No abstract

We report the results of an extended molecular dynamics simulation on the migration of photodissociated carbon monoxide in wild-type sperm whale myoglobin. Our results allow following one possible ligand migration dynamics from the distal pocket to the Xe1 cavity via a path involving the other xenon binding cavities and momentarily two additional packing defects along the pathway. Comparison with recent time resolved structural data obtained by Laue crystallography with subnanosecond to millisecond resolution shows a more than satisfactory agreement. In fact, according to time resolved crystallography, CO, after photolysis, can occupy the Xe1 and Xe4 cavities. However, no information on the trajectory of the ligand from the distal pocket to the Xe1 is available. Our results clearly show one possible path within the protein. In addition, although our data refer to a single trajectory, the local dynamics of the ligand in each cavity is sufficiently equilibrated to obtain local structural and thermodynamic information not accessible to crystallography. In particular, we show that the CO motion and the protein fluctuations are strictly correlated: free energy calculations of the migration between adjacent cavities show that the migration is not a simple diffusion but is kinetically or thermodynamically driven by the collective motions of the protein; conversely, the protein fluctuations are influenced by the ligand in such a way that the opening/closure of the passage between adjacent cavities is strictly correlated to the presence of CO in its proximity. The compatibility between time resolved crystallographic experiments and molecular dynamics simulations paves the way to a deeper understanding of the role of internal dynamics and packing defects in the control of ligand binding in heme proteins.

No abstract

The results of extended (80-ns) molecular dynamics simulations of wild-type and YQR triple mutant of sperm whale deoxy myoglobin in water are reported and compared with the results of the simulation of the intermediate(s) obtained by photodissociation of CO in the wild-type protein. The opening/closure of pathways between preexistent cavities is different in the three systems. For the photodissociated state, we previously reported a clear-cut correlation between the opening probability and the presence of the photolyzed CO in the proximity of the passage; here we show that in wild-type deoxy myoglobin, opening is almost random. In wild-type deoxy myoglobin, the passage between the distal pocket and the solvent is strictly correlated to the presence/absence of a water molecule that simultaneously interacts with the distal histidine side chain and the heme iron; conversely, in the photodissociated myoglobin, the connection with the bulk solvent is always open when CO is in the vicinity of the A pyrrole ring. In YQR deoxy myoglobin, the mutated Gln(E7)64 is stably H-bonded with the mutated Tyr(B10)29. The essential dynamics analysis unveils a different behavior for the three systems. The motion amplitude is progressively restricted in going from wild-type to YQR deoxy myoglobin and to wild-type myoglobin photoproduct. In all cases, the principal motions involve mainly the same regions, but their directions are different. Analysis of the dynamics of the preexisting cavities indicates large fluctuations and frequent connections with the solvent, in agreement with the earlier hypothesis that some of the ligand may escape from the protein through these pathways.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure–activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.