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David Simoncini

David Simoncini

session, year:
2013 2nd

Former fellow

Receiving laboratory:
MIAT Applied Mathematics and Informatics, Toulouse

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Mobility project

Computational design of proteins using a fragment-based approach and cost function network model

Computational protein design (CPD) refers to the problem of finding a sequence of amino acids corresponding to a protein with the desired three-dimensional structure, or the desired biological function and is a longstanding goal in computational structural biology.

The project aims at developing an original CPD software based on the novel idea of fragment-based protein design with applications to the design of robust and efficient enzymes of biotechnological interest. We propose to map fragments of existing proteins to the backbone of the protein that needs to be designed (or redesigned). The backbone was  decomposed in small structural fragments, and the Protein DataBank will be queried to find matches for each fragment. The sequences of the matching pieces of proteins were gathered in a library and used to construct the designers. The pieces of sequences will be assembled and evaluated by state-of-the-art score functions. Computational optimization techniques for Weighted Constraint Networks and Weighted MaxSAT developed in the MIAT laboratory and recently adapted to CPD in collaboration with computational biologists of the CIMES team at LISBP has been applied to retrieve the optimal and suboptimal designer proteins,and extended to take into account protein flexibility. Promising designer proteins was assessed with a set of computational tools and compared to experimental data issued from CIMES-LISBP.The use of structural information in fragment-based CPD is attractive, since it reduces the size of the search space to naturally occurring protein sequences. Yet, there is no report of such method in the literature.

Biography & research interests

I obtained a Ph.D. in Computer Science from the University of Nice (France). I was studying Evolutionary Algorithms, which are some population-based optimization methods. I joined the RIKEN in Japan as a postdoc to develop computational methods for protein structure prediction. I stayed at RIKEN for two years and a half. I started working on computational protein design when I came back to France for a one year postdoc in Nice. I then became an AgreenSkills fellow and joined the INRA MIAT laboratory in Toulouse for two years where I was developing computational models for protein design. I am currently visiting scientist in the Rijken Center for Biosystems Dynamics Research (Japan) in the Laboratory for Structural Bioinformatics.

Selected publications

Simoncini, D., Schiex, T., Zhang, KYJ., 2017. Balancing exploration and exploitation in population-based sampling improves fragment-based de novo protein structure prediction. Proteins-Structure Function & Bioinformatics, 85, 5, 852-858.

C Viricel, D Simoncini, S Barbe, T Schiex. 2016. Guaranteed Weighted Counting for Affinity Computation: Beyond Determinism and Structure. International Conference on Principles and Practice of Constraint Programming, 733-750.

D Simoncini, D Allouche, S de Givry, C Delmas, S Barbe, T Schiex. 2015. Guaranteed discrete energy optimization on large protein design problems. Journal of chemical theory and computation 11 (12), 5980-5989.

ARD Voet, H Noguchi, C Addy, D Simoncini, D Terada, S Unzai, SY Park, Kam YJ Zhang, Jeremy RH Tame. 2014. Computational design of a self-assembling symmetrical ?-propeller protein. Proceedings of the National Academy  of Sciences 111 (42), 15102-15107.

D Simoncini, KYJ Zhang, 2013. Efficient Sampling in Fragment-Based Protein Structure Prediction Using an Estimation of Distribution Algorithm. PLOS ONE 8(7): e68954.

D Simoncini, F Berenger, R Shrestha, KYJ Zhang.  A probabilistic fragment-based protein structure prediction algorithm. PloS one 7 (7), e38799.


Computational protein design, combinatorial optimization, computational structural biology, macromolecular flexibility, enzyme design