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Showing 1–8 of 8 results
Advanced filters: Author: Matteo Aldeghi Clear advanced filters

  • Molecular dynamics-based approaches to calculate absolute protein–ligand binding free energy often rely on equilibrium free energy perturbation (FEP) protocols. Here, the authors study ligands binding to bromodomains and T4 lysozyme and find that both equilibrium and non-equilibrium approaches converge to the same results with the non-equilibrium method converging faster than FEP.

    • Vytautas Gapsys
    • Ahmet Yildirim
    • Bert L. de Groot
    ResearchOpen Access
    Communications Chemistry
    Volume: 4, P: 1-13

  • A number of disease-causing human transthyretin (TTR) mutations are known to lead to amyloid formation. Here the authors combine neutron crystallography, native mass spectrometry and modelling studies to characterize the T119M and S52P-TTR mutants, providing mechanistic insights into TTR amyloidosis.

    • Ai Woon Yee
    • Matteo Aldeghi
    • V. Trevor Forsyth
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-10

  • Structural and biochemical analysis of E. coli transketolase with 2′-methoxy-thiamine shows that this antivitamin selectively inhibits the bacterial enzyme via a steric clash with a critical glutamate residue, preventing cofactor activation.

    • Fabian Rabe von Pappenheim
    • Matteo Aldeghi
    • Kai Tittmann
    Research
    Nature Chemical Biology
    Volume: 16, P: 1237-1245

  • Displacement of water molecules from proteins can lead to higher affinity or selectivity of ligands, but measuring individual water binding energies is challenging. Here calculated binding free energies are used to estimate the stability of conserved water in 35 bromodomains and to predict the ease of displacement.

    • Matteo Aldeghi
    • Gregory A. Ross
    • Philip C. Biggin
    ResearchOpen Access
    Communications Chemistry
    Volume: 1, P: 1-12

  • Scientific understanding is one of the main aims of science. This Perspective discusses how advanced computational systems, and artificial intelligence in particular, can contribute to driving scientific understanding.

    • Mario Krenn
    • Robert Pollice
    • Alán Aspuru-Guzik
    Reviews
    Nature Reviews Physics
    Volume: 4, P: 761-769

  • Machine learning can be applied for the controlled synthesis of nanoparticles with precise properties. This Review discusses different machine learning approaches for the synthesis of semiconductor, metal, carbon-based and polymeric nanoparticles, and highlights key approaches for the collection of large datasets.

    • Huachen Tao
    • Tianyi Wu
    • Eugenia Kumacheva
    Reviews
    Nature Reviews Materials
    Volume: 6, P: 701-716