Tag: structure-prediction/sampling

Apr 21, 2026

AlphaFold2 and RosettaFold sometimes produce different conformations

Apr 21, 2026

AlphaFold2 generalizes to unseen secondary structures and topologies

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

AlphaFold2 predicts the holo form of proteins in most cases

Apr 21, 2026

AlphaFold3 learns intramolecular interactions faster than intermolecular interactions

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

Language-based protein folding NNs predict novelty with far lower confidence than MSA-based protein folding NNs

structure-prediction/sampling

Apr 21, 2026

MSA-based structure predictions outperform PLM-based methods

structure-prediction/sampling

Apr 21, 2026

Protein ensemble prediction methods do not generate conformations that are amenable to PPI docking

Apr 21, 2026

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

Apr 21, 2026

Structure prediction and design tools trained on monomers generalize to oligomers

structure-prediction/sampling

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 predict different conformations of a given sequence depending on if a single chain is predicted versus multiple chains

Apr 21, 2026

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

Apr 21, 2026

Structure prediction with AlphaFold improved when starting from an initial guess, rather than default initialization

Apr 21, 2026

The AlphaFold and ESMFold databases can be mined for alternate conformations

Apr 21, 2026

ddG predictions calculated from AlphaFold structures are equally accurate as those derived from experimental structures

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

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

Quartz 4

Explorer

Tag: structure-prediction/sampling

AlphaFold2 and RosettaFold sometimes produce different conformations

AlphaFold2 generalizes to unseen secondary structures and topologies

AlphaFold2 predicts Apo conformations with poorer RMSD than holo conformations

AlphaFold2 predicts ground states more often

AlphaFold2 predicts the holo form of proteins in most cases

AlphaFold3 learns intramolecular interactions faster than intermolecular interactions

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

Language-based protein folding NNs predict novelty with far lower confidence than MSA-based protein folding NNs

MSA-based structure predictions outperform PLM-based methods

Protein ensemble prediction methods do not generate conformations that are amenable to PPI docking

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

Structure prediction and design tools trained on monomers generalize to oligomers

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 predict different conformations of a given sequence depending on if a single chain is predicted versus multiple chains

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

Structure prediction with AlphaFold improved when starting from an initial guess, rather than default initialization

The AlphaFold and ESMFold databases can be mined for alternate conformations

ddG predictions calculated from AlphaFold structures are equally accurate as those derived from experimental structures

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

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