Comparison of aluminum oxide empirical potentials from cluster to nanoparticle

Aluminum oxide nanoparticles are increasingly sought in numerous technological applications. However, as the nanoparticles grow during the synthesis, two phase transitions occur. At the nanoscale, numerical simulation of the stability of the alumina phases requires the use of empirical potentials that are reliable over a large range of system sizes going from a few atoms to several hundred thousand atoms. In this work, we confronted four different empirical potentials that are currently employed for bulk alumina. We found that only two of them are correct at the molecular level when compared to DFT calculations. Furthermore, the two potentials remain the best at the nanoscale as they reproduce one or two phase transitions that were observed experimentally: from amorphous solid to cubic crystal ({\gamma}) and from cubic to hexagonal ({\alpha}, i.e. corundum) crystal.Comment: 11 pages, 8 pdf figures, 1 supplemental material pdf file, accepted in Physical Review

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

Crossing Boundaries: Tapestry Within the Context of the 21st Century

International audienceGraphical model processing is a central problem in artificial intelligence. The optimization of the combined cost of a network of local cost functions federates a variety of famous problems including CSP, SAT and Max-SAT but also optimization in stochastic variants such as Markov Random Fields and Bayesian networks. Exact solving methods for these problems typically include branch and bound and local inference-based bounds.In this paper we are interested in understanding when and how dynamic programming based optimization can be used to efficiently enforce soft local consistencies on Global Cost Functions, defined as parameterized families of cost functions of unbounded arity. Enforcing local consistencies in cost function networks is performed by applying so-called Equivalence Preserving Transformations (EPTs) to the cost functions. These EPTs may transform global cost functions and make them intractable to optimize.We identify as tractable projection-safe those global cost functions whose optimization is and remains tractable after applying the EPTs used for enforcing arc consistency. We also provide new classes of cost functions that are tractable projection-safe thanks to dynamic programming.We show that dynamic programming can either be directly used inside filtering algorithms, defining polynomially DAG-filterable cost functions, or emulated by arc consistency filtering on a Berge-acyclic network of bounded-arity cost functions, defining Berge-acyclic network-decomposable cost functions. We give examples of such cost functions and we provide a systematic way to define decompositions from existing decomposable global constraints.These two approaches to enforcing consistency in global cost functions are then embedded in a solver for extensive experiments that confirm the feasibility and efficiency of our proposal

Cost Function Networks to Solve Large Computational Protein Design Problems

International audienc

Projected Range Contractions of European Protected Oceanic Montane Plant Communities: Focus on Climate Change Impacts Is Essential for Their Future Conservation

Global climate is rapidly changing and while many studies have investigated the potential impacts of this on the distribution of montane plant species and communities, few have focused on those with oceanic montane affinities. In Europe, highly sensitive bryophyte species reach their optimum occurrence, highest diversity and abundance in the northwest hyperoceanic regions, while a number of montane vascular plant species occur here at the edge of their range. This study evaluates the potential impact of climate change on the distribution of these species and assesses the implications for EU Habitats Directive-protected oceanic montane plant communities. We applied an ensemble of species distribution modelling techniques, using atlas data of 30 vascular plant and bryophyte species, to calculate range changes under projected future climate change. The future effectiveness of the protected area network to conserve these species was evaluated using gap analysis. We found that the majority of these montane species are projected to lose suitable climate space, primarily at lower altitudes, or that areas of suitable climate will principally shift northwards. In particular, rare oceanic montane bryophytes have poor dispersal capacity and are likely to be especially vulnerable to contractions in their current climate space. Significantly different projected range change responses were found between 1) oceanic montane bryophytes and vascular plants; 2) species belonging to different montane plant communities; 3) species categorised according to different biomes and eastern limit classifications. The inclusion of topographical variables in addition to climate, significantly improved the statistical and spatial performance of models. The current protected area network is projected to become less effective, especially for specialised arctic-montane species, posing a challenge to conserving oceanic montane plant communities. Conservation management plans need significantly greater focus on potential climate change impacts, including models with higher-resolution species distribution and environmental data, to aid these communities’ long-term survival

Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis

Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth “Dialogue for Reverse Engineering Assessments and Methods” (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on “Systems Genetics” proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics

Euclidean Variable Neighborhood Search: A method for large computation protein design

Computational protein design (CPD) is an important tool for biotechnology still under development. Early applications led to proteins with novel ligand-binding functions, novel enzyme activity, and proteins that were completely "redesigned": around 2/3 of their sequence was mutated, yet their structure and stability were retained. In the last few years, CPD has allowed the creation of new protein folds, completely new enzymes, and the assembly or deassembly of multiprotein complexes. CPD methods are mainly characterized by (a) the energy function, (b) the description of the folded protein's conformational space, (c) the treatment of the unfolded state, and (d) the search method used to explore sequences and conformations. Graphical model and cost function network have been recently introduced as new search methods for CPD search allowing to find the global minimum energy conformation (with optimality proof). However, it can solved problems with less than 1003 and 484 mutations respectively with Rosetta and Amber forcefield and Dunbrack and Tuffery rotamers library. In this work, we introduce a new search method for CPD dedicated to large instances. The method, based on Variable Neighborhood Search (VNS), uses (partial) tree search in order exhibit the solutions with the lowest energy. [br/] In order to improve its behavior, we implemented a new heuristic taking advantage of the euclidean information provided by the pdb structure for neighbor variable selections. This last one used in conjunction with logarithmic size incrementation of the neighborhood size improves significantly the VNS behaviors. The resulting algorithm, called euclidean VNS, is more robust. It also outperforms Replica-Exchange Method for global minimum energy search. Furthermore, the method can provide direct correlation between energy improvement and protein structure, allowing to identify energetic hot spots in the protein backbone