Deep learning‐based structure modeling of the <i>Treponema pallidum</i> proteome: Insights into pathogenesis and syphilis vaccine development

Simon Houston, Steven Marshall, Austin Miller, Aleksander Palkowski, Javier A. Alfaro, Caroline E. Cameron

Open source

DOI: 10.1002/pro.70872
Published: 2026-11-10
Container: Protein Science
Publisher: Wiley
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1002/pro.70872,
  title = {Deep learning‐based structure modeling of the
                    <i>Treponema pallidum</i>
                    proteome: Insights into pathogenesis and syphilis vaccine development},
  author = {Simon Houston and Steven Marshall and Austin Miller and Aleksander Palkowski and Javier A. Alfaro and Caroline E. Cameron},
  year = {2026},
  journal = {Protein Science},
  doi = {10.1002/pro.70872},
  url = {https://doi.org/10.1002/pro.70872}
}

RIS

TY  - JOUR
TI  - Deep learning‐based structure modeling of the
                    <i>Treponema pallidum</i>
                    proteome: Insights into pathogenesis and syphilis vaccine development
AU  - Simon Houston
AU  - Steven Marshall
AU  - Austin Miller
AU  - Aleksander Palkowski
AU  - Javier A. Alfaro
AU  - Caroline E. Cameron
PY  - 2026
JO  - Protein Science
DO  - 10.1002/pro.70872
UR  - https://doi.org/10.1002/pro.70872
ER  -

APA

Houston, S., Marshall, S., Miller, A., Palkowski, A., Alfaro, J. A., & Cameron, C. E. (2026). Deep learning‐based structure modeling of the <i>Treponema pallidum</i> proteome: Insights into pathogenesis and syphilis vaccine development. Protein Science. https://doi.org/10.1002/pro.70872

Source records

crossref · retrieved 2026-11-10T18:22:19.347Z