AIMC Topic: Amino Acid Motifs

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Deep Learning-Assisted Discovery of Protein Entangling Motifs.

Biomacromolecules Feb 12, 2025
Natural topological proteins exhibit unique properties including enhanced stability, controlled quaternary structures, and dynamic switching properties, highlighting topology as a unique dimension in protein engineering. Although artificial design an...
Amino Acid Sequence Protein Engineering Amino Acid Motifs Deep Learning Proteins
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Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning.

Journal of proteomics Sep 26, 2024
Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequ...
Amino Acids Amino Acid Motifs Amino Acid Sequence Peptides Spectrometry, Mass, Electrospray Ionization Deep Learning
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Computational design of soluble and functional membrane protein analogues.

Nature Jun 19, 2024
De novo design of complex protein folds using solely computational means remains a substantial challenge. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topolog...
Amino Acid Motifs Receptors, G-Protein-Coupled Models, Molecular Solubility Protein Stability Proof of Concept Study Protein Folding Deep Learning Humans Proteome Computer-Aided Design Membrane Proteins
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Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions.

Frontiers in immunology May 28, 2024
INTRODUCTION: Somatic hypermutation (SHM) of immunoglobulin variable (V) regions by activation induced deaminase (AID) is essential for robust, long-term humoral immunity against pathogen and vaccine antigens. AID mutates cytosines preferentially wit...
Humans Deep Learning Immunoglobulin Variable Region Cytidine Deaminase Somatic Hypermutation, Immunoglobulin Mutation Nucleotide Motifs Amino Acid Motifs AICDA (Activation-Induced Cytidine Deaminase)
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ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.

Journal of computational biology : a journal of computational molecular cell biology Jan 21, 2022
Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under predict...
Sequence Alignment Amino Acid Motifs Sequence Analysis, Protein Models, Molecular Amino Acid Sequence Protein Conformation Deep Learning Computational Biology Proteins Neural Networks, Computer Software Algorithms
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A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation.

Scientific reports Jan 7, 2022
Protein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a no...
Amino Acid Motifs Phosphorylation Protein Kinases Protein Kinase Inhibitors Neural Networks, Computer Machine Learning Algorithms
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Improved Predicting of The Sequence Specificities of RNA Binding Proteins by Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
RNA-binding proteins (RBPs) have a significant role in various regulatory tasks. However, the mechanism by which RBPs identify the subsequence target RNAs is still not clear. In recent years, several machine and deep learning-based computational mode...
Conserved Sequence Amino Acid Motifs Sequence Analysis, Protein Deep Learning Computational Biology Humans RNA-Binding Proteins Animals
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Improvement of Neoantigen Identification Through Convolution Neural Network.

Frontiers in immunology May 25, 2021
Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are lim...
HLA Antigens Antigen Presentation Biomarkers, Tumor Amino Acid Sequence Humans Epitopes Computational Biology Epitope Mapping Conserved Sequence Neural Networks, Computer Antigens, Neoplasm Alleles Algorithms Amino Acid Motifs
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A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.

Scientific reports May 14, 2021
DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money...
Epigenesis, Genetic Epigenomics DNA Methylation Oryza Adenine Genome, Plant Neural Networks, Computer Amino Acid Motifs
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Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Journal of the American Chemical Society Mar 8, 2021
Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-li...
Cyclization Kelch-Like ECH-Associated Protein 1 NF-E2-Related Factor 2 Mutagenesis, Site-Directed Amino Acid Motifs Machine Learning Peptides Hydrogen Bonding Protein Binding Humans Recombinant Proteins Fluorescence Polarization Structure-Activity Relationship Nuclear Magnetic Resonance, Biomolecular Crystallography, X-Ray
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