Interactions between macromolecules, such as proteins and nucleic acids, are essential for
cellular functions. Experimental methods can fail to provide all the information required to …

Abstract AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind
that can predict three-dimensional (3D) structures of proteins from amino acid sequences …

The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of
proteins and their interactions, enabling a huge range of applications in protein modelling …

Deep-learning methods have revolutionized protein structure prediction and design but are
presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which …

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with
exceptionally high accuracy. Its implementation, however, lacks the code and data required …

Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …

Endosymbiotic bacteria have evolved intricate delivery systems that enable these organisms
to interface with host biology. One example, the extracellular contractile injection systems …

Artificial intelligence-based protein structure prediction methods such as AlphaFold have
revolutionized structural biology. The accuracies of these predictions vary, however, and …

Despite advances in machine learning-based protein structure prediction, we are still far
from fully understanding how proteins fold into their native conformation. The conventional …

Recent advances in machine learning and experimental biology have offered breakthrough
solutions to problems such as protein structure prediction that were long thought to be …