3D-Jury

3D-Jury is a metaserver that aggregates and compares models from various protein structure prediction servers.^[1]

Screenshot of Structure Prediction Meta Server, 3D-Jury, web interface from the Wayback Machine

The 3D-Jury algorithm takes in groups of predictions made by a collection of servers and assigns each pair a 3D-Jury score, based on structural similarity. To improve accuracy of the final model, users can select the prediction servers from which to aggregate results.^[1] The authors of 3D-Jury designed the system as a meta-predictor because earlier results concluded that the average low-energy protein conformation (by way of aggregation) fit the true conformation better than simply the lowest-energy protein conformation.^[2]

The Robetta automatic protein structure prediction server incorporates 3D-Jury into its prediction pipeline.^[3]

As of January 2024, the links to 3D-Jury originally hosted by the BioInfoBank Institute are no longer valid.^[4]

Algorithm

First, pairwise comparisons are made between every combination of models generated from chosen protein prediction servers. Each comparison is then scored using the MaxSub tool.^[5] The score, ${\displaystyle sim(M_{a,b},M_{i,j})}$, is generated by counting the number of C_α atoms in the two predictions within 3.5 Å of each other after being superpositioned.

To get a roughly 90% chance two models are of a similar fold class, the authors set a threshold of 40 as the lowest score possible for a pair of models to be annotated as "similar".^[1] The authors admittedly chose this threshold based on unpublished work.

There are two scores 3D-Jury gives: the best-model-mode score using one model from each server (${\displaystyle 3D-Jury-single(M_{a,b})}$) and the all-model-mode score that considers all models from each server (${\displaystyle 3D-Jury-all(M_{a,b})}$).^[1]

The best-model-mode score using one model per server, ${\displaystyle 3D-Jury-single(M_{a,b})}$, is calculated as,

${\displaystyle 3D-Jury-single(M_{a,b})={\frac {\sum _{i}^{N}\max _{j,a\neq i{\mbox{ OR }}b\neq j}^{N_{i}}(sim(M_{a,b},M_{i,j}))}{1+N}}}$

where ${\displaystyle N}$ is the number of servers and ${\displaystyle N_{i}}$ is the number of top ranking models (with a maximum of 10) from the server ${\displaystyle i}$, while a pairwise similarity score is calculated between models ${\displaystyle M_{a,b}}$ (model ${\displaystyle b}$ from server ${\displaystyle a}$) and ${\displaystyle M_{i,j}}$ (model ${\displaystyle j}$ from server ${\displaystyle i}$).^[1]

While the all-model-mode score considering all models from the servers, ${\displaystyle 3D-Jury-all(M_{a,b})}$, is calculated as,

${\displaystyle 3D-Jury-all(M_{a,b})={\frac {\sum _{i}^{N}\sum _{j,a\neq i{\mbox{ OR }}b\neq j}^{N_{i}}sim(M_{a,b},M_{i,j})}{1+\sum _{i}^{N}N_{i}}}}$

using similar variables as noted with the best-model-mode score.

Note, these meta-predictor scores do not take into account the confidence scores from each of the models from other servers.^[1]

See also

• Protein structure prediction
• Comparison of software for molecular mechanics modeling
• List of protein structure prediction software