Abstract
Proteins play pivotal roles in essential life processes and elucidating their three-dimensional (3D) structures is crucial for understanding their functions. AlphaFold2, an advanced artificial intelligence-based method developed by Google DeepMind, has emerged as a promising tool for predicting protein structures. In this study, we evaluated the predictive capabilities of AlphaFold2. Our findings highlight AlphaFold2's efficacy in providing valuable insights into protein structure prediction, albeit with certain limitations. While AlphaFold2 represents a significant advancement in the field, its utility is best realized when integrated with complementary experimental approaches. Consequently, combining the strengths of AlphaFold2 with experimental validation remains essential for achieving a comprehensive understanding and precise characterization of protein. structures.
Faculty Advisor
Dr. Masa Watanabe
Department/Program
Chemistry
Submission Type
in-person poster
Date
4-9-2024
Rights
Copyright the Author(s)
Recommended Citation
Zang, Yiqing
(2024)
"Using Artificial (AI) to Predict A Structure of Protein Complex,"
SACAD: John Heinrichs Scholarly and Creative Activity Days: Vol. 2024, Article 54.
Available at:
https://scholars.fhsu.edu/sacad/vol2024/iss2024/54
Included in
Biochemistry Commons, Biotechnology Commons, Other Biochemistry, Biophysics, and Structural Biology Commons, Scholarship of Teaching and Learning Commons, Structural Biology Commons
