NRPreTo (a two level NR protein prediction tool)

NRPreTo is a two level Nuclear Receptor (NR) protein subfamily prediction tool in which the first level predicts whether a protein sequence is a NR or not and second level predicts the sub-class of the said NR proteins.Only true positive proteins predicted as NRs at level-1 are taken ahead for the second level prediction.Feature selection was done at both levels independently to select important descriptors from an initial 13,494 descriptors calculated using iFeature package.We also performed hyper-parameters tuning at each level to obtained best performing model. NRPreTo successfully predicted 59 novel NR from human proteome when tested on HRPD and Refseq datasets.

NrPreTo WorkFlow

Requirements

Following python libraries must be installed in the system in order to run NRPreTo:

1. Boruta 0.3

pip install Boruta

2. Hyperopt 0.2.7

pip install hyperopt

How to run NRPreto

To run NRPreTo with default settings (feature selection = True and hyper parameters tuning = True) at both level, run the following command

  python3 main.py 

To run NRPreTo with no feature selection at both level, run the following command

  python3 main.py --feature_selection = 0

To run NRPreTo with no hyperparameter tuning at both level, run the following command

  python3 main.py --hyperparameter_tune = 0

To run NRPreTo with different combination of settings at both level, run the following command

  #for example if user wants to perform hyper-parameters tune but not feature selection, execute following line of code
  python3 main.py --feature_selection = 0 --hyperparameter_tune = 1

Data Folder

We utilized two previously-published benchmark datasets, namely benchmark dataset 1 (BD1)[2] and benchmark dataset 2 (BD2) [1], which compose of training and independent datasets with both NR and Non-NR sequences. Both benchmark datasets were prepared from the Nuclear Receptor Database (NucleaRDB Release5.0) containing 3016 NR sequences which are phylogenetically classified into seven subfamilies with each subfamily containing NR sequences from different animal species. The two benchmark datasets were prepared from this initial set by applying a cutoff of ≥40% and ≥60% similarity among sequences to generate BD1 and BD2, respectively. The detailed procedure of generating the NR and Non-NR sequences of benchmark datasets can be found in previous studies [1][2]. We then tested trained model on the human proteome collected from two NCBI Refseq (humans) [4] and Human Protein Reference Database (HPRD).

We have provided following subsets of benchmark datasets to train NRPreTo and external datasets to test NRPreTo.

1. BD1 (train (100 samples) and independent (100 samples))
2. BD2 (train (84 samples) and independent (86 samples))
3. HPRD (81 samples)
4. Refseq Dataset (83 samples)

How to generate descriptors using iFeature package

Feature generation was done using the standalone version of iFeature package as per the instructions described in the github repository at https://github.com/Superzchen/iFeature [3]. iFeature is an open source Python-based toolkit for generating a multitude of descriptors for protein and peptide sequences. For this study we calculated 13,494 descriptors which belong to seven descriptor groups. We downloaded iFeature package provided in their github repository locally and followed the instructions to calculate each of the seven descriptor group individually which were later concatenated resulting in 13,494 descriptors for each protein. This procedure was used to create features of the training, independent and external protein dataset.

Script Description

main.py

This is a main script. User need to run this script to run NRPreTo.

hyperparameter_tune.py

This scripts uses Hyperopt package in order to select best hyperparameters for NRPreTo model.

feature_rank.py

This scripts uses Borutapy package in order to select important features to train NRPreTo model.

utilities.py

This script is comprised of helper functions that are used to build the method.

model_eval.py

This script is used to evaluate model at 2 different levels. This scripts provides both confusion matrix and model performance in terms of F1, Accuracy, Presicion, Recall, ROC-AUC score and Mathews Correlation Coefficient.

config.ini

This is a configuration file for hyper-parameters tuning script (feature_rank.py). User can change hyper-paramter range by editing following this file.

Run Locally

Clone the project

  git clone https://github.com/bozdaglab/NRPreTo

Install libraries

  pip install Boruta
  pip install hyperopt

Run NRPreTo

  python3 main.py 

References

[1] Wang, P., Xiao, X. & Chou, K.-C. NR-2L: A Two-Level Predictor for Identifying Nuclear Receptor Subfamilies Based on Sequence-Derived Features. PLoS ONE 6, e23505 (2011).

[2] Ismail, H. D., Saigo, H. & Kc, D. B. RF-NR: Random Forest Based Approach for Improved Classification of Nuclear Receptors. IEEE/ACM Trans Comput Biol Bioinform 15, 1844–1852 (2018)

[3] Chen Z, Zhao P, Li F, Leier A, Marquez-Lago TT, Wang Y, Webb GI, Smith AI, Daly RJ*, Chou KC*, Song J*. iFeature: a python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics, 2018, Volume 34, Issue 14, 15 July 2018, Pages 2499–2502, doi: 10.1093/bioinformatics/bty140.

[4] O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44, D733-745 (2016).