Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information

Protein phosphorylation is catalyzed by kinases which regulate many aspects that control death, movement, and cell growth. Identification of the phosphorylation site-specific kinase-substrate relationships (ssKSRs) is important for understanding cellular dynamics and provides a fundamental basis for further disease-related research and drug design. Although several computational methods have been developed, most of these methods mainly use local sequence of phosphorylation sites and protein-protein interactions (PPIs) to construct the prediction model. While phosphorylation presents very complicated processes and is usually involved in various biological mechanisms, the aforementioned information is not sufficient for accurate prediction. In this study, we propose a new and powerful computational approach named KSRPred for ssKSRs prediction, by introducing a novel phosphorylation site-kinase network (pSKN) profiles that can efficiently incorporate the relationships between various protein kinases and phosphorylation sites. The experimental results show that the pSKN profiles can efficiently improve the prediction performance in collaboration with local sequence and PPI information. Furthermore, we compare our method with the existing ssKSRs prediction tools and the results demonstrate that KSRPred can significantly improve the prediction performance compared with existing tools.

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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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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 ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

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/.

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Prediction of Protein-Protein Interactions Based On Weighted Extreme Learning Machine And Speed Up Robot Features

10.21203/rs.3.rs-740023/v1 ◽

2021 ◽

Author(s):

JinXuan Zhai ◽

Ji-Yong An

Keyword(s):

Extreme Learning Machine ◽

Protein Interactions ◽

Evolutionary Information ◽

Protein Protein Interactions ◽

Prediction Ability ◽

Great Ability ◽

Weighted Extreme Learning Machine ◽

Comparison Results ◽

Speed Up ◽

Learning Machine

Abstract Background：Protein–protein interactions (PPIs) are involved in a number of cellular processes and play a key role inside cells. The prediction of PPIs is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. Given that high-throughput methods are expensive and time-consuming, it is a challenging task to develop efficient and accurate computational methods for predicting PPIs .Results：In the study, a novel computational approach named WELM-SURF was developed to predict PPIs. The proposed method used Position Specific Scoring Matrix (PSSM) to capture protein evolutionary information and employed Speed Up Robot Features (SURF) to extract key features from PSSM of protein sequence. Weighted Extreme Learning Machine (WELM) is featured with short training time and great ability to execute classification efficiently by optimizing the loss function of weight matrix. Therefore, WELM classifier was used to carry out classification. The cross-validation results show that WELM-SURF obtains 97.36% and 95.12% of average accuracy on yeast and human dataset, respectively. The prediction ability of WELM-SURF was also compared with those of ELM-SRUF, SVM-SURF and other existing approaches. The comparison results further verify that WELM-SURF is obviously better than other methods.Conclusion：The experimental results proved that the WELM-SURF method is very useful for predicting PPIs and can also be applied to other bioinformatics studies of protein.

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