Accelerated molecular dynamics (aMD) simulations greatly improve the efficiency of conventional molecular dynamics (cMD) for sampling biomolecular conformations, but they require proper reweighting for free energy calculation. In this work, we systematically compare the accuracy of different reweighting algorithms including the exponential average, Maclaurin series, and cumulant expansion on three model systems: alanine dipeptide, chignolin, and Trp-cage. Exponential average reweighting can recover the original free energy profiles easily only when the distribution of the boost potential is narrow (e.g., the range ≤20kBT) as found in dihedral-boost aMD simulation of alanine dipeptide. In dual-boost aMD simulations of the studied systems, exponential average generally leads to high energetic fluctuations, largely due to the fact that the Boltzmann reweighting factors are dominated by a very few high boost potential frames. In comparison, reweighting based on Maclaurin series expansion (equivalent to cumulant expansion on the first order) greatly suppresses the energetic noise but often gives incorrect energy minimum positions and significant errors at the energy barriers (∼2–3kBT). Finally, reweighting using cumulant expansion to the second order is able to recover the most accurate free energy profiles within statistical errors of ∼kBT, particularly when the distribution of the boost potential exhibits low anharmonicity (i.e., near-Gaussian distribution), and should be of wide applicability. A toolkit of Python scripts for aMD reweighting “PyReweighting” is distributed free of charge at .

With the rise in resistance to antibiotics such as methicillin, there is a need for new drugs. We report here the discovery and X-ray crystallographic structures of 10 chemically diverse compounds (benzoic, diketo, and phosphonic acids, as well as a bisamidine and a bisamine) that inhibit bacterial undecaprenyl diphosphate synthase, an essential enzyme involved in cell wall biosynthesis. The inhibitors bind to one or more of the four undecaprenyl diphosphate synthase inhibitor binding sites identified previously, with the most active leads binding to site 4, outside the catalytic center. The most potent leads are active against Staphylococcus aureus [minimal inhibitory concentration (MIC) 90 ∼0.25 μg/mL], and one potently synergizes with methicillin (fractional inhibitory concentration index = 0.25) and is protective in a mouse infection model. These results provide numerous leads for antibacterial development and open up the possibility of restoring sensitivity to drugs such as methicillin, using combination therapies.drug discovery | in silico high-throughput screening | peptidoglycan | protein structure

The proper understanding of biomolecular recognition mechanisms that take place in a drug target is of paramount importance to improve the efficiency of drug discovery and development. The intrinsic dynamic character of proteins has a strong influence on biomolecular recognition mechanisms and models such as conformational selection have been widely used to account for this dynamic association process. However, conformational changes occurring in the receptor prior and upon association with other molecules are diverse and not obvious to predict when only a few structures of the receptor are available. In view of the prominent role of protein flexibility in ligand binding and its implications for drug discovery, it is of great interest to identify receptor conformations that play a major role in biomolecular recognition before starting rational drug design efforts. In this review, we discuss a number of recent advances in computer-aided drug discovery techniques that have been proposed to incorporate receptor flexibility into structure-based drug design. The allowance for receptor flexibility provided by computational techniques such as molecular dynamics simulations or enhanced sampling techniques helps to improve the accuracy of methods used to estimate binding affinities and, thus, such methods can contribute to the discovery of novel drug leads.

Molecular dynamics simulation using enhanced sampling methods is one of the powerful computational tools used to explore protein conformations and free energy landscapes. Enhanced sampling methods often employ either an increase in temperature or a flattening of the potential energy surface to rapidly sample phase space, and a corresponding reweighting algorithm is used to recover the Boltzmann statistics. However, potential energies of complex biomolecules usually involve large fluctuations on a magnitude of hundreds of kcal/mol despite minimal structural changes during simulation. This leads to noisy reweighting statistics and complicates the obtainment of accurate final results. To overcome this common issue in enhanced conformational sampling, we propose a scaled molecular dynamics method, which modifies the biomolecular potential energy surface and employs a reweighting scheme based on configurational populations. Statistical mechanical theory is applied to derive the reweighting formula, and the canonical ensemble of simulated structures is recovered accordingly. Test simulations on alanine dipeptide and the fast folding polypeptide Chignolin exhibit sufficiently enhanced conformational sampling and accurate recovery of free energy surfaces and thermodynamic properties. The results are comparable to long conventional molecular dynamics simulations and exhibit better recovery of canonical statistics over methods which employ a potential energy term in reweighting.

BackgroundAmyloid beta (Aβ) oligomers play a critical role in the pathogenesis of Alzheimer’s disease (AD) and represent a promising target for drug development. Tramiprosate is a small-molecule Aβ anti-aggregation agent that was evaluated in phase III clinical trials for AD but did not meet the primary efficacy endpoints; however, a pre-specified subgroup analysis revealed robust, sustained, and clinically meaningful cognitive and functional effects in patients with AD homozygous for the ε4 allele of apolipoprotein E4 (APOE4/4 homozygotes), who carry an increased risk for the disease. Therefore, to build on this important efficacy attribute and to further improve its pharmaceutical properties, we have developed a prodrug of tramiprosate ALZ-801 that is in advanced stages of clinical development. To elucidate how tramiprosate works, we investigated its molecular mechanism of action (MOA) and the translation to observed clinical outcomes.ObjectiveThe two main objectives of this research were to (1) elucidate and characterize the MOA of tramiprosate via an integrated application of three independent molecular methodologies and (2) present an integrated translational analysis that links the MOA, conformation of the target, stoichiometry, and pharmacokinetic dose exposure to the observed clinical outcome in APOE4/4 homozygote subjects.MethodWe used three molecular analytical methods—ion mobility spectrometry–mass spectrometry (IMS–MS), nuclear magnetic resonance (NMR), and molecular dynamics—to characterize the concentration-related interactions of tramiprosate versus Aβ42 monomers and the resultant conformational alterations affecting aggregation into oligomers. The molecular stoichiometry of the tramiprosate versus Aβ42 interaction was further analyzed in the context of clinical pharmacokinetic dose exposure and central nervous system Aβ42 levels (i.e., pharmacokinetic–pharmacodynamic translation in humans).ResultsWe observed a multi-ligand interaction of tramiprosate with monomeric Aβ42, which differs from the traditional 1:1 binding. This resulted in the stabilization of Aβ42 monomers and inhibition of oligomer formation and elongation, as demonstrated by IMS–MS and molecular dynamics. Using NMR spectroscopy and molecular dynamics, we also showed that tramiprosate bound to Lys16, Lys28, and Asp23, the key amino acid side chains of Aβ42 that are responsible for both conformational seed formation and neuronal toxicity. The projected molar excess of tramiprosate versus Aβ42 in humans using the dose effective in patients with AD aligned with the molecular stoichiometry of the interaction, providing a clear clinical translation of the MOA. A consistent alignment of these preclinical-to-clinical elements describes a unique example of translational medicine and supports the efficacy seen in symptomatic patients with AD. This unique “enveloping mechanism” of tramiprosate also provides a potential basis for tramiprosate dose selection for patients with homozygous AD at earlier stages of disease.ConclusionWe have identifie...