Significance: Aggregation and cell-to-cell spread of TDP-43 are thought to underlie many cases of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). Additionally, the aging process and environmental toxins stimulate excessive generation of reactive oxygen and nitrogen species (ROS/RNS), thus contributing to the pathological processes of these neurodegenerative diseases. Here, we show that nitric oxide (NO)-related species promote TDP-43 proteinopathy in cell-based and animal models of FTD/ALS via S-nitrosylation of TDP-43 at critical cysteine residues that facilitate disulfide bond formation and abnormal aggregation of the protein. In contrast, expression of nonnitrosylatable mutant TDP-43 ameliorates TDP-43 aggregation, RNA binding, and cell toxicity. Thus, our study provides mechanistic insight into the ROS/RNS-induced TDP-43 proteinopathy and neurotoxicity observed in FTD/ALS patients.

Molecular dynamics simulations of biomolecules have been widely adopted in biomedical studies. As classical point-charge models continue to be used in routine biomolecular applications, there have been growing demands on developing polarizable force fields for handling more complicated biomolecular processes. Here, we focus on a recently proposed polarizable Gaussian Multipole (pGM) model for biomolecular simulations. A key benefit of pGM is its screening of all short-range electrostatic interactions in a physically consistent manner, which is critical for stable charge-fitting and is needed to reproduce molecular anisotropy. Another advantage of pGM is that each atom’s multipoles are represented by a single Gaussian function or its derivatives, allowing for more efficient electrostatics than other Gaussian-based models. In this study, we present an efficient formulation for the pGM model defined with respect to a local frame formed with a set of covalent basis vectors. The covalent basis vectors are chosen to be along each atom’s covalent bonding directions. The new local frame can better accommodate the fact that permanent dipoles are primarily aligned along covalent bonds due to the differences in electronegativity of bonded atoms. It also allows molecular flexibility during molecular simulations and facilitates an efficient formulation of analytical electrostatic forces without explicit torque computation. Subsequent numerical tests show that analytical atomic forces agree excellently with numerical finite-difference forces for the tested system. Finally, the new pGM electrostatics algorithm is interfaced with the particle mesh Ewald (PME) implementation in Amber for molecular simulations under the periodic boundary conditions. To validate the overall pGM/PME electrostatics, we conducted an NVE simulation for a small water box of 512 water molecules. Our results show that to achieve energy conservation in the polarizable model, it is important to ensure enough accuracy on both PME and induction iteration. It is hoped that the reformulated pGM model will facilitate the development of future force fields based on the pGM electrostatics for applications in biomolecular systems and processes where polarization plays crucial roles.

Proteases are an important class of enzymes, whose activity is central to many physiologic and pathologic processes. Detailed knowledge of protease specificity is key to understanding their function. Although many methods have been developed to profile specificities of proteases, few have the diversity and quantitative grasp necessary to fully define specificity of a protease, both in terms of substrate numbers and their catalytic efficiencies. We have developed a concept of “selectome”; the set of substrate amino acid sequences that uniquely represent the specificity of a protease. We applied it to two closely related members of the Matrixin family–MMP-2 and MMP-9 by using substrate phage display coupled with Next Generation Sequencing and information theory-based data analysis. We have also derived a quantitative measure of substrate specificity, which accounts for both the number of substrates and their relative catalytic efficiencies. Using these advances greatly facilitates elucidation of substrate selectivity between closely related members of a protease family. The study also provides insight into the degree to which the catalytic cleft defines substrate recognition, thus providing basis for overcoming two of the major challenges in the field of proteolysis: 1) development of highly selective activity probes for studying proteases with overlapping specificities, and 2) distinguishing targeted proteolysis from bystander proteolytic events.

Our previous article has established the theory of molecular dynamics (MD) simulations for systems modeled with the polarizable Gaussian multipole (pGM) electrostatics [Wei et al., J. Chem. Phys. 153(11), 114116 (2020)]. Specifically, we proposed the covalent basis vector framework to define the permanent multipoles and derived closed-form energy and force expressions to facilitate an efficient implementation of pGM electrostatics. In this study, we move forward to derive the pGM internal stress tensor for constant pressure MD simulations with the pGM electrostatics. Three different formulations are presented for the flexible, rigid, and short-range screened systems, respectively. The analytical formulations were implemented in the SANDER program in the Amber package and were first validated with the finite-difference method for two different boxes of pGM water molecules. This is followed by a constant temperature and constant pressure MD simulation for a box of 512 pGM water molecules. Our results show that the simulation system stabilized at a physically reasonable state and maintained the balance with the externally applied pressure. In addition, several fundamental differences were observed between the pGM and classic point charge models in terms of the simulation behaviors, indicating more extensive parameterization is necessary to utilize the pGM electrostatics.