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PEACCEL is hiring a new Senior Scientist in Paris

PEACCEL is a biotech company that offers fast & industry proven solutions for protein optimization and metabolic pathway engineering. We are looking for an individual with a solid foundation in machine learning and structural biology to join our multinational team. The candidate will support the directed evolution efforts in R&D by providing insight into the structure of the target engineered enzyme. The individual will be expected to propose directed evolution strategies, analyze screening results, and effectively communicate their observations to a broader team. The candidate will also be expected to work closely with the business development group to process and analyze customer screening results and aid in the drafting of proposals.

Skills and Responsibilities:

  • Analyse literature and patents to evaluate and identify optimal data sets as input for PEACCEL’s machine learning platform innov’SAR
  • Perform modeling and predictive rational screening based on the identified data/training sets
  • Generate reliable structural models of enzymes and their variants.
  • In-silico docking of substrates/products/transitions states in the active site of an enzyme.
  • A good understanding of wet lab molecular biology and biochemistry (enzyme expression, screening, etc).
  • A solid understanding of enzyme mechanisms.
  • Some background in chemistry.
  • A good knowledge of statistics.
  • Expertise in one or several of the core machine learning areas, eg. Artificial Neural Networks, Supervised/Unsupervised learning methods, Support Vector Machines, Bayesian approaches, Gaussian processes, Markov Models, Decision Theory etc.
  • Programming skills in R, Python, Perl and/or C++
  • Comfortable in the use of structure visualization software such as MOE, Discovery Studio, VMD, Pymol etc.
  • Experienced in computational protein design software such as Rosetta.

 

Requirements:

  • A PhD (bioinformatics, enzymology, protein biochemistry)
  • At least 5 years of post-doctoral or industrial experience.
  • Experience writing proposals and patents.
  • A solid publication record (scientific articles as well as patents).
  • Excellent written and oral communication skills.
  • Can integrate a team as team leader or be efficient team player that is comfortable coordinating and collaborating with many individuals across the company as well as with external partners (academia, industry).
  • Strong organizational skills.

Interested? If this interdisciplinary challenge appeals to you, please apply online with your complete application documents (CV +2 reference letters) as well as your earliest starting date and salary expectations at contact@peaccel.com.

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Publication of two articles describing PEACCELs’ proprietary innov’SAR technology and its successful application in the improvement of an industrial enzyme

PEACCEL  announced the publication of a two research articles describing PEACCELs’ proprietary innov’SAR technology and its application in the improvement of enzymes. The first article with the title “Application of fourier transform and proteochemometrics principles to protein engineering” was published in BMC Bioinformatics. The second article entitled “A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes” was published in Scientific Reports, a Nature Publishing Group journal,

The first article describes PEACCELs’ algorithm innov’SAR and its capacity to describe and to model properties of different proteins ranging from peptides to enzymes and receptors. The second article which was done in collaboration with the research group of Manfred Reetz, one of the leading scientists in the field of enzyme evolution illustrates the successful application of innov’SAR for the accurate prediction of improved epoxide hydrolases.

“With these publications we have achieved a further key milestone in our company development as we could successfully demonstrate the power of our innov’SAR technology to predict improved protein properties and to reduce time consuming and expensive laboratory testing. As shown in the articles innov’SAR could capture additive as well as epistatic effects of mutations and therefore obtained highly accurate predictions of their combined activities. Based on the precise innov’SAR models we could predict improved variants of an enzyme of industrial interest. The testing of a very limited number of variants (<10) resulted in the identification of several mutans with improved properties. The collaboration with M. Reetz gave us the opportunity not only to have a very critical outside opinion on our technology but also to perform a proof of concept study to demonstrate that innov’SAR can also further optimize already highly improved enzymes” commented Rudy Pandjaitan head of business development.

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