NLM DIR Seminar Schedule
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Dec. 17, 2024 Joey Thole
Training set associations drive AlphaFold initial predictions of fold-switching proteins -
Dec. 10, 2024 Amr Elsawy
AI for Age-Related Macular Degeneration on Optical Coherence Tomography -
Dec. 3, 2024 Sarvesh Soni
Toward Relieving Clinician Burden by Automatically Generating Progress Notes -
Nov. 19, 2024 Benjamin Lee
Reiterative Translation in Stop-Free Circular RNAs -
Nov. 12, 2024 Devlina Chakravarty
Fold-switching reveals blind spots in AlphaFold predictions
Scheduled Seminars on Feb. 15, 2024
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Though typically associated with a single folded state, some globular proteins remodel their secondary and/or tertiary structures in response to cellular stimuli. AlphaFold2 (AF2) readily generates one dominant protein structure for these fold-switching (a.k.a. metamorphic) proteins, but it often fails to predict their alternative experimentally observed structures. Wayment-Steele, et al. steered AF2 to predict alternative structures of a few metamorphic proteins using a method they call AF-cluster. However, their paper lacks some essential controls needed to assess AF-cluster’s reliability. We find that using ColabFold-based random sequence sampling–a method we call CF-random–is a more accurate and less computationally intense alternative to AF-cluster. In addition, CF-random effectively captures the alternative conformations of functional and membrane transport proteins with fewer predicted samples than other AF2-based enhanced sampling approaches. We suggest that CF-random predicts the alternative conformations of proteins using associative sequence homology rather than generative coevolutionary inference.