A Novel Index of Contact Frequency from Noise Protein–Protein Interaction Data Help for Accurate Interface Residue Pair Prediction

Lyu, Yanfen; Huang, He; Gong, Xinqi

doi:10.1007/s12539-020-00364-w

Cited by 3 publications

(2 citation statements)

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“…Protein-protein interactions are significant in various biological activities and processes, such as signal transmission, gene expression and transcriptional regulation ( Levy and Pereira-Leal, 2008 ; Malta et al, 2018 ; Li et al, 2019 ; Lyu et al, 2020 ; Zhao et al, 2022 ). The interactions between proteins in the body can form dimer protein complexes, trimer protein complexes, tetramer protein complexes and higher polymers.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the tetramer protein complex interaction based on CNN and SVM

Lyu

et al. 2023

Front. Genet.

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Protein-protein interactions play an important role in life activities. The study of protein-protein interactions helps to better understand the mechanism of protein complex interaction, which is crucial for drug design, protein function annotation and three-dimensional structure prediction of protein complexes. In this paper, we study the tetramer protein complex interaction. The research has two parts: The first part is to predict the interaction between chains of the tetramer protein complex. In this part, we proposed a feature map to represent a sample generated by two chains of the tetramer protein complex, and constructed a Convolutional Neural Network (CNN) model to predict the interaction between chains of the tetramer protein complex. The AUC value of testing set is 0.6263, which indicates that our model can be used to predict the interaction between chains of the tetramer protein complex. The second part is to predict the tetramer protein complex interface residue pairs. In this part, we proposed a Support Vector Machine (SVM) ensemble method based on under-sampling and ensemble method to predict the tetramer protein complex interface residue pairs. In the top 10 predictions, when at least one protein-protein interaction interface is correctly predicted, the accuracy of our method is 82.14%. The result shows that our method is effective for the prediction of the tetramer protein complex interface residue pairs.

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Section: Introductionmentioning

confidence: 99%

Prediction of the tetramer protein complex interaction based on CNN and SVM

Lyu

et al. 2023

Front. Genet.

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“…Hwang et al [ 4 ] proposed an index of Residue Contact Frequency to predict the protein monomer binding sites. Lyu et al [ 5 ] defined an index of contact frequency from the protein–protein docking result to accurately predict protein–protein interface residue pairs. Ovchinnikov predicted residue–residue interactions across protein interfaces using evolutionary information [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers

Lyu

Gong

2020

Molecules

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Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein–protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers.

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