AIMC Topic: Proteins

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Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.

PLoS computational biology
With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to o...

PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model.

Protein science : a publication of the Protein Society
Deep learning methods have played an increasingly pivotal role in advancing side-chain packing and mutation effect prediction (ΔΔG) for protein complexes. Although these two tasks are inherently closely related, they are typically treated separately ...

H2GnnDTI: hierarchical heterogeneous graph neural networks for drug-target interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying DTIs necessitate computational tools for auto...

[Research progress in mechanism models and artificial intelligence models for protein expression systems].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins are the basic building blocks of life. Studying the protein expression mechanism is essential for understanding the cellular organization principles and the development of biotechnology. Protein expression, involving transcription, translati...

[Intelligent mining, engineering, and design of proteins].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. ...

[Artificial intelligence-enhanced physics-based computational modeling technologies for proteins].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challe...

Unlocking protein networks with Predictomes: The SPOC advantage.

Molecular cell
In this issue of Molecular Cell, Schmid and Walter present "Predictomes," a machine-learning-based platform that utilizes AlphaFold-Multimer (AF-M) to identify high-confidence protein-protein interactions (PPIs). Their SPOC classifier is better than ...

Multiscale Differential Geometry Learning for Protein Flexibility Analysis.

Journal of computational chemistry
Protein structural fluctuations, measured by Debye-Waller factors or B-factors, are known to be closely associated with protein flexibility and function. Theoretical approaches have also been developed to predict B-factor values, which reflect protei...

MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.

Briefings in bioinformatics
In post-translational modification, covalent bonds on lysine and attached chemical groups significantly change proteins' physical and chemical properties. They shape protein structures, enhance function and stability, and are vital for physiological ...

Deep generative model for protein subcellular localization prediction.

Briefings in bioinformatics
Protein sequence not only determines its structure but also provides important clues of its subcellular localization. Although a series of artificial intelligence models have been reported to predict protein subcellular localization, most of them pro...