Volume 19 Issue 1, January 2022

A tool to make microbial network-oriented deep dives easier, and caring about equality.

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

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.

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.

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.

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 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.

Lineage tracing methods help unravel complexities of cell state and differentiation programs.

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

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

Viral tools are a mainstay in mammalian neuroscience, but there is room for improvement.

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

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

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

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

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

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

Frequencies of passenger mutations hint at unobserved driver mutations.

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

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

As last we can edit the immune system’s sleeping giants, as CRISPR tools advance into the world of naive CD4⁺ T cells.

Introducing iTracer, a system for long-term measurement of lineage dynamics at high resolution.

This benchmarking study compares 16 methods for integrating complex single-cell RNA and ATAC datasets and provides a guide to method choice.

This work describes mako, a tool for efficiently constructing and querying network databases with large-scale microbiome data.

Phage and robotics-assisted near-continuous evolution enables phage-assisted continuous evolution in high throughput, allowing for improved exploration of sequence space and insight into how variables affect evolution outcomes.

HaloTag variants offer distinct brightness and fluorescence lifetimes compared with HaloTag7 when labeled with rhodamines. These variants were used for multiplexed imaging with a single fluorophore and to create lifetime-based cell cycle indicators.

Fluorescent nanoantennas represent a versatile detection strategy for monitoring fast, large- and small-scale protein dynamics.

A nucleofection-based method to enable efficient gene editing of primary human resting CD4⁺ T cells.

A dual-channel recording system for high-resolution lineage tracing.

3D-CASH is a random-access microscopy approach that avoids in vivo motion artifacts by sampling each targeted neuron with a holographically shaped grid of illumination spots. The technology allows recording neuronal activity in the mouse cortex at a throughput of 20,000 neurons per second.

mBrainAligner is a cross-modal registration platform for whole mouse brains imaged with different modalities. In addition, a fluorescence micro-optical sectioning tomography-based mouse brain atlas has been generated.

FlyWire is an online community and a platform for proofreading electron microscopy-based connectome data of the Drosophila brain.