Articles in 2022

MapToCleave enables the large-scale study of miRNA precursor processing in living cells.

Advances in label-free microscopy are expanding experimental observations, from biophysics to cell biology and beyond.

Frequencies of passenger mutations hint at unobserved driver mutations.

The diverse microbial world provides unprecedented potential for exploring genome editing tools.

Method development is indispensable for obtaining complete genomes, and deciphering them.

The first single-cell transcriptome of the gastrulating human embryo.

High-throughput and ultrasensitive mass spectrometry–based approaches are gaining ground in subcellular compositional analysis.

Probing spatially organized DNA and its interacting elements in single cells will deepen our understanding of cell-type-specific gene regulation.

Emerging algorithms are extracting information about macromolecular motions from cryo-EM data.

Deep Learning based approaches for protein structure prediction have sent shock waves through the structural biology community. We anticipate far-reaching and long-lasting impact.

The splendid computational success of AlphaFold and RoseTTAFold in solving the 60-year-old problem of protein folding raises an obvious question: what new avenues should structural biology explore? We propose a strong pivot toward the goal of reading mechanism and function directly from the amino acid sequence. This ambitious goal will require new data analytical tools and an extensive database of the atomic-level structural trajectories traced out on energy landscapes as proteins perform their function.

AlphaFold is a neural-network-based approach to predicting protein structures with high accuracy. We describe how it works in general terms and discuss some anticipated impacts on the field of structural biology.

Two studies use nanopores for single-protein fingerprinting and make headway toward single-protein sequencing.

Deep learning has transformed protein structure modeling. Here we relate AlphaFold and RoseTTAFold to classical physically based approaches to protein structure prediction, and discuss the many areas of structural biology that are likely to be affected by further advances in deep learning.

Two microscopy approaches bring flexibility to mesoscopic imaging in the brain, allowing independent imaging in multiple regions simultaneously.

The release of protein structure predictions from AlphaFold will increase the number of protein structural models by almost three orders of magnitude. Structural biology and bioinformatics will never be the same, and the need for incisive experimental approaches will be greater than ever. Combining these advances in structure prediction with recent advances in cryo-electron microscopy suggests a new paradigm for structural biology.

The greatly improved prediction of protein 3D structure from sequence achieved by the second version of AlphaFold in 2020 has already had a huge impact on biological research, but challenges remain; the protein folding problem cannot be considered solved. We expect fierce competition to improve the method even further and new applications of machine learning to help illuminate proteomes and their many interactions.

Nature Methods has named protein structure prediction the Method of the Year 2021.