About Underappreciated Yet Active Conformations of Thiourea Organocatalysts.

Conformational dynamics can define the function of organocatalysts. While the accepted mechanism of Schreiner's catalyst features a double hydrogen bond to the substrate that only forms with the anti-anti conformation of its central thiourea group, our electronic-structure theory study reveals that binding of the model substrate methyl vinyl ketone prefers syn-anti conformations. We find a new mechanism featuring π stacking interactions and highlight the need for extensive structure searches for flexible molecules, especially when aiming for structure-based design of catalytic activity.

Assessing the Accuracy of Across-the-Scale Methods for Predicting Carbohydrate Conformational Energies for the Examples of Glucose and α-Maltose.

A big hurdle when entering the field of carbohydrate research stems from the complications in the analytical and computational treatment. In effect, this extremely important class of biomolecules remains underinvestigated when compared, for example, with the maturity of genomics and proteomics research. On the theory side, the commonly used empirical methods suffer from an insufficient amount of high-quality experimental data against which they can be thoroughly validated. In order to provide a pivotal point for an ascent of accurate carbohydrate simulations, we present here a structure/energy benchmark set of diverse glucose (in three isomeric forms) and α-maltose conformations at the coupled-cluster level as well as an assessment of commonly used energy functions. We observe that empirical force fields and semiempirical approaches, on average, do not reproduce accurately the reference energy hierarchies. While the force fields maintain accuracy for the low-energy structures, the semiempirical methods perform unsatisfactory for the entire range. On the contrary, density-functional approximations reproduce the reference energy hierarchies with better than chemical accuracy already at the generalized gradient approximation level (GGA). Particularly, the novel meta-GGA functional SCAN provides the accuracy of more expensive hybrid functionals at fraction of their computational cost. In conclusion, we advocate for electronic-structure theory methods to become the routine tool for simulations of carbohydrates.

First-Principles Molecular Structure Search with a Genetic Algorithm.

The identification of low-energy conformers for a given molecule is a fundamental problem in computational chemistry and cheminformatics. We assess here a conformer search that employs a genetic algorithm for sampling the low-energy segment of the conformation space of molecules. The algorithm is designed to work with first-principles methods, facilitated by the incorporation of local optimization and blacklisting conformers to prevent repeated evaluations of very similar solutions. The aim of the search is not only to find the global minimum but to predict all conformers within an energy window above the global minimum. The performance of the search strategy is (i) evaluated for a reference data set extracted from a database with amino acid dipeptide conformers obtained by an extensive combined force field and first-principles search and (ii) compared to the performance of a systematic search and a random conformer generator for the example of a drug-like ligand with 43 atoms, 8 rotatable bonds, and 1 cis/trans bond.

A variable fork rate affects timing of origin firing and S phase dynamics in Saccharomyces cerevisiae.

Activation (in the following referred to as firing) of replication origins is a continuous and irreversible process regulated by availability of DNA replication molecules and cyclin-dependent kinase activities, which are often altered in human cancers. The temporal, progressive origin firing throughout S phase appears as a characteristic replication profile, and computational models have been developed to describe this process. Although evidence from yeast to human indicates that a range of replication fork rates is observed experimentally in order to complete a timely S phase, those models incorporate velocities that are uniform across the genome. Taking advantage of the availability of replication profiles, chromosomal position and replication timing, here we investigated how fork rate may affect origin firing in budding yeast. Our analysis suggested that patterns of origin firing can be observed from a modulation of the fork rate that strongly correlates with origin density. Replication profiles of chromosomes with a low origin density were fitted with a variable fork rate, whereas for the ones with a high origin density a constant fork rate was appropriate. This indeed supports the previously reported correlation between inter-origin distance and fork rate changes. Intriguingly, the calculated correlation between fork rate and timing of origin firing allowed the estimation of firing efficiencies for the replication origins. This approach correctly retrieved origin efficiencies previously determined for chromosome VI and provided testable prediction for other chromosomal origins. Our results gain deeper insights into the temporal coordination of genome duplication, indicating that control of the replication fork rate is required for the timely origin firing during S phase.

Cell-to-Cell Communication Circuits: Quantitative Analysis of Synthetic Logic Gates.

One of the goals in the field of synthetic biology is the construction of cellular computation devices that could function in a manner similar to electronic circuits. To this end, attempts are made to create biological systems that function as logic gates. In this work we present a theoretical quantitative analysis of a synthetic cellular logic-gates system, which has been implemented in cells of the yeast Saccharomyces cerevisiae (Regot et al., 2011). It exploits endogenous MAP kinase signaling pathways. The novelty of the system lies in the compartmentalization of the circuit where all basic logic gates are implemented in independent single cells that can then be cultured together to perform complex logic functions. We have constructed kinetic models of the multicellular IDENTITY, NOT, OR, and IMPLIES logic gates, using both deterministic and stochastic frameworks. All necessary model parameters are taken from literature or estimated based on published kinetic data, in such a way that the resulting models correctly capture important dynamic features of the included mitogen-activated protein kinase pathways. We analyze the models in terms of parameter sensitivity and we discuss possible ways of optimizing the system, e.g., by tuning the culture density. We apply a stochastic modeling approach, which simulates the behavior of whole populations of cells and allows us to investigate the noise generated in the system; we find that the gene expression units are the major sources of noise. Finally, the model is used for the design of system modifications: we show how the current system could be transformed to operate on three discrete values.