A Comparative Analysis of Allergen Proteins between Plants and Animals Using Several Computational Tools and Chou’s PseAAC Concept

Abstract

<b><i>Background:</i></b> A large number of allergens are derived from plant and animal proteins. A major challenge for researchers is to study the possible allergenic properties of proteins. The aim of this study was in silico analysis and comparison of several physiochemical and structural features of plant- and animal-derived allergen proteins, as well as classifying these proteins based on Chou’s pseudo-amino acid composition (PseAAC) concept combined with bioinformatics algorithms. <b><i>Methods:</i></b> The physiochemical properties and secondary structure of plant and animal allergens were studied. The classification of the sequences was done using the PseAAC concept incorporated with the deep learning algorithm. Conserved motifs of plant and animal proteins were discovered using the MEME tool. B-cell and T-cell epitopes of the proteins were predicted in conserved motifs. Allergenicity and amino acid composition of epitopes were also analyzed via bioinformatics servers. <b><i>Results:</i></b> In comparison of physiochemical features of animal and plant allergens, extinction coefficient was different significantly. Secondary structure prediction showed more random coiled structure in plant allergen proteins compared with animal proteins. Classification of proteins based on PseAAC achieved 88.24% accuracy. The amino acid composition study of predicted B- and T-cell epitopes revealed more aliphatic index in plant-derived epitopes. <b><i>Conclusions:</i></b> The results indicated that bioinformatics-based studies could be useful in comparing plant and animal allergens.

Computational Study on Temperature Driven Structure–Function Relationship of Polysaccharide Producing Bacterial Glycosyl Transferase Enzyme

“…Motifs are basically signature sequences that aid in the identification of any protein. The e-value shows accuracy of the predicted motif; less the e-value, more the precision of the possible motifs [ 56 ]. From our study, it has been found that the GTs retrieved from all the three different environments based on growth temperatures (i.e., mesophile, thermophile, and hyperthermophile), e-value were less than 3.0e + 000.…”

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach

“…Therefore, more attention has recently been given to bioinformatics and machine learning strategies as potential tools for detecting and classifying food allergens. Among the great variety of methods, intelligence neural networks, supervised learning, support vector machines with linear kernel functions, and different classifiers such as k -nearest neighbor are used as reliable options for identifying, modeling, and predicting allergenic properties. − Wang et al developed a new deep learning model (transformer with a self-attention mechanism combining the learning models Light Gradient Boosting Machine [LightGBM] and eXtreme Gradient Boosting [XGBoost]) for the prediction of food allergens. Machine learning is proving to be a tremendously helpful solution in this field.…”

pLM4Alg: Protein Language Model-Based Predictors for Allergenic Proteins and Peptides

“…Subsequently, various approaches are proposed from different perspectives for allergen prediction, such as motif-based approaches, similarity searches, machine learning-based modeling, etc. Prediction tools, such as AllerCatPro 2.0, AlgPred 2.0, have been used extensively for preliminary allergenic risk assessment and identification of new allergens. ,,− One representative project derived from the FAO/WHO guidelines was AllerCatPro/AllerCatPro 2.0, which implemented a hierarchical workflow employing five criteria with improved similarity-checking methods for both sequences and 3D structures. , The criteria used in AllerCatPro were based on statistical analysis of existing allergens, representing a form of experience/knowledge-based decision-making. For instance, if a protein sequence shares >35% identity with known allergens over 90 windows, it would be classified into the group with strong evidence for allergenic potential. , So far, this approach has achieved a great performance in the allergen benchmark data sets .…”

“…Another representative technique is the machine learning (ML) approach where the classification criterion is determined by the ML model by learning from a separate training data set. ,,,− , Besides the selection of modeling method, the most challenging task in the ML approach is the representation or encoding of protein/peptide sequences into a numerical vector/matrix. Various encoding methods, including amino acid descriptors, amino acid composition (AAC), pseudoamino acid composition (PseAAC), dipeptide composition (DPC), amino acid descriptors (AAD), position-specific scoring matrix (PSSM), physicochemical descriptors, biomedical properties, k-mer dictionary-based binary representation, etc., have been widely used in predicting allergenicity and other properties/bioactivities. ,,,,,− However, these features may not always accurately represent protein sequences and simple combinations can cause high-dimensional problems as well as the feature redundancy …”