scholarly journals ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes

2018 ◽  
Author(s):  
Linh Tran ◽  
Tobias Hamp ◽  
Burkhard Rost
Keyword(s):  
Experimental Data ◽  
Protein Interactions ◽  
Web Application ◽  
Domain Knowledge ◽  
Prediction Method ◽  
Model Organisms ◽  
Evolutionary Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Link Type

AbstractMotivationProtein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods.ResultsWe extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784.

scholarly journals PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

2011 ◽  
Vol 16 (2) ◽  
Author(s):  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri ◽  
Dariusz Plewczynski
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Prediction Method ◽  
Protein Sequences ◽  
Dimensional Structure ◽  
Support Vector ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Protein Functions

AbstractProtein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

scholarly journals FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Pan ◽  
Li-Ping Li ◽  
Chang-Qing Yu ◽  
Zhu-Hong You ◽  
Zhong-Hao Ren ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Nearest Neighbor ◽  
Protein Sequences ◽  
Evolutionary Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
K Nearest Neighbor ◽  
Novel Approach ◽  
Knn Classifier ◽  
Scoring Matrix

Protein-protein interactions (PPIs) in plants are crucial for understanding biological processes. Although high-throughput techniques produced valuable information to identify PPIs in plants, they are usually expensive, inefficient, and extremely time-consuming. Hence, there is an urgent need to develop novel computational methods to predict PPIs in plants. In this article, we proposed a novel approach to predict PPIs in plants only using the information of protein sequences. Specifically, plants’ protein sequences are first converted as position-specific scoring matrix (PSSM); then, the fast Walsh–Hadamard transform (FWHT) algorithm is used to extract feature vectors from PSSM to obtain evolutionary information of plant proteins. Lastly, the rotation forest (RF) classifier is trained for prediction and produced a series of evaluation results. In this work, we named this approach FWHT-RF because FWHT and RF are used for feature extraction and classification, respectively. When applying FWHT-RF on three plants’ PPI datasets Maize, Rice, and Arabidopsis thaliana (Arabidopsis), the average accuracies of FWHT-RF using 5-fold cross validation were achieved as high as 95.20%, 94.42%, and 83.85%, respectively. To further evaluate the predictive power of FWHT-RF, we compared it with the state-of-art support vector machine (SVM) and K-nearest neighbor (KNN) classifier in different aspects. The experimental results demonstrated that FWHT-RF can be a useful supplementary method to predict potential PPIs in plants.

scholarly journals An Efficient Feature Extraction Technique Based on Local Coding PSSM and Multifeatures Fusion for Predicting Protein-Protein Interactions

2019 ◽  
Vol 15 ◽  
pp. 117693431987992 ◽  
Author(s):  
Ji-Yong An ◽  
Yong Zhou ◽  
Yu-Jun Zhao ◽  
Zi-Ji Yan
Keyword(s):  
Feature Extraction ◽  
Protein Interactions ◽  
Functional Organization ◽  
Extraction Methods ◽  
Amino Acid Sequences ◽  
Evolutionary Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Protein Protein Interactions ◽  
Local Coding

Background: Increasing evidence has indicated that protein-protein interactions (PPIs) play important roles in various aspects of the structural and functional organization of a cell. Thus, continuing to uncover potential PPIs is an important topic in the biomedical domain. Although various feature extraction methods with machine learning approaches have enhanced the prediction of PPIs. There remains room for improvement by developing novel and effective feature extraction methods and classifier approaches to identify PPIs. Method: In this study, we proposed a sequence-based feature extraction method called LCPSSMMF, which combined local coding position-specific scoring matrix (PSSM) with multifeatures fusion. First, we used a novel local coding method based on PSSM to build a new PSSM (CPSSM); the advantage of this method is that it incorporated global and local feature extraction, which can account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. Second, we adopted 2 different feature extraction methods (Local Average Group [LAG] and Bigram Probability [BP]) to capture multiple key feature information by employing the evolutionary information embedded in the CPSSM matrix. Finally, feature vectors were acquired by using multifeatures fusion method. Result: To evaluate the performance of the proposed feature extraction approach, we employed support vector machine (SVM) as a prediction classifier and applied this method to yeast and human PPI datasets. The prediction accuracies of LCPSSMMF were 93.43% and 90.41% on the yeast and human datasets, respectively. Moreover, we also compared the proposed method with the previous sequence-based approaches on the yeast datasets by using the same SVM classifier. The experimental results indicated that the performance of LCPSSMMF significantly exceeded that of several other state-of-the-art methods. It is proven that the LCPSSMMF approach can capture more local and global discriminatory information than almost all previous methods and can function remarkably well in identifying PPIs. To facilitate extensive research in future proteomics studies, we developed a LCPSSMMFSVM server, which is freely available for academic use at http://219.219.62.123:8888/LCPSSMMFSVM .

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

An Integrated Prediction Method for Identifying Protein-Protein Interactions

2020 ◽  
Vol 17 (4) ◽  
pp. 271-286
Author(s):  
Chang Xu ◽  
Limin Jiang ◽  
Zehua Zhang ◽  
Xuyao Yu ◽  
Renhai Chen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Feature Vector ◽  
Prediction Method ◽  
Feature Representation ◽  
Protein Interaction Networks ◽  
Learning Approach ◽  
Biological Processes ◽  
Integrated Learning ◽  
Protein Protein Interactions ◽  
Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

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.

Inference of Protein-Protein Interactions by Using Co-evolutionary Information

2007 ◽  
pp. 322-333
Author(s):  
Tetsuya Sato ◽  
Yoshihiro Yamanishi ◽  
Katsuhisa Horimoto ◽  
Minoru Kanehisa ◽  
Hiroyuki Toh
Keyword(s):  
Protein Interactions ◽  
Evolutionary Information ◽  
Protein Protein Interactions
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