Haopeng Xiao

If you’re at #HUPO2025, be sure to stop by my poster and learn about the latest developments in the BioPlex project!

Thank you, this could not have been done without using Rhea and Uniprot as the ground truth data. Thanks for curating and maintaining these foundational databases for biomedical research.

Thermal Tracks: A Gaussian process-based framework for universal melting curve analysis enabling unconstrained hit identification in thermal proteome profiling experiments Thermal Tracks is a Python-based statistical framework for analyzing protein thermal stability data that overcomes key limitations of existing thermal proteome profiling (TPP) work-flows. Unlike stand...

For those who perform thermal proteome profiling (TPP) experiments, you might find this new data analysis approach (and open source Python software) useful. Developed by postdoc @jfhevler.bsky.social 🙏

arxiv.org/abs/2508.096...

Excited to share that I’ve started my lab as an assistant professor at the University of Texas Southwestern Medical Center in Dallas.

Thank you to all the mentors, friends, and colleagues who have supported me throughout the years!

The results are in: top codes in Stanford #RNA 3D Folding @kaggle.com competition are competitive with CASP16-leading humans Vfold, beat AlphaFold 3. Top team’s trick was template-based modeling, not #DeepLearning. Congrats: john, odat, Eigen, + all 1706 participants! www.kaggle.com/competitions...

Thank you! It was truly a team effort. And was also inspired by some of the early works by John Dominy and Martha Stipanuk on CDO1 biology

Thank you Christian!!

lab at Dana-Farber, Nils @nilsburger.bsky.social at UTSW, Sumeet Khetarpal at UVA, Hilina, Katherine, Eric in the Fischer lab at Dana-Farber, and Ed Huttlin @edhuttlin.bsky.social and the Gygi lab @harvardcellbio.bsky.social.

Thanks to all authors and collaborators that pushed this work through the finish line. A special thanks goes to Junyi and Julius in my lab @stanfordbiochem.bsky.social and @stanford-cancer.bsky.social, Mika and Milan in the Valiant lab at Stanford Computer Science, Martha, Emma, Ed in the Chouchani

LRRC58 does so by serving as an E3 substrate adaptor that targets CDO1 for ubiquitylation and proteasomal degradation. We found that cysteine itself serves as a molecular signal to turn on and off LRRC58-mediated CDO1 degradation, and LRRC58 depletion stabilizes CDO1 to drive cysteine consumption.

Covariation MS uncovers a protein that controls cysteine catabolism - Nature A mass spectrometry-based approach globally identifies protein regulators of metabolism and reveals the role of LRRC58 in controlling cysteine catabolism.

We developed a MS and machine learning approach to globally identify protein regulators of metabolism. We found protein LRRC58 controls cellular cysteine catabolism by mediating degradation of CDO1, the rate-limiting enzyme of the catabolic cysteine shunt to taurine. www.nature.com/articles/s41...

We are seeking a life science research professional/technician (medcareers.stanford.edu/job/stanford...) and a postdoctoral fellow(postdocs.stanford.edu/prospective/...). If you are interested in using mass spec and machine learning to understand protein function and druggability please reach out!

hahaha yes maybe will announce something in the next month or so and post a hiring ad along with it- hoping to identify a postdoc candidate with expertise in mass spectrometry and data science to work on a few exciting projects!