NLM DIR Seminar Schedule
UPCOMING SEMINARS
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April 8, 2025 Jaya Srivastava
Leveraging a deep learning model to assess the impact of regulatory variants on traits and diseases -
April 15, 2025 Pascal Mutz
TBD -
April 18, 2025 Valentina Boeva, Department of Computer Science, ETH Zurich
Decoding tumor heterogeneity: computational methods for scRNA-seq and spatial omics -
April 22, 2025 Stanley Liang
TBD -
April 29, 2025 MG Hirsch
TBD
RECENT SEMINARS
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April 1, 2025 Roman Kogay
Horizontal transfer of bacterial operons into eukaryote genomes -
March 25, 2025 Yifan Yang
Adversarial Manipulation and Data Memorization in Large Language Models for Medicine -
March 11, 2025 Sofya Garushyants
Tmn – bacterial anti-phage defense system -
March 4, 2025 Sanasar Babajanyan
Evolution of antivirus defense in prokaryotes depending on the environmental virus load -
Feb. 25, 2025 Zhizheng Wang
GeneAgent: Self-verification Language Agent for Gene Set Analysis using Domain Databases
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.