A general model to predict small molecule substrates of enzymes based on machine and deep learning

For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science

Deep learning allows genome-scale prediction of Michaelis constants from structural features

AU The:Michaelis Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly constant KM describes the affinity of an enzyme : for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme–substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme–substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme’s amino acid sequence. We provide genome-scale KM predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism

Diversifying evolution of competitiveness

In many species, individuals express phenotypic characteristics that enhance their competitiveness, that is, the ability to acquire resources in competition with others. Moreover, the degree of competitiveness varies considerably across individuals and in time. By means of an evolutionary model, we provide an explanation for this finding. We make the assumption that investment into competitiveness enhances the probability to acquire a high-quality resource, but at the same time reduces the ability of exploiting acquired resources with maximal efficiency. The model reveals that under a broad range of conditions competitiveness either converges to a polymorphic state, where individuals differing in competitive ability stably coexist, or is subject to perpetual transitions between periods of high and low competitiveness. The dynamics becomes even more complex if females can evolve preferences for (or against) competitive males. In extreme cases, such preferences can even drive the population to extinction

Quantitative analysis of amino acid metabolism in liver cancer links glutamate excretion to nucleotide synthesis

Many cancer cells consume glutamine at high rates; counterintuitively, they simultaneously excrete glutamate, the first intermediate in glutamine metabolism. Glutamine consumption has been linked to replenishment of tricarboxylic acid cycle (TCA) intermediates and synthesis of adenosine triphosphate (ATP), but the reason for glutamate excretion is unclear. Here, we dynamically profile the uptake and excretion fluxes of a liver cancer cell line (HepG2) and use genome-scale metabolic modeling for in-depth analysis. We find that up to 30% of the glutamine is metabolized in the cytosol, primarily for nucleotide synthesis, producing cytosolic glutamate. We hypothesize that excreting glutamate helps the cell to increase the nucleotide synthesis rate to sustain growth. Indeed, we show experimentally that partial inhibition of glutamate excretion reduces cell growth. Our integrative approach thus links glutamine addiction to glutamate excretion in cancer and points toward potential drug targets

Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology

Extensive microdiversity within Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes

Multi-spin strings on AdS(5)xT(1,1) and operators of N=1 superconformal theory

We study rotating strings with multiple spins in the background of $AdS_5\times T^{1,1}$, which is dual to a $\mathcal{N}=1$ superconformal field theory with global symmetry $SU(2)\times SU(2)\times U(1)$ via the AdS/CFT correspondence. We analyse the limiting behaviour of macroscopic strings and discuss the identification of the dual operators and how their anomalous dimensions should behave as the global charges vary. A class of string solutions we find are dual to operators in SU(2) subsector, and our result implies that the one-loop planar dilatation operator restricted to the SU(2) subsector should be equivalent to the hamiltonian of the integrable Heisenberg spin chain.Comment: 8 pages, revtex4, twocolum

Distinct Dynamics of Endocytic Clathrin-Coated Pits and Coated Plaques

Here we classify endocytic structures at the adherent (bottom) surface of many cells in culture into shorter-lived coated pits and longer-lived coated plaques which internalize by different mechanisms

HVOF-Deposited WCCoCr as Replacement for Hard Cr in Landing Gear Actuators

WCCoCr coatings deposited by HVOF can replace hard Cr on landing gear components. Powders with two different WC particle sizes (micro and nano-) and geometries have been employed to study the effects on the coating’s properties. Moreover, coatings produced employing two sets of parameters resulting in high and low flame temperatures have been evaluated. Minor differences in microstructure and morphology were observed for the two powders employing the same spraying parameters, but the nano-sized powder exhibited a higher spraying efficiency. However, more significant microstructural changes result when the low- and high-energy spray parameters are used. Moreover, results of various tests which include adhesion, wear, salt fog corrosion resistance, liquid immersion, and axial fatigue strength, indicate that the coatings produced with high-energy flame are similar in behavior. On the other hand, the nanostructured low-energy flame coating exhibited a significantly lower salt fog corrosion resistanc

DNA polymerase η contributes to genome-wide lagging strand synthesis

DNA polymerase η (pol η) is best known for its ability to bypass UV-induced thymine-thymine (T-T) dimers and other bulky DNA lesions, but pol η\ua0also has other cellular roles. Here, we present evidence that pol η competes with DNA polymerases α and δ\ua0for the synthesis of the lagging strand genome-wide, where it also shows a preference for T-T in the DNA template. Moreover, we found that the C-terminus of pol η,\ua0which contains a PCNA-Interacting Protein motif is required for pol η\ua0to function in lagging strand synthesis. Finally, we provide evidence that a pol η dependent signature is also found to be lagging strand specific in patients with skin cancer. Taken together, these findings provide insight into the physiological role of DNA synthesis by pol η and have implications for our understanding of how our genome is replicated to avoid mutagenesis, genome instability and cancer