AIMC Topic: Amino Acids

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SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures.

BMC bioinformatics Oct 3, 2023
BACKGROUND: The relationship between the sequence of a protein, its structure, and the resulting connection between its structure and function, is a foundational principle in biological science. Only recently has the computational prediction of prote...
Neural Networks, Computer Proteins Amino Acids Amino Acid Sequence Protein Folding
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Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.

PloS one Sep 18, 2023
Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mos...
Neural Networks, Computer Algorithms Drug Discovery Amino Acids Amino Acid Sequence
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AmorProt: Amino Acid Molecular Fingerprints Repurposing-Based Protein Fingerprint.

Biochemistry Aug 25, 2023
As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data-driven predictions without the need for expensive ex...
Artificial Intelligence Algorithms Amino Acids Peptide Mapping Amyloidogenic Proteins
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Prediction of thermophilic protein using 2-D general series correlation pseudo amino acid features.

Methods (San Diego, Calif.) Aug 19, 2023
The demand for thermophilic protein has been increasing in protein engineering recently. Many machine-learning methods for identifying thermophilic proteins have emerged during this period. However, most machine learning-based thermophilic protein id...
Machine Learning Amino Acids Hydrogen Bonding
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Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach.

Journal of chemical theory and computation Aug 8, 2023
Explainable and interpretable unsupervised machine learning helps one to understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restric...
Machine Learning Peptide Fragments Amino Acids Amino Acid Sequence Trypsin
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A Highly Sensitive Model Based on Graph Neural Networks for Enzyme Key Catalytic Residue Prediction.

Journal of chemical information and modeling Jul 3, 2023
Determining the catalytic site of enzymes is a great help for understanding the relationship between protein sequence, structure, and function, which provides the basis and targets for designing, modifying, and enhancing enzyme activity. The unique l...
Neural Networks, Computer Proteins Amino Acids Amino Acid Sequence Catalytic Domain
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Lite-SeqCNN: A Light-Weight Deep CNN Architecture for Protein Function Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Jun 5, 2023
The short-and-long range interactions amongst amino-acids in a protein sequence are primarily responsible for the function performed by the protein. Recently convolutional neural network (CNN)s have produced promising results on sequential data inclu...
Neural Networks, Computer Databases, Factual Proteins Amino Acids Amino Acid Sequence
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GraphPLBR: Protein-Ligand Binding Residue Prediction With Deep Graph Convolution Network.

IEEE/ACM transactions on computational biology and bioinformatics Jun 5, 2023
The intermolecular interactions between proteins and ligands occur through site-specific amino acid residues in the proteins, and the identification of these key residues plays a critical role in both interpreting protein function and facilitating dr...
Neural Networks, Computer Protein Binding Proteins Amino Acids Ligands
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An end-to-end deep learning method for protein side-chain packing and inverse folding.

Proceedings of the National Academy of Sciences of the United States of America May 30, 2023
Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to ...
Deep Learning Proteins Amino Acids Molecular Conformation Protein Conformation Protein Folding
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: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities.

Journal of chemical information and modeling May 26, 2023
Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geom...
Machine Learning Computers Protein Binding Proteins Amino Acids
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