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 …

Water solubility and structural stability are key merits for proteins defined by the primary
sequence and 3D-conformation. Their manipulation represents important aspects of the …

Proteins are macromolecules that mediate a significant fraction of the cellular processes that
underlie life. An important task in bioengineering is designing proteins with specific 3D …

Language models have recently emerged as a powerful machine-learning approach for
distilling information from massive protein sequence databases. From readily available …

There has been considerable recent progress in protein structure prediction using deep
neural networks to predict inter-residue distances from amino acid sequences,–. Here we …

Rational protein engineering requires a holistic understanding of protein function. Here, we
apply deep learning to unlabeled amino-acid sequences to distill the fundamental features …

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

Computational methods for protein structure modelling are routinely used to complement
experimental structure determination, thus they help to address a broad spectrum of …

Proteins are the molecular machinery of the human body, and their malfunctioning is often
responsible for diseases, making them crucial targets for drug discovery. The three …

The computational de novo protein design is increasingly applied to address a number of
key challenges in biomedicine and biological engineering. Successes in expanding …