NLM DIR Seminar Schedule
UPCOMING SEMINARS
-
Feb. 17, 2026 Zhaohui Liang
Heterogeneous Graph Re-ranking for CLIP-based Medical Cross-modal Retrieval -
Feb. 19, 2026 Jean Thierry-Mieg
On Magic2, an innovative hardware-friendly RNA-seq analyzer -
Feb. 24, 2026 Ajith Viswanathan Asari Pankajam
TBD -
March 3, 2026 Gianlucca Goncalves Nicastro
TBD -
March 5, 2026 Hasan Balci
TBD
RECENT SEMINARS
-
Feb. 5, 2026 Lana Yeganova
From Algorithms to Insights: Bridging AI and Topic Discovery for Large-Scale Biomedical Literature Analysis. -
Jan. 29, 2026 Mehdi Bagheri Hamaneh
FastSpel: A simple peptide spectrum predictor that achieves deep learning-level performance at a fraction of the computational cost -
Jan. 22, 2026 Mario Flores
AI Pipeline for Characterization of the Tumor Microenvironment -
Jan. 20, 2026 Anastasia Gulyaeva
Diversity and evolution of the ribovirus class Stelpaviricetes -
Jan. 8, 2026 Won Gyu Kim
LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery
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.