AIMC Topic: Databases, Protein

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ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties.

Nature communications
The UniProt database is a valuable resource for biocatalyst discovery, yet predicting enzymatic functions remains challenging, especially for low-similarity sequences. Identifying superior enzymes with enhanced catalytic properties is even harder. To...

DeepAssembly2: A Web Server for Protein Complex Structure Assembly Based on Domain-Domain Interactions.

Journal of molecular biology
Proteins often perform biological functions by forming complexes, thereby accurately predicting the structure of protein complexes is crucial to understanding and mastering their functions, as well as facilitating drug discovery. Protein monomeric st...

Enhancing Enzyme Commission Number Prediction With Contrastive Learning and Agent Attention.

Proteins
The accurate prediction of enzyme function is crucial for elucidating disease mechanisms and identifying drug targets. Nevertheless, existing enzyme commission (EC) number prediction methods are limited by database coverage and the depth of sequence ...

Predicting protein-protein interaction with interpretable bilinear attention network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Protein-protein interactions (PPIs) play the key roles in myriad biological processes, helping to understand the protein function and disease pathology. Identification of PPIs and their interaction types through wet experime...

Learning the rules of peptide self-assembly through data mining with large language models.

Science advances
Peptides are ubiquitous and important biomolecules that self-assemble into diverse structures. Although extensive research has explored the effects of chemical composition and exterior conditions on self-assembly, a systematic study consolidating the...

Deep-ProBind: binding protein prediction with transformer-based deep learning model.

BMC bioinformatics
Binding proteins play a crucial role in biological systems by selectively interacting with specific molecules, such as DNA, RNA, or peptides, to regulate various cellular processes. Their ability to recognize and bind target molecules with high speci...

PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors.

Proteins
In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to encode the information about prote...

DeepMVD: A Novel Multiview Dynamic Feature Fusion Model for Accurate Protein Function Prediction.

Journal of chemical information and modeling
Proteins, as the fundamental macromolecules of life, play critical roles in various biological processes. Recent advancements in intelligent protein function prediction methods leverage sequences, structures, and biomedical literature data. Among the...

TrGPCR: GPCR-Ligand Binding Affinity Prediction Based on Dynamic Deep Transfer Learning.

IEEE journal of biomedical and health informatics
Predicting G protein-coupled receptor (GPCR) -ligand binding affinity plays a crucial role in drug development. However, determining GPCR-ligand binding affinities is time-consuming and resource-intensive. Although many studies used data-driven metho...

PredIDR2: Improving accuracy of protein intrinsic disorder prediction by updating deep convolutional neural network and supplementing DisProt data.

International journal of biological macromolecules
Intrinsically disordered proteins (IDPs) or regions (IDRs) are widespread in proteomes, and involved in several important biological processes and implicated in many diseases. Many computational methods for IDR prediction are being developed to decre...