LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec

Abstract

Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved an AUC of 0.976 with an accuracy of 98.4% using 5-fold cross-validation. By using independent test dataset, we compared the LSTM-PHV with existing state-of-the-art PPI predictors including DeepViral. In predicting PPIs between human and unknown or new virus, the LSTM-PHV presented higher performance than the existing predictors when they were trained by multiple host protein-including datasets. LSTM-PHV learnt multiple host protein sequence contexts more efficiently than the DeepViral. Interestingly, learning of only sequence contexts as words presented remarkably high performances. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server that is freely available at http://kurata35.bio.kyutech.ac.jp/.

Comprehensive characterization of human–virus protein-protein interactions reveals disease comorbidities and potential antiviral drugs

“…For example, P-HIPSTer is a useful method to predict human–viral PPIs based on structural information [11] . LSTM-PHV is another method, which combines the long short-term memory (LSTM) model with the word2vec embedding method [12] . However, these experimentally or computationally predicted PPIs are scattered in the literature, which limits the use of the valuable resource.…”

Accurate Prediction of Virus-Host Protein-Protein Interactions via a Siamese Neural Network Using Deep Protein Sequence Embeddings

“…Sun et al (2017) created a dataset using human protein references database (HPRD), which contains human-human PPIs. Tsukiyama et al (2021) and Guo et al (2008) performed a 5-fold cross validation (CV) experiment, whereas Sun et al (2017) used a 10-fold CV setting. We evaluated our STEP architecture using the exact same datasets with the exact same data splits as the authors of the compared methods.…”

“…Table 1: Overview of the results of comparative evaluation of STEP on LSTM-PHV (Tsukiyama et al 2021), Yeast (Guo et al 2008), and Human PPI (Sun et al 2017) datasets. For LSTM-PHV and Yeast PPI datasets, we applied a 5-fold cross validation similar to the authors of the given studies.…”

“…In recent years, deep learning-based approaches (Sun et al 2017, Wang et al 2017, Xu et al, 2020, Tsukiyama et al, 2021) have become popular and have increasingly superseded traditional machine learning approaches for the prediction of PPIs. Often these approaches use known PPIs from established PPI databases (e.g., BioGrid, IntAct, STRING, HPRD, VirHostNet-Oughtred et al 2020, Orchard et al 2014, Szklarczyk et al 2018, Keshava Prasad et al 2009, Guirimand et al 2015) to generate datasets to train deep neural network architectures.…”

“…Chen et al (2019) developed a deep learning framework employing a Siamese neural architecture to predict binary and multi-class PPIs. Tsukiyama et al (2021) recently proposed an LSTM-based model on top of a classical word2vec embedding of sequences to predict human-virus PPIs by using protein sequences. Using the same embedding technique, Liu-Wei et al (2021) developed an approach, which predicts host-virus protein-protein interactions for multiple viruses considering their taxonomic relationships.…”