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About trRosettaRNA
The input to trRosettaRNA is the nucleotide sequence or a multiple sequence alignment (MSA) of the query RNA. Shown in Figure 1a, the trRosettaRNA works as follows.(1) When the sequence of query RNA or MSA is submitted, an attention-based nerual network (Figure 1b) is applied to predict the inter-nuclotide distance distribtuions.
(2) The predicted distance distribtuions are converted into smooth restraints, which are used to guide the Rosetta to build 3D structure models based direct energy mimization.
Figure 1. The flowchart and network architecture of the trRosettaRNA algorithm.
trRosettaRNA has been validated in blind tests, including CASP15 and RNA-Puzzles (Figure 2), which suggests that that the automated predictions by trRosettaRNA are competitive to the predictions by the top human groups on natural RNAs.
Figure 2. Blind test results in CASP15 and RNA-Puzzles.
The trRosettaRNA server runs BLASTN and Infernal on the RNAcentral database to generate MSA.
At the end of trRosettaRNA neural network, we use a simple ResNet module to predict the lDDT(Local Distance Difference Test) of the predicted structure model. The lDDT score ranges from 0 to 100, with higher values indicating better accuracy.
Figure 3 shows the relationship between the predicted (pLDDT) and the real lDDT for modeling on 50 RNAs used for benchmark tests. The Pearson correlation coefficients for global and local metrics are 0.798 and 0.736, respectively.
Figure 3. The relationship between the predicted lDDT (pLDDT) and the real lDDT.
Job submission
Job submission guide:(1) Input a RNA sequence in FASTA format or a MSA in A3M/FASTA/A2M/STO format.
(2) Specify your input type.
(3) (Optional) Provide your custom secondary structure (If not provided, the secondary structure will be predicted by SPOT-RNA).
(4) (Optional) Provide your email address.
(5) (Optional) Assign your target name.
(6) Choose whether to keep your results private.
(7) Submit.
Notice: Due to computing resource limitation, we now allow no more than 20 running/pending jobs per user at the same time.
Figure 4. The "Submit" section in trRosettaRNA home page.
Output explanation
The trRosettaRNA modeling results are generally summarized in a webpage, the link of which is sent to the user upon job completion if the email address has been provided during submission(see an example of the trRosettaRNA output). A tallbar file containing the key modeling results can be downloaded from the top of this page.This section contains:
(1) Predicted 3D model visualization.
(2) Model quality estimation.
(3) Modeling method description.
(4) Separate download links for predicted structure, MSA (multiple sequence alignment) and inter-nucleotide distances.
Figure 5. The "Predicted Structure Models" section in trRosettaRNA result page.
This section visualizes predicted 2D information including:
(1) Contact map, which displays the predicted probability of nucleotide pairs being in contact, i.e., the minimum distance between all atoms of them is less than 8Å.
(2) Distance map, which displays the predicted distance (3 - 40 Å) between nucleotide pairs.
(1) Contact map, which displays the predicted probability of nucleotide pairs being in contact, i.e., the minimum distance between all atoms of them is less than 8Å.
(2) Distance map, which displays the predicted distance (3 - 40 Å) between nucleotide pairs.
Figure 6. The "Predicted 2D Information" section in trRosettaRNA result page.
This section visualizes the secondary structures including those:
(1) predicted by SPOT-RNA or input by user.
(2) extracted from the predicted 3D structure.
(1) predicted by SPOT-RNA or input by user.
(2) extracted from the predicted 3D structure.
Figure 7. The "Secondary structures" section in trRosettaRNA result page.
How to cite trRosettaRNA?
Please cite the following articles when you use the trRosettaRNA server:Need more help?
If you have more questions or comments about the server, please email yangjy
sdu.edu.cn.