(s):  
JUN CAI ◽  
YING HUANG ◽  
LIANG JI ◽  
YANDA LI
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
Messenger Rna ◽  
Expression Profiles ◽  
Support Vector ◽  
Svm Classifier ◽  
Good Prediction ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
High Throughput Experiments

In post-genomic biology, researchers in the field of proteome focus their attention on the networks of protein interactions that control the lives of cells and organisms. Protein-protein interactions play a useful role in dynamic cellular machinery. In this paper, we developed a method to infer protein-protein interactions based on the theory of support vector machine (SVM). For a given pair of proteins, a new strategy of calculating cross-correlation function of mRNA expression profiles was used to encode SVM vectors. We compared the performance with other methods of inferring protein-protein interaction. Results suggested that, through five-fold cross validation, our SVM model achieved a good prediction. It enables us to show that expression profiles in transcription level can be used to distinguish physical or functional interactions of proteins as well as sequence contents. Lastly, we applied our SVM classifier to evaluate data quality of interaction data sets from four high-throughput experiments. The results show that high-throughput experiments sacrifice some accuracy in determination of interactions because of limitation of experiment technologies.

scholarly journals Feature Design for Protein Interface hotspots using KFC2 and Rosetta

2019 ◽  
Author(s):  
Franziska Seeger ◽  
Anna Little ◽  
Yang Chen ◽  
Tina Woolf ◽  
Haiyan Cheng ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Scoring Function ◽  
Selection Strategy ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Forward Selection ◽  
Protein Protein Interaction ◽  
Combined Features ◽  
Improved Performance

AbstractProtein-protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally charac-terizing protein residues that contribute the most to protein-protein interaction affin-ity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein-protein interfaces provides important information about how to inhibit therapeutically relevant protein-protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein-protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A 2-way and 3-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.

Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

2014 ◽  
Vol 4 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Cellular Systems ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Free Distribution ◽  
Protein Protein Interaction ◽  
Domain Interactions

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Prediction of Protein-Protein Interaction Based on Weighted Feature Fusion

2019 ◽  
Vol 16 (4) ◽  
pp. 263-274
Author(s):  
Chunhua Zhang ◽  
Sijia Guo ◽  
Jingbo Zhang ◽  
Xizi Jin ◽  
Yanwen Li ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Feature Fusion ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Fusion Algorithm ◽  
Maximum Margin ◽  
Protein Protein Interaction ◽  
Maximum Margin Criterion ◽  
Complete Protein

Protein-protein interactions play an important role in biological and cellular processes. Biochemistry experiment is the most reliable approach identifying protein-protein interactions, but it is time-consuming and expensive. It is one of the important reasons why there is only a little fraction of complete protein-protein interactions networks available by far. Hence, accurate computational methods are in a great need to predict protein-protein interactions. In this work, we proposed a new weighted feature fusion algorithm for protein-protein interactions prediction, which extracts both protein sequence feature and evolutionary feature, for the purpose to use both global and local information to identify protein-protein interactions. The method employs maximum margin criterion for feature selection and support vector machine for classification. Experimental results on 11188 protein pairs showed that our method had better performance and robustness. Performed on the independent database of Helicobacter pylori, the method achieved 99.59% sensitivity and 93.66% prediction accuracy, while the maximum margin criterion is 88.03%. The results indicated that our method was more efficient in predicting protein-protein interaction compared with other six state-of-the-art peer methods.

scholarly journals 2P2I HUNTER : a tool for filtering orthosteric protein–protein interaction modulators via a dedicated support vector machine

2014 ◽  
Vol 11 (90) ◽  
pp. 20130860 ◽  
Author(s):  
Véronique Hamon ◽  
Raphael Bourgeas ◽  
Pierre Ducrot ◽  
Isabelle Theret ◽  
Laura Xuereb ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Chemical Space ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Target Class ◽  
Chemical Library ◽  
Protein Protein Interaction ◽  
Svm Model

Over the last 10 years, protein–protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this ‘high-hanging fruit’ is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database ( http://2p2idb.cnrs-mrs.fr/ ) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard D ragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns . This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

scholarly journals Protein-Protein Interaction Reveals Synergistic Discrimination of Cancer Phenotype

2010 ◽  
Vol 9 ◽  
pp. CIN.S3899 ◽  
Author(s):  
Jianghui Xiong ◽  
Juan Liu ◽  
Simon Rayner ◽  
Yinghui Li ◽  
Shanguang Chen
Keyword(s):  
Protein Interactions ◽  
Gene Pair ◽  
Gene Networks ◽  
Mapk Pathway ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Multiple Gene ◽  
Protein Protein Interaction ◽  
Cancer Phenotype ◽  
Cancer Phenotypes

Cancer is a disease associated with the deregulation of multiple gene networks. Microarray data has permitted researchers to identify gene panel markers for diagnosis or prognosis of cancer but these are not sufficient to make specific mechanistic assertions about phenotype switches. We propose a strategy to identify putative mechanisms of cancer phenotypes by protein-protein interactions (PPI). We first extracted the logic status of a PPI via the relative expression of the corresponding gene pair. The joint association of a gene pair on a cancer phenotype was calculated by entropy minimization and assessed using a support vector machine. A typical predictor is “ If Src high-expression, and Cav-1 low-expression, then cancer.“ We achieved 90% accuracy on test data with a majority of predictions associated with the MAPK pathway, focal adhesion, apoptosis and cell cycle. Our results can aid in the development of phenotype discrimination biomarkers and identification of putative therapeutic interference targets for drug development.

scholarly journals Protein-Protein Interaction Prediction using PCA and SVR-PHCS

2015 ◽  
Vol 9 (1) ◽  
pp. 1-12
Author(s):  
Saeideh Mahmoudian ◽  
Abdulaziz Yousef ◽  
Nasrollah Moghadam Charkari
Keyword(s):  
Protein Interactions ◽  
Principal Component ◽  
Classification Problem ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Classification Problems ◽  
Machine Learning Classification ◽  
Regression Methods ◽  
Protein Protein Interaction ◽  
Noise Data

Protein-Protein Interactions (PPIs) play a key role in many biological systems. Thus, identifying PPIs is critical for understanding cellular processes. Many experimental techniques were applied to predict PPIs. The data extracted using these techniques are incomplete and noisy. In this regard, a number of computational methods include machine learning classification techniques have been developed to reduce the noise data and predict new PPIs. Since, using regression methods to solve classification problems has good results in other applications. Therefore, in this paper, a regression view is applied to the PPI prediction classification problem, so a new approach is proposed using Principal Component Analysis (PCA) and Support Vector Regression (SVR) which has been improved by a new Parallel Hierarchical Cube Search (PHCS) method. Firstly, PCA algorithm is implemented to select an optimal subset of features which leads to reduce processing time and to lessen the effect of noise. Then, the PPIs would be predicted, by using SVR. To get a better performance of SVR, a new PHCS method has been applied to select the appropriate values of SVR parameters. The obtained classification accuracy of the proposed method is 74.505% on KUPS (The University of Kansas Proteomics Service) dataset which outperforms the other methods.

scholarly journals Inducible Yeast Two-Hybrid with Quantitative Measures

2021 ◽  
Author(s):  
Jesus Hernandez ◽  
Kevin D. Ross ◽  
Bruce A. Hamilton
Keyword(s):  
Protein Interactions ◽  
Interaction Strength ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Protein Protein Interaction ◽  
Growth Assay ◽  
Adh1 Promoter ◽  
Multiwell Plates ◽  
Two Hybrid ◽  
Cup1 Promoter

The yeast two-hybrid (Y2H) assay has long been used to identify new protein-protein interaction pairs and to compare relative interaction strengths. Traditional Y2H formats may be limited, however, by use of constitutive strong promoters if expressed proteins have toxic effects or post-transcriptional expression differences in yeast among a comparison group. As a step toward more quantitative Y2H assays, we modified a common vector to use an inducible CUP1 promoter, which showed quantitative induction of several "bait" proteins with increasing copper concentration. Using mouse Nxf1 (homologous to yeast Mex67p) as a model bait, copper titration achieved levels that bracket levels obtained with the constitutive ADH1 promoter. Using a liquid growth assay for an auxotrophic reporter in multiwell plates allowed log-phase growth rate to be used as a measure of interaction strength. These data demonstrate the potential for quantitative comparisons of protein-protein interactions using the Y2H system.

scholarly journals Evolution of Protein-protein Interaction Networks in Duplication-Divergence Model

2012 ◽  
Vol 22 (1) ◽  
pp. 7-14
Author(s):  
Bui Phuong Thuy ◽  
Trinh Xuan Hoang
Keyword(s):  
Experimental Data ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Living Organisms

Protein interacts with one another resulting in complex functions in living organisms. Like many other real-world networks, the networks of protein-protein interactions possess a certain degree of ordering, such as the scale-free property. The latter means that the probability $P$ to find a protein that interacts with $k$ other proteins follows a power law, $P(k) \sim k^{-\gamma}$. Protein interaction networks (PINs) have been studied by using a stochastic model, the duplication-divergence model, which is based on mechanisms of gene duplication and divergence during evolution. In this work, we show that this model can be used to fit experimental data on the PIN of yeast Saccharomyces cerevisae at two different time instances simultaneously. Our study shows that the evolution of PIN given by model is consistent with growing experimental data over time, and that the scale-free property of protein interaction network is robust against random deletion of interactions.

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

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