# Protein Structure Prediction

bAIes originally drew structural information only from AlphaFold2 distograms to build atomic-resolution conformational ensembles of intrinsically disordered proteins. It now accepts six predictors, AlphaFold2, AlphaFold3, Boltz-1, Boltz-2, ESMFold and ESMFold2, and was compared on NMR and SAXS data for 17 disordered proteins. Ensembles from every predictor reach average accuracy comparable to the original AlphaFold2 version, best on local observables such as chemical shifts, RDCs and 3J couplings, and weaker on long-range and global observables such as PREs and SAXS.

bAIes originally drew structural information only from AlphaFold2 distograms to build atomic-resolution conformational ensembles of intrinsically disordered proteins. It now accepts six predictors, AlphaFold2, AlphaFold3, Boltz-1, Boltz-2, ESMFold and ESMFold2, and was compared on NMR and SAXS data for 17 disordered proteins. Ensembles from every predictor reach average accuracy comparable to the original AlphaFold2 version, best on local observables such as chemical shifts, RDCs and 3J couplings, and weaker on long-range and global observables such as PREs and SAXS.

This work introduces two learned discrete latent protein languages: PLL, a 4,096-state contextual alphabet built on a frozen ESM-2 encoder on the sequence side, and SLL, which adapts GCP-VQVAE Lite with auxiliary sequence and confidence supervision while remaining decodable to backbone coordinates; under matched downstream training, PLLM has a fitted compute-scaling exponent of 0.038 versus 0.020 for the amino acid autoregressive model and reduces the fraction of samples below a 1.5-bit residue-composition entropy threshold by 54%, while replacing the original tokenizer with SLL reduces best validation perplexity by 34% for sequence-to-structure prediction.

This work introduces two learned discrete latent protein languages: PLL, a 4,096-state contextual alphabet built on a frozen ESM-2 encoder on the sequence side, and SLL, which adapts GCP-VQVAE Lite with auxiliary sequence and confidence supervision while remaining decodable to backbone coordinates; under matched downstream training, PLLM has a fitted compute-scaling exponent of 0.038 versus 0.020 for the amino acid autoregressive model and reduces the fraction of samples below a 1.5-bit residue-composition entropy threshold by 54%, while replacing the original tokenizer with SLL reduces best validation perplexity by 34% for sequence-to-structure prediction.