AIMC Topic: Proteins

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BridgeDPI: a novel Graph Neural Network for predicting drug-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new drugs. Network-based methods usually utilize a drug-protein association network and predict DPIs by t...

Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images.

Bioinformatics (Oxford, England)
MOTIVATION: The development of microscopic imaging techniques enables us to study protein subcellular locations from the tissue level down to the cell level, contributing to the rapid development of image-based protein subcellular location prediction...

Pre-training graph neural networks for link prediction in biomedical networks.

Bioinformatics (Oxford, England)
MOTIVATION: Graphs or networks are widely utilized to model the interactions between different entities (e.g. proteins, drugs, etc.) for biomedical applications. Predicting potential interactions/links in biomedical networks is important for understa...

EMBER: multi-label prediction of kinase-substrate phosphorylation events through deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Kinase-catalyzed phosphorylation of proteins forms the backbone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. Although on the order of 105 p...

PIPENN: protein interface prediction from sequence with an ensemble of neural nets.

Bioinformatics (Oxford, England)
MOTIVATION: The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly and challenging task, while protein sequence data...

ProteinBERT: a universal deep-learning model of protein sequence and function.

Bioinformatics (Oxford, England)
SUMMARY: Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for t...

An AI-assisted cryo-EM pipeline for structural studies of cellular extracts.

Structure (London, England : 1993)
Proteins, the building blocks of life, often form large assemblies to perform their function but are traditionally studied separately in structural biology. In this issue of Structure, Skalidis et al. (2022) present a workflow to identify members of ...

Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer.

Bioinformatics (Oxford, England)
MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam ...

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accur...

DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein model quality assessment is a key component of protein structure prediction. In recent research, the voxelization feature was used to characterize the local structural information of residues, but it may be insufficient for descri...