AIMC Topic: Peptides

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Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches.

Journal of proteome research Aug 27, 2021
Spectrum annotation is a challenging task due to the presence of unexpected peptide fragmentation ions as well as the inaccuracy of the detectors of the spectrometers. We present a deep convolutional neural network, called Slider, which learns an opt...
Peptides Tandem Mass Spectrometry Neural Networks, Computer Algorithms Databases, Factual
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How can artificial intelligence be used for peptidomics?

Expert review of proteomics Aug 23, 2021
INTRODUCTION: Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful ...
Drug Discovery Machine Learning Peptides Humans Artificial Intelligence
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HemoNet: Predicting hemolytic activity of peptides with integrated feature learning.

Journal of bioinformatics and computational biology Aug 5, 2021
Quantifying the hemolytic activity of peptides is a crucial step in the discovery of novel therapeutic peptides. Computational methods are attractive in this domain due to their ability to guide wet-lab experimental discovery or screening of peptides...
Humans Machine Learning Hemolysis Peptides Antimicrobial Peptides Amino Acid Sequence
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xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning.

Journal of chemical information and modeling Jul 30, 2021
Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring severe side effects to patients, as these therapies not only attack cancer cells but also damage nor...
Peptides Deep Learning Humans Neural Networks, Computer Male
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Protein-Peptide Binding Site Detection Using 3D Convolutional Neural Networks.

Journal of chemical information and modeling Jul 22, 2021
Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein-protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein-peptide complexes occ...
Neural Networks, Computer Peptides Binding Sites Protein Binding Proteins
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Deep learning for peptide identification from metaproteomics datasets.

Journal of proteomics Jul 8, 2021
Metaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled wi...
Algorithms Tandem Mass Spectrometry Proteomics Databases, Protein Peptides Deep Learning
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Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties.

Scientific reports Jun 30, 2021
Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity ...
Machine Learning Antineoplastic Agents Humans Amino Acid Sequence Peptides Computational Biology
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Investigation and Highly Accurate Prediction of Missed Tryptic Cleavages by Deep Learning.

Journal of proteome research Jun 17, 2021
Trypsin is one of the most important and widely used proteolytic enzymes in mass spectrometry (MS)-based proteomic research. It exclusively cleaves peptide bonds at the C-terminus of lysine and arginine. However, the cleavage is also affected by seve...
Mass Spectrometry Trypsin Deep Learning Proteomics Peptides
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Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.

Nature communications Jun 7, 2021
Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational...
Histocompatibility Antigens Class I Deep Learning Tandem Mass Spectrometry Histocompatibility Antigens Class II Proteomics Ligands Molecular Medicine Humans Extracellular Matrix Proteins Peptides Mass Spectrometry HLA Antigens Cell Line Epitopes
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Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set.

Journal of chemical information and modeling Jun 3, 2021
In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data ...
Prospective Studies Reproducibility of Results Peptides Pore Forming Cytotoxic Proteins Humans Machine Learning
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