Enhanced polymerase activity permits efficient synthesis by cancer-associated DNA polymerase ϵ variants at low dNTP levels.

Amino acid substitutions in the exonuclease domain of DNA polymerase ϵ (Polϵ) cause ultramutated tumors. Studies in model organisms suggested pathogenic mechanisms distinct from a simple loss of exonuclease. These mechanisms remain unclear for most recurrent Polϵ mutations. Particularly, the highly prevalent V411L variant remained a long-standing puzzle with no detectable mutator effect in yeast despite the unequivocal association with ultramutation in cancers. Using purified four-subunit yeast Polϵ, we assessed the consequences of substitutions mimicking human V411L, S459F, F367S, L424V and D275V. While the effects on exonuclease activity vary widely, all common cancer-associated variants have increased DNA polymerase activity. Notably, the analog of Polϵ-V411L is among the strongest polymerases, and structural analysis suggests defective polymerase-to-exonuclease site switching. We further show that the V411L analog produces a robust mutator phenotype in strains that lack mismatch repair, indicating a high rate of replication errors. Lastly, unlike wild-type and exonuclease-dead Polϵ, hyperactive variants efficiently synthesize DNA at low dNTP concentrations. We propose that this characteristic could promote cancer cell survival and preferential participation of mutator polymerases in replication during metabolic stress. Our results support the notion that polymerase fitness, rather than low fidelity alone, is an important determinant of variant pathogenicity.

Modeling to Understand Plant Protein Structure-Function Relationships-Implications for Seed Storage Proteins.

Proteins are among the most important molecules on Earth. Their structure and aggregation behavior are key to their functionality in living organisms and in protein-rich products. Innovations, such as increased computer size and power, together with novel simulation tools have improved our understanding of protein structure-function relationships. This review focuses on various proteins present in plants and modeling tools that can be applied to better understand protein structures and their relationship to functionality, with particular emphasis on plant storage proteins. Modeling of plant proteins is increasing, but less than 9% of deposits in the Research Collaboratory for Structural Bioinformatics Protein Data Bank come from plant proteins. Although, similar tools are applied as in other proteins, modeling of plant proteins is lagging behind and innovative methods are rarely used. Molecular dynamics and molecular docking are commonly used to evaluate differences in forms or mutants, and the impact on functionality. Modeling tools have also been used to describe the photosynthetic machinery and its electron transfer reactions. Storage proteins, especially in large and intrinsically disordered prolamins and glutelins, have been significantly less well-described using modeling. These proteins aggregate during processing and form large polymers that correlate with functionality. The resulting structure-function relationships are important for processed storage proteins, so modeling and simulation studies, using up-to-date models, algorithms, and computer tools are essential for obtaining a better understanding of these relationships.

Clustering and cross-linking of the wheat storage protein α-gliadin: A combined experimental and theoretical approach.

Our aim was to understand mechanisms for clustering and cross-linking of gliadins, a wheat seed storage protein type, monomeric in native state, but incorporated in network while processed. The mechanisms were studied utilizing spectroscopy and high-performance liquid chromatography on a gliadin-rich fraction, in vitro produced α-gliadins, and synthetic gliadin peptides, and by coarse-grained modelling, Monte Carlo simulations and prediction algorithms. In solution, gliadins with α-helix structures (dip at 205 nm in CD) were primarily present as monomeric molecules and clusters of gliadins (peaks at 650- and 700-s on SE-HPLC). At drying, large polymers (Rg 90.3 nm by DLS) were formed and ß-sheets increased (14% by FTIR). Trained algorithms predicted aggregation areas at amino acids 115-140, 150-179, and 250-268, and induction of liquid-liquid phase separation at P- and Poly-Q-sequences (Score = 1). Simulations showed that gliadins formed polymers by tail-to-tail or a hydrophobic core (Kratky plots and Ree = 35 and 60 for C- and N-terminal). Thus, the N-terminal formed clusters while the C-terminal formed aggregates by disulphide and lanthionine bonds, with favoured hydrophobic clustering of similar/exact peptide sections (synthetic peptide mixtures on SE-HPLC). Mechanisms of clustering and cross-linking of the gliadins presented here, contribute ability to tailor processing results, using these proteins.

Glutenin and Gliadin, a Piece in the Puzzle of their Structural Properties in the Cell Described through Monte Carlo Simulations.

Gluten protein crosslinking is a predetermined process where specific intra- and intermolecular disulfide bonds differ depending on the protein and cysteine motif. In this article, all-atom Monte Carlo simulations were used to understand the formation of disulfide bonds in gliadins and low molecular weight glutenin subunits (LMW-GS). The two intrinsically disordered proteins appeared to contain mostly turns and loops and showed "self-avoiding walk" behavior in water. Cysteine residues involved in intramolecular disulfide bonds were located next to hydrophobic peptide sections in the primary sequence. Hydrophobicity of neighboring peptide sections, synthesis chronology, and amino acid chain flexibility were identified as important factors in securing the specificity of intramolecular disulfide bonds formed directly after synthesis. The two LMW-GS cysteine residues that form intermolecular disulfide bonds were positioned next to peptide sections of lower hydrophobicity, and these cysteine residues are more exposed to the cytosolic conditions, which influence the crosslinking behavior. In addition, coarse-grained Monte Carlo simulations revealed that the protein folding is independent of ionic strength. The potential molecular behavior associated with disulfide bonds, as reported here, increases the biological understanding of seed storage protein function and provides opportunities to tailor their functional properties for different applications.

Predicting deoxynivalenol in oats under conditions representing Scandinavian production regions.

Deoxynivalenol (DON) in cereals, produced by Fusarium fungi, cause poisoning in humans and animals. Fusarium infections in cereals are favoured by humid conditions. Host species are susceptible mainly during the anthesis stage. Infections are also positively correlated with a regional history of Fusarium infections, frequent cereal production and non-tillage field management practices. Here, previously developed process-based models based on relative air humidity, rain and temperature conditions, Fusarium sporulation, host phenology and mycelium growth in host tissue were adapted and tested on oats. Model outputs were used to calculate risk indices. Statistical multivariate models, where independent variables were constructed from weather data, were also developed. Regressions of the risk indices obtained against DON concentrations in field experiments on oats in Sweden and Norway 2012-14 had coefficient of determination values (R2) between 0.84 and 0.88. Regressions of the same indices against DON concentrations in oat samples averaged for 11 × 11 km grids in farmers' fields in Sweden 2012-14 resulted in R2 values between 0.27 and 0.41 for randomly selected grids and between 0.31 and 0.62 for grids with average DON concentration above 1000 µg kg-1 grain in the previous year. When data from all three years were evaluated together, a cross-validated statistical partial least squares model resulted in R2 = 0.70 and a standard error of cross-validation (SECV) = 522 µg kg-1 grain for the period 1 April-28 August in the construction of independent variables and R2 = 0.54 and SECV = 647 µg kg-1 grain for 1 April-23 June. Factors that were not accounted for in this study probably explain large parts of the variation in DON among samples and make further model development necessary before these models can be used practically. DON prediction in oats could potentially be improved by combining weather-based risk index outputs with agronomic factors.