Dynamical Origin of Rebound versus Dissociation Selectivity during Fe-Oxo-Mediated C-H Functionalization Reactions.

The mechanism of catalytic C-H functionalization of alkanes by Fe-oxo complexes is often suggested to involve a hydrogen atom transfer (HAT) step with the formation of a radical-pair intermediate followed by diverging pathways for radical rebound, dissociation, or desaturation. Recently, we showed that in some Fe-oxo reactions, the radical pair is a nonstatistical-type intermediate and dynamic effects control rebound versus dissociation pathway selectivity. However, the effect of the solvent cage on the stability and lifetime of the radical-pair intermediate has never been analyzed. Moreover, because of the extreme complexity of motion that occurs during dynamics trajectories, the underlying physical origin of pathway selectivity has not yet been determined. For the reaction between [(TQA_Cl)FeIVO]+ and cyclohexane, here, we report explicit solvent trajectories and machine learning analysis on transition-state sampled features (e.g., vibrational, velocity, and geometric) that identified the transferring hydrogen atom kinetic energy as the most important factor controlling rebound versus nonrebound dynamics trajectories, which provides an explanation for our previously proposed dynamic matching effect in fast rebound trajectories that bypass the radical-pair intermediate. Manual control of the reaction trajectories confirmed the importance of this feature and provides a mechanism to enhance or diminish selectivity for the rebound pathway. This led to a general catalyst design principle and proof-of-principle catalyst design that showcases how to control rebound versus dissociation reaction pathway selectivity.

Evaluating Therapeutic Activity of Galectin-1 in Sarcolemma Repair of Skeletal Muscle.

Galectin-1 is a small (14.5 kDa) multifunctional protein with cell-cell and cell-ECM adhesion due to interactions with the carbohydrate recognition domain (CRD). In two types of muscular dystrophies, this lectin protein has shown therapeutic properties, including positive regulation of skeletal muscle differentiation and regeneration. Both Duchenne and limb-girdle muscular dystrophy 2B (LGMD2B) are subtypes of muscular dystrophies characterized by deficient membrane repair, muscle weakness, and eventual loss of ambulation. This chapter explains confocal techniques such as laser injury, calcium imaging, and galectin-1 localization to examine the effects of galectin-1 on membrane repair in injured LGMD2B models.

Machine learning classification of disrotatory IRC and conrotatory non-IRC trajectory motion for cyclopropyl radical ring opening.

Quasiclassical trajectory analysis is now a standard tool to analyze non-minimum energy pathway motion of organic reactions. However, due to the large amount of information associated with trajectories, quantitative analysis of the dynamic origin of reaction selectivity is complex. For the electrocyclic ring opening of cyclopropyl radical, more than 4000 trajectories were run showing that allyl radicals are formed through a mixture of disrotatory intrinsic reaction coordinate (IRC) motion as well as conrotatory non-IRC motion. Geometric, vibrational mode, and atomic velocity transition-state features from these trajectories were used for supervised machine learning analysis with classification algorithms. Accuracy >80% with a random forest model enabled quantitative and qualitative assessment of transition-state trajectory features controlling disrotatory IRC versus conrotatory non-IRC motion. This analysis revealed that there are two key vibrational modes where their directional combination provides prediction of IRC versus non-IRC motion.

Treatment with galectin-1 improves myogenic potential and membrane repair in dysferlin-deficient models.

Limb-girdle muscular dystrophy type 2B (LGMD2B) is caused by mutations in the dysferlin gene, resulting in non-functional dysferlin, a key protein found in muscle membrane. Treatment options available for patients are chiefly palliative in nature and focus on maintaining ambulation. Our hypothesis is that galectin-1 (Gal-1), a soluble carbohydrate binding protein, increases membrane repair capacity and myogenic potential of dysferlin-deficient muscle cells and muscle fibers. To test this hypothesis, we used recombinant human galectin-1 (rHsGal-1) to treat dysferlin-deficient models. We show that rHsGal-1 treatments of 48 h-72 h promotes myogenic maturation as indicated through improvements in size, myotube alignment, myoblast migration, and membrane repair capacity in dysferlin-deficient myotubes and myofibers. Furthermore, increased membrane repair capacity of dysferlin-deficient myotubes, independent of increased myogenic maturation is apparent and co-localizes on the membrane of myotubes after a brief 10min treatment with labeled rHsGal-1. We show the carbohydrate recognition domain of Gal-1 is necessary for observed membrane repair. Improvements in membrane repair after only a 10 min rHsGal-1treatment suggest mechanical stabilization of the membrane due to interaction with glycosylated membrane bound, ECM or yet to be identified ligands through the CDR domain of Gal-1. rHsGal-1 shows calcium-independent membrane repair in dysferlin-deficient and wild-type myotubes and myofibers. Together our novel results reveal Gal-1 mediates disease pathologies through both changes in integral myogenic protein expression and mechanical membrane stabilization.

Machine Learning Analysis of Direct Dynamics Trajectory Outcomes for Thermal Deazetization of 2,3-Diazabicyclo[2.2.1]hept-2-ene.

Experimentally, the thermal gas-phase deazetization of 2,3-diazabicyclo[2.2.1]hept-2-ene (1) results in the loss of N2 and the formation of bicyclo products 3 (exo) and 4 (endo) in a nonstatistical ratio, with preference for the exo product. Here, we report unrestricted M06-2X quasiclassical trajectories initialized from the concerted N2 ejection transition state that were able to replicate the experimental preference to form 3. We found that the 3:4 ratio results from the relative amounts of very fast (ballistic) exotype trajectories versus trajectories that lead to the 1,3-diradical intermediate 2. These quasiclassical trajectories provided a set of transition-state vibrational, velocity, momenta, and geometric features for the machine learning analysis. A selection of popular supervised classification algorithms (e.g., random forest) provided poor prediction of trajectory outcomes based on only transition-state vibrational quanta and energy features. However, these machine learning models provided more accurate predictions using atomic velocities and atomic positions, attaining ∼70% accuracy using initial conditions and between 85 and 95% accuracy at later reaction time steps. This increased accuracy allowed the feature importance analysis to reveal that, at the later-time analysis, the methylene bridge out-of-plane bending is correlated with trajectory outcomes for the formation of either the exo product or toward the diradical intermediate. Possible reasons for the struggle of machine learning algorithms to classify trajectories based on transition-state features is the heavily overlapping feature values, the finite but very large possible vibrational mode combinations, and the possibility of chaos as trajectories propagate. We examined this chaos by comparing a set of nearly identical trajectories that differed by only a very small scaling of the kinetic energies resulting from the transition-state reaction coordinate.