LibGENiE aims to make oligonucleotide-based protein engineering accessible.

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We provide information on standard protein properties to help reduce the sequence space and a tool to design custom oligonucleotides. These properties can serve as a starting point for designing smart libraries of reduced size. They can also be combined with additional tools not provided by LibGENiE to filter undesired mutations and enrich the library quality further.



LibGENiE requires two inputs:

  • 1. a protein sequence:
    We require the protein sequence to be between 80 and 600 residues long. There can also be no special characters in the sequence, such as \* for stop codons.

  • 2. a valid email address:
    The email address is used to share the results; please check your spam folder.


The result section contains an interactive plot to get a quick overview of your data. It contains information on the residue and sequence level.

Double-click on a specific residue in the sequence to get an overview of all 20 residues at this position. Double-click again to toggle between sequence and residue level.

Raw data can be downloaded through the button in the bottom right corner.


A sequence alignment for the input sequence is generated through three rounds of iterative psi-blast. For this, we rely on the psi-blast API provided by EMBL-EBI [1].

This MSA serves as the foundation to infer three different protein characteristics:

  1. 1. Evolutionary information
    We provide evolutionary information by processing the MSA and calculating the observed percentages (rounded down) of all 20 residues at each position.

  2. 2. Thermodynamic stability
    We self-host an API to predict stability from the protein sequence based on ACDC-NN struct [2]. The structure required to run the algorithm is modeled through the ESM-esmfold_v1 API [3, 4]. If no 3D structure can be modeled, we fall back to sequence-only predictions through ACDC-NN Seq [5].

  3. 3. Protein flexibility
    As is the case for stability, we host an API to predict flexibility from the protein sequence calculated with MEDUSA [6].

Oligo design

Oligo-pools are currently limited to < 300 bp in length. The gene has to be split into multiple sub-pools to achieve all possible single-point mutations.

This is done automatically by LibGENiE. Just provide us a DNA sequence and a maximum fragment length. The output includes all possible single-point mutations and sub-pool amplification primers.

References and Disclaimer

LibGENiE is a free tool for the community. We do not take any responsibility for the provided results and offer no warranties. Some of underlying algorithms are provided by third parties and come with their own licenses and restrictions. Please refer to the references below for more information.


[1] F. Madeira et al., “The EMBL-EBI search and sequence analysis tools APIs in 2019,” Nucleic Acids Res, vol. 47, no. W1, pp. W636–W641, Jul. 2019, doi: 10.1093/nar/gkz268.

[2] Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G., & Sanavia, T. (2021). An antisymmetric neural network to predict free energy changes in protein variants. Journal of Physics D: Applied Physics, 54(24). https://doi.org/10.1088/1361-6463/abedfb

[3] L in, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., Santos Costa, A. dos, Fazel-Zarandi, M., Sercu, T., Candido, S., & Rives, 2 Alexander. (2021). Evolutionary-scale prediction of atomic level protein structure with a language model. https://doi.org/10.1101/2022.07.20.500902

[4] https://esmatlas.com/about

[5] C. Pancotti et al., “A deep-learning sequence-based method to predict protein stability changes upon genetic variations,” Genes (Basel), vol. 12, no. 6, Jun. 2021, doi: 10.3390/genes12060911.

[6] Y. vander Meersche, G. Cretin, A. G. de Brevern, J. C. Gelly, and T. Galochkina, “MEDUSA: Prediction of Protein Flexibility from Sequence,” J Mol Biol, vol. 433, no. 11, May 2021, doi: 10.1016/j.jmb.2021.166882