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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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Download Curriculum Vitae

Mobility project

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

I obtained my ph.D. in Computer Science from the University of Nice Sophia Antipolis. I was studying evolutionary algorithms, which are some population-based search methods: my objective was to find new methods to control the search dynamics of spatially structured evolutionary algorithms.  After I passed my ph.D. I wanted to apply my methods on some specific problems, so I went to RIKEN in Tokyo in order to study protein structure prediction. I stayed there as a postdoc fellow for almost 3 years and developed a software for protein structure prediction (Edafold). I came back to France in september 2013 as an ATER at the i3s laboratory of Sophia Antipolis. I am interested by interdisciplinary work. I like to tackle problems in collaboration with researchers from various fields, each of them bringing their competences. My research interests focus on protein structure prediction and computational protein design.

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