AIMC Topic: Sequence Analysis, Protein

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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 Protein Conformation Sequence Alignment Deep Learning Computational Biology Proteins Models, Molecular Amino Acid Sequence Neural Networks, Computer Software Algorithms
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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...
Neural Networks, Computer Deep Learning Amino Acid Sequence Proteins Datasets as Topic Models, Molecular Sequence Analysis, Protein
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Protein embeddings and deep learning predict binding residues for various ligand classes.

Scientific reports Dec 13, 2021
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we propose...
Software Molecular Docking Simulation Ligands Nucleic Acids Protein Conformation Deep Learning Metals Protein Binding Binding Sites Sequence Analysis, Protein
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Protein Fold Recognition From Sequences Using Convolutional and Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
The identification of a protein fold type from its amino acid sequence provides important insights about the protein 3D structure. In this paper, we propose a deep learning architecture that can process protein residue-level features to address the p...
Computational Biology Proteins Protein Folding Sequence Analysis, Protein Algorithms Deep Learning
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Protein Fold Recognition Based on Auto-Weighted Multi-View Graph Embedding Learning Model.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Protein fold recognition is critical for studies of the protein structure prediction and drug design. Several methods have been proposed to obtain discriminative features from the protein sequences for fold recognition. However, the ensemble methods ...
Machine Learning Protein Folding Sequence Alignment Sequence Analysis, Protein Proteins Algorithms Databases, Protein Computational Biology Amino Acid Sequence
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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 Computational Biology RNA-Binding Proteins Humans Animals Deep Learning
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Enhanced Protein Structural Class Prediction Using Effective Feature Modeling and Ensemble of Classifiers.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Protein Secondary Structural Class (PSSC) information is important in investigating further challenges of protein sequences like protein fold recognition, protein tertiary structure prediction, and analysis of protein functions for drug discovery. Id...
Sequence Analysis, Protein Amino Acid Sequence Protein Structure, Secondary Machine Learning Computational Biology Support Vector Machine Proteins Databases, Protein
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Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Oct 31, 2021
The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure predicti...
Datasets as Topic Protein Conformation Sequence Analysis, Protein Computational Biology Models, Molecular Neural Networks, Computer Deep Learning
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TwinCons: Conservation score for uncovering deep sequence similarity and divergence.

PLoS computational biology Oct 29, 2021
We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a 'cost' of transforming one ...
Archaeal Proteins Conserved Sequence Sequence Analysis, Protein Metagenomics RNA, Ribosomal Deep Learning Sequence Alignment Evolution, Molecular Bacterial Proteins
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Protein Family Classification from Scratch: A CNN Based Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics Oct 8, 2021
Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of these proteins. However, compared with identified proteins, uncharacterized proteins consi...
Proteins Amino Acid Sequence Deep Learning Sequence Analysis, Protein Humans Computational Biology Algorithms Cluster Analysis
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