McVol - a program for calculating protein volumes and identifying cavities by a Monte Carlo algorithm.

In this paper, we describe a Monte Carlo method for determining the volume of a molecule. A molecule is considered to consist of hard, overlapping spheres. The surface of the molecule is defined by rolling a probe sphere over the surface of the spheres. To determine the volume of the molecule, random points are placed in a three-dimensional box, which encloses the whole molecule. The volume of the molecule in relation to the volume of the box is estimated by calculating the ratio of the random points placed inside the molecule and the total number of random points that were placed. For computational efficiency, we use a grid-cell based neighbor list to determine whether a random point is placed inside the molecule or not. This method in combination with a graph-theoretical algorithm is used to detect internal cavities and surface clefts of molecules. Since cavities and clefts are potential water binding sites, we place water molecules in the cavities. The potential water positions can be used in molecular dynamics calculations as well as in other molecular calculations. We apply this method to several proteins and demonstrate the usefulness of the program. The described methods are all implemented in the program McVol, which is available free of charge from our website at http://www.bisb.uni-bayreuth.de/software.html .

Proton-transfer pathways in photosynthetic reaction centers analyzed by profile hidden markov models and network calculations.

In the bacterial reaction center (bRC) of Rhodobacter sphaeroides, the key residues of proton transfer to the secondary quinone (Q(B)) are known. Also, several possible proton entry points and proton-transfer pathways have been proposed. However, the mechanism of the proton transfer to Q(B) remains unclear. The proton transfer to Q(B) in the bRC of Blastochloris viridis is less explored. To analyze whether the bRCs of different species use the same key residues for proton transfer to Q(B), we determined the conservation of these residues. We performed a multiple-sequence alignment based on profile hidden Markov models. Residues involved in proton transfer but not located at the protein surface are conserved or are only exchanged to functionally similar amino acids, whereas potential proton entry points are not conserved to the same extent. The analysis of the hydrogen-bond network of the bRC from R. sphaeroides and that from B. viridis showed that a large network connects Q(B) with the cytoplasmic region in both bRCs. For both species, all non-surface key residues are part of the network. However, not all proton entry points proposed for the bRC of R. sphaeroides are included in the network in the bRC of B. viridis. From our analysis, we could identify possible proton entry points. These proton entry points differ between the two bRCs. Together, the results of the conservation analysis and the hydrogen-bond network analysis make it likely that the proton transfer to Q(B) is not mediated by distinct pathways but by a large hydrogen-bond network.

Simulating the proton transfer in gramicidin A by a sequential dynamical Monte Carlo method.

The large interest in long-range proton transfer in biomolecules is triggered by its importance for many biochemical processes such as biological energy transduction and drug detoxification. Since long-range proton transfer occurs on a microsecond time scale, simulating this process on a molecular level is still a challenging task and not possible with standard simulation methods. In general, the dynamics of a reactive system can be described by a master equation. A natural way to describe long-range charge transfer in biomolecules is to decompose the process into elementary steps which are transitions between microstates. Each microstate has a defined protonation pattern. Although such a master equation can in principle be solved analytically, it is often too demanding to solve this equation because of the large number of microstates. In this paper, we describe a new method which solves the master equation by a sequential dynamical Monte Carlo algorithm. Starting from one microstate, the evolution of the system is simulated as a stochastic process. The energetic parameters required for these simulations are determined by continuum electrostatic calculations. We apply this method to simulate the proton transfer through gramicidin A, a transmembrane proton channel, in dependence on the applied membrane potential and the pH value of the solution. As elementary steps in our reaction, we consider proton uptake and release, proton transfer along a hydrogen bond, and rotations of water molecules that constitute a proton wire through the channel. A simulation of 8 mus length took about 5 min on an Intel Pentium 4 CPU with 3.2 GHz. We obtained good agreement with experimental data for the proton flux through gramicidin A over a wide range of pH values and membrane potentials. We find that proton desolvation as well as water rotations are equally important for the proton transfer through gramicidin A at physiological membrane potentials. Our method allows to simulate long-range charge transfer in biological systems at time scales, which are not accessible by other methods.