We investigated the effects of 17 widely used atomistic molecular mechanics force fields (MMFFs) on the structures and kinetics of amyloid peptide assembly. To this end, we performed large-scale all-atom molecular dynamics simulations in explicit water on the dimer of the sevenresidue fragment of the Alzheimer's amyloid-β peptide, Aβ16−22, for a total time of 0.34 ms. We compared the effects of these MMFFs by analyzing various global reaction coordinates, secondary structure contents, the fibril population, the in-register and out-of-register architectures, and the fibril formation time at 310 K. While the AMBER94, AMBER99, and AMBER12SB force fields do not predict any β-sheets, the seven force fields, AMBER96, GROMOS45a3, GROMOS53a5, GROMOS53a6, GROMOS43a1, GROMOS43a2, and GROMOS54a7, form β-sheets rapidly. In contrast, the following five force fields, AMBER99-ILDN, AMBER14SB, CHARMM22*, CHARMM36, and CHARMM36m, are the best candidates for studying amyloid peptide assembly, as they provide good balances in terms of structures and kinetics. We also investigated the assembly mechanisms of dimeric Aβ16−22 and found that the fibril formation rate is predominantly controlled by the total β-strand content.

Accurate prediction of the absolute or relative protein−ligand binding affinity is one of the major tasks in computer-aided drug design projects, especially in the stage of lead optimization. In principle, the alchemical free energy (AFE) methods such as thermodynamic integration (TI) or free-energy perturbation (FEP) can fulfill this task, but in practice, a lot of hurdles prevent them from being routinely applied in daily drug design projects, such as the demanding computing resources, slow computing processes, unavailable or inaccurate force field parameters, and difficult and unfriendly setting up and post-analysis procedures. In this study, we have exploited practical protocols of applying the CPU (central processing unit)-TI and newly developed GPU (graphic processing unit)-TI modules and other tools in the AMBER software package, combined with ff14SB/GAFF1.8 force fields, to conduct efficient and accurate AFE calculations on protein−ligand binding free energies. We have tested 134 protein−ligand complexes in total for four target proteins (BACE, CDK2, MCL1, and PTP1B) and obtained overall comparable performance with the commercial Schrodinger FEP+ program (Wang et al.

Background Aicardi-Goutières syndrome (AGS) is a severe infant or juvenile-onset autoimmune disease characterized by inflammatory encephalopathy with an elevated type 1 interferon-stimulated gene (ISG) expression signature in the brain. Mutations in seven different protein-coding genes, all linked to DNA/RNA metabolism or sensing, have been identified in AGS patients, but none of them has been demonstrated to activate the IFN pathway in the brain of an animal. The molecular mechanism of inflammatory encephalopathy in AGS has not been well defined. Adenosine Deaminase Acting on RNA 1 (ADAR1) is one of the AGS-associated genes. It carries out A-to-I RNA editing that converts adenosine to inosine at double-stranded RNA regions. Whether an AGS-associated mutation in ADAR1 activates the IFN pathway and causes autoimmune pathogenesis in the brain is yet to be determined. Methods Mutations in the ADAR1 gene found in AGS patients were introduced into the mouse genome via CRISPR/Cas9 technology. Molecular activities of the specific p.K999N mutation were investigated by measuring the RNA editing levels in brain mRNA substrates of ADAR1 through RNA sequencing analysis. IFN pathway activation in the brain was assessed by measuring ISG expression at the mRNA and protein level through real-time RT-PCR and Luminex assays, respectively. The locations in the brain and neural cell types that express ISGs were determined by RNA in situ hybridization (ISH). Potential AGS-related brain morphologic changes were assessed with immunohistological analysis. Von Kossa and Luxol Fast Blue staining was performed on brain tissue to assess calcification and myelin, respectively. Results Mice bearing the ADAR1 p.K999N were viable though smaller than wild type sibs. RNA sequencing analysis of neuron-specific RNA substrates revealed altered RNA editing activities of the mutant ADAR1 protein. Mutant mice exhibited dramatically elevated levels of multiple ISGs within the brain. RNA ISH of brain sections showed selective activation of ISG expression in neurons and microglia in a patchy pattern. ISG-15 mRNA was upregulated in ADAR1 mutant brain neurons whereas CXCL10 mRNA was elevated in adjacent astroglia. No calcification or gliosis was detected in the mutant brain. Conclusions We demonstrated that an AGS-associated mutation in ADAR1, specifically the p.K999N mutation, activates the IFN pathway in the mouse brain. The ADAR1 p.K999N mutant mouse replicates aspects of the brain interferonopathy of AGS. Neurons and microglia express different ISGs. Basal ganglia calcification and leukodystrophy seen in AGS patients were not observed in K999N mutant mice, indicating that development of the full clinical phenotype may need an additional stimulus besides AGS mutations. This mutant mouse presents a robust tool for the investigation of AGS and neuroinflammatory diseases including the modeling of potential “second hits” that enable severe phenotypes of clinically variable diseases.

We participated in the Cathepsin S (CatS) sub-challenge of the Drug Design Data Resource (D3R) Grand Challenge 3 (GC3) in 2017 to blindly predict the binding poses of 24 CatS-bound ligands, the binding affinity ranking of 136 ligands, and the binding free energies of a subset of 33 ligands in Stage 1A and Stage 2. Our submitted predictions ranked relatively well compared to the submissions from other participants. Here we present our methodologies used in the challenge. For the binding pose prediction, we employed the Glide module in the Schrodinger Suite 2017 and AutoDock Vina. For the binding affinity/free energy prediction, we carried out molecular dynamics simulations of the complexes in explicit water solvent with counter ions, and then estimated the binding free energies with our newly developed model of extended linear interaction energy (ELIE), which is inspired by two other popular end-point approaches: the linear interaction energy (LIE) method, and the molecular mechanics with Poisson-Boltzmann surface area solvation method (MM/PBSA). Our studies suggest that ELIE is a good trade-off between efficiency and accuracy, and it is appropriate for filling the gap between the high-throughput docking and scoring methods and the rigorous but much more computationally demanding methods like free energy perturbation (FEP) or thermodynamics integration (TI) in computer-aided drug design (CADD) projects.

Cannabinoids are a group of chemical compounds that have been used for thousands of years due to their psychoactive function and systemic physiological effects. There are at least two types of cannabinoid receptors, CB1 and CB2, which belong to the G protein-coupled receptor superfamily and can trigger different signaling pathways to exert their physiological functions. In this study, several representative agonists and antagonists of both CB1 and CB2 were systematically studied to predict their binding affinities and selectivity against both cannabinoid receptors using a set of hierarchical molecular modeling and simulation techniques, including homology modeling, molecular docking, molecular dynamics (MD) simulations and end point binding free energy calculations using the molecular mechanics/Poisson–Boltzmann surface area-WSAS (MM-PBSA-WSAS) method, and molecular mechanics/generalized Born surface area (MM-GBSA) free energy decomposition. Encouragingly, the calculated binding free energies correlated very well with the experimental values and the correlation coefficient square (R 2), 0.60, was much higher than that of an efficient but less accurate docking scoring function (R 2 = 0.37). The hotspot residues for CB1 and CB2 in both active and inactive conformations were identified via MM-GBSA free energy decomposition analysis. The comparisons of binding free energies, ligand–receptor interaction patterns, and hotspot residues among the four systems, namely, agonist-bound CB1, agonist-bound CB2, antagonist-bound CB1, and antagonist-bound CB2, enabled us to investigate and identify distinct binding features of these four systems, with which one can rationally design potent, selective, and function-specific modulators for the cannabinoid receptors.