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Showing 1–8 of 8 results
Advanced filters: Author: Ava P. Amini Clear advanced filters

  • Effective substrates are key to probing and harnessing protease activity. This work presents CleaveNet, an AI tool that generates efficient, selective substrates, revealing known and distinct cleavage motifs and tuning designs to target activity profiles.

    • Carmen Martin-Alonso
    • Sarah Alamdari
    • Ava P. Amini
    ResearchOpen Access
    Nature Communications
    Volume: 17, P: 1-17

  • A hierarchical cross-entropy loss is presented, which incorporates ontology structure into training and improves the out-of-distribution performance of large-scale single-cell annotation models without additional computational cost.

    • Sebastiano Cultrera di Montesano
    • Davide D’Ascenzo
    • Lorin Crawford
    ResearchOpen Access
    Nature Computational Science
    P: 1-7

  • The ability to engineer novel protein structures has tremendous scientific and therapeutic impact. Here, authors develop a generative model acting upon an angular representation of protein structures to create high quality protein backbones.

    • Kevin E. Wu
    • Kevin K. Yang
    • Ava P. Amini
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-12

  • The activity of multiple enzymes is dysregulated in cancer, but this cannot always be measured through enzyme expression. Here, the authors develop methods to measure protease activity across the organism, tissue, and single cell scales, and identify protease dysregulation in lung cancer and its response to targeted therapy.

    • Ava P. Amini
    • Jesse D. Kirkpatrick
    • Sangeeta N. Bhatia
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-16

  • An image-inspired deep-learning model is developed to generate realistic de novo protein structures and scaffolds around functional sites, which helps the search for new structures and functions in protein engineering.

    • Ava P. Amini
    • Kevin K. Yang
    News & Views
    Nature Computational Science
    Volume: 3, P: 366-367