PEffect - prediction of bacterial type III effector proteins

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(Introduction)

Revision as of 19:59, 18 November 2015

Contents

Web server

https://rostlab.org/services/pEffect/

Introduction

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.

Method design

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.

Input

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)

Output

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.

Example.

Figure 1
Fig 1: More reliable predictions better. The curves show the percentage Accuracy vs. Coverage for LocTree3 predictions above a given RI threshold (from 0=unreliable to 100=most reliable). The curves were obtained on cross-validated test sets of bacterial (gray line) and eukaryotic (black line) proteins. Half of all eukaryotic proteins are predicted at RI>65; for these Q18 is above 95% (black arrow). Half of all bacterial proteins are predicted at RI>80 and Q18 above 95% (black arrow).

Prediction reliability

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).

Runtime analysis

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
Archaea 0.8s 3.0s 10.4s 18.8s 51m2s 1m36s 3m43s
Bacteria 3.6s 1m.09s 5m25s 9m12s 27m01s 1h4m 2h10m
Eukaryota 1m37s 8m43s 44m 1h13m 4h17m 7h47m 15h6m

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.

Availability/ Download

Data

Data sets used for development and evaluation of LocTree3 can be accessed here.

Reference

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.

Nucleic Acids Researh. 2014 Jul;42(Web Server issue):W350-5 (Full Text, PDF)

Supporting Online Material


LocTree2 predicts localization for all domains of life

Goldberg T, Hamp T, Rost B.

Bioinformatics 2012 28: i458-i465 (Full Text, PDF)

Supporting Online Material

Contact

For questions, please contact localization@rostlab.org

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