Machine-Learning-Based Single-Molecule Quantification of Circulating MicroRNA Mixtures.

MicroRNAs (miRs) are small noncoding RNAs that regulate gene expression and are emerging as powerful indicators of diseases. MiRs are secreted in blood plasma and thus may report on systemic aberrations at an early stage via liquid biopsy analysis. We present a method for multiplexed single-molecule detection and quantification of a selected panel of miRs. The proposed assay does not depend on sequencing, requires less than 1 mL of blood, and provides fast results by direct analysis of native, unamplified miRs. This is enabled by a novel combination of compact spectral imaging and a machine learning-based detection scheme that allows simultaneous multiplexed classification of multiple miR targets per sample. The proposed end-to-end pipeline is extremely time efficient and cost-effective. We benchmark our method with synthetic mixtures of three target miRs, showcasing the ability to quantify and distinguish subtle ratio changes between miR targets.

Noncovalent inhibitors reveal BTK gatekeeper and auto-inhibitory residues that control its transforming activity.

Inhibition of Bruton tyrosine kinase (BTK) is a breakthrough therapy for certain B cell lymphomas and B cell chronic lymphatic leukemia. Covalent BTK inhibitors (e.g., ibrutinib) bind to cysteine C481, and mutations of this residue confer clinical resistance. This has led to the development of noncovalent BTK inhibitors that do not require binding to cysteine C481. These new compounds are now entering clinical trials. In a systematic BTK mutagenesis screen, we identify residues that are critical for the activity of noncovalent inhibitors. These include a gatekeeper residue (T474) and mutations in the kinase domain. Strikingly, co-occurrence of gatekeeper and kinase domain lesions (L512M, E513G, F517L, L547P) in cis results in a 10- to 15-fold gain of BTK kinase activity and de novo transforming potential in vitro and in vivo. Computational BTK structure analyses reveal how these lesions disrupt an intramolecular mechanism that attenuates BTK activation. Our findings anticipate clinical resistance mechanisms to a new class of noncovalent BTK inhibitors and reveal intramolecular mechanisms that constrain BTK's transforming potential.

Not just an oil slick: how the energetics of protein-membrane interactions impacts the function and organization of transmembrane proteins.

The membrane environment, its composition, dynamics, and remodeling, have been shown to participate in the function and organization of a wide variety of transmembrane (TM) proteins, making it necessary to study the molecular mechanisms of such proteins in the context of their membrane settings. We review some recent conceptual advances enabling such studies, and corresponding computational models and tools designed to facilitate the concerted experimental and computational investigation of protein-membrane interactions. To connect productively with the high resolution achieved by cognate experimental approaches, the computational methods must offer quantitative data at an atomistically detailed level. We show how such a quantitative method illuminated the mechanistic importance of a structural characteristic of multihelical TM proteins, that is, the likely presence of adjacent polar and hydrophobic residues at the protein-membrane interface. Such adjacency can preclude the complete alleviation of the well-known hydrophobic mismatch between TM proteins and the surrounding membrane, giving rise to an energy cost of residual hydrophobic mismatch. The energy cost and biophysical formulation of hydrophobic mismatch and residual hydrophobic mismatch are reviewed in the context of their mechanistic role in the function of prototypical members of multihelical TM protein families: 1), LeuT, a bacterial homolog of mammalian neurotransmitter sodium symporters; and 2), rhodopsin and the ß1- and ß2-adrenergic receptors from the G-protein coupled receptor family. The type of computational analysis provided by these examples is poised to translate the rapidly growing structural data for the many TM protein families that are of great importance to cell function into ever more incisive insights into mechanisms driven by protein-ligand and protein-protein interactions in the membrane environment.