DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Banchariya, Anjali; Rao, Atmakuri Ramakrishna

doi:10.1186/s12859-017-1587-y

Cited by 15 publications

(13 citation statements)

References 74 publications

(67 reference statements)

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“…Many computational studies 32–38 in the recent past have adopted five guidelines for developing supervised learning model-based predictor. The guidelines are given below.Prepare datasets of highest standard for training and evaluating the predictor comprehensively.Transform the sequence dataset (DNA/RNA/Protein) into numeric form by using such an encoding scheme which can reflect maximum correlation with the concerned target.Propose a competent prediction algorithm.Employ proper validation approach to measure the efficiency of the developed computational model.Built a freely accessible prediction server using the developed approach for the benefit of scientific community.…”

Section: Methodsmentioning

confidence: 99%

HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine

Meher

Sahu

Raghunandan

et al. 2019

Sci Rep

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Herbicide resistance (HR) is a major concern for the agricultural producers as well as environmentalists. Resistance to commonly used herbicides are conferred due to mutation(s) in the genes encoding herbicide target sites/proteins (GETS). Identification of these genes through wet-lab experiments is time consuming and expensive. Thus, a supervised learning-based computational model has been proposed in this study, which is first of its kind for the prediction of seven classes of GETS. The cDNA sequences of the genes were initially transformed into numeric features based on the k-mer compositions and then supplied as input to the support vector machine. In the proposed SVM-based model, the prediction occurs in two stages, where a binary classifier in the first stage discriminates the genes involved in conferring the resistance to herbicides from other genes, followed by a multi-class classifier in the second stage that categorizes the predicted herbicide resistant genes in the first stage into any one of the seven resistant classes. Overall classification accuracies were observed to be ~89% and >97% for binary and multi-class classifications respectively. The proposed model confirmed higher accuracy than the homology-based algorithms viz., BLAST and Hidden Markov Model. Besides, the developed computational model achieved ~87% accuracy, while tested with an independent dataset. An online prediction server HRGPred (http://cabgrid.res.in:8080/hrgpred) has also been established to facilitate the prediction of GETS by the scientific community.

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Section: Methodsmentioning

confidence: 99%

HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine

Meher

Sahu

Raghunandan

et al. 2019

Sci Rep

Self Cite

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“…In addition, amino acid composition is a basic feature of every protein sequence, which consists of 20 discrete numbers. Each of the numbers represent the frequency of the native amino acid residues in a protein sequence [ 39 ]. In this study, every protein sequence was represented by a 420 (20 × 20 + 20) dimensional vector by combining amino acid composition and Markov chains.…”

Section: Methodsmentioning

confidence: 99%

iAPSL-IF: Identification of Apoptosis Protein Subcellular Location Using Integrative Features Captured from Amino Acid Sequences

Tang

Xie

Chen

2018

IJMS

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Apoptosis proteins (APs) control normal tissue homeostasis by regulating the balance between cell proliferation and death. The function of APs is strongly related to their subcellular location. To date, computational methods have been reported that reliably identify the subcellular location of APs, however, there is still room for improvement of the prediction accuracy. In this study, we developed a novel method named iAPSL-IF (identification of apoptosis protein subcellular location—integrative features), which is based on integrative features captured from Markov chains, physicochemical property matrices, and position-specific score matrices (PSSMs) of amino acid sequences. The matrices with different lengths were transformed into fixed-length feature vectors using an auto cross-covariance (ACC) method. An optimal subset of the features was chosen using a recursive feature elimination (RFE) algorithm method, and the sequences with these features were trained by a support vector machine (SVM) classifier. Based on three datasets ZD98, CL317, and ZW225, the iAPSL-IF was examined using a jackknife cross-validation test. The resulting data showed that the iAPSL-IF outperformed the known predictors reported in the literature: its overall accuracy on the three datasets was 98.98% (ZD98), 94.95% (CL317), and 97.33% (ZW225), respectively; the Matthews correlation coefficient, sensitivity, and specificity for several classes of subcellular location proteins (e.g., membrane proteins, cytoplasmic proteins, endoplasmic reticulum proteins, nuclear proteins, and secreted proteins) in the datasets were 0.92–1.0, 94.23–100%, and 97.07–100%, respectively. Overall, the results of this study provide a high throughput and sequence-based method for better identification of the subcellular location of APs, and facilitates further understanding of programmed cell death in organisms.

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“…Feature extraction from protein sequences plays an important role in protein classification [1,2,3,4] of many areas, such as identification of plant pentatricopeptide repeat coding protein [5], prediction of bacterial type IV secreted effectors [6,7], identification of heat shock protein [8], prediction of mitochondrial proteins [9], etc. In general, prevailing encoding approaches of protein sequences for feature extraction include pseudo-amino acid composition (PseAAC) [8,9,10,11,12,13,14,15,16,17,18,19,20], position-specific scoring matrix (PSSM) [7,21,22,23,24,25,26,27,28,29,30], position-specific iterated blast (PSI-BLAST) [31,32,33,34,35] etc.…”

Section: Introductionmentioning

confidence: 99%

Variable Selection from a Feature Representing Protein Sequences: A Case of Classification on Bacterial Type IV Secreted Effectors

Zhang

et al. 2020

Preprint

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Background: Classification of certain proteins with specific functions is momentous for biological research. Encoding approaches of protein sequences for feature extraction play an important role in protein classification. Many computational methods (namely classifiers) are used for classification on protein sequences according to various encoding approaches. Commonly, protein sequences keep certain labels corresponding to different categories of biological functions (e.g., bacterial type IV secreted effectors or not), which makes protein prediction a fantasy. As to protein prediction, a kernel set of protein sequences keeping certain labels certified by biological experiments should be existent in advance. However, it has been hardly ever seen in prevailing researches. Therefore, unsupervised learning rather than supervised learning (e.g. classification) should be considered. As to protein classification, various classifiers may help to evaluate the effectiveness of different encoding approaches. Besides, variable selection from an encoded feature representing protein sequences is an important issue that also needs to be considered.Results: Focusing on the latter problem, we propose a new method for variable selection from an encoded feature representing protein sequences. Taking a benchmark dataset as a case, experiments are made to identify bacterial type IV secreted effectors from protein sequences, which indicates the effectiveness of our method. Conclusions: Certain variables other than an encoded feature they belong to do work for discrimination between different types of proteins. In addition, ensemble classifiers with an automatic assignment of different base classifiers do achieve a better classification result.

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DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins

Cited by 15 publications

References 74 publications

HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine

HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine

iAPSL-IF: Identification of Apoptosis Protein Subcellular Location Using Integrative Features Captured from Amino Acid Sequences

Variable Selection from a Feature Representing Protein Sequences: A Case of Classification on Bacterial Type IV Secreted Effectors

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