Since the first revelation of proteins functioning as macromolecular machines through their
three dimensional structures, researchers have been intrigued by the marvelous ways the …

Deep learning approaches have produced substantial breakthroughs in fields such as
image classification and natural language processing and are making rapid inroads in the …

Deep-learning language models have shown promise in various biotechnological
applications, including protein design and engineering. Here we describe ProGen, a …

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 …

Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …

Advanced protein structure prediction requires evolutionary information from multiple
sequence alignments (MSAs) from evolutionary couplings that are not always available …

Although knowing where a protein functions in a cell is important to characterize biological
processes, this information remains unavailable for most known proteins. Machine learning …

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as
highly reliable and effective methods for predicting protein structures. This article explores …

Computational Protein Design (CPD) has produced impressive results for engineering new
proteins, resulting in a wide variety of applications. In the past few years, various efforts have …

The prediction of protein structures from sequences is an important task for function
prediction, drug design, and related biological processes understanding. Recent advances …