Macromolecular modeling and design in Rosetta: recent methods and frameworks.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Structure, subunit organization and behavior of the asymmetric Type IIT restriction endonuclease BbvCI.

BbvCI, a Type IIT restriction endonuclease, recognizes and cleaves the seven base pair sequence 5'-CCTCAGC-3', generating 3-base, 5'-overhangs. BbvCI is composed of two protein subunits, each containing one catalytic site. Either site can be inactivated by mutation resulting in enzyme variants that nick DNA in a strand-specific manner. Here we demonstrate that the holoenzyme is labile, with the R1 subunit dissociating at low pH. Crystallization of the R2 subunit under such conditions revealed an elongated dimer with the two catalytic sites located on opposite sides. Subsequent crystallization at physiological pH revealed a tetramer comprising two copies of each subunit, with a pair of deep clefts each containing two catalytic sites appropriately positioned and oriented for DNA cleavage. This domain organization was further validated with single-chain protein constructs in which the two enzyme subunits were tethered via peptide linkers of variable length. We were unable to crystallize a DNA-bound complex; however, structural similarity to previously crystallized restriction endonucleases facilitated creation of an energy-minimized model bound to DNA, and identification of candidate residues responsible for target recognition. Mutation of residues predicted to recognize the central C:G base pair resulted in an altered enzyme that recognizes and cleaves CCTNAGC (N = any base).

Soil Weathering as an Engine for Manganese Contamination of Well Water.

Manganese (Mn) contamination of well water is recognized as an environmental health concern. In the southeastern Piedmont region of the United States, well water Mn concentrations can be >2 orders of magnitude above health limits, but the specific sources and causes of elevated Mn in groundwater are generally unknown. Here, using field, laboratory, spectroscopic, and geospatial analyses, we propose that natural pedogenetic and hydrogeochemical processes couple to export Mn from the near-surface to fractured-bedrock aquifers within the Piedmont. Dissolved Mn concentrations are greatest just below the water table and decrease with depth. Solid-phase concentration, chemical extraction, and X-ray absorption spectroscopy data show that secondary Mn oxides accumulate near the water table within the chemically weathering saprolite, whereas less-reactive, primary Mn-bearing minerals dominate Mn speciation within the physically weathered transition zone and bedrock. Mass-balance calculations indicate soil weathering has depleted over 40% of the original solid-phase Mn from the near-surface, and hydrologic gradients provide a driving force for downward delivery of Mn. Overall, we estimate that >1 million people in the southeastern Piedmont consume well water containing Mn at concentrations exceeding recommended standards, and collectively, these results suggest that integrated soil-bedrock-system analyses are needed to predict and manage Mn in drinking-water wells.

Modeling symmetric macromolecular structures in Rosetta3.

Symmetric protein assemblies play important roles in many biochemical processes. However, the large size of such systems is challenging for traditional structure modeling methods. This paper describes the implementation of a general framework for modeling arbitrary symmetric systems in Rosetta3. We describe the various types of symmetries relevant to the study of protein structure that may be modeled using Rosetta's symmetric framework. We then describe how this symmetric framework is efficiently implemented within Rosetta, which restricts the conformational search space by sampling only symmetric degrees of freedom, and explicitly simulates only a subset of the interacting monomers. Finally, we describe structure prediction and design applications that utilize the Rosetta3 symmetric modeling capabilities, and provide a guide to running simulations on symmetric systems.

Spatial modeling for groundwater arsenic levels in North Carolina.

To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area.

Progress in modeling of protein structures and interactions.

The prediction of the structures and interactions of biological macromolecules at the atomic level and the design of new structures and interactions are critical tests of our understanding of the interatomic interactions that underlie molecular biology. Equally important, the capability to accurately predict and design macromolecular structures and interactions would streamline the interpretation of genome sequence information and allow the creation of macromolecules with new and useful functions. This review summarizes recent progress in modeling that suggests that we are entering an era in which high-resolution prediction and design will make increasingly important contributions to biology and medicine.

Geometry of dynamically available empty space is the key to near-arrest dynamics.

We study several examples of kinetically constrained lattice models using dynamically accessible volume as an order parameter. Thereby we identify two distinct regimes exhibiting dynamical slowing, with a sharp threshold between them. These regimes are identified both by a new response function in dynamically available volume, as well as directly in the dynamics. Results for the self-diffusion constant in terms of the connected hole density are presented, and some evidence is given for scaling in the limit of dynamical arrest.

Exact solution of a jamming transition: closed equations for a bootstrap percolation problem.

Jamming, or dynamical arrest, is a transition at which many particles stop moving in a collective manner. In nature it is brought about by, for example, increasing the packing density, changing the interactions between particles, or otherwise restricting the local motion of the elements of the system. The onset of collectivity occurs because, when one particle is blocked, it may lead to the blocking of a neighbor. That particle may then block one of its neighbors, these effects propagating across some typical domain of size named the dynamical correlation length. When this length diverges, the system becomes immobile. Even where it is finite but large the dynamics is dramatically slowed. Such phenomena lead to glasses, gels, and other very long-lived nonequilibrium solids. The bootstrap percolation models are the simplest examples describing these spatio-temporal correlations. We have been able to solve one such model in two dimensions exactly, exhibiting the precise evolution of the jamming correlations on approach to arrest. We believe that the nature of these correlations and the method we devise to solve the problem are quite general. Both should be of considerable help in further developing this field.

Clarification of the bootstrap percolation paradox.

We study the onset of the bootstrap percolation transition as a model of generalized dynamical arrest. Our results apply to two dimensions, but there is no significant barrier to extending them to higher dimensionality. We develop a new importance-sampling procedure in simulation, based on rare events around "holes", that enables us to access bootstrap lengths beyond those previously studied. By framing a new theory in terms of paths or processes that lead to emptying of the lattice we are able to develop systematic corrections to the existing theory and compare them to simulations. Thereby, for the first time in the literature, it is possible to obtain credible comparisons between theory and simulation in the accessible density range.

Predicting the beta-helix fold from protein sequence data.

A method is presented that uses beta-strand interactions to predict the parallel right-handed beta-helix super-secondary structural motif in protein sequences. A program called BetaWrap implements this method and is shown to score known beta-helices above non-beta-helices in the Protein Data Bank in cross-validation. It is demonstrated that BetaWrap learns each of the seven known SCOP beta-helix families, when trained primarily on beta-structures that are not beta-helices, together with structural features of known beta-helices from outside the family. BetaWrap also predicts many bacterial proteins of unknown structure to be beta-helices; in particular, these proteins serve as virulence factors, adhesins, and toxins in bacterial pathogenesis and include cell surface proteins from Chlamydia and the intestinal bacterium Helicobacter pylori. The computational method used here may generalize to other beta-structures for which strand topology and profiles of residue accessibility are well conserved.