Enzymatic Degradation of Organophosphorus Pesticides and Nerve Agents by EC: 3.1.8.2

The organophosphorus substances, including pesticides and nerve agents (NAs), represent highly toxic compounds. Standard decontamination procedures place a heavy burden on the environment. Given their continued utilization or existence, considerable efforts are being made to develop environmentally friendly methods of decontamination and medical countermeasures against their intoxication. Enzymes can offer both environmental and medical applications. One of the most promising enzymes cleaving organophosphorus compounds is the enzyme with enzyme commission number (EC): 3.1.8.2, called diisopropyl fluorophosphatase (DFPase) or organophosphorus acid anhydrolase from Loligo Vulgaris or Alteromonas sp. JD6.5, respectively. Structure, mechanisms of action and substrate profiles are described for both enzymes. Wild-type (WT) enzymes have a catalytic activity against organophosphorus compounds, including G-type nerve agents. Their stereochemical preference aims their activity towards less toxic enantiomers of the chiral phosphorus center found in most chemical warfare agents. Site-direct mutagenesis has systematically improved the active site of the enzyme. These efforts have resulted in the improvement of catalytic activity and have led to the identification of variants that are more effective at detoxifying both G-type and V-type nerve agents. Some of these variants have become part of commercially available decontamination mixtures.

Highlighting Human Enzymes Active in Different Metabolic Pathways and Diseases: The Case Study of EC 1.2.3.1 and EC 2.3.1.9

Enzymes are key proteins performing the basic functional activities in cells. In humans, enzymes can be also responsible for diseases, and the molecular mechanisms underlying the genotype to phenotype relationship are under investigation for diagnosis and medical care. Here, we focus on highlighting enzymes that are active in different metabolic pathways and become relevant hubs in protein interaction networks. We perform a statistics to derive our present knowledge on human metabolic pathways (the Kyoto Encyclopaedia of Genes and Genomes (KEGG)), and we found that activity aldehyde dehydrogenase (NAD(+)), described by Enzyme Commission number EC 1.2.1.3, and activity acetyl-CoA C-acetyltransferase (EC 2.3.1.9) are the ones most frequently involved. By associating functional activities (EC numbers) to enzyme proteins, we found the proteins most frequently involved in metabolic pathways. With our analysis, we found that these proteins are endowed with the highest numbers of interaction partners when compared to all the enzymes in the pathways and with the highest numbers of predicted interaction sites. As specific enzyme protein test cases, we focus on Alpha-Aminoadipic Semialdehyde Dehydrogenase (ALDH7A1, EC 2.3.1.9) and Acetyl-CoA acetyltransferase, cytosolic and mitochondrial (gene products of ACAT2 and ACAT1, respectively; EC 2.3.1.9). With computational approaches we show that it is possible, by starting from the enzyme structure, to highlight clues of their multiple roles in different pathways and of putative mechanisms promoting the association of genes to disease.

Effect of Sequence Padding on the Performance of Protein-Based Deep Learning Models

Background The use of raw amino acid sequences as input for protein-based deep learning models has gained popularity in recent years. This scheme obliges to manage proteins with different lengths, while deep learning models require same-shape input. To accomplish this, zeros are usually added to each sequence up to a established common length in a process called zero-padding. However, the effect of different padding strategies on model performance and data structure is yet unknown. Results We analysed the impact of different ways of padding the amino acid sequences in a hierarchical Enzyme Commission number prediction problem. Our results show that padding has an effect on model performance even when there are convolutional layers implied. We propose and implement four novel types of padding the amino acid sequences. Conclusions The present study highlights the relevance of the step of padding the one-hot encoded amino acid sequences when building deep learning-based models for Enzyme Commission number prediction. The fact that this has an effect on model performance should raise awareness on the need of justifying the details of this step on future works. The code of this analysis is available at https://github.com/b2slab/padding_benchmark.

Hayai-Annotation Plants: an ultra-fast and comprehensive functional gene annotation system in plants

Summary Hayai-Annotation Plants is a browser-based interface for an ultra-fast and accurate functional gene annotation system for plant species using R. The pipeline combines the sequence-similarity searches, using USEARCH against UniProtKB (taxonomy Embryophyta), with a functional annotation step. Hayai-Annotation Plants provides five layers of annotation: i) protein name; ii) gene ontology terms consisting of its three main domains (Biological Process, Molecular Function and Cellular Component); iii) enzyme commission number; iv) protein existence level; and v) evidence type. It implements a new algorithm that gives priority to protein existence level to propagate GO and EC information and annotated Arabidopsis thaliana representative peptide sequences (Araport11) within 5 min at the PC level. Availability and implementation The software is implemented in R and runs on Macintosh and Linux systems. It is freely available at https://github.com/kdri-genomics/Hayai-Annotation-Plants under the GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

Hayai-Annotation Plants: an ultra-fast and comprehensive gene annotation system in plants

SummaryHayai-Annotation Plants is a browser-based interface for an ultra-fast and accurate gene annotation system for plant species using R. The pipeline combines the sequence-similarity searches, using USEARCH against UniProtKB (taxonomy Embryophyta), with a functional annotation step. Hayai-Annotation Plants provides five layers of annotation: 1) gene name; 2) gene ontology terms consisting of its three main domains (Biological Process, Molecular Function, and Cellular Component); 3) enzyme commission number; 4) protein existence level; 5) and evidence type. In regard to speed and accuracy, Hayai-Annotation Plants annotated Arabidopsis thaliana (Araport11, representative peptide sequences) within five minutes with an accuracy of 96.4 %.Availability and ImplementationThe software is implemented in R and runs on Macintosh and Linux systems. It is freely available at https://github.com/kdri-genomics/Hayai-Annotation-Plants under the GPLv3 license.

EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

Ferrochelatase: the convergence of the porphyrin biosynthesis and iron transport pathways

Ferrochelatase (also known as PPIX ferrochelatase; Enzyme Commission number 4.9.9.1.1) catalyzes the insertion of ferrous iron into PPIX to form heme. This reaction unites the biochemically synchronized pathways of porphyrin synthesis and iron transport in nearly all living organisms. The ferrochelatases are an evolutionarily diverse family of enzymes with no more than six active site residues known to be perfectly conserved. The availability of over thirty different crystal structures, including many with bound metal ions or porphyrins, has added tremendously to our understanding of ferrochelatase structure and function. It is generally believed that ferrous iron is directly channeled to ferrochelatase in vivo, but the identity of the suspected chaperone remains uncertain despite much recent progress in this area. Identification of a conserved metal ion binding site at the base of the active site cleft may be an important clue as to how ferrochelatases acquire iron, and catalyze desolvation during transport to the catalytic site to complete heme synthesis.