iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model

Abstract

The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.

ACP-Dnnel: Anti-coronavirus Peptides Prediction based on Deep Neural Network Ensemble Learning

“…Their evaluation incorporated all of the currently available predictors. iACVP [26] is the latest anti-coronavirus peptide prediction model, which concentrates the advantages of multiple models in the early stage and constructs an independent validation dataset, in order to compare model performance more objectively, this paper also using the ACVP-M dataset as the independence validation datasets, we evaluated the nine state-of-the-art predictors and compared their performance against our proposed predictor. 6.…”

“…The model was constructed based on a deep neural network. The external test accuracy was 93.9%, and the MCC value was 0.87. iACVP [26] Combining RF with word-embedding word2vec, and extracting contextual feature information in peptide sequences by word-embedding word2vec technology for prediction of ACovPs, PACVP [27] used the stacking learning framework, the first layer is responsible for feature extraction, and the second layer through the logistic regression algorithm (LR) to train the final model and accomplish the prediction of ACovPs.…”

A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins

“…The sequence-based analysis included the occurrence frequency of amino acids (AA) and the relative occurrence frequency of aromatic amino acids (aromaticity) [ 72 , 73 , 74 , 75 , 76 ].…”

Analysis, Modeling, and Target-Specific Predictions of Linear Peptides Inhibiting Virus Entry

“…In these designs, amino acid composition, amino acid sequences, motif structures, and physicochemical properties were considered as features. Recently, numerous new predictors for antiviral and anticoronavirus activities have been published. − These predictors were built using machine-learning methods such as random forest and support vector machine.…”