shiny-pred: a server for the prediction of protein disordered regions

Introduction

Experimental structure resolution of intrinsically disordered proteins/intrinsically disordered regions (IDP/IDRs) is complex, lengthy and expensive, leading to a variety of computational approaches being developed (He et al., 2009). Over 60 computational protein disorder prediction servers are currently available, although not all publicly. Methods can be classified in one of the following categories (Atkins et al., 2015): (i) Ab initio or sequence-based, (ii) clustering, (iii) template based, and (iv) meta or consensus.

shiny-pred is an ab initio predictor, which means it relies exclusively on amino acid sequence information to make disordered predictions. It uses prediction models based on convolutional neural networks and reduced protein alphabets. Currently there are three available models, each one built using the same training protein data from PDB (Berman et al., 2000) but differing on the convolutional neural network architecture. Since it doesn't rely in sequence profiles to make predictions, it is fast to be used in proteome-wide disorder scenarios. It performs at the same level or outperforms other state of the art sequence-only methods, achieving accuracy levels of 0.76 and AUC of 0.85 on the publicly available CASP10 dataset (Monastyrskyy et al., 2014), at faster speeds.

Methods

Operation

Our tool has two operation modes; predicting disordered residues in protein sequences (prediction) and benchmarking the predictor performance against sequences with known disorder information (benchmark). The mode is selected automatically based on the format of the input sequences. Users can either upload a sequence file, type/paste a sequence into the text area or select pre-loaded examples from a list.

When in prediction mode, the amino acid sequences are expected to be in FASTA format (Figure 1). In benchmark mode, input sequences in FASTA format are expected to have an additional line containing the disorder information (D=disorder, O=ordered). Multiple sequences can be submitted at once; several examples for different types of submissions (prediction and benchmark modes) are made available as examples. In both modes, the application will show a result panel, where for each input sequence a graph with the probability of disorder per residue is plotted (Figure 2).

Figure 1. Input sequence format (prediction mode).

Figure 2. Prediction results.

(1) Prediction mode

The workflow for protein disorder prediction is:

• (i) Input the target sequences (in FASTA format) in the text area;

• (ii) Select the model to use for the prediction (default is cnn-128-ker-local) and submit the sequence for prediction;

• (iii) Visualize and download results.

(2) Benchmark mode

In benchmark mode, input sequences are expected to have an extra line with the actual disorder information to be used as benchmark. Result tables will populate two extra columns (actual class and match) with the actual disorder information and if the prediction was accurate for the current residue. An extra panel (Benchmark) shows the ROC curve along with other common binary metrics (sensitivity, specificity, balance accuracy and Matthews correlation coefficient).

Use cases

We use shiny-pred to predict disordered regions within the publicly available CASP10 benchmark dataset. The dataset contains 94 target sequences, each one annotated with the disorder/order information at the residue level. The annotated dataset is provided as an example (‘CASP_all’) and it can be selected form the example selection list on the ‘Sequence Input’ tab. Figure 3 shows the input panel after the dataset is selected and loaded. Predictions per sequence can be viewed and downloaded from the ‘Results’ tab while the ‘Benchmark’ tab provides a summary of the performance using binary and statistical metrics. Figure 4 shows the server performance for the input dataset, achieving an AUC value of 0.85 and balance accuracy of 0.75.

Figure 3. Input sequence format (benchmark mode).

Figure 4. Predictor benchmarking.

Summary

This article presents shiny-pred, a sequence-only ab initio web application for predicting protein disorder. It's based on reduced amino acid alphabets and convolutional neural networks, being fast and accurate, it is suitable for large proteome-wide experiments.

Software availability

Software available from: https://gmu-binf.shinyapps.io/shiny-pred

Source code available from: https://github.com/mauricioob/shiny-pred

Archived source code as at time of publication: https://doi.org/10.5281/zenodo.2567259 (Mauricio, 2019).

License: GNU public license (GPL-3)