A molecular glue approach to control the half-life of CRISPR-based technologies.

Cas9 is a programmable nuclease that has furnished transformative technologies, including base editors and transcription modulators (e.g., CRISPRi/a), but several applications of these technologies, including therapeutics, mandatorily require precision control of their half-life. For example, such control can help avert any potential immunological and adverse events in clinical trials. Current genome editing technologies to control the half-life of Cas9 are slow, have lower activity, involve fusion of large response elements (> 230 amino acids), utilize expensive controllers with poor pharmacological attributes, and cannot be implemented in vivo on several CRISPR-based technologies. We report a general platform for half-life control using the molecular glue, pomalidomide, that binds to a ubiquitin ligase complex and a response-element bearing CRISPR-based technology, thereby causing the latter's rapid ubiquitination and degradation. Using pomalidomide, we were able to control the half-life of large CRISPR-based technologies (e.g., base editors, CRISPRi) and small anti-CRISPRs that inhibit such technologies, allowing us to build the first examples of on-switch for base editors. The ability to switch on, fine-tune and switch-off CRISPR-based technologies with pomalidomide allowed complete control over their activity, specificity, and genome editing outcome. Importantly, the miniature size of the response element and favorable pharmacological attributes of the drug pomalidomide allowed control of activity of base editor in vivo using AAV as the delivery vehicle. These studies provide methods and reagents to precisely control the dosage and half-life of CRISPR-based technologies, propelling their therapeutic development.

Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.

Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called KD-seq, it determines the absolute affinity of protein-ligand interactions. We also apply ProBound to profile the kinetics of kinase-substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

Rates and Patterns of Clonal Oncogenic Mutations in the Normal Human Brain.

Although oncogenic mutations have been found in nondiseased, proliferative nonneural tissues, their prevalence in the human brain is unknown. Targeted sequencing of genes implicated in brain tumors in 418 samples derived from 110 individuals of varying ages, without tumor diagnoses, detected oncogenic somatic single-nucleotide variants (sSNV) in 5.4% of the brains, including IDH1 R132H. These mutations were largely present in subcortical white matter and enriched in glial cells and, surprisingly, were less common in older individuals. A depletion of high-allele frequency sSNVs representing macroscopic clones with age was replicated by analysis of bulk RNA sequencing data from 1,816 nondiseased brain samples ranging from fetal to old age. We also describe large clonal copy number variants and that sSNVs show mutational signatures resembling those found in gliomas, suggesting that mutational processes of the normal brain drive early glial oncogenesis. This study helps understand the origin and early evolution of brain tumors. SIGNIFICANCE: In the nondiseased brain, clonal oncogenic mutations are enriched in white matter and are less common in older individuals. We revealed early steps in acquiring oncogenic variants, which are essential to understanding brain tumor origins and building new mutational baselines for diagnostics.This article is highlighted in the In This Issue feature, p. 1.