Bioactive hydrolysates from casein: generation, identification, andin silicotoxicity and allergenicity prediction of peptides

2018 ◽  
Vol 98 (9) ◽  
pp. 3416-3426 ◽  
Author(s):  
Maolin Tu ◽  
Hanxiong Liu ◽  
Ruyi Zhang ◽  
Hui Chen ◽  
Fengjiao Fan ◽  
...  
Keyword(s):  
Allergenicity Prediction

scholarly journals Predicting Immunogenicity Risk in Biopharmaceuticals

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Nikolet Doneva ◽  
Irini Doytchinova ◽  
Ivan Dimitrov
Keyword(s):  
Machine Learning Algorithms ◽  
Computational Tools ◽  
Binding Motifs ◽  
B Cell Epitopes ◽  
Crucial Step ◽  
Histocompatibility Complex ◽  
Alignment Algorithms ◽  
Experimental Approaches ◽  
Dynamics Simulations ◽  
Allergenicity Prediction

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

An Alignment-Independent Platform for Allergenicity Prediction

2020 ◽  
pp. 147-153
Author(s):  
Ivan Dimitrov ◽  
Irini Doytchinova
Keyword(s):  
Allergenicity Prediction

In silico allergenicity prediction of several lipid transfer proteins

2016 ◽  
Vol 60 ◽  
pp. 32-42 ◽  
Author(s):  
Cristiano Garino ◽  
Jean Daniel Coïsson ◽  
Marco Arlorio
Keyword(s):  
In Silico ◽  
Lipid Transfer ◽  
Lipid Transfer Proteins ◽  
Allergenicity Prediction

Computational allergenicity prediction of transgenic proteins expressed in genetically modified crops

2010 ◽  
Vol 33 (3) ◽  
pp. 410-422 ◽  
Author(s):  
Alok Kumar Verma ◽  
Amita Misra ◽  
Swarna Subash ◽  
Mukul Das ◽  
Premendra D. Dwivedi
Keyword(s):  
Genetically Modified ◽  
Genetically Modified Crops ◽  
Allergenicity Prediction

scholarly journals AllergenFP: allergenicity prediction by descriptor fingerprints

2013 ◽  
Vol 30 (6) ◽  
pp. 846-851 ◽  
Author(s):  
I. Dimitrov ◽  
L. Naneva ◽  
I. Doytchinova ◽  
I. Bangov
Keyword(s):  
Allergenicity Prediction

Accuracy of protein allergenicity prediction can be improved by taking into account data on allergenic protein discontinuous peptides

2013 ◽  
Vol 31 (1) ◽  
pp. 59-64 ◽  
Author(s):  
Anatoly O. Bragin ◽  
Pavel S. Demenkov ◽  
Nickolay A. Kolchanov ◽  
Vladimir A. Ivanisenko
Keyword(s):  
Allergenic Protein ◽  
Protein Allergenicity ◽  
Allergenicity Prediction ◽  
Protein Allergenicity Prediction

Protein allergenicity prediction on the basis of conformational peptides

2012 ◽  
Vol 2 (1) ◽  
pp. 18-22
Author(s):  
A. O. Bragin ◽  
P. S. Demenkov ◽  
V. A. Ivanisenko
Keyword(s):  
Protein Allergenicity ◽  
Allergenicity Prediction ◽  
Protein Allergenicity Prediction

Allergenicity prediction by artificial neural networks

2014 ◽  
Vol 28 (4) ◽  
pp. 282-286 ◽  
Author(s):  
Ivan Dimitrov ◽  
Lyudmila Naneva ◽  
Ivan Bangov ◽  
Irini Doytchinova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Allergenicity Prediction

scholarly journals AllerStat: Finding Statistically Significant Allergen-Specific Patterns in Protein Sequences by Machine Learning

2021 ◽  
Author(s):  
Kento Goto ◽  
Norimasa Tamehiro ◽  
Takumi Yoshida ◽  
Hiroyuki Hanada ◽  
Takuto Sakuma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Sequences ◽  
Allergic Reactions ◽  
Proof Of Concept ◽  
Specific Amino Acid ◽  
Novel Foods ◽  
Allergenic Proteins ◽  
Data Driven Approach ◽  
Allergenicity Prediction

Cutting-edge technologies such as genome editing and synthetic biology allow us to produce novel foods and functional proteins. However, their toxicity and allergenicity must be accurately evaluated. Allergic reactions are caused by specific amino-acid sequences in proteins (Allergen Specific Patterns, ASPs), of which, many remain undiscovered. In this study, we introduce a data-driven approach and a machine-learning (ML) method to find undiscovered ASPs. The proposed method enables an exhaustive search for amino-acid subsequences whose frequencies are statistically significantly higher in allergenic proteins. As a proof-of-concept (PoC), we created a database containing 21,154 proteins of which the presence or absence of allergic reactions are already known, and the proposed method was applied to the database. The detected ASPs in the PoC study were consistent with known biological findings, and the allergenicity prediction accuracy using the detected ASPs was higher than extant approaches.

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