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

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.

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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 ◽

Protein Protein Interactions ◽

Learning Framework ◽

Topological Features ◽

Ppi Networks ◽

Ppi Prediction

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.

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Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

Scientific Reports ◽

10.1038/s41598-021-96265-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yang Li ◽

Zheng Wang ◽

Li-Ping Li ◽

Zhu-Hong You ◽

Wen-Zhun Huang ◽

...

Keyword(s):

Protein Interactions ◽

Protein Sequence ◽

Large Scale ◽

False Positive Rate ◽

Computational Method ◽

Evolutionary Information ◽

Local Alignment ◽

Protein Interaction Data ◽

Sequence Information ◽

Protein Protein Interactions

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

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Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform

Intelligent Computing Theories and Application - Lecture Notes in Computer Science ◽

10.1007/978-3-030-60802-6_13 ◽

2020 ◽

pp. 132-142

Author(s):

Jie Pan ◽

Zhu-Hong You ◽

Chang-Qing Yu ◽

Li-Ping Li ◽

Xin-ke Zhan

Keyword(s):

Wavelet Transform ◽

Protein Interactions ◽

Protein Sequence ◽

Sequence Information ◽

Protein Protein Interactions ◽

Complex Wavelet Transform ◽

Complex Wavelet

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Prediction of high-throughput protein-protein interactions based on protein sequence information

2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) ◽

10.1109/cibcb.2015.7300310 ◽

2015 ◽

Author(s):

Yixun Li ◽

Behzad Rezaei ◽

Alioune Ngom ◽

Luis Rueda

Keyword(s):

High Throughput ◽

Protein Interactions ◽

Protein Sequence ◽

Sequence Information ◽

Protein Protein Interactions

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

Evolutionary Bioinformatics ◽

10.1177/1176934319844522 ◽

2019 ◽

Vol 15 ◽

pp. 117693431984452 ◽

Cited By ~ 7

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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Novel Computational Methods to Design Protein-Protein Interactions

Biophysical Journal ◽

10.1016/j.bpj.2013.11.3622 ◽

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

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Computational Methods For Predicting Protein–Protein Interactions

Protein – Protein Interaction - Advances in Biochemical Engineering/Biotechnology ◽

10.1007/10_2007_089 ◽

2008 ◽

pp. 247-267 ◽

Cited By ~ 13

Author(s):

Sylvain Pitre ◽

Md Alamgir ◽

James R. Green ◽

Michel Dumontier ◽

Frank Dehne ◽

...

Keyword(s):

Computational Methods ◽

Protein Interactions ◽

Protein Protein Interactions

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

Current Drug Metabolism ◽

10.2174/1389200219666180829121038 ◽

2019 ◽

Vol 20 (3) ◽

pp. 177-184 ◽

Cited By ~ 16

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.

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