<i>In Silico</i> Categorization of <i>in Vivo</i> Intrinsic Clearance Using Machine Learning

Machine learning has recently become popular and much used within the life science research domain, e.g., for finding quantitative structure–activity relationships (QSARs) between molecular structures and different biological end points. In the work presented here, we have applied orthogonal partial least-squares (OPLS), principal component analysis (PCA), and random forests (RF) methods for classification as well as regression analysis to a publicly available <i>in vivo</i> data set in order to assess the intrinsic metabolic clearance (CL_int) in humans. The derived classification models are able to identify compounds with CL_int lower and higher than 1500 mL/min, respectively, with nearly 80% accuracy. The most relevant descriptors are of lipophilicity and charge/polarizability types. Furthermore, the accuracy from a classification model based on regression analysis, using the 1500 mL/min cutoff, is also around 80%. These results suggest the usefulness of machine learning techniques to derive robust and predictive models in the area of <i>in vivo</i> ADMET (absorption, distribution, metabolism, elimination, and toxicity) modeling

The Central Role of Gln63 for the Hydrogen Bonding Network and UV–Visible Spectrum of the AppA BLUF Domain

In blue-light sensing using flavin (BLUF) domains, the side-chain orientation of key residues close to the flavin chromophore is still under debate. We report quantum refinements of the wild-type AppA BLUF protein from Rhodobacter sphaeroides starting from two published X-ray structures (1YRX and 2IYG) with different arrangements of the residues around the chromophore. Quantum refinement uses the same experimental X-ray raw data as conventional refinement, but includes data from quantum mechanics/molecular mechanics (QM/MM) calculations as restraints, which is expected to be more reliable than the normally employed MM data. In addition to quantum refinement, pure QM/MM geometry optimizations are performed for the 1YRX and 2IYG structures and for five models derived therefrom. Vertical excitation energies are computed at the QM­(DFT/MRCI)/MM level to assess the resulting structures. The experimental absorption maximum of the dark state of wild-type AppA is well reproduced for structures that contain the Gln63 residue in 1YRX-type orientation. The computed excitation energies are red-shifted for structures with a flipped Gln63 residue in 2IYG-type orientation. The calculated 1YRX- and 2IYG-type hydrogen-bonding networks are discussed in detail, particularly with regard to the orientation of the chromophore and the Gln63, Trp104, and Met106 residues

A Pragmatic Approach Using First-Principle Methods to Address Site of Metabolism with Implications for Reactive Metabolite Formation

A majority of xenobiotics are metabolized by cytochrome P450 (CYP) enzymes. The discovery of drug candidates with low propensity to form reactive metabolites and low clearance can be facilitated by understanding CYP-mediated xenobiotic metabolism. Being able to predict the sites where reactive metabolites form is beneficial in drug design to produce drug candidates free of reactive metabolite issues. Herein, we report a pragmatic protocol using first-principle density functional theory (DFT) calculations for predicting sites of epoxidation and hydroxylation of aromatic substrates mediated by CYP. The method is based on the relative stabilities of the CYP-substrate intermediates or the substrate epoxides. Consequently, it concerns mainly the electronic reactivity of the substrates. Comparing to the experimental findings, the presented protocol gave excellent first-ranked epoxidation site predictions of 83%, and when the test was extended to CYP-mediated sites of aromatic hydroxylation, satisfactory results were also obtained (73%). This indicates that our assumptions are valid and also implies that the intrinsic reactivities of the substrates are in general more important than their binding poses in proteins, although the protocol may benefit from the addition of docking information

Cooperative Modes of Action of Antimicrobial Peptides Characterized with Atomistic Simulations: A Study on Cecropin B

Antimicrobial peptides (AMPs) are widely occurring host defense agents of interest as one route for addressing the growing problem of multidrug-resistant pathogens. Understanding the mechanisms behind their antipathogen activity is instrumental in designing new AMPs. Herein, we present an all-atom molecular dynamics and free energy study on cecropin B (CB) and its constituent domains. We find a cooperative mechanism in which CB inserts into an anionic model membrane with its amphipathic N-terminal segment, supported by the hydrophobic C-terminal segment of a second peptide. The two peptides interact via a Glu···Lys salt bridge and together sustain a pore in the membrane. Using a modified membrane composition, we demonstrate that when the lower leaflet is overall neutral, insertion of the cationic segment is retarded and thus this mode of action is membrane specific. The observed mode of action utilizes a flexible hinge, a common structural motif among AMPs, which allows CB to insert into the membrane using either or both termini. Data from both unbiased trajectories and enhanced sampling simulations indicate that a requirement for CB to be an effective AMP is the interaction of its hydrophobic C-terminal segment with the membrane. Simulations of these segments in isolation reveal their aggregation in the membrane and a different mechanism of supporting pore formation. Together, our results show the complex interaction of different structural motifs of AMPs and, in particular, a potential role for electronegative side chains in an overall cationic AMP