Genetic-Algorithm-Based Optimization of a Peptidic Scaffold for Sequestration and Hydration of CO

Biomimicry is a strategy that makes practical use of evolution to find efficient and sustainable ways to produce chemical compounds or engineer products. Exploring the natural machinery of enzymes for the production of desired compounds is a highly profitable investment, but the design of efficient biomimetic systems remains a considerable challenge. An ideal biomimetic system self-assembles in solution, binds a desired range of substrates and catalyzes reactions with turnover rates similar to the native system. To this end, tailoring catalytic functionality in engineered peptides generally requires site-directed mutagenesis or the insertion of additional amino acids, which entails an intensive search across chemical and sequence space. Here we discuss a novel strategy for the computational design of biomimetic compounds and processes that consists of a) characterization of the wild-type and biomimetic systems; b) identification of key descriptors for optimization; c) an efficient search through sequence and chemical space to tailor the catalytic capabilities of the biomimetic system. Through this proof-of-principle study, we are able to decisively understand and identify whether a given scaffold is useful, appropriate and tailorable for a given, desired task.

Lessons from Nature: Computational Design of Biomimetic Compounds and Processes

Negar Ashari Astani, Esra Bozkurt, Nicholas John Browning, Elizabeth Claire Brunk, Pablo Campomanes Ramos, Marta Andreia Da Silva Perez, Ursula Röthlisberger

Through millions of years of evolution, Nature has accomplished the development of highly efficient and sustainable processes and the idea to understand and copy natural strategies is therefore very appealing. However, in spite of intense experimental and computational research, it has turned out to be a difficult task to design efficient biomimetic systems. Here we discuss a novel strategy for the computational design of biomimetic compounds and processes that consists of i) target selection; ii) atomistic and electronic characterization of the wild type system and the biomimetic compounds; iii) identification of key descriptors through feature selection iv) choice of biomimetic template and v) efficient search of chemical and sequence space for optimization of the biomimetic system. As a proof-of-principles study, this general approach is illustrated for the computational design of a 'green' catalyst mimicking the action of the zinc metalloenzyme Human Carbonic Anhydrase (HCA). HCA is a natural model for CO2 fixation since the enzyme is able to convert CO2 into bicarbonate. Very recently, a weakly active HCA mimic based on a trihelical peptide bundle was synthetized. We have used quantum mechanical/molecular mechanical (QM/MM) Car-Parrinello simulations to study the mechanisms of action of HCA and its peptidic mimic and employed the obtained information to guide the design of improved biomimetic analogues. Applying a genetic algorithm based optimization procedure, we were able to re-engineer and optimize the biomimetic system towards its natural counter part. In a second example, we discuss a similar strategy for the design of biomimetic sensitizers for use in dye-sensitized solar cells.

2014

Integrating rate based models into multi-objective optimisation of process designs using surrogate models

François Maréchal, Levent Sinan Teske

Multi-objective optimisation (MOO) of super-structured process designs are expensive in CPU- time because of the high number of potential configurations and operation conditions to be calculated. Thus single process units are generally represented by simple models like equilibrium based (chemical or phase equilibrium) or specific short cut models. In the development of new processes, kinetic effects or mass transport limitations in certain process units may play an important role, especially in multiphase chemical reactors. Therefore, it is desirable to represent such process unit by experimentally derived RBMs (rate based models) in the process flowsheet simulators used for the extensive MOO. This increases the trust engineers have in the results and allows enriching the process simulations with newest experimental findings. As most RBMs are iteratively solved, a direct incorporation would cause higher CPU-time that penalises the use of MOO. A global surrogate model (SUMO) of a RBM was successfully generated to allow its incorporation into a process design & optimisation (PDO) tool which makes use of an evolutionary MOO. The methodology was applied to a fluidised bed methanation reactor in the process chain from wood to Synthetic Natural Gas (SNG). Two types of surrogate model, an ordinary Kriging interpolation and an artificial neural network, were generated and compared to its underlying rate based model and the chemical equilibrium model. The analysis showed that kinetic limitations have significant influence on the result already for standard bulk gas chemical components. From experimental experience it can be stated that a significant amount of chemical compounds, which are near to complete conversion according the thermodynamic equilibrium, are measured in the gas phase after the reactor. Thus including RBMs in PDO improves the quality of results. This approach allows a significant improvement of information exchange between process design & optimisation workflow and experimental development of PUs in early stages of process development.

2013

Pushing the Frontiers of First-Principles Based Computer Simulations of Chemical and Biological Systems

Negar Ashari Astani, Elizabeth Claire Brunk, Pablo Campomanes Ramos, Basile Curchod, Polydefkis Diamantis, Andrey Laktionov, Marilisa Neri, Giulia Palermo, Thomas James Penfold, Ursula Röthlisberger, Ivano Tavernelli, Stefano Vanni

The Laboratory of Computational Chemistry and Biochemistry is active in the development and application of first-principles based simulations of complex chemical and biochemical phenomena. Here, we review some of our recent efforts in extending these methods to larger systems, longer time scales and increased accuracies. Their versatility is illustrated with a diverse range of applications, ranging from the determination of the gas phase structure of the cyclic decapeptide gramicidin S, to the study of G protein coupled receptors, the interaction of transition metal based anti-cancer agents with protein targets, the mechanism of action of DNA repair enzymes, the role of metal ions in neurodegenerative diseases and the computational design of dye-sensitized solar cells. Many of these projects are done in collaboration with experimental groups from the Institute of Chemical Sciences and Engineering (ISIC) at the EPFL.

2011

Integrating rate based models into multi-objective optimisation of process designs using surrogate models

François Maréchal, Levent Sinan Teske

2013