Prediction of protein–protein interaction sites using patch-based residue characterization

Qiu, Zhijun; Wang, Xicheng

doi:10.1016/j.jtbi.2011.10.021

Cited by 20 publications

(8 citation statements)

References 93 publications

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“…Such methods also consider only sites of the complex under examination, disregarding disparate sites involved in other interactions [ 3 , 4 ]. In response to these shortcomings, computational methods for the prediction of PPISs have been developed, starting with Jones and Thornton’s pioneering analysis of surface patches [ 5 , 6 ], and many predictors have since been published [ 7 - 43 ], utilizing a wide variety of algorithmic approaches to the problem.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic approaches to protein-protein interaction site prediction

Aumentado-Armstrong

Istrate

Murgita

2015

Algorithms Mol Biol

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Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

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Section: Introductionmentioning

confidence: 99%

Algorithmic approaches to protein-protein interaction site prediction

Aumentado-Armstrong

Istrate

Murgita

2015

Algorithms Mol Biol

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“…For example, Agrawal et al [ 11 ] proposed a computational tool—named a spatial interaction map (SIM)—that utilizes the structure of unbound proteins to detect the residues from PPIs. Qiu et al [ 12 ] presented a novel residue characterization model, based on 3D structures, for the purpose of detecting PPIs. These computational methods—based on structural data—identify the interaction domain by analyzing the hydrophobicity, solvation, protrusion, and accessibility of residues.…”

Section: Introductionmentioning

confidence: 99%

PCLPred: A Bioinformatics Method for Predicting Protein–Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation

Wang

You

et al. 2018

IJMS

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Protein–protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the Saccharomyces cerevisiae dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.

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“…Qiu et al [11] extracted properties based on 3D structure to build a patch-based model and a residue-based model. For the residue-based model, they achieved a specificity rate of 70% and a sensitivity of 0.78; for patch-based model, they achieved a success rate of 0.8.…”

Section: Introductionmentioning

confidence: 99%

“…In most cases, machine learning methods with features are proposed to predict protein-protein interaction sites. Popular algorithms are Support Vector Machine (SVM) [4,14,15], Random Forest (RF) [11,[16][17][18], neural network (NN) [19] and so on. Chen et al [14] constructed an integrative profile by developing a support vector machine ensemble.…”

Section: Introductionmentioning

confidence: 99%

A novel feature extraction scheme for prediction of protein–protein interaction sites

Jing

2015

Mol. BioSyst.

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Identifying protein-protein interaction (PPI) sites plays an important and challenging role in some topics of biology. Although many methods have been proposed, this problem is still far away to be solved. Here, a feature selection approach with an 11-sliding window and random forest algorithm is proposed, which is called DX-RF. This method has achieved an accuracy of 88.79%, recall of 82.09%, and precision of 85.76% with top-ranked 34 features on the Hetero test dataset and has 91.6% accuracy, 89.2% precision, 83.54% recall with top-ranked 25 features set on the Homo test dataset. Compared to other methods, the results indicate that the DX-RF method has a strong ability to select relevance features to get a higher performance. Moreover, in order to further understand protein interactions, feature analysis in this study is also performed.

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Prediction of protein–protein interaction sites using patch-based residue characterization

Cited by 20 publications

References 93 publications

Algorithmic approaches to protein-protein interaction site prediction

Algorithmic approaches to protein-protein interaction site prediction

PCLPred: A Bioinformatics Method for Predicting Protein–Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation

A novel feature extraction scheme for prediction of protein–protein interaction sites

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