Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes
AlphaFold2's 93 million parameters, shaped by the evolutionary record of protein structure encoded in the Protein Data Bank and in sequence alignments, are conventionally treated only as machinery for converting sequence to structure. We propose they are also a scientific object that can be analyzed directly: a learned encoding of protein conformational organization that can be probed and characterized. By smoothing the Evoformer's weight tensors with a Gaussian convolution and scaling the result, we show that the trained model produces physically structured conformational landscapes. Under pe
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- PossiblePossibly related (embedding) · 68%Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1 →
- PossiblePossibly related (embedding) · 59%Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language →
- LinkedLinked via arxiv author · 85%Kaustav Mehta →
“Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes”
- PossiblePossibly related (embedding) · 46%Alignment with experimental data improves protein generative modeling - Nature →