The pH-regulated M2 proton channel from the influenza A virus has a His-tetrad in its transmembrane (TM) domain that is essential for proton conduction. At neutral pH, the tetrad has been observed in two distinct configurations, the His-box and dimer-of-dimers, with similar backbone structures suggesting competing models for proton conduction. Here, we propose that both conformations can play a role. In support of this hypothesis, we used molecular dynamics simulations based on density functional theory to simulate the M2-TM domain and force-field-based simulations to estimate the relevant free-energy barriers. Both configurations are stable on accessible simulation time scales, and transitions between them occur faster than the millisecond time scale of proton conduction. Moreover, the deprotonation energy is too high for spontaneous conduction, consistent with their occurrence in the low-current regime. Our computations support a multiconfiguration model with different population levels, thereby connecting experimental data obtained under different conditions.

The tetrameric M2 proton channel of influenza A virus is an integral membrane protein responsible for the acidification of the viral interior. Drugs such as amantadine target the transmembrane region of wild type M2 by acting as pore blockers. However, a number of mutations affecting this domain confer drug resistance, prompting the need for alternative inhibitors. The availability of high-resolution structures of drug-bound M2, paired with computational investigations, revealed that inhibitors can bind at different sites, and provided useful insights in understanding the principles governing proton conduction. Here, we investigated by computation the energetic and geometric factors determining the relative stability of pore blockers at individual sites of different M2 strains. We found that local free energy minima along the translocation pathway of positively charged chemical species correspond to experimentally determined binding sites of inhibitors. Then, by examining the structure of water clusters hydrating each site, as well as of those displaced by binding of hydrophobic scaffolds, we predicted the binding preferences of M2 ligands. This information can be used to guide the identification of novel classes of inhibitors.

Transition metal oxide materials are of great utility, with a diversity of topical applications ranging from catalysis to electronic devices. Because of their widespread importance in materials science, there is increasing interest in developing computational tools capable of reliable prediction of transition metal oxide phase behavior and properties. The workhorse of materials theory is density functional theory (DFT). Accordingly, we have investigated the impact of various correlation and exchange approximations on their ability to predict the properties of NiO using DFT. We have chosen NiO as a particularly challenging representative of transition metal oxides in general. In so doing, we have provided validation for the use of the r2SCAN density functional for predicting the materials properties of oxides. r2SCAN yields accurate structural properties of NiO and a local spin moment that notably persists under pressure, consistent with experiment. The outcome of our study is a pragmatic scheme for providing electronic structure data to enable the parameterization of interatomic potentials using state-of-the-art artificial intelligence (AI) and machine learning (ML) methodologies. The latter is essential to allow large scale molecular dynamics simulations of bulk and surface materials phase behavior and properties with ab initio accuracy.

SignificanceThe conduction of protons through the highly restricted paths of transmembrane proteins is an essential process of living systems and an intriguing problem in modern physical chemistry. The small size of the influenza M2 proton channel makes it an ideal system for the study of proton transport across a membrane. Additionally, the M2 channel has medical relevance as an anti-flu drug target. These high-resolution structures of the channel were obtained by crystallizing the protein in a membrane-like environment and reveal networks of hydrogen-bonded waters that change with temperature and pH. The locations of these waters, in conjunction with molecular dynamics simulations that predict their hydrogen bond orientations, provide insight into the mechanism of proton stabilization and transduction within the channel.The matrix 2 (M2) protein from influenza A virus is a proton channel that uses His37 as a selectivity filter. Here we report high-resolution (1.10 Å) cryogenic crystallographic structures of the transmembrane domain of M2 at low and high pH. These structures reveal that waters within the pore form hydrogen-bonded networks or “water wires” spanning 17 Å from the channel entrance to His37. Pore-lining carbonyl groups are well situated to stabilize hydronium via second-shell interactions involving bridging water molecules. In addition, room temperature crystallographic structures indicate that water becomes increasingly fluid with increasing temperature and decreasing pH, despite the higher electrostatic field. Complementary molecular dynamics simulations reveal a collective switch of hydrogen bond orientations that can contribute to the directionality of proton flux as His37 is dynamically protonated and deprotonated in the conduction cycle.

The influenza A virus M2 proton channel (A/M2) is the target of the antiviral drugs amantadine and rimantadine, whose use has been discontinued due to widespread drug resistance. Among the handful of drug-resistant mutants, S31N is found in more than 95% of the currently circulating viruses and shows greatly decreased inhibition by amantadine. The discovery of inhibitors of S31N has been hampered by the limited size, polarity, and dynamic nature of its amantadine-binding site. Nevertheless, we have discovered small-molecule drugs that inhibit S31N with potencies greater than amantadines potency against WT M2. Drug binding locks the protein into a well-defined conformation, and the NMR structure of the complex shows the drug bound in the homotetrameric channel, threaded between the side chains of Asn31. Unrestrained molecular dynamics simulations predicted the same binding site. This S31N inhibitor, like other potent M2 inhibitors, contains a charged ammonium group. The ammonium binds as a hydrate to one of three sites aligned along the central cavity that appear to be hotspots for inhibition. These sites might stabilize hydronium-like species formed as protons diffuse through the outer channel to the proton-shuttling residue His37 near the cytoplasmic end of the channel.

Developing an understanding of the mechanism of voltage-gated ion channels in molecular terms requires knowledge of the structure of the active and resting conformations. Although the active-state conformation is known from x-ray structures, an atomic resolution structure of a voltage-dependent ion channel in the resting state is not currently available. This has motivated various efforts at using computational modeling methods and molecular dynamics (MD) simulations to provide the missing information. A comparison of recent computational results reveals an emerging consensus on voltage-dependent gating from computational modeling and MD simulations. This progress is highlighted in the broad context of preexisting work about voltage-gated channels.

Anti-influenza drugs, amantadine and rimantadine, targeting the M2 channel from influenza A virus are no longer effective because of widespread drug resistance. S31N is the predominant and amantadine-resistant M2 mutant, present in almost all of the circulating influenza A strains as well as in the pandemic 2009 H1N1 and the highly pathogenic H5N1 flu strains. Thus, there is an urgent need to develop second-generation M2 inhibitors targeting the S31N mutant. However, the S31N mutant presents a huge challenge to drug discovery, and it has been considered undruggable for several decades. Using structural information, classical medicinal chemistry approaches, and M2-specific biological testing, we discovered benzyl-substituted amantadine derivatives with activity against both S31N and WT, among which 4-(adamantan-1-ylaminomethyl)-benzene-1,3-diol (44) is the most potent dual inhibitor. These inhibitors demonstrate that S31N is a druggable target and provide a new starting point to design novel M2 inhibitors that address the problem of drug-resistant influenza A infections.

Objective

To characterize progression of geographic atrophy (GA) associated with age-related macular degeneration in AREDS as measured by digitized fundus photographs.

Methods

Fundus photographs from 181 of 4757 AREDS participants with a GA area of at least 0.5 disc areas at baseline or from participants who developed bilateral GA during follow-up were scanned, digitized, and evaluated longitudinally. Geographic atrophy area was determined using planimetry. Rates of progression from noncentral to central GA and of vision loss following development of central GA included the entire AREDS cohort.

Results

Median initial lesion size was 4.3 mm(2). Average change in digital area of GA from baseline was 2.03 mm(2) (standard error of the mean, 0.24 mm(2)) at 1 year, 3.78 mm(2) (0.24 mm(2)) at 2 years, 5.93 mm(2) (0.34 mm(2)) at 3 years, and 1.78 mm(2) (0.086 mm(2)) per year overall. Median time to developing central GA after any GA diagnosis was 2.5 years (95% confidence interval, 2.0-3.0). Average visual acuity decreased by 3.7 letters at first documentation of central GA, and by 22 letters at year 5.

Conclusions

Growth of GA area can be reliably measured using standard fundus photographs that are digitized and subsequently graded at a reading center. Development of GA is associated with subsequent further growth of GA, development of central GA, and loss in central vision.