Exercise:AllergenicityPrediction

Exercise written by Carolina Barra Quaglia

Introduction

Climate change and the increasing demand for sustainable food production are driving the search for alternative protein sources. Algae are particularly interesting because they are abundant, can be cultivated without agricultural land, and may provide a valuable source of proteins and other nutrients.

However, introducing a new protein source into the human diet also raises an important question: could some of these proteins cause allergic reactions?

Food allergens are generally proteins that are recognized by the immune system and can trigger an inappropriate immune response in susceptible individuals. Experimental characterization of every protein in a new food source is expensive and time-consuming. Bioinformatics can therefore play an important role in identifying proteins with potential allergenic properties and prioritizing candidates for experimental investigation.

In this exercise, we will investigate the potential allergenicity of proteins from brown algae (Phaeophyceae, taxid: 2870), with particular attention to Ectocarpus siliculosus.

We will first explore how known allergens and sequence similarity can be used to assess potential allergenicity. We will then build our own machine-learning classifier for allergen prediction using a Random Forest. Finally, you will try to improve the predictor using alternative features, preprocessing strategies, machine-learning algorithms, or other approaches of your choice.

Learning Objectives

After completing this exercise, you should be able to:

• Identify appropriate databases and experimental evidence for investigating protein allergenicity

• Explain how sequence similarity to known allergens can provide evidence of potential cross-reactivity or sensitisation

• Use sequence-search tools such as BLAST to investigate relationships between allergens

• Explain how existing bioinformatics predictions can be used as proxies when a dedicated predictor is unavailable

• Describe the major steps required to construct a supervised machine-learning predictor from biological sequence data

• Explain why sequence clustering is important when separating biological data into training and test sets

• Train and evaluate a Random Forest classifier for protein allergenicity.

• Interpret commonly used measures of predictive performance.

• Identify potential limitations of a machine-learning model and propose strategies for improving its performance.

• Design and evaluate an improved allergenicity-prediction strategy.

PART A: Investigating Allergenicity

Imagine that researchers at DTU Food are developing a new high-protein food product based on brown algae.

Before such a product could be considered safe for consumers, one important question would be whether proteins from the algae could potentially act as allergens or cross-react with allergens to which people are already exposed.

It is your job to investigate whether brown algae (Phaeophyceae, taxid: 2870), and in particular Ectocarpus siliculosus, contain proteins that could present an allergenic risk.

Where Would You Start?

Every good bioinformatics investigation starts by asking what information is already available.

Q1A: What would be your first approach to determine whether brown algae contain potential allergens?

Consider what biological information you would search for and what properties of the proteins might provide evidence of allergenic potential.

Q1B: Where would you look for experimental evidence that a protein has previously been associated with an allergic response?

Think about databases containing experimentally characterized allergens, epitopes, antibody responses, or other immunological measurements.

Now imagine that no computational predictor specifically designed for allergenicity has yet been developed.

Q1C: How could you attempt to predict allergenicity in silico?

Which existing protein predictions or sequence properties could potentially be used as proxies for allergenicity?

There is not necessarily a single correct solution here. Think about what biological properties characterize allergens and what computational tools could provide information about those properties.

Sequence Similarity and Cross-Reactivity

Allergic reactions are not always restricted to the protein that originally caused sensitization. Antibodies recognizing one allergen may sometimes recognize structurally or evolutionarily related proteins from another organism.

ALK has developed allergen immunotherapy treatments against house dust mites. One of the major allergens from Dermatophagoides pteronyssinus is Der p 1.

Following prolonged allergen immunotherapy, many patients show reduced allergic symptoms towards house dust mites. Interestingly, reductions in reactions to some other allergens have also been observed.

Let us investigate one possible explanation.

Go to the NCBI BLAST server:

https://blast.ncbi.nlm.nih.gov/Blast.cgi

Retrieve the protein sequence of Der p 1 and investigate whether similar proteins occur among known allergens from other species.

In particular, consider allergens associated with papaya and pineapple (Ananas).

Q2: Can you identify a possible molecular explanation for cross-reactivity between these allergens?

Consider:

Sequence similarity.

Protein family and function.

Conserved regions that could potentially be recognized by the same antibodies.

Do your observations support the hypothesis that these proteins could exhibit immunological cross-reactivity? Explain your reasoning.

Back to Brown Algae

We can now return to our original food-safety problem.

Q3: Based on your investigation, could an immunotherapy targeting an existing allergen potentially reduce allergic responses to proteins found in brown algae?

Use sequence or protein-family evidence to justify your answer.

What would have to be true about the algal proteins for such cross-reactivity to be plausible?

PART B: Develop your own allergenicity prediction

Now we are going to develop an allergenicity predictor using random forest

Follow the code in google Colab. Here you will find some hints on how to use google colab: https://colab.research.google.com/notebooks/basic_features_overview.ipynb#scrollTo=WUtu4316QSHL

https://colab.research.google.com/drive/1vNR_jJeJrMdVoGcTL3iPCKKsXqPi62he?usp=sharing

Note the different steps involved in the creation of the predictor.

1. Data collection and filtering (Data upload)

2. Clustering

3. Train and test data split

4. Training

5. Evaluation

Report the performance of your model.

PART C

Now it is your turn to improve the allergenicity prediction. Optimize the code to improve performance. Here you are allowed to use chatGPT and other language models so give your best hit, and try to beat the earliest performance. There are many things that can be improved such as changing the random forest for other machine learning architectures, add a regularization term to avoid overfitting, you can do some pre-processing of the data or be more creative and find other ways to improve allergenicity prediction. This is a potential project to continue in the mini research project of the second part of the group, and original projects are always evaluated extra-positively!

(Hint: If you are not very familiar with programming you can optimise the given code and asking chatgpt) Here you can find some documentation from sci-kit learn and examples: https://scikit-learn.org/stable/index.html

In the hand in, share your code, and report the new performance.