AIMC Topic: Peptides
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Discovering de novo peptide substrates for enzymes using machine learning.
Nature communications
Dec 7, 2018
The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal ...
Isolation and identification of immunomodulatory selenium-containing peptides from selenium-enriched rice protein hydrolysates.
Food chemistry
Sep 20, 2018
The RAW264.7 cell model was employed to screen immunomodulatory selenium-containing peptides from selenium-enriched rice protein hydrolysates (SPHs). Moreover, the selenium-containing peptides of high-activity protein hydrolysates were purified by Se...
Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning.
Analytical chemistry
Aug 29, 2018
The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this predict...
Identification and characterization of antioxidative peptides derived from simulated in vitro gastrointestinal digestion of walnut meal proteins.
Food research international (Ottawa, Ont.)
Aug 23, 2018
The aim of this study was to isolate and identify antioxidant peptides from defatted walnut meal proteins hydrolysates (DWMPH) prepared by simulated gastrointestinal digestion, and to evaluate the protective effect of the selected antioxidant peptide...
PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.
Frontiers in immunology
Jul 31, 2018
Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibact...
Semi-supervised machine learning for automated species identification by collagen peptide mass fingerprinting.
BMC bioinformatics
Jun 26, 2018
BACKGROUND: Biomolecular methods for species identification are increasingly being utilised in the study of changing environments, both at the microscopic and macroscopic levels. High-throughput peptide mass fingerprinting has been largely applied to...
Designing Anticancer Peptides by Constructive Machine Learning.
ChemMedChem
May 29, 2018
Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to d...
Recurrent Neural Network Model for Constructive Peptide Design.
Journal of chemical information and modeling
Jan 22, 2018
We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequenc...
Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.
BMC bioinformatics
Dec 28, 2017
BACKGROUND: Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, m...
Aggregation-Induced Emission Probe for Specific Turn-On Quantification of Soluble Transferrin Receptor: An Important Disease Marker for Iron Deficiency Anemia and Kidney Diseases.
Analytical chemistry
Dec 22, 2017
Transferrin receptor (TfR) is overexpressed on the surface of many cancer cells due to its vital roles in iron circulation and cellular respiration. Soluble transferrin receptor (sTfR), a truncated extracellular form of TfR in serum, is an important ...
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