DMFold generates a large set of structural
models by different MSAs as inputs. These models are ranked by
predicted TM-score (pTM-score for multimer) or predicted LDDT (pLDDT for monomer) and top 5 models are selected with the highest predicted scores.
Query structure is shown in cartoon, while the structural analog is displayed using backbone trace.
(b)
Ranking of proteins is based on TM-score of the structural alignment between the query structure and known structures in the PDB library.
(c)
RMSDa is the RMSD between residues that are structurally aligned by TM-align.
(d)
IDENa is the percentage sequence identity in the structurally aligned region.
(e)
Cov. represents the coverage of the alignment by TM-align and is equal to the number of structurally aligned residues divided by length of the query protein.
Download full result of the above consensus prediction.
Click the graph to show a high resolution version.
(a)
CscoreGO is the confidence score of predicted GO terms. CscoreGO values range in between [0-1]; where a higher value indicates a better confidence in predicting the function using the template.
(b)
The graph shows the predicted terms within the Gene Ontology hierachy for Molecular Function. Confidently predicted terms are color coded by CscoreGO:
Download full result of the above consensus prediction.
Click the graph to show a high resolution version.
(a)
CscoreGO is the confidence score of predicted GO terms. CscoreGO values range in between [0-1]; where a higher value indicates a better confidence in predicting the function using the template.
(b)
The graph shows the predicted terms within the Gene Ontology hierachy for Biological Process. Confidently predicted terms are color coded by CscoreGO:
Download full result of the above consensus prediction.
Click the graph to show a high resolution version.
(a)
CscoreGO is the confidence score of predicted GO terms. CscoreGO values range in between [0-1]; where a higher value indicates a better confidence in predicting the function using the template.
(b)
The graph shows the predicted terms within the Gene Ontology hierachy for Cellular Component. Confidently predicted terms are color coded by CscoreGO:
Click on the radio buttons to visualize predicted active site residues.
(a)
CscoreEC is the confidence score for the Enzyme Commission (EC) number prediction. CscoreEC values range in between [0-1]; where a higher score indicates a more reliable EC number prediction.
(b)
TM-score is a measure of global structural similarity between query and template protein.
(c)
RMSDa is the RMSD between residues that are structurally aligned by TM-align.
(d)
IDENa is the percentage sequence identity in the structurally aligned region.
(e)
Cov. represents the coverage of global structural alignment and is equal to the number of structurally aligned residues divided by length of the query protein.
Click on the radio buttons to visualize predicted binding site and residues.
(a)
CscoreLB is the confidence score of predicted binding site. CscoreLB values range in between [0-1]; where a higher score indicates a more reliable ligand-binding site prediction.
(b)
BS-score is a measure of local similarity (sequence & structure) between template binding site and predicted binding site in the query structure. Based on large scale benchmarking analysis, we have observed that a BS-score >1 reflects a significant local match between the predicted and template binding site.
(c)
TM-score is a measure of global structural similarity between query and template protein.
(d)
RMSDa the RMSD between residues that are structurally aligned by TM-align.
(e)
IDENa is the percentage sequence identity in the structurally aligned region.
(f)
Cov. represents the coverage of global structural alignment and is equal to the number of structurally aligned residues divided by length of the query protein.
[Click result.zip to download all results on this page]
References:
1.
Wei Zheng, Quancheng Liu, Qiqige Wuyun, P. Lydia Freddolino, Yang Zhang. DMFold: A deep learning platform for protein complex structure and function predictions based on DeepMSA2. In preparation.
2.
Wei Zheng, Qiqige Wuyun, Yang Li, Chengxin Zhang, P Lydia Freddolino, Yang Zhang. Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data. Nature Methods, in press (2023).
3.
Wei Zheng, Qiqige Wuyun, Peter L Freddolino, Yang Zhang. Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15. 1-20. doi:10.1002/prot.26585. Proteins. (2023).