AIMC Topic: Peptides

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Accurate Prediction of y Ions in Beam-Type Collision-Induced Dissociation Using Deep Learning.

Analytical chemistry May 24, 2022
Peptide fragmentation spectra contain critical information for the identification of peptides by mass spectrometry. In this study, we developed an algorithm that more accurately predicts the high-intensity peaks among the peptide spectra. The trainin...
Tandem Mass Spectrometry Ions Deep Learning Proteomics Peptides
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Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.

Analytical chemistry May 12, 2022
The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tan...
Humans Infant, Newborn Proteomics Tandem Mass Spectrometry Proteolysis Peptides Deep Learning
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Accelerating All-Atom Simulations and Gaining Mechanistic Understanding of Biophysical Systems through State Predictive Information Bottleneck.

Journal of chemical theory and computation Apr 6, 2022
An effective implementation of enhanced sampling algorithms for molecular dynamics simulations requires knowledge of the approximate reaction coordinate describing the relevant mechanisms in the system. In this work, we focus on the recently develop...
Phospholipids Peptides Artificial Intelligence Thermodynamics Molecular Dynamics Simulation
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Identification of antimicrobial peptides from the human gut microbiome using deep learning.

Nature biotechnology Mar 3, 2022
The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LST...
Anti-Bacterial Agents Animals Humans Mice Gastrointestinal Microbiome Antimicrobial Peptides Peptides Deep Learning Adenosine Monophosphate
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MSSort-DIA: A deep learning classification tool of the peptide precursors quantified by OpenSWATH.

Journal of proteomics Feb 26, 2022
OpenSWATH is an analysis toolkit commonly used for data independent acquisition (DIA). Although the output of OpenSWATH is controlled at 1% false discovery rate (FDR), the output report still contains many peptide precursors with low similarity fragm...
Peptides Deep Learning Tandem Mass Spectrometry Proteomics Software
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ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information.

Molecules (Basel, Switzerland) Feb 24, 2022
Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer...
Amino Acid Sequence Antineoplastic Agents Deep Learning Humans Neoplasms Peptides
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De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update.

Journal of chemical information and modeling Feb 7, 2022
Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeuti...
Proteins Neural Networks, Computer Artificial Intelligence Machine Learning Peptides
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A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.

International journal of molecular sciences Feb 3, 2022
As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Ab...
Leukemia Learning Curve Databases as Topic Entropy Humans Cluster Analysis Neoplasm Proteins Phosphoproteins Amino Acid Sequence Phosphorylation Deep Learning ROC Curve Neural Networks, Computer Peptides
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AMP: Species-Specific Prediction of Anti-microbial Peptides Using Zero and Few Shot Learning.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
Evolution of drug-resistant microbial species is one of the major challenges to global health. Development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel a...
Machine Learning Peptides Anti-Bacterial Agents Amino Acid Sequence
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Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.

PLoS computational biology Jan 28, 2022
Machine learning with multi-layered artificial neural networks, also known as "deep learning," is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neur...
Sequence Analysis, Protein Macaca mulatta Computational Biology Amino Acid Sequence Models, Biological Peptides Humans Binding Sites Deep Learning Animals
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