PEffect - prediction of bacterial type III effector proteins
The type III secretion system is one of the causes of a wide range of bacterial infections in human, animals and plants. This system comprises a hollow needle-like structure localized on the surface of bacterial cells that injects specific bacterial proteins, the so-called effectors, directly into the cytoplasm of a host cell. During infection, effectors convert host resources to their advantage and promote pathogenicity.
We - Tatyana Goldberg, Burkhard Rost and Yana Bromberg - at BrombergLab and RostLab developed a novel method, pEffect that predicts bacterial type III effector proteins. In our method, we combine sequence-based homology searches and advanced machine learning to accurately predict effector proteins. We use information encoded in the entire protein sequence for our predictions.
pEffect is a method that combines sequence similarity-based inferences (PSI-BLAST) with de-novo predictions using machine learning techniques (Support Vector Machines; SVM). For a query protein it first runs PSI-BLAST to identify a homolog in the set of known and annotated effector proteins. If such a homolog is available, then its annotation (i.e. type III effector) is being transferred to a query protein. If a homolog is not available, pEffect triggers an SVM that predicts effector proteins through searches of k-consecutive residues that are known from annotated proteins.
The input to the server is:
1. one or more FASTA-formatted protein sequences. The sequences must be in one-letter amino acid code (not case-sensitive). The allowed amino acids are: ACDEFGHIKLMNPQRSTVWY and X (unknown). Example.
a. e-mail address: a notification for completed prediction result and the access link are sent to the provided email address (Optional)
For every query protein, result contains four basic values:
1. the protein identifier as provided by the user
2. the reliability score of a prediction on a 0-100 scale with 100 being the most confident prediction
3. prediction of a protein to be a type III effector
4. annotation type of the prediction (PSI-BLAST or SVM)
For PSI-BLAST predictions, the web site provides ‘per click’ on the annotation type (i.e. PSI-BLAST) the information about the closest homolog and its PSI-BLAST alignment to query.
Every prediction result is supported by a Reliability Index (RI) measuring the strength of a prediction. The RI is a value between 0 and 100, with 100 denoting the most confident predictions.
We rigorously evaluated the reliability of pEffect predictions on a non-redundant test set of proteins (Fig. 1). We observed that at the default threshold of RI>50, over 87% of all predictions of type III effectors are correct and 95% of all effectors in our set are identified (Fig. 2; black arrow). At a higher RI>80 effector predictions are correct 96% of the time, but only 78% of all effectors in the set are identified (Fig. 2; gray arrow).
pEffect is built to run a homology-based PSI-BLAST; if no hit is identified then a de-novo SVM prediction is used.
While PSI-BLAST searches are fast, SVM's runtime depends on the number of query protein sequences. We measured pEffect's runtime on a Dell M605 machine with a Six-Core AMD Opteron processor (2.4 GHz, 6MB and 75W ACP) running on Linux.
|1 Sequence||100 Sequences||500 Sequences||1000 Sequences||3000 Sequences||5000 Sequences||10000 Sequences|
Note: to increase server's response time we store all PSI-BLAST profile files (required for LocTree2) in the PredictProtein cache (current size: results for >11Mio sequences). These can be retrieved from the cache very fast. For novel protein sequences for which we don't have PSI-BLAST profiles in the cache the runtimes increases substantially.
- The LocTree3 web server is available at https://rostlab.org/services/loctree3/
- LocTree3 predictions can also be accessed through the PredictProtein service
- Standalone version of LocTree3 can be downloaded as a Debian package here
Data sets used for development and evaluation of LocTree3 can be accessed here.
LocTree3 prediction of localization
Goldberg T, Hecht M, Hamp T, Karl T, Yachdav G, Ahmed N, Altermann U, Angerer P, Ansorge S, Balasz K, Bernhofer M, Betz A, Cizmadija L, Do KT, Gerke J, Greil R, Joerdens V, Hastreiter M, Hembach K, Herzog M, Kalemanov M, Kluge M, Meier A, Nasir H, Neumaier U, Prade V, Reeb J, Sorokoumov A, Troshani I, Vorberg S, Waldraff S, Zierer J, Nielsen H, Rost B.
LocTree2 predicts localization for all domains of life
Goldberg T, Hamp T, Rost B.
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