Motif scaffolding builds a protein around a prescribed structural motif -- an active site, a binding epitope, a metal site -- so that the motif is presented in its required geometry. The task is usually posed with the motif pinned down: its position along the sequence is given, and several rigid fragments inherit their mutual pose from a native structure. Many applications supply neither. We present NomadFlow, unindexed multi-motif scaffolding on all-atom SE(3) flow matching: the motif enters as a condition, a group projection preserves the internal geometry of each group exactly, and the groups move freely relative to one another until a set time, after which they act only as a condition. We assemble a preliminary benchmark for this setting, varying the granularity of the partition and the structural context around the motif, and obtain unique successes on 19 of 21 tasks. The trained model proposes a diversity of valid mutual poses, and on several tasks the native arrangement is among them. The shape of that solution space, however, varies strongly with the geometry of the condition: across tasks the successful layouts form a wide cloud, collapse onto the native pose, or contract to a connected low-dimensional family -- a continuum of poses rather than a set of isolated solutions. Multi-motif methods are therefore better compared by the space of layouts they propose and the successful part of it they retain than by yield or fold diversity alone. Code and weights: https://github.com/ipermyakov/nomadflow.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.