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Nitrophorin 4 (NP4) is a heme protein that reversibly binds nitric oxide (NO), with release rates modulated by pH change. High-resolution structures of NP4 revealed that pH changes and NO binding induce a large conformational rearrangement in two loops that serve to protect the heme-bound NO molecule from solvent. We used extended (110 ns) molecular dynamics simulations of NP4 at pH 5 and pH 7, modeled by selective deprotonation of acidic groups. Conformational and dynamic changes were observed, consistent with those found in the crystal. Further, major solvent movement and NO escape were observed at pH 7, while the ligand remained in the heme binding pocket at pH 5. As a control, we also performed molecular dynamics (MD) simulations of sperm whale myoglobin, where NO migration into the interior cavities of the protein was observed, consistent with previous reports. We constructed a kinetic model of ligand escape to quantitatively relate the microscopic rate constants to the observed rates, and tested the predictions against the experimental data. The results suggest that release rates of diatomic molecules from heme proteins can be varied by several orders of magnitude through modest adjustments in geminate rebinding and gating behavior. [ABSTRACT FROM AUTHOR]

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/ Contact: dimaio@cs.wisc.edu [ABSTRACT FROM AUTHOR]