Virtual 2-D map of the fungal proteome

Proteomics analysis of resistance mechanism of Trichoderma harzianum under U(VI) stress

Trichoderma harzianum has a certain resistance to Hexavalent Uranium (U(VI)), but its resistance mechanism is unknown. Based on proteomics sequencing using DIA mode, differentially expressed proteins (DEPs) of Trichoderma harzianum under U(VI) stress were identified. GO enrichment, KEGG annotation analysis and DEPs annotation were performed. The results showed that 8 DEPs, 8 DEPs and 15 DEPs were obtained in the low-dose, medium-dose and high-dose groups, respectively. The functional classification of GO demonstrated that DEPs were associated with 17 molecular functions, 5 biological processes, and 5 cellular components. Furthermore, DEPs were enriched in transport and catabolism, energy metabolism, translation, and signal transduction. These findings showed that Trichoderma harzianum was significantly changed in protein expression and signaling pathway after U(VI) exposure. Therefore, these results have provided Trichoderma harzianum with a theoretical background that can be applied to environmental cleanup.

FungiProteomeDB: a database for the molecular weight and isoelectric points of the fungal proteomes

Proteins' molecular weight (MW) and isoelectric point (pI) are crucial for their subcellular localization and subsequent function. These are also useful in 2D gel electrophoresis, liquid chromatography-mass spectrometry and X-ray protein crystallography. Moreover, visualizations like a virtual 2D proteome map of pI vs. MW are worthwhile to discuss the proteome diversity among different species. Although the genome sequence data of the fungi kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the MW and pI of the fungi proteins and reported them in, FungiProteomeDB, an online database (DB) https://vision4research.com/fungidb/. We analyzed the proteome of 685 fungal species that contain 7 127 141 protein sequences. The DB provides an easy-to-use and efficient interface for various search options, summary statistics and virtual 2D proteome map visualizations. The MW and pI of a protein can be obtained by searching the name of a protein, a keyword or a list of accession numbers. It also allows querying protein sequences. The DB will be helpful in hypothesis formulation and in various biotechnological applications. Database URL https://vision4research.com/fungidb/.

In silico evaluation of NO donor heterocyclic vasodilators as SARS-CoV-2 Mpro protein inhibitor

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which causes COVID-19 disease has been exponentially increasing throughout the world. The mortality rate is increasing gradually as effective treatment is unavailable to date. In silico based screening for novel testable hypotheses on SARS-CoV-2 M^pro protein to discover the potential lead drug candidate is an emerging area along with the discovery of a vaccine. Administration of NO-releasing agents, NO inducers or the NO gas itself may be useful as therapeutics in the treatment of SARS-CoV-2. In the present study, a 3D structure of SARS-CoV-2 M^pro protein was used for the rational setting of inhibitors to the binding pocket of enzyme which proposed that phenyl furoxan derivative gets efficiently dock in the target pocket. Molecular docking and molecular dynamics simulations helped to investigate possible effective inhibitor candidates bound to SARS-CoV-2 M^pro substrate binding pocket. Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations revealed energetic contributions of active site residues of M^pro in binding with most stable proposed NO donor heterocyclic vasodilator inhibitor molecules. Furthermore, principal component analysis (PCA) showed that the NO donor heterocyclic inhibitor molecules 14, 16, 18 and 19 was strongly bound to catalytic core of SARS-CoV-2 M^pro protein, limiting its movement to form stable complex as like control. Thus, overall in silico investigations revealed that 5-oxopiperazine-2-carboxylic acid coupled furoxan derivatives was found to be key pharmacophore in drug design for the treatment of SARS-CoV-2, a global pandemic disease with a dual mechanism of action as NO donor and a worthwhile ligand to act as SARS-CoV-2 M^pro protein inhibitor.Communicated by Ramaswamy H. Sarma.

Virtual 2D map of cyanobacterial proteomes

Cyanobacteria are prokaryotic Gram-negative organisms prevalent in nearly all habitats. A detailed proteomics study of Cyanobacteria has not been conducted despite extensive study of their genome sequences. Therefore, we conducted a proteome-wide analysis of the Cyanobacteria proteome and found Calothrix desertica as the largest (680331.825 kDa) and Candidatus synechococcus spongiarum as the smallest (42726.77 kDa) proteome of the cyanobacterial kingdom. A Cyanobacterial proteome encodes 312.018 amino acids per protein, with a molecular weight of 182173.1324 kDa per proteome. The isoelectric point (pI) of the Cyanobacterial proteome ranges from 2.13 to 13.32. It was found that the Cyanobacterial proteome encodes a greater number of acidic-pI proteins, and their average pI is 6.437. The proteins with higher pI are likely to contain repetitive amino acids. A virtual 2D map of Cyanobacterial proteome showed a bimodal distribution of molecular weight and pI. Several proteins within the Cyanobacterial proteome were found to encode Selenocysteine (Sec) amino acid, while Pyrrolysine amino acids were not detected. The study can enable us to generate a high-resolution cell map to monitor proteomic dynamics. Through this computational analysis, we can gain a better understanding of the bias in codon usage by analyzing the amino acid composition of the Cyanobacterial proteome.

PlantMWpIDB: a database for the molecular weight and isoelectric points of the plant proteomes

The molecular weight and isoelectric point of the proteins are very important parameters that control their subcellular localization and subsequent function. Although the genome sequence data of the plant kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the molecular weight and isoelectric point of the plant proteins and reported them in this database. A database, PlantMWpIDB, containing protein data from 342 plant proteomes was created to provide information on plant proteomes for hypothesis formulation in basic research and for biotechnological applications. The Molecular weight and isoelectric point (pI) are important molecular parameters of proteins that are useful when conducting protein studies involving 2D gel electrophoresis, liquid chromatography-mass spectrometry, and X-ray protein crystallography. PlantMWpIDB provides an easy-to-use and efficient interface for search options and generates a summary of basic protein parameters. The database represents a virtual 2D proteome map of plants, and the molecular weight and pI of a protein can be obtained by searching on the name of a protein, a keyword, or by a list of accession numbers. The PlantMWpIDB database also allows one to query protein sequences. The database can be found in the following link https://plantmwpidb.com/ . The individual 2D virtual proteome map of the plant kingdom will enable us to understand the proteome diversity between different species. Further, the molecular weight and isoelectric point of individual proteins can enable us to understand their functional significance in different species.

Proteome-pI 2.0: proteome isoelectric point database update

Proteome-pI 2.0 is an update of an online database containing predicted isoelectric points and pKa dissociation constants of proteins and peptides. The isoelectric point-the pH at which a particular molecule carries no net electrical charge-is an important parameter for many analytical biochemistry and proteomics techniques. Additionally, it can be obtained directly from the pKa values of individual charged residues of the protein. The Proteome-pI 2.0 database includes data for over 61 million protein sequences from 20 115 proteomes (three to four times more than the previous release). The isoelectric point for proteins is predicted by 21 methods, whereas pKa values are inferred by one method. To facilitate bottom-up proteomics analysis, individual proteomes were digested in silico with the five most commonly used proteases (trypsin, chymotrypsin, trypsin + LysC, LysN, ArgC), and the peptides' isoelectric point and molecular weights were calculated. The database enables the retrieval of virtual 2D-PAGE plots and customized fractions of a proteome based on the isoelectric point and molecular weight. In addition, isoelectric points for proteins in NCBI non-redundant (nr), UniProt, SwissProt, and Protein Data Bank are available in both CSV and FASTA formats. The database can be accessed at http://isoelectricpointdb2.org.

Virtual 2D mapping of the viral proteome reveals host-specific modality distribution of molecular weight and isoelectric point

A proteome-wide study of the virus kingdom based on 1.713 million protein sequences from 19,128 virus proteomes was conducted to construct an overall proteome map of the virus kingdom. Viral proteomes encode an average of 386.214 amino acids per protein with the variation in the number of protein-coding sequences being host-specific. The proteomes of viruses of fungi hosts (882.464) encoded the greatest number of amino acids, while the viral proteome of bacterial host (210.912) encoded the smallest number of amino acids. Viral proteomes were found to have a host-specific amino acid composition. Leu (8.556%) was the most abundant and Trp (1.274%) the least abundant amino acid in the collective proteome of viruses. Viruses were found to exhibit a host-dependent molecular weight and isoelectric point of encoded proteins. The isoelectric point (pI) of viral proteins was found in the acidic range, having an average pI of 6.89. However, the pI of viral proteins of algal (pI 7.08) and vertebrate (pI 7.09) hosts was in the basic range. The virtual 2D map of the viral proteome from different hosts exhibited host-dependent modalities. The virus proteome from algal hosts and archaea exhibited a bimodal distribution of molecular weight and pI, while the virus proteome of bacterial host exhibited a trimodal distribution, and the virus proteome of fungal, human, land plants, invertebrate, protozoa, and vertebrate hosts exhibited a unimodal distribution.