scholarly journals An immunologically friendly classification of non-peptidic ligands

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Lindy Edwards ◽  
Rebecca Jackson ◽  
James A Overton ◽  
Randi Vita ◽  
Nina Blazeska ◽  
...  
Keyword(s):  
Experimental Data ◽  
Single Letter ◽  
Ncbi Taxonomy ◽  
Web Interface ◽  
Immune Epitope ◽  
Improve Access ◽  
Immune Epitope Database

Abstract The Immune Epitope Database (IEDB) freely provides experimental data regarding immune epitopes to the scientific public. The main users of the IEDB are immunologists who can easily use our web interface to search for peptidic epitopes via their simple single-letter codes. For example, ‘A’ stands for ‘alanine’. Similarly, users can easily navigate the IEDB’s simplified NCBI taxonomy hierarchy to locate proteins from specific organisms. However, some epitopes are non-peptidic, such as carbohydrates, lipids, chemicals and drugs, and it is more challenging to consistently name them and search upon, making access to their data more problematic for immunologists. Therefore, we set out to improve access to non-peptidic epitope data in the IEDB through the simplification of the non-peptidic hierarchy used in our search interfaces. Here, we present these efforts and their outcomes. Database URL:  http://www.iedb.org/

scholarly journals The Immune Epitope Database: How Data Are Entered and Retrieved

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Ward Fleri ◽  
Kerrie Vaughan ◽  
Nima Salimi ◽  
Randi Vita ◽  
Bjoern Peters ◽  
...  
Keyword(s):  
Experimental Data ◽  
Biomedical Research ◽  
Nonhuman Primates ◽  
Allergic Diseases ◽  
Research Community ◽  
Easy Access ◽  
Immune Epitope ◽  
Free Service ◽  
Epitope Discovery ◽  
Immune Epitope Database

Easy access to a vast collection of experimental data on immune epitopes can greatly facilitate the development of therapeutics and vaccines. The Immune Epitope Database and Analysis Resource (IEDB) was developed to provide such a resource as a free service to the biomedical research community. The IEDB contains epitope and assay information related to infectious diseases, autoimmune diseases, allergic diseases, and transplant/alloantigens for humans, nonhuman primates, mice, and any other species studied. It contains T cell, B cell, MHC binding, and MHC ligand elution experiments. Its data are curated primarily from the published literature and also include direct submissions from researchers involved in epitope discovery. This article describes the process of capturing data from these sources and how the information is organized in the IEDB data. Different approaches for querying the data are then presented, using the home page search interface and the various specialized search interfaces. Specific examples covering diverse applications of interest are given to highlight the power and functionality of the IEDB.

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

scholarly journals Epitope Specific Antibodies and T Cell Receptors in the Immune Epitope Database

2018 ◽  
Vol 9 ◽  
Author(s):  
Swapnil Mahajan ◽  
Randi Vita ◽  
Deborah Shackelford ◽  
Jerome Lane ◽  
Veronique Schulten ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptors ◽  
Specific Antibodies ◽  
Cell Receptors ◽  
Immune Epitope ◽  
Immune Epitope Database

scholarly journals KiMoSys 2.0: an upgraded database for submitting, storing and accessing experimental data for kinetic modeling

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Hugo Mochão ◽  
Pedro Barahona ◽  
Rafael S Costa
Keyword(s):  
Experimental Data ◽  
Metabolic Networks ◽  
Model Simulation ◽  
Web Interface ◽  
Concentration Data ◽  
Web Based ◽  
Share Data ◽  
Visualization Tools ◽  
Filter Mechanism ◽  
Machine Readable

Abstract The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL:  www.kimosys.org

Are Trust and Reciprocity Related within Individuals?

2013 ◽  
Vol 13 (1) ◽  
pp. 249-270 ◽  
Author(s):  
Tamas Kovacs ◽  
Marc Willinger
Keyword(s):  
Experimental Data ◽  
Strongly Correlated ◽  
Positive Relation ◽  
Strategy Method ◽  
Positive Correlation ◽  
Investment Game ◽  
Trust And Reciprocity ◽  
Within Subjects ◽  
New Evidence

AbstractWe provide new evidence about a positive correlation between the own amount sent and the own amount returned in the investment game. Our analysis relies on the experimental data collected under the strategy method. While the percentage returned is independent of the amount received for most of our subjects, it is strongly correlated to their amount sent as a trustor. Our analysis is based on a two-way classification of subjects: according to their trusting type and according to their reciprocal type. We show the existence of a strong positive relation between trusting types and reciprocal types within subjects.

Evaluation of experimental data from a GPR system for detection and classification of buried mines

1994 ◽  
Author(s):  
Bertil Brusmark ◽  
Staffan Abrahamson ◽  
Dan Axelsson ◽  
Anders Gustafsson ◽  
Hans Strifors
Keyword(s):  
Experimental Data

scholarly journals Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

2020 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Neelam Sharma ◽  
Salman Sadullah Usmani ◽  
Gajendra P S Raghava
Keyword(s):  
Scientific Community ◽  
Prediction Models ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Independent Validation ◽  
Immune Epitope ◽  
Learning Techniques ◽  
Wide Range ◽  
Immune Epitope Database

Abstract Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

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