Deep Learning

  • Munsamy, G., Lindner, S., Lorenz, P., Ferruz, N. ZymCTRL: a conditional language model for the controllable generation of artificial enzymes

    Ferruz, N. et al. From sequence to function through structure: deep learning for protein design. Comput. Struct. Biotechnol. J. (2022) doi:10.1016/j.csbj.2022.11.014.

    Ferruz, N. & Höcker, B. Controllable protein design with language models. Nat. Mach. Intell. 4, 521–532 (2022)

    Ferruz, N., Schmidt, S. & Höcker, B. ProtGPT2 is a deep unsupervised language model for protein design. Nat. Commun. 13, 4348 (2022)

    Ferruz, N. & Höcker, B. Dreaming ideal protein structures. Nat. Biotechnol. 40, 171–172 (2022).

Evolutionarily Conserved Fragments

  • Ferruz, N., Schmidt, S. & Höcker, B. ProteinTools : a toolkit to analyze protein structures. Nucleic Acids Res. 49, W559–W566 (2021).

    Ferruz, N., Michel, F., Lobos, F., Schmidt, S. & Höcker, B. Fuzzle 2.0: Ligand Binding in Natural Protein Building Blocks. Front. Mol. Biosci. 8, (2021).

    Ferruz, N., Noske, J. & Höcker, B. Protlego: A Python package for the analysis and design of chimeric proteins. Bioinforma. Oxf. Engl. 37, 3182–3189 (2021).

    Ferruz, N. et al. Identification and Analysis of Natural Building Blocks for Evolution-Guided Fragment-Based Protein Design. J. Mol. Biol. 432, 3898–3914 (2020).

Large-scale Molecular Dynamics

  • Kröger, P., Shanmugaratnam, S., Ferruz, N., Schweimer, K. & Höcker, B. A comprehensive binding study illustrates ligand recognition in the periplasmic binding protein PotF. Struct. Lond. Engl. 1993 29, 433-443.e4 (2021).

    Ferruz, N. et al. Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs. Sci. Rep. 8, 897 (2018).

    Ferruz, N., Tresadern, G., Pineda-Lucena, A. & De Fabritiis, G. Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme. Sci. Rep. 6, 30275 (2016).

    Ferruz, N., Harvey, M. J., Mestres, J. & De Fabritiis, G. Insights from Fragment Hit Binding Assays by Molecular Simulations. J. Chem. Inf. Model. 55, 2200–2205 (2015).

    Lauro, G. et al. Reranking Docking Poses Using Molecular Simulations and Approximate Free Energy Methods. J. Chem. Inf. Model. 54, 2185–2189 (2014).

See Google Scholar, ORCID, or ResearchGate for more info.