In Silico Drug Profiling of the Human Kinome Based on a Molecular Marker for Cross Reactivity

2008 ◽  
Vol 5 (5) ◽  
pp. 728-738 ◽  
Author(s):  
Xi Zhang ◽  
Ariel Fernández
Keyword(s):  
Molecular Marker ◽  
In Silico ◽  
Cross Reactivity ◽  
Human Kinome

scholarly journals Potential Antigenic Cross-reactivity Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Dengue Viruses

2020 ◽  
Author(s):  
Yaniv Lustig ◽  
Shlomit Keler ◽  
Rachel Kolodny ◽  
Nir Ben-Tal ◽  
Danit Atias-Varon ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Envelope Protein ◽  
False Positive ◽  
In Silico ◽  
Rapid Test ◽  
Cross Reactivity ◽  
Lateral Flow ◽  
Immunoglobulin M ◽  
Enzyme Linked Immunosorbent Assay ◽  
Elisa Testing

Abstract Background Coronavirus disease 2019 (COVID-19) and dengue fever are difficult to distinguish given shared clinical and laboratory features. Failing to consider COVID-19 due to false-positive dengue serology can have serious implications. We aimed to assess this possible cross-reactivity. Methods We analyzed clinical data and serum samples from 55 individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To assess dengue serology status, we used dengue-specific antibodies by means of lateral-flow rapid test, as well as enzyme-linked immunosorbent assay (ELISA). Additionally, we tested SARS-CoV-2 serology status in patients with dengue and performed in-silico protein structural analysis to identify epitope similarities. Results Using the dengue lateral-flow rapid test we detected 12 positive cases out of the 55 (21.8%) COVID-19 patients versus zero positive cases in a control group of 70 healthy individuals (P = 2.5E−5). This includes 9 cases of positive immunoglobulin M (IgM), 2 cases of positive immunoglobulin G (IgG), and 1 case of positive IgM as well as IgG antibodies. ELISA testing for dengue was positive in 2 additional subjects using envelope protein directed antibodies. Out of 95 samples obtained from patients diagnosed with dengue before September 2019, SARS-CoV-2 serology targeting the S protein was positive/equivocal in 21 (22%) (16 IgA, 5 IgG) versus 4 positives/equivocal in 102 controls (4%) (P = 1.6E−4). Subsequent in-silico analysis revealed possible similarities between SARS-CoV-2 epitopes in the HR2 domain of the spike protein and the dengue envelope protein. Conclusions Our findings support possible cross-reactivity between dengue virus and SARS-CoV-2, which can lead to false-positive dengue serology among COVID-19 patients and vice versa. This can have serious consequences for both patient care and public health.

scholarly journals Evaluation of cross-reactivity between casein components using inhibition assay and in silico analysis

2020 ◽  
Author(s):  
Michihiro Naito ◽  
Teruaki Matsui ◽  
Chikako Yamada ◽  
Kazunori Tagami ◽  
Komei Ito ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Cross Reactivity ◽  
Inhibition Assay ◽  
Silico Analysis

scholarly journals Development of a Cartilage Oligomeric Matrix Protein Neo-Epitope Assay for the Detection of Intra-Thecal Tendon Disease

2020 ◽  
Vol 21 (6) ◽  
pp. 2155
Author(s):  
Roger Smith ◽  
Patrik Önnerfjord ◽  
Kristin Holmgren ◽  
Shacko di Grado ◽  
Jayesh Dudhia
Keyword(s):  
Molecular Marker ◽  
Cartilage Oligomeric Matrix Protein ◽  
Matrix Protein ◽  
Competitive Inhibition ◽  
Objective Assessment ◽  
Clinical Signs ◽  
Tendon Injury ◽  
Cross Reactivity ◽  
Polyclonal Antiserum ◽  
Synovial Fluids

The diagnosis of tendon injury relies on clinical signs and diagnostic imaging but imaging is subjective and does not always correlate with clinical signs. A molecular marker would potentially offer a sensitive and specific diagnostic tool that could also provide objective assessment of healing for the comparison of different treatments. Cartilage Oligomeric Matrix Protein (COMP) has been used as a molecular marker for osteoarthritis in humans and horses but assays for the protein in tendon sheath synovial fluids have shown overlap between horses affected by tendinopathy and controls. We hypothesized that quantifying a COMP neoepitope would be more discriminatory of injury. COMP fragments were purified from synovial fluids of horses with intra-thecal tendon injuries and media from equine tendon explants, and mass spectrometry of a consistent and abundant fragment revealed a ~100 kDa COMP fragment with a new N-terminus at the 78th amino-acid (NH2-TPRVSVRP) located just outside the junctional region of the protein. A competitive inhibition ELISA based on a polyclonal antibody raised to this sequence yielded more than a 10-fold rise in the mean neoepitope levels for tendinopathy cases compared to controls (5.3 ± 1.3 µg/mL (n = 7) versus 58.8 ± 64.3 µg/mL (n = 13); p = 0.002). However, there was some cross-reactivity of the neoepitope polyclonal antiserum with intact COMP, which could be blocked by a peptide spanning the neoepitope. The modified assay demonstrated a lower concentration but a significant > 500-fold average rise with tendon injury (2.5 ± 2.2 ng/mL (n = 6) versus 1029.8 ± 2188.8 ng/ml (n = 14); p = 0.013). This neo-epitope assay therefore offers a potentially useful marker for clinical use.

scholarly journals Mapping B-Cell Epitopes for Nonspecific Lipid Transfer Proteins of Legumes Consumed in India and Identification of Critical Residues Responsible for IgE Binding

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1269
Author(s):  
Ankita Mishra ◽  
Ashok Kumar
Keyword(s):  
B Cell ◽  
In Silico ◽  
Mung Bean ◽  
Pigeon Pea ◽  
Cross Reactivity ◽  
Lipid Transfer ◽  
Lipid Transfer Proteins ◽  
Garden Pea ◽  
B Cell Epitopes ◽  
Critical Residues

Nonspecific lipid transfer proteins (nsLTPs) have been categorized as panallergens and display widespread occurrence across plant-kingdom. Present study, investigated B-cell epitopes for LTPs from chickpea, mung-bean, cowpea, pigeon-pea, and soybean via in silico methods. In-silico predicted regions were evaluated for epitope-conservancy and property-based peptide similarity search by different allergen databases. Additionally, the in-silico predicted regions were compared with the experimentally validated epitopes of peach-LTP. Sequence-homology studies showed that chickpea and mung-bean LTPs shared significant homology, i.e., >70% and >60%, respectively, with other LTP allergens from lentil, garden-pea, peanut, etc. Phylogenetic-analysis also showed chickpea and mung-bean LTPs to be closely related to allergenic LTPs from lentil and peanut, respectively. Epitope-conservation analysis showed that two of the predicted B-cell epitopic regions in chickpea and mung-bean LTPs were also conserved in other allergenic LTPs from peach, peanut, garden-pea, lentil, and green-bean, and might serve as conserved B-cell epitopes of the LTP protein family. Property-distance index values for chickpea and mung-bean LTPs also showed that most of the epitopes shared similarity with the reported allergens like-lentil, peanut, apple, plum, tomato, etc. Present findings, may be explored for identification of probable allergenicity of novel LTPs, on the basis of the reported conserved B-cell epitopes, responsible for potential cross-reactivity.

scholarly journals In silico approach toward the identification of unique peptides from viral protein infection: Application to COVID-19

2020 ◽  
Author(s):  
Benjamin C. Orsburn ◽  
Conor Jenkins ◽  
Sierra M. Miller ◽  
Benjamin A Neely ◽  
Namandje N Bumpus
Keyword(s):  
In Silico ◽  
Human Microbiome ◽  
Protein Sequences ◽  
Cross Reactivity ◽  
Clinical Diagnostics ◽  
Peptide Fragmentation ◽  
Proteomics Data ◽  
Variable Regions ◽  
Data Processing Method ◽  
Similar Chemical

SummaryWe describe a method for rapid in silico selection of diagnostic peptides from newly described viral pathogens and applied this approach to SARS-CoV-2/COVID-19. This approach is multi-tiered, beginning with compiling the theoretical protein sequences from genomic derived data. In the case of SARS-CoV-2 we begin with 496 peptides that would be produced by proteolytic digestion of the viral proteins. To eliminate peptides that would cause cross-reactivity and false positives we remove peptides from consideration that have sequence homology or similar chemical characteristics using a progressively larger database of background peptides. Using this pipeline, we can remove 47 peptides from consideration as diagnostic due to the presence of peptides derived from the human proteome. To address the complexity of the human microbiome, we describe a method to create a database of all proteins of relevant abundance in the saliva microbiome. By utilizing a protein-based approach to the microbiome we can more accurately identify peptides that will be problematic in COVID-19 studies which removes 12 peptides from consideration. To identify diagnostic peptides, another 7 peptides are flagged for removal following comparison to the proteome backgrounds of viral and bacterial pathogens of similar clinical presentation. By aligning the protein sequences of SARS-CoV-2 field isolates deposited to date we can identify peptides for removal due to their presence in highly variable regions that may lead to false negatives as the pathogen evolves. We provide maps of these regions and highlight 3 peptides that should be avoided as potential diagnostic or vaccine targets. Finally, we leverage publicly deposited proteomics data from human cells infected with SARS-CoV-2, as well as a second study with the closely related MERS-CoV to identify the two proteins of highest abundance in human infections. The resulting final list contains the 24 peptides most unique and diagnostic of SARS-CoV-2 infections. These peptides represent the best targets for the development of antibodies are clinical diagnostics. To demonstrate one application of this we model peptide fragmentation using a deep learning tool to rapidly generate targeted LCMS assays and data processing method for detecting CoVID-19 infected patient samples.Graphical Abstract

scholarly journals In silico analysis of cross reactivity among phospholipases from Hymenoptera species

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 2
Author(s):  
Yuliana Emiliani ◽  
Andrés Sánchez ◽  
Marlon Munera ◽  
Jorge Sánchez ◽  
Dilia Aparicio
Keyword(s):  
Phylogenetic Analysis ◽  
In Silico ◽  
Membrane Lipids ◽  
In Silico Analysis ◽  
3D Models ◽  
Cross Reactivity ◽  
Amino Acid Sequences ◽  
Conserved Regions ◽  
Antigenic Regions

Background: Phospholipases are enzymes with the capacity to hydrolyze membrane lipids and have been characterized in several allergenic sources, such as hymenoptera species. However, cross-reactivity among phospholipases allergens are little understood. The objective of this study was to determine potential antigenic regions involved in cross-reactivity among allergens of phospholipases using an in silico approach. Methods: In total, 18 amino acids sequences belonging to phospholipase family derived from species of the order hymenoptera were retrieved from the UniProt database to perform phylogenetic analysis to determine the closest molecular relationship. Multialignment was done to identify conserved regions and matched with antigenic regions predicted by ElliPro server. 3D models were obtained from modeling by homology and were used to locate cross-reactive antigenic regions. Results: Phylogenetic analysis showed that the 18 phospholipases split into four monophyletic clades (named here as A, B, C and D). Phospholipases from A clade shared an amino acid sequences’ identity of 79%. Antigenic patches predicted by Ellipro were located in highly conserved regions, suggesting that they could be involved in cross-reactivity in this group (Ves v 1, Ves a 1 and Ves m 1). Conclusions: At this point, we advanced to the characterization of potential antigenic sites involved in cross-reactivity among phospholipases. Inhibition assays are needed to confirm our finding.

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