DeepUMQA-X Results for job X202502151321395392 (dongl.test.single-chain)

The quality of "model1.pdb" (Ranked third)

TM-score:0.795 Global-lDDT:0.653
Evaluated Model Inter-residue distance deviation Per-residue lDDT with atoms

The quality of "model2.pdb" (Ranked first)

TM-score:0.934 Global-lDDT:0.787
Evaluated Model Inter-residue distance deviation Per-residue lDDT with atoms

The quality of "model3.pdb" (Ranked fourth)

TM-score:0.805 Global-lDDT:0.639
Evaluated Model Inter-residue distance deviation Per-residue lDDT with atoms

The quality of "model4.pdb" (Ranked fifth)

TM-score:0.768 Global-lDDT:0.622
Evaluated Model Inter-residue distance deviation Per-residue lDDT with atoms

The quality of "model5.pdb" (Ranked second)

TM-score:0.91 Global-lDDT:0.735
Evaluated Model Inter-residue distance deviation Per-residue lDDT with atoms

Note: Ranking by Global-lDDT. If there are multiple large proteins, Jmol may not be able to display them due to memory limitations.

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Reference

Dong Liu^✝, Biao Zhang^✝, Jun Liu, Hui Li*, Le Song* and Guijun Zhang*. GraphCPLMQA: Assessing protein model quality based on deep graph coupled networks using protein language model. Briefings in Bioinformatics, 2023, 25(1):bbad420.
Jun Liu, Kailong Zhao and Guijun Zhang*. Improved model quality assessment using sequence and structural information by enhanced deep neural networks. Briefings in Bioinformatics, 2023, 24(1): bbac507.
Saisai Guo^✝, Jun Liu^✝, Xiaogen Zhou, Guijun Zhang*. DeepUMQA: Ultrafast Shape Recognition-based Protein Model Quality Assessment using Deep Learning. Bioinformatics, 2022, 38(7): 1895-1903.