Generative protein design can now rapidly produce de novo binders with high affinity and functional activity against a wide range of targets, including lethal snake venom toxins. However, so far most reported successes rely on new-to-nature scaffolds with limited therapeutic precedent. Single-domain antibodies (VHHs) o...
M. D. Overath, Emil V. S. Lundquist, Kasper H. Björnsson et al.· bioRxiv· 0 citations
Odin-Multi is presented, a binder design framework that optimises a shared binder sequence against several complexes simultaneously, applying attractive objectives to on-targets and repulsive objectives to off-targets, and widens the range of binding behaviours accessible to computational design.
Valentas Brasas, C. R. Christensen, Kasper H. Björnsson et al.· bioRxiv· 0 citations
Mass spectrometry-based proteomics increasingly relies on machine learning, yet existing models are trained for defined supervised tasks such as peptide identification, de novo sequencing or fragment intensity prediction, limiting transfer across datasets, instruments and acquisition methods. Here we present InstaNovo-...
M. Nieuwoudt, Marco Reverenna, Divanisha Patel et al.· bioRxiv· 1 citation· ⚡1
Deep generative models have become a leading approach for designing therapeutic molecules, yet efficiently exploring vast biomolecular sequence spaces remains difficult, particularly for targets with limited training data. The prior distribution that seeds a generative model shapes which regions of sequence space it ex...
E. S. Engdal, Jonathan Funk, O. Bacarreza et al.· bioRxiv· 0 citations
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