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ICACT20250017 Question.1
Questioner: kbadal@aggies.ncat.edu    2025-02-14 오후 11:38:56 		ICACT20250017 Answer.1
Answer by Auhor lqingge@ncat.edu   2025-02-14 오후 11:38:56
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https://www.youtube.com/watch?v=ww46LYbCgZw https://www.youtube.com/watch?v=ww46LYbCgZw
ICACT20250017 Question.2
Questioner: benjikone@yahoo.fr    2025-03-04 오후 10:47:57 		ICACT20250017 Answer.2
Answer by Auhor lqingge@ncat.edu   2025-03-04 오후 10:47:57
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Your study presents PSF as a powerful AI-driven web tool for protein scaffold filling. Given the rapid advancements in protein structure prediction with models like AlphaFold, how do you see PSF complementing or integrating with such models to enhance protein sequence reconstruction? Thank you for your excellent question. While AlphaFold and similar models have revolutionized protein structure prediction, PSF addresses a different but complementary challenge in the protein engineering pipeline. PSF specifically focuses on the scaffold filling problem, where we start with a defined structural scaffold and seek to identify optimal sequences that will fold into that target structure. This is particularly valuable in de novo protein design, where the scaffold may represent a novel fold or function not found in nature. We see PSF integrating with tools like AlphaFold in several ways. First, AlphaFold-predicted structures can serve as input scaffolds for PSF, allowing researchers to explore sequence diversity compatible with those structures. Second, PSF-generated sequences can be validated through AlphaFold to assess their foldability, creating an iterative design-validation cycle.