Tag: conformational-dynamics/modeling

Apr 21, 2026

AF2 RMSD to ground truth correlated with RMSD between conformations

Apr 21, 2026

AlphaFold2 and RosettaFold sometimes produce different conformations

Apr 21, 2026

AlphaFold2 predicts Apo conformations with poorer RMSD than holo conformations

Apr 21, 2026

AlphaFold2 predicts ground states more often

Apr 21, 2026

AlphaFold3 performs comparably to AlphaFold2 when predicting multiple conformations of fold-switching proteins

Apr 21, 2026

AlphaFold3 universally predicts the active state of ligand-bound GPCRs, even when the ligand is an antagonist

Apr 21, 2026

Alternate conformations can be sampled by inverting AlphaFold2 and backpropagating along specific user-picked collective variables

Apr 21, 2026

Alternate conformations can be sampled with MSA-based structure prediction methods using custom PDB databases and subsampled MSAs

Apr 21, 2026

Conformationally stabilizing mutations can be identified from sequence clusters that are predicted to fold into that conformation when passed to AlphaFold2

Apr 21, 2026

Protein folding neural networks can be combined with VAEs for conformational modeling

Apr 21, 2026

Protein structure prediction methods are unable to predict the energetics of a conformational landscape unless explicitly trained for that purpose

Apr 21, 2026

Structure prediction from conformational samplers tends to be worse than those from vanilla structure prediction methods

Apr 21, 2026

Structure prediction methods undersample the conformational space they find to be high-confidence

Apr 21, 2026

Structure prediction neural networks trained on the same structural dataset are likely to predict the same conformation

Apr 21, 2026

The AlphaFold and ESMFold databases can be mined for alternate conformations

Apr 21, 2026

pLDDT and PAE inversely correlated with protein dynamics in dynamic naturally occurring proteins, but not de novo proteins

Apr 21, 2026

pTM is less lenient than pLDDT toward structural novelty when predicting alternate conformations

Apr 21, 2026

Biomolecular diffusion models cannot reproduce the equilibrium dynamics of the simulations they are trained on

Apr 21, 2026

Conformational sampling by AlphaFold3-generation methods can be achieved by scaling the pair representation

Apr 21, 2026

Guidance potentials can be added to diffusion-based structure prediction for enhanced sampling of protein conformations

Apr 21, 2026

Masking ESMFold can sometimes sample alternate conformations

Apr 21, 2026

Mutation of allosteric residues can influence the equilibrium dynamics of designed proteins

conformational-dynamics/modeling

Apr 21, 2026

The latent space of VAEs can encode the conformational landscape of dynamic proteins

conformational-dynamics/modeling

Quartz 4

Explorer

Tag: conformational-dynamics/modeling

AF2 RMSD to ground truth correlated with RMSD between conformations

AlphaFold2 and RosettaFold sometimes produce different conformations

AlphaFold2 predicts Apo conformations with poorer RMSD than holo conformations

AlphaFold2 predicts ground states more often

AlphaFold3 performs comparably to AlphaFold2 when predicting multiple conformations of fold-switching proteins

AlphaFold3 universally predicts the active state of ligand-bound GPCRs, even when the ligand is an antagonist

Alternate conformations can be sampled by inverting AlphaFold2 and backpropagating along specific user-picked collective variables

Alternate conformations can be sampled with MSA-based structure prediction methods using custom PDB databases and subsampled MSAs

Conformationally stabilizing mutations can be identified from sequence clusters that are predicted to fold into that conformation when passed to AlphaFold2

Protein folding neural networks can be combined with VAEs for conformational modeling

Protein structure prediction methods are unable to predict the energetics of a conformational landscape unless explicitly trained for that purpose

Structure prediction from conformational samplers tends to be worse than those from vanilla structure prediction methods

Structure prediction methods undersample the conformational space they find to be high-confidence

Structure prediction neural networks trained on the same structural dataset are likely to predict the same conformation

The AlphaFold and ESMFold databases can be mined for alternate conformations

pLDDT and PAE inversely correlated with protein dynamics in dynamic naturally occurring proteins, but not de novo proteins

pTM is less lenient than pLDDT toward structural novelty when predicting alternate conformations

Biomolecular diffusion models cannot reproduce the equilibrium dynamics of the simulations they are trained on

Conformational sampling by AlphaFold3-generation methods can be achieved by scaling the pair representation

Guidance potentials can be added to diffusion-based structure prediction for enhanced sampling of protein conformations

Masking ESMFold can sometimes sample alternate conformations

Mutation of allosteric residues can influence the equilibrium dynamics of designed proteins

The latent space of VAEs can encode the conformational landscape of dynamic proteins