Solvent Isotherms and Structural Transitions in Nanoparticle Superlattice Assembly.

We introduce a Molecular Theory for Compressible Fluids (MOLT-CF) that enables us to compute free energies and other thermodynamic functions for nanoparticle superlattices with any solvent content, including the dry limit. Quantitative agreement is observed between MOLT-CF and united-atom molecular dynamics simulations performed to assess the reliability and precision of the theory. Among other predictions, MOLT-CF shows that the amount of solvent within the superlattice decreases approximately linearly with its vapor pressure and that in the late stages of drying, solvent-filled voids form at lattice interstitials. Applied to single-component superlattices, MOLT-CF predicts fcc-to-bcc Bain transitions for decreasing vapor pressure and for increasing ligand length, both in agreement with experimental results. We explore the stability of other single-component phases and show that the C14 Frank-Kasper phase, which has been reported in experiments, is not a global free-energy minimum. Implications for precise assembly and prediction of multicomponent nanoparticle systems are discussed.

Ensemble-based screening of natural products and FDA-approved drugs identified potent inhibitors of SARS-CoV-2 that work with two distinct mechanisms.

The recent outbreak of SARS-CoV-2 is responsible for high morbidity and mortality rate across the globe. This requires an urgent identification of drugs and other interventions to overcome this pandemic. Computational drug repurposing represents an alternative approach to provide a more effective approach in search for COVID-19 drugs. Selected natural product known to have antiviral activities were screened, and based on their hits; a similarity search with FDA approved drugs was performed using computational methods. Obtained drugs from similarity search were assessed for their stability and inhibition against SARS-CoV-2 targets. Diosmin (DB08995) was found to be a promising drug that works with two distinct mechanisms, preventing viral replication and viral fusion into the host cell. Isoquercetin (DB12665) and rutin (DB01698) work by inhibiting viral replication and preventing cell entry, respectively. Our analysis based on molecular dynamics simulation and MM-PBSA binding free energy calculation suggests that diosmin, isoquercetin, rutin and other similar flavone glycosides could serve as SARS-CoV-2 inhibitor, hence an alternative solution to treat COVID-19 upon further clinical validation.

Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization.

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.

Potential of Matrix Metalloproteinase Inhibitors for the Treatment of Local Tissue Damage Induced by a Type P-I Snake Venom Metalloproteinase.

Snake bite envenoming is a public health problem that was recently included in the list of neglected tropical diseases of the World Health Organization. In the search of new therapies for the treatment of local tissue damage induced by snake venom metalloproteinases (SVMPs), we tested the inhibitory activity of peptidomimetic compounds designed as inhibitors of matrix metalloproteinases on the activities of the SVMP Batx-I, from Bothrops atrox venom. The evaluated compounds show great potential for the inhibition of Batx-I proteolytic, hemorrhagic and edema-forming activities, especially the compound CP471474, a peptidomimetic including a hydroxamate zinc binding group. Molecular dynamics simulations suggest that binding of this compound to the enzyme is mediated by the electrostatic interaction between the hydroxamate group and the zinc cofactor, as well as contacts, mainly hydrophobic, between the side chain of the compound and amino acids located in the substrate binding subsites S1 and S1 ' . These results show that CP471474 constitutes a promising compound for the development of co-adjuvants to neutralize local tissue damage induced by snake venom metalloproteinases.

Interactions between Triterpenes and a P-I Type Snake Venom Metalloproteinase: Molecular Simulations and Experiments.

Small molecule inhibitors of snake venom metalloproteinases (SVMPs) could provide a means to rapidly halt the progression of local tissue damage following viperid snake envenomations. In this study, we examine the ability of candidate compounds based on a pentacyclic triterpene skeleton to inhibit SVMPs. We leverage molecular dynamics simulations to estimate the free energies of the candidate compounds for binding to BaP1, a P-I type SVMP, and compare these results with experimental assays of proteolytic activity inhibition in a homologous enzyme (Batx-I). Both simulation and experiment suggest that betulinic acid is the most active candidate, with the simulations predicting a standard binding free energy of Δ G ∘ = - 11.0 ± 1.4 kcal/mol. The simulations also reveal the atomic interactions that underlie binding between the triterpenic acids and BaP1, most notably the electrostatic interaction between carboxylate groups of the compounds and the zinc cofactor of BaP1. Together, our simulations and experiments suggest that occlusion of the S1 ' subsite is essential for inhibition of proteolytic activity. While all active compounds make hydrophobic contacts in the S1 ' site, ß -boswellic acid, with its distinct carboxylate position, does not occlude the S1 ' site in simulation and exhibits negligible activity in experiment.

Investigating molecular dynamics-guided lead optimization of EGFR inhibitors.

The epidermal growth factor receptor (EGFR) is part of an extended family of proteins that together control aspects of cell growth and development, and thus a validated target for drug discovery. We explore in this work the suitability of a molecular dynamics-based end-point binding free energy protocol to estimate the relative affinities of a virtual combinatorial library designed around the EGFR model inhibitor 6{1} as a tool to guide chemical synthesis toward the most promising compounds. To investigate the validity of this approach, selected analogs including some with better and worse predicted affinities relative to 6{1} were synthesized, and their biological activity determined. To understand the binding determinants of the different analogs, hydrogen bonding and van der Waals contributions, and water molecule bridging in the EGFR-analog complexes were analyzed. The experimental validation was in good qualitative agreement with our theoretical calculations, while also a 6-dibromophenyl-substituted compound with enhanced inhibitory effect on EGFR compared to the reference ligand was obtained.