Accurate top protein variant discovery via low-N pick-and-validate machine learning.

A strategy to obtain the greatest number of best-performing variants with least amount of experimental effort over the vast combinatorial mutational landscape would have enormous utility in boosting resource producibility for protein engineering. Toward this goal, we present a simple and effective machine learning-based strategy that outperforms other state-of-the-art methods. Our strategy integrates zero-shot prediction and multi-round sampling to direct active learning via experimenting with only a few predicted top variants.

Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning models.

The sequence in the 5' untranslated regions (UTRs) is known to affect mRNA translation rates. However, the underlying regulatory grammar remains elusive. Here, we propose MTtrans, a multi-task translation rate predictor capable of learning common sequence patterns from datasets across various experimental techniques.

Parallel engineering and activity profiling of a base editor system.

Selecting the most suitable existing base editors and engineering new variants for installing specific base conversions with maximal efficiency and minimal undesired edits are pivotal for precise genome editing applications. Here, we present a platform for creating and analyzing a library of engineered base editor variants to enable head-to-head evaluation of their editing performance at scale. Our comprehensive comparison provides quantitative measures on each variant's editing efficiency, purity, motif preference, and bias in generating single and multiple base conversions, while uncovering undesired higher indel generation rate and noncanonical base conversion for some of the existing base editors.

Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities.

The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA. Considering only the residues in proximity to the target DNA as potential sites to optimise Cas9's activity, the number of combinatorial variants to screen through is too massive for a wet-lab experiment. Here we generate and cross-validate ten in silico and experimental datasets of multi-domain combinatorial mutagenesis libraries for Cas9 engineering, and demonstrate that a machine learning-coupled engineering approach reduces the experimental screening burden by as high as 95% while enriching top-performing variants by ∼7.

Optochemical Control of mTOR Signaling and mTOR-Dependent Autophagy.

As an important regulator of cell metabolism, proliferation, and survival, mTOR (mammalian target of rapamycin) signaling provides both a potential target for cancer treatment and a research tool for investigation of cell metabolism. One inhibitor for both mTORC1 and mTORC2 pathways, OSI-027, exhibited robust anticancer efficacy but induced side effects. Herein, we designed a photoactivatable OSI-027 prodrug, which allowed the release of OSI-027 after light irradiation to inhibit the mTOR signaling pathway, triggering autophagy and leading to cell death.

A Combinatorial CRISPR-Cas9 Screen Identifies Ifenprodil as an Adjunct to Sorafenib for Liver Cancer Treatment.

Systematic testing of existing drugs and their combinations is an attractive strategy to exploit approved drugs for repurposing and identifying the best actionable treatment options. To expedite the search among many possible drug combinations, we designed a combinatorial CRISPR-Cas9 screen to inhibit druggable targets. Coblockade of the N-methyl-d-aspartate receptor (NMDAR) with targets of first-line kinase inhibitors reduced hepatocellular carcinoma (HCC) cell growth.