Molecular square dancing in CO-CO collisions

Knowledge of rotational energy transfer (RET) involving carbon monoxide (CO) molecules is crucial for the interpretation of astrophysical data. As of now, our nearly perfect understanding of atom-molecule scattering shows that RET usually occurs by only a simple “bump” between partners. To advance molecular dynamics to the next step in complexity, we studied molecule-molecule scattering in great detail for collision between two CO molecules. Using advanced imaging methods and quasi-classical and fully quantum theory, we found that a synchronous movement can occur during CO-CO collisions, whereby a bump is followed by a move similar to a “do-si-do” in square dancing. This resulted in little angular deflection but high RET to both partners, a very unusual combination. The associated conditions suggest that this process can occur in other molecule-molecule systems.

Prediction of models for ordered solvent in macromolecular structures by a classifier based upon resolution-independent projections of local feature data

Current software tools for the automated building of models for macromolecular X-ray crystal structures are capable of assembling high-quality models for ordered macromolecule and small-molecule scattering components with minimal or no user supervision. Many of these tools also incorporate robust functionality for modelling the ordered water molecules that are found in nearly all macromolecular crystal structures. However, no current tools focus on differentiating these ubiquitous water molecules from other frequently occurring multi-atom solvent species, such as sulfate, or the automated building of models for such species. PeakProbe has been developed specifically to address the need for such a tool. PeakProbe predicts likely solvent models for a given point (termed a `peak') in a structure based on analysis (`probing') of its local electron density and chemical environment. PeakProbe maps a total of 19 resolution-dependent features associated with electron density and two associated with the local chemical environment to a two-dimensional score space that is independent of resolution. Peaks are classified based on the relative frequencies with which four different classes of solvent (including water) are observed within a given region of this score space as determined by large-scale sampling of solvent models in the Protein Data Bank. Designed to classify peaks generated from difference density maxima, PeakProbe also incorporates functionality for identifying peaks associated with model errors or clusters of peaks likely to correspond to multi-atom solvent, and for the validation of existing solvent models using solvent-omit electron-density maps. When tasked with classifying peaks into one of four distinct solvent classes, PeakProbe achieves greater than 99% accuracy for both peaks derived directly from the atomic coordinates of existing solvent models and those based on difference density maxima. While the program is still under development, a fully functional version is publicly available. PeakProbe makes extensive use of cctbx libraries, and requires a PHENIX licence and an up-to-date phenix.python environment for execution.