Mots-Clés

Pipeline bioinformatics data analyses biological sequences enzymes evolution

Description

Context:

The availability of molecules to produce cellular energy has evolved along Earth history. Electron transport chain (ETC) have thus adapted to different available electron donors and acceptors. In particular, enzymes of the ETC have adapted to different quinones – small molecules involved in the shuttling of electrons between the enzymes of the ETC. The overarching goal of this project is to understand the adaptation of ETC enzymes to the variety of quinones found across the tree of life.

In the frame of a collaborative project funded by the French Research Agency involving the TrEE team of the TIMC lab (Grenoble, location of the internship), the BIP lab (“Bioénergétique et Ingénierie des Protéines”, Marseille) and the LCB lab (“Laboratoire de Chimie Bactérienne”, Marseille), we offer a 6-months internship that could be prolonged with a 2-year contract as “Ingénieur d’Étude” (funded).

Keywords:

Pipeline; bioinformatics; data analyses; biological sequences; enzymes; evolution

Objectives:

The Master 2 project consists first in the design of a sequence analysis pipeline that will then be applied to ETC enzymes (of which Marseille collaborators are experts). The goal is to link precise amino-acid positions in these enzymes, and the capacity of these enzymes to interact with different quinone types (e. g., of high or low electro-chemical potential).   

Establishing the pipeline will consist in the following steps:

• the design of HMM (Hidden Markov Model) protein profiles for the automatized sequence similarity search of the enzymes of interest in publicly available genomes (NCBI and GTDB databases)
• the calculation of highly accurate multiple sequence alignments (MSA) of the enzymes by taking into account the available 3D protein structures
• the analyses of these MSA, including the extraction of co-evolving amino-acid positions, and those specific to the interaction with quinone(s). To this end, meta-data generated by the host team that are readily available (prediction of produced quinones based on genomic annotation) will be integrated
• the valorization of the generated results by the creation of scripts to generate visualization of the identified positions on the 3D structure of the enzymes (e.g., pyMol)

Finally, the results will be interpreted in collaboration with the partners of the project. In addition to the above, the intern will be involved in testing, finalizing and industrializing the code of a software dedicated to the detection of co-evolving positions in proteins, that is currently being developed in the team in Python. Depending on the results and his/her interest, the intern could be involved in the design of wet lab experiments to test the proposed hypotheses.

Profile of the candidate:

We are seeking a candidate willing to develop skills in the development of pipeline of analyses for massive biological data, with an interest in the evolution of biological functions.

A training in bioinformatics, in the Python programming language, and in using a Linux environment are required. Notions of sequence analyses and of structural biology/biochemistry would be a bonus.

The project being funded for the next 4 years, the Master 2 project could be followed by a professional experience as “Ingénieur d’Étude en bioinformatique” in the frame of a 2-year contract.

Environment:

The TREE team @TIMC lab (CNRS, Université Grenoble Alpes): We are part of a highly inter-disciplinary team, gathering biochemists, biophysicist, molecular microbiologists, biostatisticians and bioinformaticians, with a common strong interest in microbial evolution. The lab is located on the Campus of La Tronche, in close vicinity to Grenoble (Tram B).

• The team website: http://www.timc.fr/en/tree
• The computational biology group website: https://tree-timc.github.io/compbio

References:

(1) Abby et al., BBA Bioenergetics (2020) 1861:148259; (2) Kazemzadeh et al., Mol Biol Evol (2023) msad219; (3) Elling et al., bioRxiv (2024); (4) Szyttenholm et al., BBA Bioenergetics (2020) 10.1016/j.bbabio.2020.148252