ProAll-D: protein allergen detection using long short term memory - a deep learning approach

Abstract

Background: An allergic reaction is the immune system's overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach's positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens. Method: This work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration. Results: The Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour's Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %. Conclusion: As the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data.
 ©2022 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
 Keywords
 Allergen prediction; ACC transformation; LSTM model; Gaussian naive bayes; Classifier; Extra tree classifier; Bagging classifier; ADA boost; Linear discriminant analysis; Quadratic discriminant analysis

Computational prediction of allergenic proteins based on multi-feature fusion

“…This dataset had been studied by ProAll-D project and could be freely accessible from https://doi.org/10.17632/ tjmt97xpjf.1. Proteins with high homology had been removed by CD-HIT program for avoiding sequence redundancy and ensuring the objectivity of experimental results (Shanthappa and Kumar, 2022).…”

“…Furthermore, ensemble approaches, such as proAP and AlgPred 2.0, have also been developed based on both sequence similarity and motif eliciting strategy (Soeria-Atmadja et al, 2006;Wang et al, 2013;Sharma et al, 2021b). In recent years, several feature vector-based approaches have been reported, including APPEL, AllerTOP, AllergenFP, AllerCatPro, ProAll-D (Cui et al, 2007;Dimitrov et al, 2013;Dimitrov et al, 2014;Nguyen et al, 2022;Shanthappa and Kumar, 2022). In general, they take sequence-derived compositional, evolutionary, structural and physicochemical information into consideration and achieve allergenic protein classification by using machine learning or deep learning models (Wang et al, 2021;Ao et al, 2022;Wu et al, 2023).…”