Deep Learning Application in Protein Structure Understanding–FoxP Series of Proteins as an Example
DOI:
https://doi.org/10.47611/jsrhs.v14i1.8992Keywords:
Deep Learning, AlphaFold2, Protein Structure Prediction, AI4ScienceAbstract
This study explores the application of deep learning (DL) in understanding protein structures, focusing on the FoxP2 protein as an example. The integration of DL tools, such as AlphaFold2 (AF2) and ChatGPT, enables analysis of FoxP2's structure, accuracy, and function, with implications for its role in speech and language development. AF2's predictions were compared to experimental data, revealing strengths in identifying key regions like DNA-binding domains but limitations in accuracy and reliability in less structured areas. Additionally, ChatGPT demonstrated effectiveness in providing supplementary biological insights, such as the impact of mutations like ARG553 and potential clinical implications. The findings highlight the utility of DL in accelerating protein research while emphasizing the need for caution in interpreting predictions.
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