Unknown

Dataset Information

0

An integration of deep learning with feature embedding for protein-protein interaction prediction.

ABSTRACT: Protein-protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein-protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein-protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein-protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew's correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein-protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI.

SUBMITTER: Yao Y 

PROVIDER: S-EPMC6585896 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

Json Xml
altmetric image

Publications

An integration of deep learning with feature embedding for protein-protein interaction prediction.

Yao Yu Y   Du Xiuquan X   Diao Yanyu Y   Zhu Huaixu H  

PeerJ 20190617

Protein-protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein-protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein-protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In th  ...[more]

Similar Datasets

Unknown Protein Interaction Interface Region Prediction by Geometric Deep Learning.
| S-EPMC8428585 | biostudies-literature
Unknown SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.
| S-EPMC7869013 | biostudies-literature
Unknown Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration : Malonylation site prediction.
| S-EPMC7682087 | biostudies-literature
Unknown Sequence-based prediction of protein protein interaction using a deep-learning algorithm.
| S-EPMC5445391 | biostudies-literature
Unknown A deep-learning framework for multi-level peptide-protein interaction prediction.
| S-EPMC8443569 | biostudies-literature
Unknown AptaNet as a deep learning approach for aptamer-protein interaction prediction.
| S-EPMC7971039 | biostudies-literature
Unknown DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.
| S-EPMC8459562 | biostudies-literature
Unknown A new paradigm for applying deep learning to protein–ligand interaction prediction
| S-EPMC10998640 | biostudies-literature
Unknown RNA secondary structure prediction using deep learning with thermodynamic integration.
| S-EPMC7878809 | biostudies-literature
Unknown scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data.
| S-EPMC10833085 | biostudies-literature