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Amino Acids

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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
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...

PTGAC Model: A machine learning approach for constructing phylogenetic tree to compare protein sequences.

Journal of bioinformatics and computational biology
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...

Transformer-based deep learning for predicting protein properties in the life sciences.

eLife
Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap b...

DeepPRObind: Modular Deep Learner that Accurately Predicts Structure and Disorder-Annotated Protein Binding Residues.

Journal of molecular biology
Current sequence-based predictors of protein-binding residues (PBRs) belong to two distinct categories: structure-trained vs. intrinsic disorder-trained. Since disordered PBRs differ from structured PBRs in several ways, including ability to bind mul...

Prediction of lysine HMGylation sites using multiple feature extraction and fuzzy support vector machine.

Analytical biochemistry
Protein 3-hydroxyl-3-methylglutarylation (HMGylation) is newly discovered lysine acylation modification in mitochondrion. The accurate identification of HMGylation sites is the premise and key to further explore the molecular mechanisms of HMGylation...

DeepBSRPred: deep learning-based binding site residue prediction for proteins.

Amino acids
MOTIVATION: Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand t...

Long-distance dependency combined multi-hop graph neural networks for protein-protein interactions prediction.

BMC bioinformatics
BACKGROUND: Protein-protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of...

Entropy and Variability: A Second Opinion by Deep Learning.

Biomolecules
BACKGROUND: Analysis of the distribution of amino acid types found at equivalent positions in multiple sequence alignments has found applications in human genetics, protein engineering, drug design, protein structure prediction, and many other fields...

RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction.

Journal of chemical information and modeling
The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational methods have been proposed in the past. However, the majority of the past methods are time consuming and...

Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method.

Computers in biology and medicine
Drug targets must be identified and positioned correctly to research and manufacture new drugs. In this study, rather than using traditional methods for drug expansion, the drug target is determined using machine learning. Machine learning has genera...