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Updated: Jul 21, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Prediction of CD44 Structure by Deep Learning-Based Protein Modeling.

Chiara Camponeschi¹, Benedetta Righino¹, Davide Pirolli¹

¹Institute of Chemical Sciences and Technologies ''Giulio Natta'' (SCITEC)-CNR, 00168 Rome, Italy.

|July 29, 2023

Summary

Structural prediction of CD44s, a key cell receptor, was achieved using deep learning. AlphaFold2 showed superior accuracy in modeling the full-length CD44s structure, including its transmembrane helix.

Keywords:

artificial intelligence hyaluronan-binding domain immune response intrinsically disordered regions molecular dynamics simulations

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Area of Science:

Biochemistry
Structural Biology
Cell Biology

Background:

CD44 is a crucial cell surface receptor mediating cell-matrix and cell-cell interactions, particularly with hyaluronic acid.
Its ubiquitous expression and role in signaling pathways highlight its importance in physiological and pathological processes.
Understanding CD44 structure is vital for developing targeted therapies for diseases linked to its dysregulation.

Purpose of the Study:

To predict the full-length structure of the CD44s isoform using advanced deep learning methods.
To evaluate the performance of different deep learning tools in structural prediction of CD44.
To identify regions of CD44s important for its function and potential therapeutic targeting.

Main Methods:

Utilized deep learning tools: D-I-TASSER, AlphaFold2, and RoseTTAFold for full-length CD44s structural prediction.
Performed molecular dynamics simulations on the most accurate predicted model.
Compared predicted structures with experimentally determined hyaluronan-binding domain (HABD) structures.

Main Results:

All three deep learning methods accurately predicted the HABD of CD44.
AlphaFold2 demonstrated superior performance compared to D-I-TASSER and RoseTTAFold in structural accuracy and transmembrane helix prediction.
Predicted low-confidence regions corresponded to known disordered regions of CD44s, crucial for its activity.

Conclusions:

Deep learning models, particularly AlphaFold2, are effective for predicting the full-length CD44s structure.
The predicted structure provides insights into CD44s mechanism of action and its role in cellular processes.
Structural insights can guide the development of novel therapeutic strategies targeting CD44 in various diseases.