Qualitative and quantitative prediction of food allergen epitopes based on machine learning combined with in vitro experimental validation

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

“…faced challenges in unifying the feature dimension of the embeddings. In a study by Yu et al, each property in AAindex of a peptide was the average of all residues, resulting in a less promising performance for allergenic peptide prediction (accuracy = 72.2%) . In addition, amino acid descriptors such as DPPS, 3Z-scale, 5-Zscale, HESH, FASGAI, ISA-ECI, and E-scale, yielded inferior performance than AAindex .…”

“…However, this simplistic view overlooks complexities during initial allergen exposure, such as allergen biochemistry, surrounding innate immune stimulants, allergen stability in tissues and mucosa, and dosage and duration of interaction with the immune system . The majority of allergens are protein/peptides, which are widely present in foods, personal care products, and the environment. − Notably, many allergens, especially in plants and foods, belong to a few protein families . The reason certain proteins act as allergens remains unclear, but they often share characteristics contributing to allergenicity, including small size, water solubility, glycosylation, repetitive structures, numerous disulfide bonds, resistance to heat, acid, and proteolysis, ligand-binding property, ability to interact with membranes and lipids, and intrinsic adjuvant property. ,, …”

“…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 …”

Machine Learning-Based Prediction of Free IgE Concentration in Allergic Rhinitis Patients Treated with Allergen Immunotherapy and Omalizumab

“…Machine learning is a type of artificial intelligence that can be used to learn from data and make predictions. Machine learning algorithms are gradually gaining ground as tools that can be very effective at predicting free IgE concentration and other immunological biomarkers in patients with allergic diseases [10][11][12] . In this study, we are going to use a machine learning algorithm to predict free IgE in allergic rhinitis patients treated with allergen immunotherapy and omalizumab 5,6,9,13 .…”