Computational Methods For Predicting Protein–Protein Interactions

2008 ◽  
pp. 247-267 ◽  
Author(s):  
Sylvain Pitre ◽  
Md Alamgir ◽  
James R. Green ◽  
Michel Dumontier ◽  
Frank Dehne ◽  
...  
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Novel Computational Methods to Design Protein-Protein Interactions

2014 ◽  
Vol 106 (2) ◽  
pp. 654a-655a
Author(s):  
Alice Qinhua Zhou ◽  
Corey S. O'Hern ◽  
Lynne Regan
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Protein Protein Interactions

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches

2019 ◽  
Vol 20 (3) ◽  
pp. 177-184 ◽  
Author(s):  
Nantao Zheng ◽  
Kairou Wang ◽  
Weihua Zhan ◽  
Lei Deng
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Protein Interactions ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Host Protein ◽  
Protein Protein Interactions ◽  
Protein Motifs

Background:Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions.Methods:In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods.Results:We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions.Conclusion:The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

2020 ◽  
Vol 207 ◽  
pp. 112764 ◽  
Author(s):  
Yuran Qiu ◽  
Xinyi Li ◽  
Xinheng He ◽  
Jun Pu ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals A High Efficient Biological Language Model for Predicting Protein–Protein Interactions

Cells ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 122 ◽  
Author(s):  
Yanbin Wang ◽  
Zhu-Hong You ◽  
Shan Yang ◽  
Xiao Li ◽  
Tong-Hai Jiang ◽  
...  
Keyword(s):  
Computational Methods ◽  
Language Processing ◽  
Protein Interactions ◽  
Protein Sequence ◽  
Processing Technology ◽  
Limiting Factors ◽  
Sequence Information ◽  
Biological Sequences ◽  
Protein Protein Interactions ◽  
High Efficient

Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a “bio-word” segmentation system and a word representation model used for learning the distributed representation for each “bio-word”. The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of “bio language processing technology,” which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.

An improved method for predicting interactions between virus and human proteins

2017 ◽  
Vol 15 (01) ◽  
pp. 1650024 ◽  
Author(s):  
Byungmin Kim ◽  
Saud Alguwaizani ◽  
Xiang Zhou ◽  
De-Shuang Huang ◽  
Byunkyu Park ◽  
...  
Keyword(s):  
Amino Acid ◽  
Computational Methods ◽  
Protein Interactions ◽  
Human Papillomaviruses ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Host Proteins ◽  
Human Ppis ◽  
Human Proteins ◽  
Key Features

The interaction of virus proteins with host proteins plays a key role in viral infection and consequent pathogenesis. Many computational methods have been proposed to predict protein–protein interactions (PPIs), but most of the computational methods are intended for PPIs within a species rather than PPIs across different species such as virus–host PPIs. We developed a method that represents key features of virus and human proteins of variable length into a feature vector of fixed length. The key features include the relative frequency of amino acid triplets (RFAT), the frequency difference of amino acid triplets (FDAT) between virus and host proteins, and amino acid composition (AC). We constructed several support vector machine (SVM) models to evaluate our method and to compare our method with others on PPIs between human and two types of viruses: human papillomaviruses (HPV) and hepatitis C virus (HCV). Comparison of our method to others with same datasets of HPV–human PPIs and HCV–human PPIs showed that the performance of our method is significantly higher than others in all performance measures. Using the SVM model with gene ontology (GO) annotations of proteins, we predicted new HPV–human PPIs. We believe our approach will be useful in predicting heterogeneous PPIs.

scholarly journals Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer–Based Relevance Vector Machine

2019 ◽  
Vol 15 ◽  
pp. 117693431984452 ◽  
Author(s):  
Ji-Yong An ◽  
Zhu-Hong You ◽  
Yong Zhou ◽  
Da-Fu Wang
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Relevance Vector Machine ◽  
Prediction Performance ◽  
Experimental Results ◽  
Sequence Information ◽  
Gray Wolf ◽  
Protein Protein Interactions ◽  
Homologous Proteins ◽  
Gray Wolf Optimizer

Protein-protein interactions (PPIs) are essential to a number of biological processes. The PPIs generated by biological experiment are both time-consuming and expensive. Therefore, many computational methods have been proposed to identify PPIs. However, most of these methods are limited as they are difficult to compute and rely on a large number of homologous proteins. Accordingly, it is urgent to develop effective computational methods to detect PPIs using only protein sequence information. The kernel parameter of relevance vector machine (RVM) is set by experience, which may not obtain the optimal solution, affecting the prediction performance of RVM. In this work, we presented a novel computational approach called GWORVM-BIG, which used Bi-gram (BIG) to represent protein sequences on a position-specific scoring matrix (PSSM) and GWORVM classifier to perform classification for predicting PPIs. More specifically, the proposed GWORVM model can obtain the optimum solution of kernel parameters using gray wolf optimizer approach, which has the advantages of less control parameters, strong global optimization ability, and ease of implementation compared with other optimization algorithms. The experimental results on yeast and human data sets demonstrated the good accuracy and efficiency of the proposed GWORVM-BIG method. The results showed that the proposed GWORVM classifier can significantly improve the prediction performance compared with the RVM model using other optimizer algorithms including grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO). In addition, the proposed method is also compared with other existing algorithms, and the experimental results further indicated that the proposed GWORVM-BIG model yields excellent prediction performance. For facilitating extensive studies for future proteomics research, the GWORVMBIG server is freely available for academic use at http://219.219.62.123:8888/GWORVMBIG .

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