AIMC Topic: Peptides

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Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore.

ACS nano Dec 19, 2023
Protein post-translational modifications (PTMs) play a crucial role in countless biological processes, profoundly modulating protein properties on both spatial and temporal scales. Protein PTMs have also emerged as reliable biomarkers for several dis...
Protein Processing, Post-Translational alpha-Synuclein Nanopores Peptides Deep Learning
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De novo design of high-affinity binders of bioactive helical peptides.

Nature Dec 18, 2023
Many peptide hormones form an α-helix on binding their receptors, and sensitive methods for their detection could contribute to better clinical management of disease. De novo protein design can now generate binders with high affinity and specificity ...
Biosensing Techniques Mass Spectrometry Computer-Aided Design Deep Learning Luminescent Measurements Diffusion Parathyroid Hormone Proteins Models, Molecular Peptides Glucagon Substrate Specificity Protein Structure, Secondary
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Structure, dynamics, and stability of the smallest and most complex 7 protein knot.

The Journal of biological chemistry Dec 10, 2023
Proteins can spontaneously tie a variety of intricate topological knots through twisting and threading of the polypeptide chains. Recently developed artificial intelligence algorithms have predicted several new classes of topological knotted proteins...
Protein Structure, Tertiary Ureaplasma urealyticum Artificial Intelligence Peptides Bacterial Proteins Models, Molecular Protein Folding
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Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning.

Journal of chemical information and modeling Dec 6, 2023
Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, ...
Humans Machine Learning Deep Learning Inflammation Anti-Inflammatory Agents Algorithms Peptides
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PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features.

Scientific reports Nov 28, 2023
Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and...
Peptides Deep Learning Software Proteins Amino Acid Sequence
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IF-AIP: A machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy.

Computers in biology and medicine Nov 19, 2023
BACKGROUND: The most commonly used therapy currently for inflammatory and autoimmune diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects. Recently, some anti-inflammatory peptides (AIPs) have been found to be a ...
Anti-Inflammatory Agents Algorithms Peptides Machine Learning Software
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Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

Frontiers in immunology Nov 17, 2023
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic reg...
Peptides Humans Artificial Intelligence Leukocytes, Mononuclear Pandemics Viral Vaccines SARS-CoV-2 COVID-19 Epitopes, T-Lymphocyte
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Use of tree-based machine learning methods to screen affinitive peptides based on docking data.

Molecular informatics Nov 9, 2023
Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four differen...
Algorithms Peptides Machine Learning
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The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.

Acta crystallographica. Section D, Structural biology Nov 3, 2023
Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedu...
Peptides Proteins Models, Molecular Crystallography, X-Ray Artificial Intelligence Protein Conformation Cryoelectron Microscopy
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Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries.

Journal of proteome research Nov 1, 2023
Spectral libraries are useful resources in proteomic data analysis. Recent advances in deep learning allow tandem mass spectra of peptides to be predicted from their amino acid sequences. This enables predicted spectral libraries to be compiled, and ...
Deep Learning Peptide Library Databases, Protein Software Proteomics Peptides
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