The main protease (Mpro) of SARS-CoV-2 is a key antiviral drug target. While most Mpro inhibitors have a γ-lactam glutamine surrogate at the P1 position, we recently discovered several Mpro inhibitors have hydrophobic moieties at the P1 site, including calpain inhibitors II and XII, which are also active against human cathepsin L, a host-protease that is important for viral entry. In this study, we solved X-ray crystal structures of Mpro in complex with calpain inhibitors II and XII, and three analogs of GC-376. The structure of Mpro with calpain inhibitor II confirmed the S1 pocket can accommodate a hydrophobic methionine side chain, challenging the idea that a hydrophilic residue is necessary at this position. Interestingly, the structure of calpain inhibitor XII revealed an unexpected, inverted binding pose. Taken together, the biochemical, computational, structural, and cellular data presented herein provide new directions for the development of dual inhibitors as SARS-CoV-2 antivirals.

The main protease (Mpro) of SARS-CoV-2, the pathogen responsible for the COVID-19 pandemic, is a key antiviral drug target. While most SARS-CoV-2 Mpro inhibitors have a γ-lactam glutamine surrogate at the P1 position, we recently discovered several Mpro inhibitors have hydrophobic moieties at the P1 site, including calpain inhibitors II/XII, which are also active against human cathepsin L, a host-protease that is important for viral entry. To determine the binding mode of these calpain inhibitors and establish a structure-activity relationship, we solved X-ray crystal structures of Mpro in complex with calpain inhibitors II and XII, and three analogues of GC-376, one of the most potent Mpro inhibitors in vitro. The structure of Mpro with calpain inhibitor II confirmed the S1 pocket of Mpro can accommodate a hydrophobic methionine side chain, challenging the idea that a hydrophilic residue is necessary at this position. Interestingly, the structure of calpain inhibitor XII revealed an unexpected, inverted binding pose where the P1’ pyridine inserts in the S1 pocket and the P1 norvaline is positioned in the S1’ pocket. The overall conformation is semi-helical, wrapping around the catalytic core, in contrast to the extended conformation of other peptidomimetic inhibitors. Additionally, the structures of three GC-376 analogues UAWJ246, UAWJ247, and UAWJ248 provide insight to the sidechain preference of the S1’, S2, S3 and S4 pockets, and the superior cell-based activity of the aldehyde warhead compared with the α-ketoamide. Taken together, the biochemical, computational, structural, and cellular data presented herein provide new directions for the development of Mpro inhibitors as SARS-CoV-2 antivirals.

Coulomb interactions play a major role in determining the thermodynamics, structure, and dynamics of condensed-phase systems, but often present significant challenges. Computer simulations usually use periodic boundary conditions to minimize corrections from finite cell boundaries but the long range of the Coulomb interactions generates significant contributions from distant periodic images of the simulation cell, usually calculated by Ewald sum techniques. This can add significant overhead to computer simulations and hampers the development of intuitive local pictures and simple analytic theory. In this paper, we present a general framework based on local molecular field theory to accurately determine the contributions from long-ranged Coulomb interactions to the potential of mean force between ionic or apolar hydrophobic solutes in dilute aqueous solutions described by standard classical point charge water models. The simplest approximation leads to a short solvent (SS) model, with truncated solvent–solvent and solute–solvent Coulomb interactions and long-ranged but screened Coulomb interactions only between charged solutes. The SS model accurately describes the interplay between strong short-ranged solute core interactions, local hydrogen-bond configurations, and long-ranged dielectric screening of distant charges, competing effects that are difficult to capture in standard implicit solvent models.

Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-based calculations, but with a fraction of the cost, enabling simulations of large systems over long timescales. However, implicit in the construction of neural network potentials is an assumption of locality, wherein atomic arrangements on the nanometer-scale are used to learn interatomic interactions. Because of this assumption, the resulting neural network models cannot describe long-range interactions that play critical roles in dielectric screening and chemical reactivity. Here, we address this issue by introducing the self-consistent field neural network — a general approach for learning the long-range response of molecular systems in neural network potentials that relies on a physically meaningful separation of the interatomic interactions — and demonstrate its utility by modeling liquid water with and without applied fields.