An effective coarse-grained model for biological simulations: recent refinements and validations.

Exploring the free energy landscape of proteins and modeling the corresponding functional aspects presents a major challenge for computer simulation approaches. This challenge is due to the complexity of the landscape and the enormous computer time needed for converging simulations. The use of various simplified coarse grained (CG) models offers an effective way of sampling the landscape, but most current models are not expected to give a reliable description of protein stability and functional aspects.

Realistic simulation of the activation of voltage-gated ion channels.

Understanding the detailed mechanism of the activation of voltage-gated ion channels has been a problem of great current interest. Reliable molecular simulations of voltage effects present a major challenge because meaningful converging microscopic simulations are not yet available and macroscopic treatments involve major uncertainties regarding the dielectric constant used and other key features. The current work has overcome some of the above challenges by using our recently developed coarse-grained (CG) model in simulating the activation of the Kv1.

Coarse grained model for exploring voltage dependent ion channels.

The relationship between the membrane voltage and the gating of voltage activated ion channels and other systems have been a problem of great current interest. Unfortunately, reliable molecular simulations of external voltage effects present a major challenge, since meaningful converging microscopic simulations are not yet available and macroscopic treatments involve major uncertainties in terms of the dielectric used and other key features. This work extends our coarse grained (CG) model to simulations of membrane/protein systems under external potential.

Coarse-grained (multiscale) simulations in studies of biophysical and chemical systems.

Recent years have witnessed an explosion in computational power, leading to attempts to model ever more complex systems. Nevertheless, there remain cases for which the use of brute-force computer simulations is clearly not the solution. In such cases, great benefit can be obtained from the use of physically sound simplifications.

On the energetics of translocon-assisted insertion of charged transmembrane helices into membranes.

The understanding of the mechanism of insertion of transmembrane (TM) helixes through the translocon presents a major open challenge. Although the experimental information about the partition of the inserted helices between the membrane and the solution contains crucial information about this process, it is not clear how to extract this information. In particular, it is not clear how to rationalize the small apparent insertion energy, ΔG(app), of an ionized residue in the center of a TM helix.

Multiscale simulations of protein landscapes: using coarse-grained models as reference potentials to full explicit models.

Evaluating the free-energy landscape of proteins and the corresponding functional aspects presents a major challenge for computer simulation approaches. This challenge is due to the complexity of the landscape and the enormous computer time needed for converging simulations. The use of simplified coarse-grained (CG) folding models offers an effective way of sampling the landscape but such a treatment, however, may not give the correct description of the effect of the actual protein residues.

Effective approach for calculations of absolute stability of proteins using focused dielectric constants.

The ability to predict the absolute stability of proteins based on their corresponding sequence and structure is a problem of great fundamental and practical importance. In this work, we report an extensive, refinement and validation of our recent approach (Roca et al., FEBS Lett 2007;581:2065-2071) for predicting absolute values of protein stability DeltaG(fold).

Separating true positive predicted residue contacts from false positive ones in mainly alpha proteins, using constrained Metropolis MC simulations.

We present a method that significantly improves the accuracy of predicted proximal residue pairs in protein molecules. Computational methods for predicting pairs of amino acids that are distant in the protein sequence but close in the protein 3D structure can benefit attempts to in silico recognize the fold of a protein molecule. Unfortunately, currently available methods suffer from low predictive accuracy.

Prediction of distant residue contacts with the use of evolutionary information.

In this work we present a novel correlated mutations analysis (CMA) method that is significantly more accurate than previously reported CMA methods. Calculation of correlation coefficients is based on physicochemical properties of residues (predictors) and not on substitution matrices. This results in reliable prediction of pairs of residues that are distant in protein sequence but proximal in its three dimensional tertiary structure.