We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy
and residue-residue distance signed error in protein models and uses these predictions to …

The 3D structure of a protein can be predicted from its amino acid sequence with high
accuracy for a large fraction of cases because of the availability of large quantities of …

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of
Structure Prediction (CASP14) conference. We explored network architectures that …

Protein model refinement is the last step applied to improve the quality of a predicted protein
model. Currently, the most successful refinement methods rely on extensive conformational …

Knowledge of protein structures is essential to understand proteins' functions, evolution,
dynamics, stabilities, and interactions and for data-driven protein-or drug design. Yet …

The prediction of interresidue contacts and distances from coevolutionary data using deep
learning has considerably advanced protein structure prediction. Here, we build on these …

The trRosetta (transform-restrained Rosetta) server is a web-based platform for fast and
accurate protein structure prediction, powered by deep learning and Rosetta. With the input …

Deep learning has transformed protein structure modeling. Here we relate AlphaFold and
RoseTTAFold to classical physically based approaches to protein structure prediction, and …

Proteins are essential to life, and understanding their structure can facilitate a mechanistic
understanding of their function. Through an enormous experimental effort 1, 2, 3, 4, the …

Motivation The computational prediction of a protein structure from its sequence generally
relies on a method to assess the quality of protein models. Most assessment methods rank …