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PhD position: Molecular Models and Methods for Computational Protein Design



The remarkable properties of proteins, particularly of enzymes (high catalytic efficiency, regio/stereo-selectivity) have long been recognized and exploited in industry. Enzyme-driven processes are expected to play an increasing role in industry renewal over the next decades, potentially replacing unsustainable manufacturing processes and contributing to the raise of bioeconomy. An important step in this direction consists in reliably accelerating the conception of new proteins endowed with property/function fully suitable and optimized for the targeted application. In this regard, structure-based Computational Protein Design (CPD) has been attracting increasing attention as the rational way of designing new proteins and enzymes with practical impact in biotechnology, nanotechnology, green chemistry and synthetic biology.


By combining physico-chemical models governing relations between protein amino-acid composition and protein 3D structure with optimization algorithms, CPD seeks to identify sequences that fold into a given 3D-scaffold and possess the targeted biochemical properties. The objective is thus to considerably narrow down the number of mutants for experimental tests while greatly increasing the odds of designing the desired protein. Using CPD, proteins with new and optimized functions/properties have been successfully designed. However, to generalize these particular achievements, CPD methods face several challenges. Methodological advances in the Computational Protein Design field are thus needed to bring more efficiency and reliability into the design of ad hoc and optimized proteins for a wide range of critical applications in various sectors (bioenergy, chemicals, food, feed, pharmaceutical, cosmetic…) such as the production of high-valued molecules, the development of novel eco-friendly bioprocesses and the valorization of renewable carbon resources.


PhD proposal

 We are looking for a highly motivated candidate for a three year PhD to develop and apply models and methods for Computational Protein Design. The work will take place in an ongoing project involving a team of structural biologists specialized in molecular modeling and design from LISBP (Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés, Toulouse, UMR INSA INRA 792 – CNRS 5504) and computer scientists specialized in discrete optimization from MIAT (Unité de Mathématiques et Informatiques de Toulouse, INRA 875). Together, we have developed game-changing artificial-intelligence-based optimization technologies for protein design. Based on the combination of a cutting-edge exact combinatorial optimization technique in Artificial Intelligence (Cost Function Networks, CFN) with molecular modelling methods, these new CPD methods speed up the search process of the most relevant sequences by several orders of magnitude compared to exact methods commonly used in CPD [1,4]. Hence, they are able to handle highly complex protein design problems that previously could not be solved exactly.


The aim of the PhD will be to bring these improvements to a new level by further exploiting and extending these methods to handle more complex protein design problems and overcome other CPD challenges. Notably, CPD approaches based on the learning of structural and evolutionary related data will be investigated. The foreseen work will also target the integration of large-scale molecular flexibility into design process and solving multi-state design problems. Methods will be extensively tested in large-scale protein design experiments.





Eligibility and duties: Motivated candidates with an academic degree at the Masters level in Molecular Modeling of proteins with a strong taste for algorithms, programming and some mathematics.


The work will include method development, implementation and testing on protein design problems with practical impact in biotechnology, nanotechnology… as well as writing of research papers. Molecular modeling and programming skills will be extremely beneficial. Some knowledge in machine learning is welcome. A good knowledge of spoken and written English is a highly valuable asset. A good knowledge of spoken French is also appreciated. If needed, training can be provided.


A PhD grant is provided for 3 years, at a current level of 1,757€/month (gross salary). Applications should include a CV, a brief description of research interests and past research experience, copies of exam-certificates and grades, a copy of your Master’s thesis (or a summary) and a cover letter motivating why you are interested in the position. The candidates are encouraged to provide contact information of at most two reference persons.


Applications should be sent by email to :


Sophie Barbe :

Laboratoire d’Ingénierie des Systèmes Biologiques et des Procédés

INSA de Toulouse (INRA 792 - CNRS 5504)

135 avenue de Rangueil

31077 Toulouse CEDEX 04

Phone: (+33)




Thomas Schiex :

Laboratoire de Mathématiques et Informatiques Appliquées de Toulouse

INRA de Toulouse (INRA 875)

Chemin de Borde Rouge - Auzeville, CS 52627

31326 Castanet Tolosan Cedex, France

Phone: (+33)



1- Traoré S, Allouche D, Andre I, De Givry S, Katsirelos G, Schiex T, Barbe S (2013). A new framework for computational protein design through cost function network optimization. Bioinformatics, 29 (17), 129-2136.

2- Simoncini D, Allouche D, de Givry S, Delmas C, Barbe S, Schiex T (2015) Guaranteed Discrete Energy Optimization on Large Protein Design Problems. J Chem Theory Comput 11(12):5980-9.

3- Traoré S, Roberts KE, Allouche D, Donald BR, André I, Schiex T, Barbe S (2016). Fast search algorithms for Computational Protein Design. J Comput Chem. (12) 1048-58.

4- Traoré S, Allouche D, André I, Schiex T, Barbe S (2016). Deterministic search methods for Computational Protein Design. In Computational Protein Design.  Springer. 1529:107-123.

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