Behaviour of small solutes and large drugs in a lipid bilayer from computer simulations

To reach their biological target, drugs have to cross cell membranes, and understanding passive membrane permeation is therefore crucial for rational drug design. Molecular dynamics simulations offer a powerful way of studying permeation at the single molecule level. Starting from a computer model proven to be able to reproduce the physical properties of a biological membrane, the behaviour of small solutes and large drugs in a lipid bilayer has been studied. Analysis of dihedral angles shows that a few nanosesconds are sufficient for the simulations to converge towards common values for those angles, even if the starting structures belong to different conformations. Results clearly show that, despite their difference in size, small solutes and large drugs tend to lie parallel to the bilayer normal and that, when moving from water solution into biomembranes, permeants lose degrees of freedom. This explains the experimental observation that partitioning and permeation are highly affected by entropic effects and are size-dependent. Tilted orientations, however, occur when they make possible the formation of hydrogen bonds. This helps to understand the reason why hydrogen bonding possibilities are an important parameter in cruder approaches which predict drug absorption after administration. Interestingly, hydration is found to occur even in the membrane core, which is usually considered an almost hydrophobic region. Simulations suggest the possibility for highly polar compounds like acetic acid to cross biological membranes while hydrated. These simulations prove useful for drug design in rationalising experimental observations and predicting solute behaviour in biomembranes

The role of size and charge for blood-brain barrier permeation of drugs and fatty acids

The lipid bilayer is the diffusion barrier of biological membranes. Highly protective membranes such as the blood-brain barrier (BBB) are reinforced by ABC transporters such as P-glycoprotein (MDR1, ABCB1) and multidrug resistance associated proteins (MRPs, ABCCs). The transporters bind their substrates in the cytosolic lipid bilayer leaflet before they reach the cytosol and flip them to the outer leaflet. The large majority of drugs targeted to the central nervous system (CNS) are intrinsic substrates of these transporters. Whether an intrinsic substrate can cross the BBB depends on whether passive influx is higher than active efflux. In this paper, we show that passive influx can be estimated quantitatively on the basis of Stokesian diffusion, taking into account the ionization constant and the cross-sectional area of the molecule in its membrane bond conformation, as well as the lateral packing density of the membrane. Active efflux by ABC transporters was measured. The calculated net flux is in excellent agreement with experimental results. The approach is exemplified with several drugs and fatty acid analogs. It shows that compounds with small cross-sectional areas (A(D) > 70 A(2)) and/or intermediate or low charge exhibit higher passive influx than efflux and, therefore, cross the BBB despite being intrinsic substrates. Large (A(D) < 70 A(2)) or highly charged compounds show higher efflux than influx. They cannot cross the BBB and are, thus, apparent substrates for ABC transporters. The strict size and charge limitation for BBB permeation results from the synergistic interaction between passive influx and active efflux

Classification of protein fold classes by knot theory and prediction of folds by neural networks: A combined theoretical and experimental approach

We present different means of classifying protein structure. One is made rigorous by mathematical knot invariants that coincide reasonably well with ordinary graphical fold classification and another classification is by packing analysis. Furthermore when constructing our mathematical fold classifications, we utilize standard neural network methods for predicting protein fold classes from amino acid sequences. We also make an analysis of the redundancy of the structural classifications in relation to function and ligand binding. Finally we advocate the use of combining the measurement of the VA, VCD, Raman, ROA, EA and ECD spectra with the primary sequence as a way to improve both the accuracy and reliability of fold class prediction schemes