Long-standing research on electric and electromagnetic field interactions with biological cells and their subcellular structures has mainly focused on the low- and high-frequency regimes. Biological effects at intermediate frequencies between 100 and 300 kHz have been recently discovered and applied to cancer cells as a therapeutic modality called Tumor Treating Fields (TTFields). TTFields are clinically applied to disrupt cell division, primarily for the treatment of glioblastoma multiforme (GBM). In this review, we provide an assessment of possible physical interactions between 100 kHz range alternating electric fields and biological cells in general and their nano-scale subcellular structures in particular. This is intended to mechanistically elucidate the observed strong disruptive effects in cancer cells. Computational models of isolated cells subject to TTFields predict that for intermediate frequencies the intracellular electric field strength significantly increases and that peak dielectrophoretic forces develop in dividing cells. These findings are in agreement with in vitro observations of TTFields’ disruptive effects on cellular function. We conclude that the most likely candidates to provide a quantitative explanation of these effects are ionic condensation waves around microtubules as well as dielectrophoretic effects on the dipole moments of microtubules. A less likely possibility is the involvement of actin filaments or ion channels.

We describe the activation of out-of-equilibrium collective oscillations of a macromolecule as a classical phonon condensation phenomenon. If a macromolecule is modeled as an open system—that is, it is subjected to an external energy supply and is in contact with a thermal bath to dissipate the excess energy—the internal nonlinear couplings among the normal modes make the system undergo a nonequilibrium phase transition when the energy input rate exceeds a threshold value. This transition takes place between a state where the energy is incoherently distributed among the normal modes and a state where the input energy is channeled into the lowest-frequency mode entailing a coherent oscillation of the entire molecule. The model put forward in the present work is derived as the classical counterpart of a quantum model proposed a long time ago by Fröhlich in an attempt to explain the huge speed of enzymatic reactions. We show that such a phenomenon is actually possible. Two different and complementary THz near-field spectroscopic techniques—a plasmonic rectenna and a microwire near-field probe—have been used in two different labs to eliminate artifacts. By considering an aqueous solution of a model protein, the bovine serum albumin, we find that this protein displays a remarkable absorption feature around 0.314 THz, when driven in a stationary out-of-thermal equilibrium state by means of optical pumping. The experimental outcomes are in very good qualitative agreement with the theory developed in the first part of the paper and in excellent quantitative agreement with the theoretical result, allowing us to identify the observed spectral feature with a collective oscillation of the entire molecule.

The dynamical properties and diffusive behavior of a collection of mutually interacting particles are numerically investigated for two types of long-range interparticle interactions: Coulomb-electrostatic and dipole-electrodynamic. It is shown that when the particles are uniformly distributed throughout the accessible space, the self-diffusion coefficient is always lowered by the considered interparticle interactions, irrespective of their attractive or repulsive character. This fact is also confirmed by a simple model to compute the correction to the Brownian diffusion coefficient due to the interactions among the particles. These interactions are also responsible for the onset of dynamical chaos and an associated chaotic diffusion which still follows an Einstein-Fick-like law for the mean-square displacement as a function of time. Transitional phenomena are observed for Coulomb-electrostatic (repulsive) and dipole-electrodynamic (attractive) interactions considered both separately and in competition. The outcomes reported in this paper clearly indicate a feasible experimental method to probe the activation of resonant electrodynamic interactions among biomolecules.

Some clinical studies have indicated activity of ivermectin, a macrocyclic lactone, against COVID-19, but a biological mechanism initially proposed for this anti-viral effect is not applicable at physiological concentrations. This in silico investigation explores potential modes of action of ivermectin and 14 related compounds, by which the infectivity and morbidity of the SARS-CoV-2 virus may be limited. Binding affinity computations were performed for these agents on several docking sites each for models of (1) the spike glycoprotein of the virus, (2) the CD147 receptor, which has been identified as a secondary attachment point for the virus, and (3) the alpha-7 nicotinic acetylcholine receptor (α7nAChr), an indicated point of viral penetration of neuronal tissue as well as an activation site for the cholinergic anti-inflammatory pathway controlled by the vagus nerve. Binding affinities were calculated for these multiple docking sites and binding modes of each compound. Our results indicate the high affinity of ivermectin, and even higher affinities for some of the other compounds evaluated, for all three of these molecular targets. These results suggest biological mechanisms by which ivermectin may limit the infectivity and morbidity of the SARS-CoV-2 virus and stimulate an α7nAChr-mediated anti-inflammatory pathway that could limit cytokine production by immune cells.

Cytochrome P450 monooxygenases (CYPs/P450s) are well known for their role in organisms’ primary and secondary metabolism. Among 20 P450s of the tuberculosis-causing Mycobacterium tuberculosis H37Rv, CYP128A1 is particularly important owing to its involvement in synthesizing electron transport molecules such as menaquinone-9 (MK9). This study employs different in silico approaches to understand CYP128 P450 family’s distribution and structural aspects. Genome data-mining of 4250 mycobacterial species has revealed the presence of 2674 CYP128 P450s in 2646 mycobacterial species belonging to six different categories. Contrast features were observed in the CYP128 gene distribution, subfamily patterns, and characteristics of the secondary metabolite biosynthetic gene cluster (BGCs) between M. tuberculosis complex (MTBC) and other mycobacterial category species. In all MTBC species (except one) CYP128 P450s belong to subfamily A, whereas subfamily B is predominant in another four mycobacterial category species. Of CYP128 P450s, 78% was a part of BGCs with CYP124A1, or together with CYP124A1 and CYP121A1. The CYP128 family ranked fifth in the conservation ranking. Unique amino acid patterns are present at the EXXR and CXG motifs. Molecular dynamic simulation studies indicate that the CYP128A1 bind to MK9 with the highest affinity compared to the azole drugs analyzed. This study provides comprehensive comparative analysis and structural insights of CYP128A1 in M. tuberculosis.

The prediction of the geometry and strength governing small molecule-protein interactions remains a paramount challenge in drug discovery due to their complex and dynamic nature. A number of machine learning (ML) methods have been proposed to complement and improve on physics-based tools such as molecular docking, usually by mapping three dimensional features of individual poses to their closeness to experimental structures and/or to binding affinities. Here, we introduce Dockbox2 (DBX2), a novel approach that encodes ensembles of computational poses within a graph neural network architecture via simple energy-based features derived from molecular docking. The model was jointly trained to predict binding pose likelihood as a node-level task and binding affinity as a graph-level task using the PDBbind dataset and demonstrated significant performance in comprehensive, retrospective docking and virtual screening experiments. Our results encourage further exploration of ML models based on conformational ensembles to provide more accurate estimates of small molecule-protein interactions and thermodynamics. The DBX2 code is available at https://github.com/jp43/DockBox2.