Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

In vitro expression and analysis of the 826 human G protein-coupled receptors

ABSTRACT G protein-coupled receptors (GPCRs) are involved in all human physiological systems where they are responsible for transducing extracellular signals into cells. GPCRs signal in response to a diverse array of stimuli including light, hormones, and lipids, where these signals affect downstream cascades to impact both health and disease states. Yet, despite their importance as therapeutic targets, detailed molecular structures of only 30 GPCRs have been determined to date. A key challenge to their structure determination is adequate protein expression. Here we report the quantification of protein expression in an insect cell expression system for all 826 human GPCRs using two different fusion constructs. Expression characteristics are analyzed in aggregate and among each of the five distinct subfamilies. These data can be used to identify trends related to GPCR expression between different fusion constructs and between different GPCR families, and to prioritize lead candidates for future structure determination feasibility

A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T

We report a search on a sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne-year exposure collected by PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and electrons. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such dark matter into photon final states. In particular, we present the first direct detection limits for an axial-vector (vector) interaction which are the strongest in the mass range from 25 to 45 (35 to 50) keV/c$^2$

Progress on Crowding Effect in Cell-like Structures

Several biological macromolecules, such as proteins, nucleic acids, and polysaccharides, occupy about 30% of the space in cells, resulting in a crowded macromolecule environment. The crowding effect within cells exerts an impact on the functions of biological components, the assembly behavior of biomacromolecules, and the thermodynamics and kinetics of metabolic reactions. Cell-like structures provide confined and independent compartments for studying the working mechanisms of cells, which can be used to study the physiological functions arising from the crowding effect of macromolecules in cells. This article mainly summarizes the progress of research on the macromolecular crowding effects in cell-like structures. It includes the effects of this crowding on actin assembly behavior, tubulin aggregation behavior, and gene expression. The challenges and future trends in this field are presented at the end of the paper

Local Void Fractions and Bubble Velocity in Vertical Air-Water Two-Phase Flows Measured by Needle-Contact Capacitance Probe

Multiphase flow measurements have become increasingly important in a wide range of industrial fields. In the present study, a dual needle-contact capacitance probe was newly designed to measure local void fractions and bubble velocity in a vertical channel, which was verified by digital high-speed camera system. The theoretical analyses and experiments show that the needle-contact capacitance probe can reliably measure void fractions with the readings almost independent of temperature and salinity for the experimental conditions. In addition, the trigger-level method was chosen as the signal processing method for the void fraction measurement, with a minimum relative error of −4.59%. The bubble velocity was accurately measured within a relative error of 10%. Meanwhile, dynamic response of the dual needle-contact capacitance probe was analyzed in detail. The probe was then used to obtain raw signals for vertical pipe flow regimes, including plug flow, slug flow, churn flow, and bubbly flow. Further experiments indicate that the time series of the output signals vary as the different flow regimes and are consistent with each flow structure