AIMC Topic: Peptides

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AAPred-CNN: Accurate predictor based on deep convolution neural network for identification of anti-angiogenic peptides.

Methods (San Diego, Calif.) Jan 12, 2022
Recently, deep learning techniques have been developed for various bioactive peptide prediction tasks. However, there are only conventional machine learning-based methods for the prediction of anti-angiogenic peptides (AAP), which play an important r...
Neural Networks, Computer Machine Learning Angiogenic Proteins Peptides Deep Learning
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Effective prediction of short hydrogen bonds in proteins via machine learning method.

Scientific reports Jan 10, 2022
Short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms lie within 2.7 Å, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry an...
Machine Learning Peptides Hydrogen Bonding Models, Molecular Amino Acid Sequence Proteins Databases, Protein
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Harnessing protein folding neural networks for peptide-protein docking.

Nature communications Jan 10, 2022
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...
Models, Molecular Amino Acid Sequence Protein Interaction Domains and Motifs Neural Networks, Computer Binding Sites Peptides Protein Conformation, alpha-Helical Protein Binding Software Protein Conformation, beta-Strand Molecular Docking Simulation Proteins Protein Interaction Mapping Protein Folding
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Deep learning approaches for data-independent acquisition proteomics.

Expert review of proteomics Dec 28, 2021
INTRODUCTION: Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.
Peptides Deep Learning Proteome Protein Processing, Post-Translational Humans Proteomics
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ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.

Scientific reports Dec 8, 2021
Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) h...
Amino Acid Sequence Chemical Phenomena Drug Discovery Antineoplastic Agents ROC Curve Computational Biology Humans Deep Learning Reproducibility of Results Peptides Neural Networks, Computer Algorithms
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UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.

International journal of molecular sciences Dec 4, 2021
Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly....
Databases, Protein Internet Dietary Proteins Taste Peptides Computational Biology Machine Learning Support Vector Machine Algorithms
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Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO.

Journal of chemical theory and computation Nov 18, 2021
An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we appl...
Molecular Dynamics Simulation Peptides Protein Binding Machine Learning Algorithms
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Amino acid environment affinity model based on graph attention network.

Journal of bioinformatics and computational biology Nov 13, 2021
Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that ...
Neural Networks, Computer Amino Acids Peptides Proteins
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A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides.

International journal of molecular sciences Nov 13, 2021
As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these...
Spiders Neurotoxins Deep Learning Spider Venoms Animals Databases, Protein Peptides
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Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques.

BMC bioinformatics Nov 10, 2021
BACKGROUND: Antimicrobial peptides are promising tools to fight against ever-growing antibiotic resistance. However, despite many advantages, their toxicity to mammalian cells is a critical obstacle in clinical application and needs to be addressed.
Pore Forming Cytotoxic Proteins Peptides Drug Resistance, Microbial Machine Learning Animals
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