Blinded predictions of distribution coefficients in the SAMPL5 challenge

In the context of the SAMPL5 challenge water-cyclohexane distribution coefficients for 53 drug-like molecules were predicted. Four different models based on molecular dynamics free energy calculations were tested. All models initially assumed only one chemical state present in aqueous or organic phases. Model A is based on results from an alchemical annihilation scheme; model B adds a long range correction for the Lennard Jones potentials to model A; model C adds charging free energy corrections; model D applies the charging correction from model C to ionizable species only. Model A and B perform better in terms of mean-unsigned error ([Formula: see text] D units − 95 % confidence interval) and determination coefficient [Formula: see text] , while charging corrections lead to poorer results with model D ([Formula: see text] and [Formula: see text] ). Because overall errors were large, a retrospective analysis that allowed co-existence of ionisable and neutral species of a molecule in aqueous phase was investigated. This considerably reduced systematic errors ([Formula: see text] and [Formula: see text] ). Overall accurate [Formula: see text] predictions for drug-like molecules that may adopt multiple tautomers and charge states proved difficult, indicating a need for methodological advances to enable satisfactory treatment by explicit-solvent molecular simulations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10822-016-9969-1) contains supplementary material, which is available to authorized users

Partial atomic charges and their impact on the free energy of solvation

ree energies of solvation (DG) in water and n-octanol have been computed for common drug molecules by molecular dynamics simulations with an additive fixed-charge force field. The impact of the electrostatic interactions was investigated by computing the partial atomic charges with four methods that all fit the charges from the quantum mechanically determined electrostatic potential (ESP). Due to the redistribution of electron density that occurs when molecules are transferred from gas phase to condensed phase, the polarization impact was also investigated. By computing the partial atomic charges with the solutes placed in a conductor- like continuum, the charges were effectively polarized to take the polarization effects into account. No polarization correction term or similar was considered, only the partial atomic charges. Results show that free energies are very sensitive to the choice of atomic charges and that DG can differ by several kBT depending on the charge computing method. Inclusion of polarization effects makes the solutes too hydrophilic with most methods and in vacuo charges make the solutes too hydrophobic. The restrained-ESP methods together with effectively polarized charges perform well in our test set and also when applied to a larger set of molecules. The effect of water models is also highlighted and shows that the conclusions drawn are valid for different three-point models. Partitioning between an aqueous and a hydrophobic phase is also described better if the two environment’s polarization is taken into account, but again the results are sensitive to the charge calculation method. Overall, the results presented here show that effectively polarized charges can improve the description of solvating a drug-like molecule in a solvent and that the choice of partial atomic charges is crucial to ensure that molecular simulations produce reliable results. 2012 Wiley Periodicals, Inc

In Silico Design of Antimicrobial Peptides

The rapid spread of drug-resistant pathogenic microbial strains has created an urgent need for the development of new anti-infective molecules, having different mechanism of action in comparison to existing drugs. Natural antimicrobial peptides (AMPs) represent a novel class of molecules with a broad spectrum of activity and a low rate in inducing bacterial resistance. In particular, linear alphahelical cationic antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement of the innate defense against microbes. However, until now, many AMPs have failed in clinical trials because of several drawbacks that strongly limit their applicability such as degradation, cytotoxicity, and high production cost. Thus, to overcome the limitations of native peptides, a rational in silico approach to AMPs design becomes a promising strategy that drastically reduce production costs and the time required for evaluation of activity and toxicity. This chapter focuses on the strategies and methods for de novo design of potentially active AMPs. In particular, statistical-based design strategies and MD methods for modelling AMPs are elucidated