Water molecules play a key role in many biomolecular systems, particularly when bound at protein–ligand interfaces. However, molecular simulation studies on such systems are hampered by the relatively long time scales over which water exchange between a protein and solvent takes place. Grand canonical Monte Carlo (GCMC) is a simulation technique that avoids this issue by attempting the insertion and deletion of water molecules within a given structure. The approach is constrained by low acceptance probabilities for insertions in congested systems, however. To address this issue, here, we combine GCMC with nonequilibium candidate Monte Carlo (NCMC) to yield a method that we refer to as grand canonical nonequilibrium candidate Monte Carlo (GCNCMC), in which the water insertions and deletions are carried out in a gradual, nonequilibrium fashion. We validate this new approach by comparing GCNCMC and GCMC simulations of bulk water and three protein binding sites. We find that not only is the efficiency of the water sampling improved by GCNCMC but that it also results in increased sampling of ligand conformations in a protein binding site, revealing new water-mediated ligand-binding geometries that are not observed using alternative enhanced sampling techniques.

Antigen processing is an immunological mechanism by which intracellular peptides are transported to the cell surface while bound to Major Histocompatibility Complex molecules, where they can be surveyed by circulating CD8+ or CD4+ T-cells, potentially triggering an immunological response. The antigen processing pathway is a complex multistage filter that refines a huge pool of potential peptide ligands derived from protein degradation into a smaller ensemble for surface presentation. Each stage presents unique challenges due to the number of ligands, the polymorphic nature of MHC and other protein constituents of the pathway and the nature of the interactions between them. Predicting the ensemble of displayed peptide antigens, as well as their immunogenicity, is critical for improving T cell vaccines against pathogens and cancer. Our predictive abilities have always been hindered by an incomplete empirical understanding of the antigen processing pathway. In this review, we highlight the role of computational and structural approaches in improving our understanding of antigen processing, including structural biology, computer simulation, and machine learning techniques, with a particular focus on the MHC-I pathway.

Water molecules at protein–ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.

Mycobacterium tuberculosis (Mtb) is one of the most successful human pathogens. Several cytokines are known to increase virulence of bacterial pathogens, leading us to investigate whether Interferon-γ (IFN-γ), a central regulator of the immune defense against Mtb, has a direct effect on the bacteria. We found that recombinant and T-cell derived IFN-γ rapidly induced a dose-dependent increase in the oxygen consumption rate (OCR) of Mtb, consistent with increased bacterial respiration. This was not observed in attenuated Bacillus Calmette–Guérin (BCG), and did not occur for other cytokines tested, including TNF-α. IFN-γ binds to the cell surface of intact Mtb, but not BCG. Mass spectrometry identified mycobacterial membrane protein large 10 (MmpL10) as the transmembrane binding partner of IFN-γ, supported by molecular modelling studies. IFN-γ binding and the OCR response was absent in Mtb Δmmpl10 strain and restored by complementation with wildtype mmpl10. RNA-sequencing and RT-PCR of Mtb exposed to IFN-γ revealed a distinct transcriptional profile, including genes involved in virulence. In a 3D granuloma model, IFN-γ promoted Mtb growth, which was lost in the Mtb Δmmpl10 strain and restored by complementation, supporting the involvement of MmpL10 in the response to IFN-γ. Finally, IFN-γ addition resulted in sterilization of Mtb cultures treated with isoniazid, indicating clearance of phenotypically resistant bacteria that persist in the presence of drug alone. Together our data are the first description of a mechanism allowing Mtb to respond to host immune activation that may be important in the immunopathogenesis of TB and have use in novel eradication strategies.