AIMC Topic: Proteins

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Unlocking protein networks with Predictomes: The SPOC advantage.

Molecular cell Mar 20, 2025
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 ...
Protein Interaction Maps Computational Biology Proteins Humans Machine Learning Protein Interaction Mapping
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Multiscale Differential Geometry Learning for Protein Flexibility Analysis.

Journal of computational chemistry Mar 15, 2025
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...
Machine Learning Proteins Protein Conformation
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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 Mar 4, 2025
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 ...
Neural Networks, Computer Protein Processing, Post-Translational Software Proteins Humans Computational Biology Algorithms Lysine Cluster Analysis Convolutional Neural Networks
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Deep generative model for protein subcellular localization prediction.

Briefings in bioinformatics Mar 4, 2025
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...
Cytoplasm Subcellular Fractions Cell Nucleus Protein Transport Computational Biology Humans Proteins Deep Learning
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Relational similarity-based graph contrastive learning for DTI prediction.

Briefings in bioinformatics Mar 4, 2025
As part of the drug repurposing process, it is imperative to predict the interactions between drugs and target proteins in an accurate and efficient manner. With the introduction of contrastive learning into drug-target prediction, the accuracy of dr...
Proteins Drug Repositioning Deep Learning Computational Biology Neural Networks, Computer Algorithms Humans
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TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.

Briefings in bioinformatics Mar 4, 2025
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment ...
Models, Molecular Protein Conformation Proteins Databases, Protein Neural Networks, Computer Software Deep Learning Computational Biology Molecular Docking Simulation
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Sul-BertGRU: an ensemble deep learning method integrating information entropy-enhanced BERT and directional multi-GRU for S-sulfhydration sites prediction.

Bioinformatics (Oxford, England) Mar 4, 2025
MOTIVATION: S-sulfhydration, a crucial post-translational protein modification, is pivotal in cellular recognition, signaling processes, and the development and progression of cardiovascular and neurological disorders, so identifying S-sulfhydration ...
Protein Processing, Post-Translational Proteins Entropy Deep Learning Computational Biology
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Enhanced O-glycosylation site prediction using explainable machine learning technique with spatial local environment.

Bioinformatics (Oxford, England) Feb 4, 2025
MOTIVATION: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning (ML) models primarily relied on primary or secondary protein structural and re...
Protein Processing, Post-Translational Neural Networks, Computer Computational Biology Proteins Machine Learning Glycosylation
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MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network.

Bioinformatics (Oxford, England) Feb 4, 2025
MOTIVATION: The increasing accessibility of large-scale protein sequences through advanced sequencing technologies has necessitated the development of efficient and accurate methods for predicting protein function. Computational prediction models hav...
Databases, Protein Neural Networks, Computer Amino Acid Sequence Algorithms Gene Ontology Computational Biology Sequence Analysis, Protein Proteins
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AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.

Protein science : a publication of the Protein Society Feb 1, 2025
Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences...
Protein Conformation Mutation, Missense Databases, Protein Models, Molecular Computational Biology Proteins Humans Machine Learning Software
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