AIMC Topic: Proteins

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3D-equivariant graph neural networks for protein model quality assessment.

Bioinformatics (Oxford, England)
MOTIVATION: Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highl...

Improving target-disease association prediction through a graph neural network with credibility information.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Identifying effective target-disease associations (TDAs) can alleviate the tremendous cost incurred by clinical failures of drug development. Although many machine learning models have been proposed to predict potential novel TDAs rapidly, their cred...

Contrastive learning of protein representations with graph neural networks for structural and functional annotations.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Although protein sequence data is growing at an ever-increasing rate, the protein universe is still sparsely annotated with functional and structural annotations. Computational approaches have become efficient solutions to infer annotations for unlab...

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all ...

LambdaPP: Fast and accessible protein-specific phenotype predictions.

Protein science : a publication of the Protein Society
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first in...

DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces.

Bioinformatics (Oxford, England)
MOTIVATION: Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning...

Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Compound-protein interaction (CPI) plays an essential role in drug discovery and is performed via expensive molecular docking simulations. Many artificial intelligence-based approaches have been proposed in this regard. Recently, two type...

Deep learning of protein sequence design of protein-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protei...

How to Design Peptides.

Methods in molecular biology (Clifton, N.J.)
Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial ...