Anisotropic selection in cellular genetic algorithms

In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure: we can obtain the same takeover time with the two techniques although the spreading of the best individual is different. We then give experimental results that show to what extent AS promotes the emergence of niches that support low coupling and high cohesion. Finally, using a cGA with anisotropic selection on a Quadratic Assignment Problem we show the existence of an anisotropic optimal value for which the best average performance is observed. Further work will focus on the selective pressure self-adjustment ability provided by this new selection scheme

On the Influence of Selection Operators on Performances in Cellular Genetic Algorithms

In this paper, we study the influence of the selective pressure on the performance of cellular genetic algorithms. Cellular genetic algorithms are genetic algorithms where the population is embedded on a toroidal grid. This structure makes the propagation of the best so far individual slow down, and allows to keep in the population potentially good solutions. We present two selective pressure reducing strategies in order to slow down even more the best solution propagation. We experiment these strategies on a hard optimization problem, the quadratic assignment problem, and we show that there is a value for of the control parameter for both which gives the best performance. This optimal value does not find explanation on only the selective pressure, measured either by take over time and diversity evolution. This study makes us conclude that we need other tools than the sole selective pressure measures to explain the performances of cellular genetic algorithms

Centric selection: a way to tune the exploration/exploitation trade-off

In this paper, we study the exploration / exploitation trade-off in cellular genetic algorithms. We define a new selection scheme, the centric selection, which is tunable and allows controlling the selective pressure with a single parameter. The equilibrium model is used to study the influence of the centric selection on the selective pressure and a new model which takes into account problem dependent statistics and selective pressure in order to deal with the exploration / exploitation trade-off is proposed: the punctuated equilibria model. Performances on the quadratic assignment problem and NK-Landscapes put in evidence an optimal exploration / exploitation trade-off on both of the classes of problems. The punctuated equilibria model is used to explain these results

Computational protein design to accelerate the conception of fine-tuned biocatalysts

The remarkable properties of enzymes (high catalytic efficiency, regio- and stereo-selectivity) have been recognized and largely exploited in biocatalysis. Accordingly, enzyme-driven processes should play an increasing role in the next decades, potentially substituting chemical processes and contributing to the raise of bioeconomy. However, to foresee a viable future to biocatalysis, advances in R&D are required to accelerate the delivery of fine-tuned enzymes displaying high chemical specificity on non-cognate substrates, high efficiency and better stability in reaction conditions. To this end, structure-based Computational Protein Design (CPD) is a promising strategy to fully rationalize and speed-up the conception of new enzymes while reducing associated human and financial costs. By combining physico-chemical models governing relations between protein amino-acid composition and their 3D structure with optimization algorithms, CPD seeks to identify sequences that fold into a given 3D-scaffold and possess the targeted biochemical properties. Starting from a huge search space, the protein sequence-conformation space, this in silico pre-screening aims to considerably narrow down the number of mutants tested at experimental level while substantially increasing the chances of reaching the desired enzyme. While CPD is still a very young and rapidly evolving field, success stories of computationally designed proteins highlight future prospects of this field. Nonetheless, despite landmark achievements, the success rate of the current computational approaches remains low, and designed enzymes are often way less efficient than their natural counterparts. Therefore, several limitations of the CPD still need to be addressed to improve its efficiency, predictability and reliability. Herein, we present our methodological advances in the CPD field that enabled overcoming technological bottlenecks and hence propose innovative CPD methods to explore large sequence-conformation spaces while providing more accuracy and robustness than classical approaches. Our CPD methods speed-up search across vast sequence-conformation spaces by several orders of magnitude, find the minimum energy enzyme design and generate exhaustive lists of near-optimal sequences, defining small mutant libraries. These new methods, in rupture with classical approaches are based on efficient algorithms issued from recent research in artificial intelligence. The performance and accuracy of our computer-aided enzyme design methods have been evaluated and validated on various types of protein design problems. This work was partially funded by INRA/Région Midi-Pyrénées, IDEX Toulouse, Agreenskills and the French National Research Agency (PROTICAD, ANR-12-MONU-0015-03)

Cost Function Networks to Solve Large Computational Protein Design Problems

International audienc

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The Grizzly, October 23, 2014

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Cost Function Networks to Solve Large Computational Protein Design Problems

International audienc

Computational design of symmetrical eight-bladed beta-propeller proteins

β-Propeller proteins form one of the largest families of protein structures, with a pseudo-symmetrical fold made up of subdomains called blades. They are not only abundant but are also involved in a wide variety of cellular processes, often by acting as a platform for the assembly of protein complexes. WD40 proteins are a subfamily of propeller proteins with no intrinsic enzymatic activity, but their stable, modular architecture and versatile surface have allowed evolution to adapt them to many vital roles. By computationally reverse-engineering the duplication, fusion and diversification events in the evolutionary history of a WD40 protein, a perfectly symmetrical homologue called Tako8 was made. If two or four blades of Tako8 are expressed as single polypeptides, they do not self-assemble to complete the eight-bladed architecture, which may be owing to the closely spaced negative charges inside the ring. A different computational approach was employed to redesign Tako8 to create Ika8, a fourfold-symmetrical protein in which neighbouring blades carry compensating charges. Ika2 and Ika4, carrying two or four blades per subunit, respectively, were found to assemble spontaneously into a complete eight-bladed ring in solution. These artificial eight-bladed rings may find applications in bionanotechnology and as models to study the folding and evolution of WD40 proteins