nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine

Abstract

As inorganic nitrogen compounds are essential for basic building blocks of life (e.g., nucleotides and amino acids), the role of biological nitrogen-fixation (BNF) is indispensible. All nitrogen fixing microbes rely on the same nitrogenase enzyme for nitrogen reduction, which is in fact an enzyme complex consists of as many as 20 genes. However, the occurrence of six genes viz., nifB, nifD, nifE, nifH, nifK, and nifN has been proposed to be essential for a functional nitrogenase enzyme. Therefore, identification of these genes is important to understand the mechanism of BNF as well as to explore the possibilities for improving BNF from agricultural sustainability point of view. Further, though the computational tools are available for the annotation and phylogenetic analysis of nifH gene sequences alone, to the best of our knowledge no tool is available for the computational prediction of the above mentioned six categories of nitrogen-fixation (nif) genes or proteins. Thus, we proposed an approach, which is first of its kind for the computational identification of nif proteins encoded by the six categories of nif genes. Sequence-derived features were employed to map the input sequences into vectors of numeric observations that were subsequently fed to the support vector machine as input. Two types of classifier were constructed: (i) a binary classifier for classification of nif and non-nitrogen-fixation (non-nif) proteins, and (ii) a multi-class classifier for classification of six categories of nif proteins. Higher accuracies were observed for the combination of composition-transition-distribution (CTD) feature set and radial kernel, as compared to the other feature-kernel combinations. The overall accuracies were observed >90% in both binary and multi-class classifications. The developed approach further achieved >92% accuracy, while evaluated with blind (independent) test datasets. The developed approach also produced higher accuracy in identifying nif proteins, while evaluated using proteome-wide datasets of several species. Furthermore, we established a prediction server nifPred (http://webapp.cabgrid.res.in/nifPred) to assist the scientific community for proteome-wide identification of six categories of nif proteins. Besides, the source code of nifPred is also available at https://github.com/PrabinaMeher/nifPred. The developed web server is expected to supplement the transcriptional profiling and comparative genomics studies for the identification and functional annotation of genes related to BNF.

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

“…One of the most important tasks in machine learning-based prediction using biological sequence data is to encode the sequences into numeric features, as machine learning algorithms (MLA) can only take numerical inputs [2,[77][78][79][80]. Further, the miRNA sequences are only 20-24 nucleotides long, which is also a limitation to generate large number of discriminative features.…”

NIFtHool: an informatics program for identification of NifH proteins using deep neural networks

“…nifPred, a multi NifH proteins classifier, that uses 13,500 values per sequence, involves four manual methods to obtain the components and data to be trained, having a high specificity. 14 NIFtHool was compared with two models that work with two different embedding vectors to identification of mitochondrial proteins of Plasmodium falciparum 25 and DNA-binding proteins. 23 The three models showed positive results in sensitivity, specificity, and accuracy, so the selection of the embedding vector method was adequate.…”

“…However, their investigations did not produce a computer tool. Meher et al (2018) developed nifPred, a machine learning (ML) software, to perform a sequence classification into NifH or non-NifH proteins. This informatics tool converts multi-categorical sort of gene sequences into one of the six types of the Nif proteins encoded by the nif operon using a high computational performance.…”

“…This informatics tool converts multi-categorical sort of gene sequences into one of the six types of the Nif proteins encoded by the nif operon using a high computational performance. 14 Constant supervision is necessary to guide the program in all phases of the system, which causes the increment of computational cost. 15 Nowadays, some algorithms are registered in literature to make predictions of NifH proteins based on gene sequences.…”

Phylogeny of Nitrogenase Structural and Assembly Components Reveals New Insights into the Origin and Distribution of Nitrogen Fixation across Bacteria and Archaea

“…A study of 2000 complete genomes available in 2012 led to proposing NifHDKENB ( Figure 1 ) as the minimum criteria for computational prediction of diazotrophy [ 20 ]. This six gene criterion has been widely used as diagnostic for diazotrophy in culture-independent studies [ 21 , 22 , 23 , 24 ].…”