An Optimization Rule for In Silico Identification of Targeted Overproduction in Metabolic Pathways

2013 ◽  
Vol 10 (4) ◽  
pp. 914-926
Author(s):  
Mouli Das ◽  
C.A. Murthy ◽  
Rajat K. De
Keyword(s):  
In Silico ◽  
Metabolic Pathways ◽  
Optimization Rule ◽  
In Silico Identification

scholarly journals A Global Analysis of Enzyme Compartmentalization to Glycosomes

Pathogens ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 281 ◽  
Author(s):  
Hina Durrani ◽  
Marshall Hampton ◽  
Jon N. Rumbley ◽  
Sara L. Zimmer
Keyword(s):  
In Silico ◽  
Metabolic Pathways ◽  
Common Ancestor ◽  
Global Analysis ◽  
Meta Analysis ◽  
Peptide Identification ◽  
Identification Scheme ◽  
In Silico Identification ◽  
Transcriptome Sequences ◽  
Localization Score

In kinetoplastids, the first seven steps of glycolysis are compartmentalized into a glycosome along with parts of other metabolic pathways. This organelle shares a common ancestor with the better-understood eukaryotic peroxisome. Much of our understanding of the emergence, evolution, and maintenance of glycosomes is limited to explorations of the dixenous parasites, including the enzymatic contents of the organelle. Our objective was to determine the extent that we could leverage existing studies in model kinetoplastids to determine the composition of glycosomes in species lacking evidence of experimental localization. These include diverse monoxenous species and dixenous species with very different hosts. For many of these, genome or transcriptome sequences are available. Our approach initiated with a meta-analysis of existing studies to generate a subset of enzymes with highest evidence of glycosome localization. From this dataset we extracted the best possible glycosome signal peptide identification scheme for in silico identification of glycosomal proteins from any kinetoplastid species. Validation suggested that a high glycosome localization score from our algorithm would be indicative of a glycosomal protein. We found that while metabolic pathways were consistently represented across kinetoplastids, individual proteins within those pathways may not universally exhibit evidence of glycosome localization.

In Silico Identification and Molecular Characterization of Extracellular Cathepsin L Proteases from Giardia duodenalis

2020 ◽  
Vol 17 (4) ◽  
pp. 342-351
Author(s):  
Sergio A. Durán-Pérez ◽  
José G. Rendón-Maldonado ◽  
Lucio de Jesús Hernandez-Diaz ◽  
Annete I. Apodaca-Medina ◽  
Maribel Jiménez-Edeza ◽  
...  
Keyword(s):  
In Silico ◽  
Cathepsin L ◽  
Three Dimensional ◽  
Giardia Duodenalis ◽  
Evolutionary Genetics ◽  
Extracellular Proteins ◽  
In Silico Identification ◽  
Dimensional Models ◽  
Three Dimensional Models

Background: The protozoan Giardia duodenalis, which causes giardiasis, is an intestinal parasite that commonly affects humans, mainly pre-school children. Although there are asymptomatic cases, the main clinical features are chronic and acute diarrhea, nausea, abdominal pain, and malabsorption syndrome. Little is currently known about the virulence of the parasite, but some cases of chronic gastrointestinal alterations post-infection have been reported even when the infection was asymptomatic, suggesting that the cathepsin L proteases of the parasite may be involved in the damage at the level of the gastrointestinal mucosa. Objective: The aim of this study was the in silico identification and characterization of extracellular cathepsin L proteases in the proteome of G. duodenalis. Methods: The NP_001903 sequence of cathepsin L protease from Homo sapienswas searched against the Giardia duodenalisproteome. The subcellular localization of Giardia duodenaliscathepsin L proteases was performed in the DeepLoc-1.0 server. The construction of a phylogenetic tree of the extracellular proteins was carried out using the Molecular Evolutionary Genetics Analysis software (MEGA X). The Robetta server was used for the construction of the three-dimensional models. The search for possible inhibitors of the extracellular cathepsin L proteases of Giardia duodenaliswas performed by entering the three-dimensional structures in the FINDSITEcomb drug discovery tool. Results: Based on the amino acid sequence of cathepsin L from Homo sapiens, 8 protein sequences were identified that have in their modular structure the Pept_C1A domain characteristic of cathepsins and two of these proteins (XP_001704423 and XP_001704424) are located extracellularly. Threedimensional models were designed for both extracellular proteins and several inhibitory ligands with a score greater than 0.9 were identified. In vitrostudies are required to corroborate if these two extracellular proteins play a role in the virulence of Giardia duodenalisand to discover ligands that may be useful as therapeutic targets that interfere in the mechanism of pathogenesis generated by the parasite. Conclusion: In silicoanalysis identified two proteins in the Giardia duodenalisprotein repertoire whose characteristics allowed them to be classified as cathepsin L proteases, which may be secreted into the extracellular medium to act as virulence factors. Three-dimensional models of both proteins allowed the identification of inhibitory ligands with a high score. The results suggest that administration of those compounds might be used to block the endopeptidase activity of the extracellular cathepsin L proteases, interfering with the mechanisms of pathogenesis of the protozoan parasite Giardia duodenalis.

In silico identification of novel small molecule umami peptide from ovotransferrin

2021 ◽  
Author(s):  
Wenzhu Zhao ◽  
Jingbo He ◽  
Zhipeng Yu ◽  
Sijia Wu ◽  
Jianrong Li ◽  
...  
Keyword(s):  
Small Molecule ◽  
In Silico ◽  
In Silico Identification

In silico identification of potential inhibitor for TP53-induced glycolysis and apoptosis regulator in head and neck squamous cell carcinoma

3 Biotech ◽  
2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Vaishali Chandel ◽  
Prem Prakash Sharma ◽  
Seema A. Nayar ◽  
Niraj Kumar Jha ◽  
Saurabh Kumar Jha ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
In Silico ◽  
Neck Squamous Cell Carcinoma ◽  
Potential Inhibitor ◽  
In Silico Identification
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