Biomedical subjects

Michael Feig

Publications and source records attributed to Michael Feig.

23 records · Page 2Linked to original sources

PubMed2003

New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations.

In a recent article (Lee, M. S.; Salsbury, F. R. Jr.; Brooks, C. L., III. J Chem Phys 2002, 116, 10606), we demonstrated that generalized Born (GB) theory provides a good approximation to Poisson electrostatic solvation energy calculations if one uses the same definitions of molecular volume for each. In this work, we present a new and improved analytic method for reproducing the Lee-Richards molecular volume, which is the most common volume definition for Poisson calculations. Overall, 1% errors are achieved for absolute solvation energies of a large set of proteins and relative solvation energies of protein conformations. We also introduce an accurate SASA approximation that uses the same machinery employed by our GB method and requires a small addition of computational cost. The combined methodology is shown to yield an efficient and accurate implicit solvent representation for simulations of biopolymers.

Algorithms↗

PubMed2003

An implicit membrane generalized born theory for the study of structure, stability, and interactions of membrane proteins.

Exploiting recent developments in generalized Born (GB) electrostatics theory, we have reformulated the calculation of the self-electrostatic solvation energy to account for the influence of biological membranes. Consistent with continuum Poisson-Boltzmann (PB) electrostatics, the membrane is approximated as an solvent-inaccessible infinite planar low-dielectric slab. The present membrane GB model closely reproduces the PB electrostatic solvation energy profile across the membrane. The nonpolar contribution to the solvation energy is taken to be proportional to the solvent-exposed surface area (SA) with a phenomenological surface tension coefficient. The proposed membrane GB/SA model requires minor modifications of the pre-existing GB model and appears to be quite efficient. By combining this implicit model for the solvent/bilayer environment with advanced computational sampling methods, like replica-exchange molecular dynamics, we are able to fold and assemble helical membrane peptides. We examine the reliability of this model and approach by applications to three membrane peptides: melittin from bee venom, the transmembrane domain of the M2 protein from Influenza A (M2-TMP), and the transmembrane domain of glycophorin A (GpA). In the context of these proteins, we explore the role of biological membranes (represented as a low-dielectric medium) in affecting the conformational changes in melittin, the tilt of transmembrane peptides with respect to the membrane normal (M2-TMP), helix-to-helix interactions in membranes (GpA), and the prediction of the configuration of transmembrane helical bundles (GpA). The present method is found to perform well in each of these cases and is anticipated to be useful in the study of folding and assembly of membrane proteins as well as in structure refinement and modeling of membrane proteins where a limited number of experimental observables are available.

Binding Sites↗

PubMed2002

Evaluating CASP4 predictions with physical energy functions.

Physical energy scoring functions based on implicit solvation models are tested by evaluating predictions from the most recent CASP4 competition. The best performing scoring functions are identified along with the best protocol for preparing structures before energies are evaluated. Ranking of structures with the best scoring functions is compared across CASP4 targets to establish when physical scoring functions can be expected to reliably distinguish structures that are most similar to the native fold in a set of misfolded or unfolded protein conformations. The results are used to interpret previous studies where scoring functions were tested on the standard decoy sets by Park, Levitt, and Baker. We show that the best physical scoring functions can be applied successfully in automated consensus scoring applications where a single best conformation has to be selected from a set of structures from different sources. Finally, the potential for better protein structure scoring functions is discussed with a suggestion for an empirically parameterized linear combination of energy components.

Animals↗

PubMed2002

Solvation and hydration of proteins and nucleic acids: a theoretical view of simulation and experiment.

Many theoretical, computational, and experimental techniques recently have been successfully used for description of the solvent distribution around macromolecules. In this Account, we consider recent developments in the areas of protein and nucleic acid solvation and hydration as seen by experiment, theory, and simulations. We find that in most cases not only the general phenomena of solvation but even local hydration patterns are more accurately discussed in the context of water distributions rather than individual molecules of water. While a few localized or high-residency waters are often associated with macromolecules in solution (or crystals from aqueous liquors), these are readily and accurately included in this more general description. The goal of this Account is to review the theoretical models used for the description of the interfacial solvent structure on the border near DNA and protein molecules. In particular, we hope to highlight the progress in this field over the past five years with a focus on comparison of simulation and experimental results.

Computer Simulation↗

PubMed2002

Evolution and physics in comparative protein structure modeling.

From a physical perspective, the native structure of a protein is a consequence of physical forces acting on the protein and solvent atoms during the folding process. From a biological perspective, the native structure of proteins is a result of evolution over millions of years. Correspondingly, there are two types of protein structure prediction methods, de novo prediction and comparative modeling. We review comparative protein structure modeling and discuss the incorporation of physical considerations into the modeling process. A good starting point for achieving this aim is provided by comparative modeling by satisfaction of spatial restraints. Incorporation of physical considerations is illustrated by an inclusion of solvation effects into the modeling of loops.

Biophysical Phenomena↗