AIMC Topic: Peptides

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Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.

Nature chemistry May 16, 2024
Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high poten...
Amino Acid Sequence Glucagon-Like Peptide-1 Receptor Receptors, Glucagon Glucagon-Like Peptide-1 Receptor Agonists Drug Design Hypoglycemic Agents Machine Learning Peptides Humans
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A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.

Interdisciplinary sciences, computational life sciences May 11, 2024
Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identification of THPs can provide crucial insights for drug-discovery and pharmaceutical industries as th...
Neoplasms Peptides Computational Biology Humans Algorithms Support Vector Machine
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Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF.

Nature communications May 10, 2024
Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible pro...
Ions Deep Learning Proteomics HLA Antigens Tandem Mass Spectrometry Databases, Protein Software Peptides Humans
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The role and future prospects of artificial intelligence algorithms in peptide drug development.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie May 6, 2024
Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to ...
Artificial Intelligence Machine Learning Humans Peptides Drug Discovery Drug Development Algorithms Animals
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ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier.

Computational biology and chemistry May 2, 2024
Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhibit cancer cell growth and survival. Traditional classification approaches for ACPs are expensive and time-consuming. This paper proposes a pre-traine...
Peptides Antineoplastic Agents Humans Neural Networks, Computer
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ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites.

Journal of molecular graphics & modelling Apr 17, 2024
This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-vi...
Peptides Protein Binding Machine Learning Algorithms Databases, Protein Humans Computational Biology Neural Networks, Computer Proteins Binding Sites
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Genome-scale annotation of protein binding sites via language model and geometric deep learning.

eLife Apr 17, 2024
Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accuratel...
Peptides Proteins Deep Learning Binding Sites Protein Binding
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Stack-AAgP: Computational prediction and interpretation of anti-angiogenic peptides using a meta-learning framework.

Computers in biology and medicine Apr 9, 2024
BACKGROUND: Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of cancer treatment, recent investigations into peptides with anti-angiogenic properties have yielded en...
Humans Machine Learning Algorithms Peptides Computational Biology Angiogenesis Inhibitors
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One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates.

The journal of physical chemistry. B Apr 3, 2024
Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sa...
Discriminant Analysis Molecular Dynamics Simulation Protein Folding Thermodynamics Machine Learning Peptides
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GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure.

PloS one Mar 27, 2024
Neoantigens are tumor-derived peptides and are biomarkers that can predict prognosis related to immune checkpoint inhibition by estimating their binding to major histocompatibility complex (MHC) proteins. Although deep neural networks have been prima...
Antigens, Neoplasm Molecular Structure Melanoma Peptides Humans Neural Networks, Computer
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