AIMC Topic: Databases, Protein

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DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest.

Methods (San Diego, Calif.)
Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protei...

EnzChemRED, a rich enzyme chemistry relation extraction dataset.

Scientific data
Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzym...

DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Proteins
Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulati...

Artificial Intelligence Learns Protein Prediction.

Cold Spring Harbor perspectives in biology
From over to , the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: leaped forward in protein structure predi...

PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.

BMC bioinformatics
BACKGROUND: Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of o...

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

International journal of molecular sciences
Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exp...

Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites.

Nature communications
Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trad...

Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins.

The journal of physical chemistry. B
Chemical structures of protein surfaces govern intermolecular interaction, and protein functions include specific molecular recognition, transport, self-assembly, etc. Therefore, the relationship between the chemical structure and protein functions p...

Protein ligand binding site prediction using graph transformer neural network.

PloS one
Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to ...

DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.

BMC bioinformatics
BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep lear...