Prediction of protein structure from sequence has been intensely studied for many decades,
owing to the problem's importance and its uniquely well-defined physical and computational …

Abstract Protein Structure Prediction is a central topic in Structural Bioinformatics. Since
the'60s statistical methods, followed by increasingly complex Machine Learning and recently …

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

Large language models have recently been shown to develop emergent capabilities with
scale, going beyond simple pattern matching to perform higher level reasoning and …

Most proteins fold into 3D structures that determine how they function and orchestrate the
biological processes of the cell. Recent developments in computational methods for protein …

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

Unsupervised contact prediction is central to uncovering physical, structural, and functional
constraints for protein structure determination and design. For decades, the predominant …

Many high-throughput experimental technologies have been developed to assess the
effects of large numbers of mutations (variation) on phenotypes. However, designing …

Protein language models (pLMs) have emerged as potent tools for predicting and designing
protein structure and function, and the degree to which these models fundamentally …

Residue-residue coevolution has been observed across a number of protein-protein
interfaces, but the extent of residue coevolution between protein families on the whole …