NLM DIR Seminar Schedule
UPCOMING SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 15, 2025 Noam Rotenberg
Cell phenotypes in the biomedical literature: a systematic analysis and the NLM CellLink text mining corpus
RECENT SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
June 10, 2025 Aleksandra Foerster
Interactions at pre-bonding distances and bond formation for open p-shell atoms: a step toward biomolecular interaction modeling using electrostatics -
June 3, 2025 MG Hirsch
Interactions among subclones and immunity controls melanoma progression -
May 29, 2025 Harutyun Sahakyan
In silico evolution of globular protein folds from random sequences
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.