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

Untersuchungen des molekularen Konformationsraumes

Flexible organic molecules and biomolecules can adopt a variety of energetically favorable conformations that differ in chemical and physical properties. The identification of such low-energy conformers is a fundamental problem in molecular physics and computational chemistry. Here we describe our efforts to develop methods exploring molecular conformational spaces at the first-principles level. We present a genetic algorithm (GA) based search for sampling the conformational space of molecules. This GA is available in the Python library Fafoom and has been developed in this thesis. The GA search aims not only at finding the global minimum, but also at identifying all conformers within a certain energy window. The implementation of the GA search 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 performance of the GA search is evaluated for seven amino-acid dipeptides and eight drug-like molecules. The evaluation focuses on: (i) how well the GA search can reproduce the reference data; and (ii) how well the conformational space is covered. Our study shows that the GA search samples the conformational space of the evaluated molecules with high accuracy and efficiency. For the purpose of the investigation of the dynamics of the conformational ensemble, we propose a strategy to construct a reduced energy surface from low-energy minima and selected transition states. The strategy selects pairs of conformers for the optimization of the transition states. The resulting energy barriers are then arranged into a barrier tree, a convenient representation of a high- dimensional energy surface. The method is evaluated for: (i) the alanine tetrapeptide, at the force-field level, where it matches the findings of free- energy simulations; and (ii) a synthetic peptide, employing first principles, where the resulting barrier-tree representation helps interpreting the experiment. Accurate predictions of properties, e.g. catalytic activity, require identification of energetically favorable 3D structures. We investigate the relation between the adopted 3D structures and the catalytic activity in eight (thio)urea based compounds. The conformational preferences of the (thio)urea based compounds significantly differ between each other. The investigation of the interaction between an example thiourea catalyst and a model substrate reveals that only in its active form can the catalyst activate the substrate.Flexible organische und biologische Moleküle können verschiedene 3D Konformationen annehmen, die unterschiedliche chemische und physikalische Eigenschaften aufweisen. Die Suche nach energetisch günstigen Konformeren ist ein fundamentales Problem der Molekülphysik und Computerchemie. In der vorliegenden Arbeit stellen wir Methoden vor, die der Untersuchung des molekularen Konformationsraumes dienen und die ab initio-Methoden verwenden. Wir präsentieren eine Suchtechnik, die unter Verwendung eines genetischen Algorithmus (GA) den Konformationsraum durchsucht. Diese Suchtechnik wurde als Teil der Python-Bibliothek Fafoom implementiert, die im Rahmen dieser Doktorarbeit entwickelt wurde. Ziel der GA-basierten Suchtechnik ist es das Auffinden des globalen Minimums und aller lokalen Minima in einem bestimmen Energiefenster. Die effiziente Verwendung rechenintensiver ab initio-Methoden wird durch die Durchführung lokaler Optimierungen und das Vermeiden der Auswertung von bekannten Lösungen unterstützt. Die Suchtechnik wurde eingesetzt um den Konformationsraum von sieben Dipeptiden und acht Arzneistoff-ähnlichen Molekülen zu untersuchen. Im Anschluss wurden folgende Punkte überprüft: (i) wie gut kann die Suchtechnik die Referenzdaten reproduzieren; und (ii) wie gut ist der Konformationsraum erforscht worden. Unsere Studie zeigt, dass die GA-basierte Suchtechnik den Konformationsraum der untersuchten Moleküle mit hoher Genauigkeit und Effizienz probt. Wir präsentieren eine Strategie, die eine vereinfachte Darstellung der Energiefläche bietet um eine Untersuchung der Vielfalt des Konformationsensembles zu ermöglichen. Die vereinfachte Darstellung besteht aus energetisch günstigen lokalen Minima und ausgewählten Übergangszuständen. Die resultierenden Energiebarrieren werden verwendet um die vieldimensionale Energiefläche in Form eines Energiebaumes anschaulich darzustellen. Folgende Moleküle wurden mit der Methode untersucht: (i) das Alanin-Tetrapeptid mit Hilfe von Molekülmechanik-Rechnungen und (ii) ein synthetisches Peptid unter Verwendung von ersten Prinzipien. Die für das Alanin-Tetrapeptid gewonnenen Resultate stimmen mit den Erkenntnissen aus Vergleichssimulationen überein. Das für das synthetische Peptid konstruierte Energie-Baumdiagramm unterstützt die Interpretation von experimentellen Daten. Die Bestimmung von energetisch günstigen Konformeren ist zur korrekten Vorhersage von Eigenschaften notwendig. Wir untersuchen den Zusammenhang zwischen der 3D-Struktur und der katalytischen Aktivität von acht (Thio-)Harnstoffverbindungen. Die Unterschiede zwischen den strukturellen Präferenzen von den (Thio-)Harnstoffen sind signifikant. Die Untersuchung der Interaktion zwischen einem Thioharnstoff basierten Katalysator und einem Modellsubstrat hat ergeben, dass nur ein bestimmtes Konformer des Katalysators das Substrat aktivieren kann

Cell-to-Cell Communication Circuits

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.Peer Reviewe

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 <i>anti-anti</i> conformation of its central thiourea group, our electronic-structure theory study reveals that binding of the model substrate methyl vinyl ketone prefers <i>syn-anti</i> 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

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 <i>anti-anti</i> conformation of its central thiourea group, our electronic-structure theory study reveals that binding of the model substrate methyl vinyl ketone prefers <i>syn-anti</i> 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

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