Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

Abstract

Motivation Sequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information. Results We present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short. Availability and implementation The implementation is available at https://github.com/muhaochen/seq_ppi.git. Supplementary information Supplementary data are available at Bioinformatics online.

Current challenges for epitope-agnostic TCR interaction prediction and a new perspective derived from image classification

“…Using this type of interaction map as the model input differs from the prevailing approach seen in sequence-based prediction models, not only in the field of TCR-epitope prediction, but also for the modeling of other molecular interactions. The interacting molecules are usually supplied to a model as separate or concatenated inputs (1, 3,5,6,15,18,20,25,26). Those types of models need to learn an internal representation for each molecule separately, before being combined again in deeper layers.…”

Sequence representations and their utility for predicting protein-protein interactions

“…These methods have included the Support Vector Machine [15], Random Forests [29] and autoencoders [6]. More recently, studies such as DPPI [31], DNN-PPI [32], and PIPR [33] have explored deep learning frameworks for PPI prediction. Note that these newer deep learning-based approaches are end-to-end classification models and do not specifically focus on the feature construction technique.…”

“…All of these approaches are primarily based on the Convolutional Neural Network and have a deep architecture. The most recent of these approaches, PIPR [33], provides an end-to-end deep GRU (gated recurrent unit) based architecture for PPI prediction. The results reported [33] show an improvement in classification results (refer Table 3) compared to other benchmark methods -including other deep learning based approaches.…”

“…In these experiments, we compare the results of our methods (M9 and M10) on Guo's yeast dataset with baseline approaches SVM-AC [15], kNN-CTD [17], EELM-PCA [46], SVM-MCD [27], MLP [47], RF-LPQ [28], SAE [6], DNN-PPI [32], DPPI [31] and SRGRU, SCNN and PIPR from [33]. The results for the baseline approaches are taken from [33]. Table 3, M9 and M10 outperform many approaches (SVM-AC, kNN-CTD, EELM-PCA, SVM-MCD, SAE, DNN-PPI, SRGRU) and perform at par with DPPI and SCNN.…”

Evaluating Representations for Gene Ontology Terms

“…The first data resource is the sequence-to-sequence relationship. For example, proteinprotein interaction network and structural homology are often used to constraint the fact that closely related proteins should have similar GO labels [4,29]. The second data resource is in fact the Gene Ontology itself.…”