Motivation Predicting protein structures with high accuracy is a critical challenge for the
broad community of life sciences and industry. Despite progress made by deep neural …

Protein structure predictions from deep learning models like AlphaFold2, despite their
remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular …

Computing protein structure from amino acid sequence information has been a long‐
standing grand challenge. Critical assessment of structure prediction (CASP) conducts …

Significant progress has been made in protein structure prediction in recent years. However,
it remains challenging for AlphaFold2 and other deep learning-based methods to predict …

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

Protein structure prediction beyond AlphaFold | Nature Machine Intelligence Skip to main
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AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment.
Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or …

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

Atomic interactions play essential roles in protein folding, structure stabilization, and function
performance. Recent advances in deep learning‐based methods have achieved impressive …

Despite the immense progress recently witnessed in protein structure prediction, the
modeling accuracy for proteins that lack sequence and/or structure homologs remains to be …