AIMC Topic: 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...
Sequence Analysis, Protein Models, Biological Humans Peptides Amino Acid Sequence Animals Macaca mulatta Deep Learning Binding Sites 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...
Models, Molecular Amino Acid Sequence Protein Conformation Sequence Alignment Amino Acid Motifs Deep Learning Computational Biology Proteins Software Algorithms Neural Networks, Computer Sequence Analysis, Protein
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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...
Amino Acid Sequence Datasets as Topic Sequence Analysis, Protein Deep Learning Proteins Models, Molecular Neural Networks, Computer
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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-...
Proteins Amino Acid Sequence Neural Networks, Computer Machine Learning Computational Biology
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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...
Machine Learning Databases, Protein Peptides Models, Molecular Proteins Amino Acid Sequence Hydrogen Bonding
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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...
Molecular Docking Simulation Protein Binding Proteins Software Peptides Protein Interaction Mapping Protein Folding Protein Interaction Domains and Motifs Models, Molecular Amino Acid Sequence Binding Sites Neural Networks, Computer Protein Conformation, alpha-Helical Protein Conformation, beta-Strand
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Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.

Scientific reports Jan 7, 2022
In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrom...
Protein Conformation Protein Processing, Post-Translational Glutamic Acid Proteins Reproducibility of Results Structure-Activity Relationship Deep Learning Models, Molecular Computational Biology Amino Acid Sequence
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Re-evaluating Deep Neural Networks for Phylogeny Estimation: The Issue of Taxon Sampling.

Journal of computational biology : a journal of computational molecular cell biology Jan 5, 2022
Deep neural networks (DNNs) have been recently proposed for quartet tree phylogeny estimation. Here, we present a study evaluating recently trained DNNs in comparison to a collection of standard phylogeny estimation methods on a heterogeneous collect...
Neural Networks, Computer Computer Simulation Phylogeny Evolution, Molecular Amino Acid Sequence Deep Learning Classification Computational Biology Databases, Genetic
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DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks.

International journal of molecular sciences Dec 17, 2021
Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein's function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharma...
Proteins Deep Learning Models, Chemical Computational Biology Amino Acid Sequence Solubility
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Adaptive machine learning for protein engineering.

Current opinion in structural biology Dec 9, 2021
Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool. However, when using these models to suggest new protein designs, one must deal with the vast combinatoria...
Machine Learning Proteins Amino Acid Sequence Protein Engineering
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