Identifying protein–protein interaction sites in transient complexes with temperature factor, sequence profile and accessible surface area

Amino Acids ◽  
2009 ◽  
Vol 38 (1) ◽  
pp. 263-270 ◽  
Author(s):  
Rong Liu ◽  
Wenchao Jiang ◽  
Yanhong Zhou
Keyword(s):  
Surface Area ◽  
Protein Interaction ◽  
Accessible Surface Area ◽  
Temperature Factor ◽  
Sequence Profile ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Accessible Surface ◽  
Factor Sequence ◽  
Protein Interaction Sites

scholarly journals Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

2018 ◽  
Vol 35 (14) ◽  
pp. 2395-2402 ◽  
Author(s):  
Xiaoying Wang ◽  
Bin Yu ◽  
Anjun Ma ◽  
Cheng Chen ◽  
Bingqiang Liu ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Protein Interaction ◽  
Feature Selection Method ◽  
Feature Space ◽  
Supplementary Information ◽  
Sequence Profile ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Imbalance Problem ◽  
Ppi Networks

Abstract Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples. Results A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2–15.7% and 6.1–18.9% higher than the other existing tools, respectively. Availability and implementation The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

Crystals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 640 ◽  
Author(s):  
Chang Lu ◽  
Zhe Liu ◽  
Bowen Kan ◽  
Yingli Gong ◽  
Zhiqiang Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Surface Area ◽  
Transmembrane Protein ◽  
Pearson Correlation ◽  
Transmembrane Proteins ◽  
Correlation Coefficients ◽  
Accessible Surface Area ◽  
Interaction Sites ◽  
Accessible Surface

Transmembrane proteins (TMPs) play vital and diverse roles in many biological processes, such as molecular transportation and immune response. Like other proteins, many major interactions with other molecules happen in TMPs’ surface area, which is important for function annotation and drug discovery. Under the condition that the structure of TMP is hard to derive from experiment and prediction, it is a practical way to predict the TMP residues’ surface area, measured by the relative accessible surface area (rASA), based on computational methods. In this study, we presented a novel deep learning-based predictor TMP-SSurface for both alpha-helical and beta-barrel transmembrane proteins (α-TMP and β-TMP), where convolutional neural network (CNN), inception blocks, and CapsuleNet were combined to construct a network framework, simply accepting one-hot code and position-specific score matrix (PSSM) of protein fragment as inputs. TMP-SSurface was tested against an independent dataset achieving appreciable performance with 0.584 Pearson correlation coefficients (CC) value. As the first TMP’s rASA predictor utilizing the deep neural network, our method provided a referenceable sample for the community, as well as a practical step to discover the interaction sites of TMPs based on their sequence.

A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction Sites

2015 ◽  
Vol 14 (7) ◽  
pp. 746-760 ◽  
Author(s):  
Zhi-Sen Wei ◽  
Jing-Yu Yang ◽  
Hong-Bin Shen ◽  
Dong-Jun Yu
Keyword(s):  
Random Forests ◽  
Protein Interaction ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Protein Interaction Sites
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