An improved model for prediction of de novo designed proteins with diverse geometries.

Saved in:
Bibliographic Details
Title: An improved model for prediction of de novo designed proteins with diverse geometries.
Authors: Orr B; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.; Biophysics Graduate Program, University of California, San Francisco; San Francisco, CA 94143, USA., Crilly SE; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA., Akpinaroglu D; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, CA 94143, USA., Zhu E; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA., Keiser MJ; Department of Pharmaceutical Chemistry, University of California, San Francisco; San Francisco, CA 94143, USA.; Quantitative Biosciences Institute, University of California, San Francisco; San Francisco, CA 94143, USA., Kortemme T; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.; Biophysics Graduate Program, University of California, San Francisco; San Francisco, CA 94143, USA.; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, CA 94143, USA.; Quantitative Biosciences Institute, University of California, San Francisco; San Francisco, CA 94143, USA.; Chan Zuckerberg Biohub; San Francisco, CA 94143, USA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2025 Jun 06. Date of Electronic Publication: 2025 Jun 06.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.1101/2025.06.02.657515