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: Data Collection

1. A) What would be your first approach to determine if the algae contains potential allergens? B) Where would you look for experimental evidence? C) How would you envision predicting in-silico allergy if no allergenicity predictor would been developed yet? Which predictions could you use as a proxy? 2. ALK has developed a treatment for allergy immunotherapy against mites by inducing tolerance to specific common antigens, being one of the most common antigens (Der p 1). It has been observed that after prolonged subcutaneous injections or oral treatment, 72% of the patients do not exhibit allergy symptoms towards dust. In addition, people having other allergies such as allergy to ananas or papaya are not observed anymore. Can you explain why? Hint: https://blast.ncbi.nlm.nih.gov/Blast.cgi 3. Do you think the immunotherapy treatment can also be useful to reduce allergies against brown algae? Justify your answer

PART B 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.