AIMC Topic: Amino Acid Sequence

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A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.

International journal of molecular sciences Feb 3, 2022
As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Ab...
Cluster Analysis Neoplasm Proteins Phosphorylation Deep Learning Neural Networks, Computer ROC Curve Peptides Entropy Humans Leukemia Learning Curve Amino Acid Sequence Databases as Topic Phosphoproteins
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AMP: Species-Specific Prediction of Anti-microbial Peptides Using Zero and Few Shot Learning.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
Evolution of drug-resistant microbial species is one of the major challenges to global health. Development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel a...
Machine Learning Amino Acid Sequence Anti-Bacterial Agents Peptides
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Boltzmann Machine Learning and Regularization Methods for Inferring Evolutionary Fields and Couplings From a Multiple Sequence Alignment.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
The inverse Potts problem to infer a Boltzmann distribution for homologous protein sequences from their single-site and pairwise amino acid frequencies recently attracts a great deal of attention in the studies of protein structure and evolution. We ...
Amino Acid Sequence Sequence Alignment Machine Learning Proteins
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A two-step ensemble learning for predicting protein hot spot residues from whole protein sequence.

Amino acids Jan 30, 2022
Protein hot spot residues are functional sites in protein-protein interactions. Biological experimental methods are traditionally used to identify hot spot residues, which is laborious and time-consuming. Thus a variety of computational methods were ...
Proteins Databases, Protein Machine Learning Protein Binding Amino Acid Sequence
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Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.

PLoS computational biology Jan 28, 2022
Machine learning with multi-layered artificial neural networks, also known as "deep learning," is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neur...
Binding Sites Humans Sequence Analysis, Protein Models, Biological Deep Learning Macaca mulatta Animals Peptides Amino Acid Sequence Computational Biology
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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...
Amino Acid Motifs Sequence Analysis, Protein Amino Acid Sequence Protein Conformation Sequence Alignment Algorithms Proteins Neural Networks, Computer Models, Molecular Deep Learning Software Computational Biology
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Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.

Scientific reports Jan 17, 2022
Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during conformational search. In this context, prediction of large in...
Deep Learning Proteins Neural Networks, Computer Models, Molecular Sequence Analysis, Protein Datasets as Topic Amino Acid Sequence
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GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction.

BMC bioinformatics Jan 11, 2022
BACKGROUND: Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-...
Computational Biology Machine Learning Proteins Neural Networks, Computer Amino Acid Sequence
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Effective prediction of short hydrogen bonds in proteins via machine learning method.

Scientific reports Jan 10, 2022
Short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms lie within 2.7 Å, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry an...
Models, Molecular Amino Acid Sequence Proteins Hydrogen Bonding Machine Learning Databases, Protein Peptides
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Harnessing protein folding neural networks for peptide-protein docking.

Nature communications Jan 10, 2022
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...
Amino Acid Sequence Binding Sites Protein Interaction Mapping Proteins Models, Molecular Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Protein Folding Protein Interaction Domains and Motifs Neural Networks, Computer Software Molecular Docking Simulation Protein Binding Peptides
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