Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture

Zhao, Zhenni; Gong, Xinqi

doi:10.1109/tcbb.2017.2706682

Cited by 32 publications

(30 citation statements)

References 16 publications

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“…With the advent of deep learning models in bioinformatics [ 113 ], we believe that these neural network architectures should be tried to further improve the accuracy of the MoRF predictions. This claim is supported by the fact that a few accurate deep learning models that predict residue-level protein interactions were recently published, including the predictor of residue-residue contacts in protein structures [ 114 ], and the predictor of residue-residue interactions in protein complexes [ 115 ].…”

Section: Discussionmentioning

confidence: 99%

Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions

Katuwawala

Peng

Yang

et al. 2019

Computational and Structural Biotechnology Journal

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Molecular recognition features (MoRFs) are short protein-binding regions that undergo disorder-to-order transitions (induced folding) upon binding protein partners. These regions are abundant in nature and can be predicted from protein sequences based on their distinctive sequence signatures. This first-of-its-kind survey covers 14 MoRF predictors and six related methods for the prediction of short protein-binding linear motifs, disordered protein-binding regions and semi-disordered regions. We show that the development of MoRF predictors has accelerated in the recent years. These predictors depend on machine learning-derived models that were generated using training datasets where MoRFs are annotated using putative disorder. Our analysis reveals that they generate accurate predictions. We identified eight methods that offer area under the ROC curve (AUC) ≥ 0.7 on experimentally-validated test datasets. We show that modern MoRF predictors accurately find experimentally annotated MoRFs even though they were trained using the putative disorder annotations. They are relatively highly-cited, particularly the methods available as webservers that on average secure three times more citations than methods without this option. MoRF predictions contribute to the experimental discovery of protein-protein interactions, annotation of protein functions and computational analysis of a variety of proteomes, protein families, and pathways. We outline future development and application directions for these tools, stressing the importance to develop novel tools that would target interactions of disordered regions with other types of partners.

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Section: Discussionmentioning

confidence: 99%

Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions

Katuwawala

Peng

Yang

et al. 2019

Computational and Structural Biotechnology Journal

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“…We compared the performance of our method with the previous method [ 16 ]. When at least 1 protein–protein interaction interface of each protein trimer was correctly predicted, the accuracy of our method is 76.92% and of the previous method [ 16 ] is 31.1% in the top 10 predictions. The accuracy of our method is higher than them.…”

Section: Resultsmentioning

confidence: 99%

“…At present, there are a few methods to predict interface residue pairs of protein trimer. Zhao et al [ 16 ] took the sequence feature as input in multilayered Long Short-Term Memory networks to predict interface residue pairs of protein trimer. In this paper, we want to develop a new and effective method for prediction of interface residue pairs in protein trimers.…”

Section: Introductionmentioning

confidence: 99%

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

Lyu

Gong

2020

Molecules

Self Cite

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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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“…Besides the DCA models, some machine/deep learning methods have been developed to predict the reside-residue contacts between protein-protein interfaces with [32] or without producing the joint MSA for the complex [33][34][35][36]. However, the accuracy of these methods is still relatively low because of the impact of joint MSAs.…”

Section: Introductionmentioning

confidence: 99%

Accurate prediction of residue-residue contacts across homo-oligomeric protein interfaces through deep leaning

Yan

Huang

2020

Preprint

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Protein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein interactions. Recently, deep learning has led to a breakthrough in intraprotein contact prediction, achieving an unusual high accuracy in recent CASP structure prediction challenges. However, due to the limited number of known homologous protein-protein interactions and the challenge to generate joint multiple sequence alignments (MSA) of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue-residue contacts across homo-oligomeric protein interfaces, named as DeepHomo, by integrating evolutionary coupling, sequence conservation, distance map, docking pattern, and physic-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-CAPRI targets. It was shown that DeepHomo achieved a high accuracy of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis (DCA) and machine learning (ML)-based approaches. Integrating predicted contacts into protein docking with blindly predicted monomer structures also significantly improved the docking accuracy. The present study demonstrated the success of DeepHomo in inter-protein contact prediction. It is anticipated that DeepHomo will have a far-reaching implication in the inter-protein contact and structure prediction for protein-protein interactions.

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Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture

Cited by 32 publications

References 16 publications

Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions

Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions

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

Accurate prediction of residue-residue contacts across homo-oligomeric protein interfaces through deep leaning

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