AIMC Topic: Amino Acid Sequence

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In Silico Screening and Optimization of Cell-Penetrating Peptides Using Deep Learning Methods.

Biomolecules Mar 13, 2023
Cell-penetrating peptides (CPPs) have great potential to deliver bioactive agents into cells. Although there have been many recent advances in CPP-related research, it is still important to develop more efficient CPPs. The development of CPPs by in s...
Cell-Penetrating Peptides Protein Transport Humans Amino Acid Sequence Deep Learning
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RiRPSSP: A unified deep learning method for prediction of regular and irregular protein secondary structures.

Journal of bioinformatics and computational biology Mar 9, 2023
Protein secondary structure prediction (PSSP) is an important and challenging task in protein bioinformatics. Protein secondary structures (SSs) are categorized in regular and irregular structure classes. Regular SSs, representing nearly 50% of amino...
Amino Acids Deep Learning Amino Acid Sequence Proteins Neural Networks, Computer
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Protein Design Using Physics Informed Neural Networks.

Biomolecules Mar 1, 2023
The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been su...
Neural Networks, Computer Amino Acid Sequence Physics Molecular Dynamics Simulation Proteins
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ProteInfer, deep neural networks for protein functional inference.

eLife Feb 27, 2023
Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large ...
Amino Acid Sequence Computational Biology Proteins Algorithms Neural Networks, Computer Software
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Hierarchical graph learning for protein-protein interaction.

Nature communications Feb 25, 2023
Protein-Protein Interactions (PPIs) are fundamental means of functions and signalings in biological systems. The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and und...
Protein Interaction Mapping Deep Learning Proteins Humans Amino Acid Sequence Protein Interaction Maps
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Predicting mechanical properties of silk from its amino acid sequences via machine learning.

Journal of the mechanical behavior of biomedical materials Feb 22, 2023
The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, i...
Animals Biomechanical Phenomena Machine Learning Spiders Amino Acid Sequence Silk
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The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors.

Current opinion in structural biology Feb 18, 2023
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between...
Amino Acid Sequence Deep Learning Proteins Computational Biology
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Prediction of drug protein interactions based on variable scale characteristic pyramid convolution network.

Methods (San Diego, Calif.) Feb 15, 2023
MOTIVATION: In the process of drug screening, it is significant to improve the accuracy of drug-target binding affinity prediction. A multilayer convolutional neural network is one of the most popular existing methods for predicting affinity based on...
Deep Learning Amino Acid Sequence Proteins Neural Networks, Computer Algorithms
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PTGAC Model: A machine learning approach for constructing phylogenetic tree to compare protein sequences.

Journal of bioinformatics and computational biology Feb 10, 2023
This work proposes a machine learning-based phylogenetic tree generation model based on agglomerative clustering (PTGAC) that compares protein sequences considering all known chemical properties of amino acids. The proposed model can serve as a suita...
Phylogeny Cluster Analysis Machine Learning Amino Acid Sequence Amino Acids
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Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.

Current opinion in structural biology Feb 9, 2023
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct a...
Deep Learning Proteins Cryoelectron Microscopy Amino Acid Sequence Protein Conformation Models, Molecular
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