NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Dec. 10, 2024 Amr Elsawy
AI for Age-Related Macular Degeneration on Optical Coherence Tomography -
Dec. 17, 2024 Joey Thole
TBD -
Jan. 7, 2025 Qiao Jin
TBD -
Jan. 14, 2025 Ryan Bell
TBD -
Jan. 21, 2025 Qingqing Zhu
TBD
RECENT SEMINARS
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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 -
Nov. 5, 2024 Max Burroughs
Revisiting the co-evolution of multicellularity and immunity across the tree of life -
Nov. 4, 2024 Finn Werner
African Swine Fever Virus transcription – from transcriptome to molecular structure
Scheduled Seminars on Nov. 12, 2024
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
In recent years, advances in artificial intelligence have significantly transformed the field of structural biology, particularly in protein structure prediction. While AlphaFold2 often predicts structures that resemble known conformations in the Protein Data Bank (PDB), the question remains: has the protein structure prediction problem truly been solved? AlphaFold2 typically produces models representing a single conformational state with limited structural diversity. To address this, various enhanced sampling techniques have been developed to broaden the range of predicted structures or to steer predictions toward specific conformers or folds. However, subsampling of multiple sequence alignments (MSAs) and other methods aimed at increasing structural diversity can sometimes result in lower-confidence models, often favoring familiar structures from its training data over novel folds. We explored both the strengths and potential limitations of AlphaFold (AF2 and AF3), particularly in predicting the alternative conformations of fold-switching proteins. These features point to approaches in predicting alternative conformations more reliably.