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@labstructbioinf

Laboratory of Structural Bioinformatics

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The amount of biological data available from different experimental approaches (genome and transcriptome sequencing, structure determination, functional assays) is enormous. In our group, we use computational techniques such as deep learning, molecular dynamics simulations and sequence analysis to make sense of this data. We are particularly interested in understanding how protein folds have evolved and how protein structures and functions are encoded by the 20 amino acid alphabet.

Useful links:

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  1. pLM-BLAST pLM-BLAST Public

    Detection of remote homology by comparison of protein language model representations

    Python 53 6

  2. localpdb localpdb Public

    Python package to manage protein structures and their annotations

    Python 43 5

  3. EdgeGat EdgeGat Public

    graph neural network layer for Deep Graph Library with pytorch backend

    Python 28 3

  4. DeepCoil DeepCoil Public

    Fast and accurate prediction of coiled coil domains in protein sequences. ​

    Python 24 3

  5. rossmann-toolbox rossmann-toolbox Public

    Prediction and re-engineering of the cofactor specificity of Rossmann-fold proteins

    Python 12 4

  6. PiPred PiPred Public

    Deep learning based prediction of pi-helices in protein sequences

    Python 10 1

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