Dmitry A. Kondrashov

Optimization and evaluation of a coarse-grained model of protein motion using x-ray crystal data.

Simple coarse-grained models, such as the Gaussian network model, have been shown to capture some of the features of equilibrium protein dynamics. We extend this model by using atomic contacts to define residue interactions and introducing more than one interaction parameter between residues. We use B-factors from 98 ultra-high resolution (<or=1.0 A) x-ray crystal structures to optimize the interaction parameters. The average correlation between Gaussian network-model fluctuation predictions and the B-factors is 0.64 for the data set, consistent with a previous large-scale study. By separating residue interactions into covalent and noncovalent, we achieve an average correlation of 0.74, and addition of ligands and cofactors further improves the correlation to 0.75. However, further separating the noncovalent interactions into nonpolar, polar, and mixed yields no significant improvement. The addition of simple chemical information results in better prediction quality without increasing the size of the coarse-grained model.

Structure of Human J-type Co-chaperone HscB Reveals a Tetracysteine Metal-binding Domain

Iron-sulfur proteins play indispensable roles in a broad range of biochemical processes. The biogenesis of iron-sulfur proteins is a complex process that has become a subject of extensive research. The final step of iron-sulfur protein assembly involves transfer of an iron-sulfur cluster from a cluster-donor to a cluster-acceptor protein. This process is facilitated by a specialized chaperone system, which consists of a molecular chaperone from the Hsc70 family and a co-chaperone of the J-domain family. The 3.0Å crystal structure of a human mitochondrial J-type co-chaperone HscB revealed an L-shaped protein that resembles Escherichia coli HscB. The important difference between the two homologs is the presence of an auxiliary metal-binding domain at the N terminus of human HscB that coordinates a metal via the tetracysteine consensus motif CWXCX9–13FCXXCXXXQ. The domain is found in HscB homologs from animals and plants as well as in magnetotactic bacteria. The metal-binding site of the domain is structurally similar to that of rubredoxin and several zinc finger proteins containing rubredoxin-like knuckles. The normal mode analysis of HscB revealed that this L-shaped protein preferentially undergoes a scissors-like motion that correlates well with the conformational changes of human HscB observed in the crystals.

Sampling of the native conformational ensemble of myoglobin via structures in different crystalline environments.

Proteins sample multiple conformational substates in their native environment, but the process of crystallization selects the conformers that allow for close packing. The population of conformers can be shifted by varying the environment through a range of crystallization conditions, often resulting in different space groups and changes in the packing arrangements. Three high resolution structures of myoglobin (Mb) in different crystal space groups are presented, including one in a new space group P6122 and two structures in space groups P212121 and P6. We compare coordinates and anisotropic displacement parameters (ADPs) from these three structures plus an existing structure in space group P21. While the overall changes are small, there is substantial variation in several external regions with varying patterns of crystal contacts across the space group packing arrangements. The structural ensemble containing four different crystal forms displays greater conformational variance (Cα rmsd of 0.54–0.79 Å) in comparison to a collection of four Mb structures with different ligands and mutations in the same crystal form (Cα rmsd values of 0.28–0.37 Å). The high resolution of the data enables comparison of both the magnitudes and directions of ADPs, which are found to be suppressed by crystal contacts. A composite dynamic profile of Mb structural variation from the four structures was compared with an independent structural ensemble developed from NMR refinement. Despite the limitations and biases of each method, the ADPs of the crystallographic ensemble closely match the positional variance from the solution NMR ensemble with linear correlation of 0.8. This suggests that crystal packing selects conformers representative of the solution ensemble, and several different crystal forms give a more complete view of the plasticity of a protein structure. Proteins 2008.

Topological features of rugged fitness landscapes in sequence space

The factors that determine the tempo and mode of protein evolution continue to be a central question in molecular evolution. Traditionally, studies of protein evolution focused on the rates of amino acid substitutions. More recently, with the availability of sequence data and advanced experimental techniques, the focus of attention has shifted toward the study of evolutionary trajectories and the overall layout of protein fitness landscapes. In this review we describe the effect of epistasis on the topology of evolutionary pathways that are likely to be found in fitness landscapes and develop a simple theory to connect the number of maladapted genotypes to the topology of fitness landscapes with epistatic interactions. Finally, we review recent studies that have probed the extent of epistatic interactions and have begun to chart the fitness landscapes in protein sequence space.

Creating protein models from electron-density maps using particle-filtering methods

MOTIVATION One bottleneck in high-throughput protein crystallography is interpreting an electron-density map, that is, fitting a molecular model to the 3D picture crystallography produces. Previously, we developed ACMI (Automatic Crystallographic Map Interpreter), an algorithm that uses a probabilistic model to infer an accurate protein backbone layout. Here, we use a sampling method known as particle filtering to produce a set of all-atom protein models. We use the output of ACMI to guide the particle filters sampling, producing an accurate, physically feasible set of structures. RESULTS We test our algorithm on 10 poor-quality experimental density maps. We show that particle filtering produces accurate all-atom models, resulting in fewer chains, lower sidechain RMS error and reduced R factor, compared to simply placing the best-matching sidechains on ACMIs trace. We show that our approach produces a more accurate model than three leading methods--Textal, Resolve and ARP/WARP--in terms of main chain completeness, sidechain identification and crystallographic R factor. AVAILABILITY Source code and experimental density maps available at http://ftp.cs.wisc.edu/machine-learning/shavlik-group/programs/acmi/

Structure and dynamics of [gamma]-SNAP: Insight into flexibility of proteins from the SNAP family

Soluble N‐ethylmaleimide‐sensitive factor attachment protein gamma (γ‐SNAP) is a member of an eukaryotic protein family involved in intracellular membrane trafficking. The X‐ray structure of Brachydanio rerio γ‐SNAP was determined to 2.6 Å and revealed an all‐helical protein comprised of an extended twisted‐sheet of helical hairpins with a helical‐bundle domain on its carboxy‐terminal end. Structural and conformational differences between multiple observed γ‐SNAP molecules and Sec17, a SNAP family protein from yeast, are analyzed. Conformational variation in γ‐SNAP molecules is matched with great precision by the two lowest frequency normal modes of the structure. Comparison of the lowest‐frequency modes from γ‐SNAP and Sec17 indicated that the structures share preferred directions of flexibility, corresponding to bending and twisting of the twisted sheet motif. We discuss possible consequences related to the flexibility of the SNAP proteins for the mechanism of the 20S complex disassembly during the SNAP receptors recycling. Proteins 2008.

Transition Pathway Calculation Using Interpolated Parameters From Swarms Of Trajectories

Understanding the mechanism of conformational changes in macromolecules requires the knowledge of the intermediate states. A version of the string method, which uses multiple short dynamics trajectories to propagate the pathway, was recently developed by Pan et al. Here we use data from swarms of trajectories calculated at discrete points in phase space to interpolate the average displacement and variance at arbitrary points. This is tested on model potentials using statistics from actual swarms of trajectories. We use the interpolated parameters to compute the Markovian propagators from one point on the transition path to the next. We use them to obtain a time-dependent action of a path, which can be optimized to produce the highest probability pathway. We describe the optimization protocol and demonstrate that in artificial flat potentials the existing string method cannot correct problems such as loops in the initial path, while the new method produces the correct pathway (Figure shows pathway in 2D potential). We further illustrate the utility of our method by applying it to protein conformational transitions, such as the KcsA potassium channel, and comparing its performance to existing transition pathway methods.View Large Image | View Hi-Res Image | Download PowerPoint Slide