Postdoctoral Position on novel algorithms for annotating and fitting of electron density

About the NANO-D research group at INRIA

The NANO-D group, led by Stephane Redon at INRIA, develops novel multiscale, adaptive modeling and simulation methods, which automatically focus computational resources on the most relevant parts of the nanosystems under study. All algorithms developed by the group are gathered into SAMSON, an open-architecture software platform designed by NANO-D (SAMSON: Software for Adaptive Modeling and Simulation Of Nanosystems).

During the twentieth century, the development of macroscopic engineering has been largely stimulated by progress in numerical design and prototyping: cars, planes, boats, and many other manufactured objects are nowadays designed and tested on computers. Digital prototypes have progressively replaced actual ones, and effective computer-aided engineering tools have helped cut costs and reduce production cycles of these macroscopic systems.

The twenty-first century is most likely to see a similar development at the atomic scale. Indeed, the recent years have seen tremendous progress in nanotechnology - in particular in the ability to control matter at the atomic scale. Similar to what has happened with macroscopic engineering, powerful and generic computational tools will be employed to engineer complex nanosystems, through modeling and simulation. The NANO-D group is funded through ANR grants, an ARC grant, and an ERC Starting Grant (http://nano-d.inrialpes.fr). 

Protein–Protein Interactions and Cryo Electron Microscopy

Critical Assessment of PRediction of Interactions (CAPRI) demonstrates that currently it is very difficult or even impossible to predict domain-domain interactions when no complex templates are available (Lensink and Wodak 2010). Therefore, in many cases, the only way to obtain the structure of a multi-domain protein or a complex of proteins is to use a combination of complimentary techniques. In particular, cryo-electron microscopy (cryoEM) may provide a mid-to-low-resolution picture for many systems (Lawson et al. 2011). Additional symmetry information can be also used sometimes to make structural predictions.

Cryo-electron microscopy (cryoEM) is a popular experimental method which yields electron densities of large multi-domain proteins at low-to-mid resolution. This method allows to capture macromolecular complexes in different conformational or structural states, which are physiologically more relevant compared to structures obtained by X-ray crystallography. Various computational methods exist for post-processing of electron densities (Fabiola and Chapman 2005). They either model the 3D structure of the whole complex starting from the amino acid sequence, if resolution allows, or fit known structures of smaller templates into the electron density of the complex.

Another way to narrow down the docking search space is to use a priori knowledge about molecular symmetry. Symmetry is a frequent structural trait in molecular systems. For example, most of the water-soluble and membrane proteins found in living cells are composed of symmetrical subunits, and nearly all structural proteins (such as microfilaments, intermediate filaments, and microtubules in eukaryotic cells) form long oligomeric chains of identical subunits.

Task 1
Medium resolution cryoEM electron density maps pose another challenge, as was announced recently (Lawson et al. 2011). At sub-nanometer resolution, α-helices become resolvable, and as the resolution improves further, β-sheets become discernible, eventually showing strand separation. In this intermediate (~5-10 Å) resolution range, tools for automatic identification and localisation of secondary structure elements become quite valuable. The first task will be to use supervised learning and exhaustive search techniques to automatically identify regions inside the map that belong to different structural traits, α-helices or β-sheets. First, the appropriate set of structural descriptors responsible for the secondary structure characterisation needs to be found. Second, regions with most similarities to either of the secondary structure motifs are to be detected.

Task 2
Grounded on the two recently developed exhaustive–search FFT-based algorithms for fitting a structure into a density map (Hoang et al. 2013; Derevyanko and Grudinin), this task deals with symmetry in the formulation of the fitting problem. More precisely, symmetry restricts the search space and should make the search algorithm faster and more precise. An algorithm for automatic recognition of the point group symmetry in electron density maps has to be developed as well.

References

  • Lensink, M.F., and S.J. Wodak. 2010. “Docking and Scoring Protein Interactions: Capri 2009.” Proteins: Structure, Function, and Bioinformatics
  • Lawson, CL, ML Baker, C Best, C Bi, M Dougherty, P Feng, G van Ginkel, B Devkota, I Lagerstedt, SJ Ludtke, RH Newman, TJ Oldfield, I Rees, G Sahni, R Sala, S Velankar, J Warren, JD Westbrook, K Henrick, GJ Kleywegt, HM Berman, and W Chiu. 2011. “Emdatabank.Org: Unified Data Resource for Cryoem.” Nucleic Acids Res 39(Database issue): D456–64.
  • Fabiola, F, and MS Chapman. 2005. “Fitting of High-Resolution Structures Into Electron Microscopy Reconstruction Images.” Structure 13(3): 389–400.
  • Hoang, T.V., Cavin, X., and Ritchie, D. W. “gEMfitter: A Highly Parallel FFT-Based 3D Density Fitting Tool With GPU Texture Memory Acceleration: gEMfitter: A GPU-Accelerated 3D Density Fitting Tool.” Journal of Structural Biology 2013.
  • Derevyanko, G., and Grudinin, S. “HermiteFit: Fast fitting atomic structures into cryo-EM density maps using 3D orthogonal Hermite functions.” Under Revision.

Desired profile

We are looking for creative, passionate and hard-working individuals with exceptional talent for computer science and mathematics. Excellent oral, written and interpersonal communication skills are essential (working language will be English – knowledge of French is a plus).

Requirements

  • Strong computer science background (knowledge of machine learning is a plus)
  • Strong knowledge of applied math (linear algebra, signal processing, Fourier analysis, polynomial expansions)
  • Strong oral, written and interpersonal communication skills (working language: English – knowing French is a plus)
  • Good knowledge of C++
  • Fair knowledge of parallel computing frameworks (threading libraries and GPU)
  • Fair knowledge of structural biology
  • Ability to work independently and with a team

Benefits

  • Possibility of French courses
  • Help for housing
  • Scientific resident card and help to obtain visa (for both you and your spouse)

Salary and duration

  • 2,620.84 € gross/month for 18 months

  • Monthly salary after taxes: around 2,138 € (medical insurance included).

About Grenoble

Grenoble is the capital city of the French Alps. Combining the urban life-style of southern France with a unique mountain setting, it is ideally situated for outdoor activities. The Grenoble area is today an important centre of industry and science (second largest in France). Dedicated to an ambitious policy in the arts, the city is host to numerous cultural institutions. With 60,000 students (including 6,000 foreign students), Grenoble is the third largest student area in France.

To apply

Send an email to Sergei Grudinin (sergei.grudinin@inria.fr) with:

  • A resume
  • A motivation letter
  • Two recommendation letters