Computational model predicts protein binding sites of a luminescent ligand equipped with guanidiniocarbonyl-pyrrole groups.

The 14-3-3 protein family, one of the first discovered phosphoserine/phosphothreonine binding proteins, has attracted interest not only because of its important role in the cell regulatory processes but also due to its enormous number of interactions with other proteins. Here, we use a computational approach to predict the binding sites of the designed hybrid compound featuring aggregation-induced emission luminophores as a potential supramolecular ligand for 14-3-3ζ in the presence and absence of C-Raf peptides. Our results suggest that the area above and below the central pore of the dimeric 14-3-3ζ protein is the most probable binding site for the ligand. Moreover, we predict that the position of the ligand is sensitive to the presence of phosphorylated C-Raf peptides. With a series of experiments, we confirmed the computational prediction of two C 2 related, dominating binding sites on 14-3-3ζ that may bind to two of the supramolecular ligand molecules.

Classifier uncertainty: evidence, potential impact, and probabilistic treatment.

Classifiers are often tested on relatively small data sets, which should lead to uncertain performance metrics. Nevertheless, these metrics are usually taken at face value. We present an approach to quantify the uncertainty of classification performance metrics, based on a probability model of the confusion matrix. Application of our approach to classifiers from the scientific literature and a classification competition shows that uncertainties can be surprisingly large and limit performance evaluation. In fact, some published classifiers may be misleading. The application of our approach is simple and requires only the confusion matrix. It is agnostic of the underlying classifier. Our method can also be used for the estimation of sample sizes that achieve a desired precision of a performance metric.

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation.

Evaluating binary classifications is a pivotal task in statistics and machine learning, because it can influence decisions in multiple areas, including for example prognosis or therapies of patients in critical conditions. The scientific community has not agreed on a general-purpose statistical indicator for evaluating two-class confusion matrices (having true positives, true negatives, false positives, and false negatives) yet, even if advantages of the Matthews correlation coefficient (MCC) over accuracy and F1 score have already been shown.In this manuscript, we reaffirm that MCC is a robust metric that summarizes the classifier performance in a single value, if positive and negative cases are of equal importance. We compare MCC to other metrics which value positive and negative cases equally: balanced accuracy (BA), bookmaker informedness (BM), and markedness (MK). We explain the mathematical relationships between MCC and these indicators, then show some use cases and a bioinformatics scenario where these metrics disagree and where MCC generates a more informative response.Additionally, we describe three exceptions where BM can be more appropriate: analyzing classifications where dataset prevalence is unrepresentative, comparing classifiers on different datasets, and assessing the random guessing level of a classifier. Except in these cases, we believe that MCC is the most informative among the single metrics discussed, and suggest it as standard measure for scientists of all fields. A Matthews correlation coefficient close to +1, in fact, means having high values for all the other confusion matrix metrics. The same cannot be said for balanced accuracy, markedness, bookmaker informedness, accuracy and F1 score.

Bayesian Data Integration Questions Classic Study on Protease Self-Digest Kinetics.

We combine Bayesian data integration with kinetic modeling to rigorously identify reaction mechanisms. This approach forces models to be consistent not only with kinetic measurements but with all available information. We revisit a classic study on trypsin self-digest acceleration by colloidal silica. Bayesian data integration reveals that the mechanism suggested in that study is inconsistent with its presented data. We propose an improved hypothesis. However, the detailed mechanism of the surface reaction cannot be inferred from the available data.

Accelerated trypsin autolysis by affinity polymer templates.

Self-cleavage of proteins is an important natural process that is difficult to control externally. Recently a new mechanism for the accelerated autolysis of trypsin was discovered involving polyanionic template polymers; however it relies on unspecific interactions and is inactive at elevated salt loads. We have now developed affinity copolymers that bind to the surface of proteases by specific recognition of selected amino acid residues. These are highly efficient trypsin inhibitors with low nanomolar IC50 levels and operate at physiological conditions. In this manuscript we show how these affinity copolymers employ the new mechanism of polymer-assisted self-digest (PAS) and act as a template for multiple protease molecules. Their elevated local concentration leads to accelerated autolysis on the accessible surface area and shields complexed areas. The resulting extremely efficient trypsin inhibition was studied by SDS-PAGE, gel filtration, CD, CZE and ESI-MS. We also present a simple theoretical model that simulates most experimental findings and confirms them as a result of multivalency and efficient reversible templating. For the first time, mass spectrometric kinetic analysis of the released peptide fragments gives deeper insight into the underlying mechanism and reveals that polymer-bound trypsin cleaves much more rapidly with low specificity at predominantly uncomplexed surface areas.

Is Magnetic Bistability of Carbenes a General Phenomenon? Isolation of Simple Aryl(trifluoromethyl)carbenes in Both Their Singlet and Triplet States.

p-Tolyl(trifluoromethyl)carbene and the related fluorenyl(trifluoromethyl)carbene were synthesized in solid argon and characterized by IR, UV-vis, and electron paramagnetic resonance spectroscopy as well as by quantum mechanical calculations. The carbenes can be generated in both their triplet and singlet states, and both states coexist under the conditions of matrix isolation. According to our calculations, the singlet and triplet states of these carbenes are energetically nearly degenerate in the gas phase. Warming of matrices containing pure triplet p-tolyl(trifluoromethyl)carbene from 3 to 25 K leads to an interconversion of up to 20% of the triplet into the singlet state. This interconversion is thermally irreversible, and cooling back to 3 K does not change the singlet to triplet ratio. Irradiation at 365 nm results in a complete singlet to triplet interconversion, whereas 450 nm irradiation produces again up to 20% of the singlet state. An alternative way to generate the singlet carbene is the reaction of the triplet with water molecules by annealing water-doped matrices at 25 K. This results in the irreversible formation of a hydrogen-bonded complex between the singlet carbene and water. For fluorenyl(trifluoromethyl)carbene, very similar results are obtained, but the yield of the singlet state is even higher. Magnetic bistability of carbenes seems to be a general phenomenon that only depends on the singlet-triplet gap rather than on the nature of the carbene.

Capture of CO2 by a Cationic Nickel(I) Complex in the Gas Phase and Characterization of the Bound, Activated CO2 Molecule by Cryogenic Ion Vibrational Predissociation Spectroscopy.

We describe a systematic method for the preparation and spectroscopic characterization of a CO2 molecule coordinated to an activated bisphenoidal nickel(I) compound containing a tetraazamacrocyclic ligand in the gas phase. The resulting complex was then structurally characterized by using mass-selected vibrational predissociation spectroscopy. The results indicate that a highly distorted CO2 molecule is bound to the metal center in an η(2)-C,O coordination mode, thus establishing an efficient and rational method for the preparation of metal-activated CO2 for further studies using ion chemistry techniques.

Thermodynamics of water dimer dissociation in the primary hydration shell of the iodide ion with temperature-dependent vibrational predissociation spectroscopy.

The strong temperature dependence of the I(-)·(H2O)2 vibrational predissociation spectrum is traced to the intracluster dissociation of the ion-bound water dimer into independent water monomers that remain tethered to the ion. The thermodynamics of this process is determined using van't Hoff analysis of key features that quantify the relative populations of H-bonded and independent water molecules. The dissociation enthalpy of the isolated water dimer is thus observed to be reduced by roughly a factor of three upon attachment to the ion. The cause of this reduction is explored with electronic structure calculations of the potential energy profile for dissociation of the dimer, which suggest that both reduction of the intrinsic binding energy and vibrational zero-point effects act to weaken the intermolecular interaction between the water molecules in the first hydration shell. Additional insights are obtained by analyzing how classical trajectories of the I(-)·(H2O)2 system sample the extended potential energy surface with increasing temperature.