Paper Summary
Paperzilla title
Building with Weird Amino Acids: A New Tool for Protein Design
This paper introduces RareFold, a deep learning model that predicts the 3D structures of proteins containing both common and unusual amino acids. Researchers used RareFold to create a new tool called EvoBindRare, which successfully designed linear and cyclic peptides that bind to a target protein, demonstrating the potential for developing next-generation peptide therapeutics.
Possible Conflicts of Interest
P.B. is a cofounder of and shareholder in Cyclic Therapeutics.
Identified Weaknesses
Limited Experimental Validation of RareFold
While the authors provide experimental validation for EvoBindRare, the direct validation of RareFold's structure prediction accuracy is limited to a small test set of 174 structures, which might not be fully representative of the diversity of proteins with NCAAs. More extensive benchmarking on larger and diverse datasets is needed to fully assess RareFold's performance, especially for rare or novel NCAAs.
The study focuses on 29 NCAAs, which is a small subset of the over 500 known NCAA types. The model's ability to generalize to other, less common, NCAAs remains unclear and requires further investigation.
Computational Cost of EvoBindRare
Although RareFold itself is relatively efficient, the design process using EvoBindRare still requires significant computational resources, especially for longer peptides and larger design spaces. This computational cost could limit its applicability for high-throughput design or complex design tasks.
Limited Generalizability of EvoBindRare
The EvoBindRare framework is currently only demonstrated on a single target protein (Ribonuclease). Further validation on different targets and with different binding modes is needed to assess its general applicability for peptide binder design.
Rating Explanation
The development of RareFold and EvoBindRare represents a significant advance in protein structure prediction and design with NCAAs. The combination of a novel token-based architecture with experimental validation makes this a strong contribution to the field. However, the limited scope of NCAAs included in the study and the need for further benchmarking and validation on larger and more diverse datasets prevent a rating of 5. The acknowledged conflict of interest is noted but does not appear to directly impact the scientific validity of the work.
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File Information
Original Title:
RareFold: Structure prediction and design of proteins with noncanonical amino acids
Uploaded:
September 08, 2025 at 08:01 PM
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