A platform for multimodal pooled genetic screens reveals regulators of liver function.

Organ function requires coordinated activities of thousands of genes in distinct, spatially organized cell types. Understanding the basis of emergent tissue function requires approaches to dissect the genetic control of diverse cellular and tissue phenotypes . Here, we develop paired imaging and sequencing methods to construct large-scale, multi-modal genotype-phenotypes maps in tissue with pooled genetic perturbations.

Molecularly defined and spatially resolved cell atlas of the whole mouse brain.

In mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues, including several brain regions (for example, refs.

A molecularly defined and spatially resolved cell atlas of the whole mouse brain.

In mammalian brains, tens of millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells in the brain have so far hindered our understanding of the molecular and cellular basis of its functions. Recent advances in spatially resolved single-cell transcriptomics have allowed systematic mapping of the spatial organization of molecularly defined cell types in complex tissues.

Accurate positioning of functional residues with robotics-inspired computational protein design.

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods.

De novo protein fold families expand the designable ligand binding site space.

A major challenge in designing proteins de novo to bind user-defined ligands with high affinity is finding backbones structures into which a new binding site geometry can be engineered with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place ("match") these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2.

Recent advances in de novo protein design: Principles, methods, and applications.

The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank.

Expanding the space of protein geometries by computational design of de novo fold families.

Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature's ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space.

Macromolecular modeling and design in Rosetta: recent methods and frameworks.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities.

Structure of the AAA protein Msp1 reveals mechanism of mislocalized membrane protein extraction.

The AAA protein Msp1 extracts mislocalized tail-anchored membrane proteins and targets them for degradation, thus maintaining proper cell organization. How Msp1 selects its substrates and firmly engages them during the energetically unfavorable extraction process remains a mystery. To address this question, we solved cryo-EM structures of Msp1-substrate complexes at near-atomic resolution.

Design, construction, and characterization of a set of biosensors for aromatic compounds.

Aromatic pollutants in the environments pose significant threat to human health due to their persistence and toxicity. Here, we report the design and comprehensive characterization of a set of aromatic biosensors constructed using green fluorescence protein as the reporter and aromatics-responsive transcriptional regulators, namely, NahR, XylS, HbpR, and DmpR, as the detectors. The genetic connections between the detectors and the reporter were carefully adjusted to achieve fold inductions far exceeding those reported in previous studies.

Studies of the drug resistance response of sensitive and drug-resistant strains in a microfluidic system.

Since the discovery of bacterial drug resistance, its dynamics have been the focus in biophysics studies. In this paper, we used a new microfluidic system to monitor the responses of sensitive and drug-resistant strains of E. coli in different β-lactam ceftriaxone concentrations at the single cell level and traced each individual cell's states such as cell length, GFP protein expression and growth rate.