AIMC Topic: Membrane Proteins

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Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4.

Frontiers in bioscience (Landmark edition) Nov 29, 2024
BACKGROUND: Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 () in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, 's compre...
Gene Expression Regulation, Neoplastic Humans Membrane Proteins Machine Learning Neoplasms Cytoskeletal Proteins Myristoylated Alanine-Rich C Kinase Substrate Cell Proliferation Aneuploidy
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A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma.

Briefings in bioinformatics Nov 22, 2024
Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti...
Humans Deep Learning Liver Neoplasms Algorithms Prognosis Signal Transduction Programmed Cell Death 1 Receptor Nucleotidyltransferases B7-H1 Antigen Membrane Proteins Carcinoma, Hepatocellular Immune Checkpoint Inhibitors
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Machine Learning Diagnostic Model for Hepatocellular Carcinoma Based on Liquid-Liquid Phase Separation and Ferroptosis-Related Genes.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology Oct 7, 2024
BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) represents a primary liver malignancy with a multifaceted molecular landscape. The interplay between liquid-liquid phase separation (LLPS) and ferroptosis-a regulated form of cell death-has garnered int...
GTP Phosphohydrolases Ferroptosis Phase Separation Liver Neoplasms Carcinoma, Hepatocellular Gene Expression Regulation, Neoplastic Membrane Proteins Prognosis Gene Expression Profiling Humans Machine Learning
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ProteinMAE: masked autoencoder for protein surface self-supervised learning.

Bioinformatics (Oxford, England) Dec 1, 2023
SUMMARY: The biological functions of proteins are determined by the chemical and geometric properties of their surfaces. Recently, with the booming progress of deep learning, a series of learning-based surface descriptors have been proposed and achie...
Natural Language Processing Protein Domains Supervised Machine Learning Membrane Proteins Binding Sites
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Identification of membrane protein types via deep residual hypergraph neural network.

Mathematical biosciences and engineering : MBE Nov 6, 2023
A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: Hig...
Membrane Proteins Neural Networks, Computer
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Neural potentials of proteins extrapolate beyond training data.

The Journal of chemical physics Aug 28, 2023
We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trai...
Neural Networks, Computer Membrane Proteins
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GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning.

Nucleic acids research Jun 9, 2023
Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limite...
Membrane Proteins Nucleic Acids Binding Sites Algorithms Deep Learning
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CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.

Nucleic acids research Jul 5, 2022
Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational appro...
Binding Sites Protein Interaction Mapping Software Ligands Deep Learning Protein Conformation Membrane Proteins
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PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning.

Bioinformatics (Oxford, England) Apr 12, 2022
MOTIVATION: Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regulation and RNA degradation. Accurate identification of binding sites on RNA-binding proteins (RBPs) i...
Binding Sites Deep Learning Membrane Proteins Computational Biology Protein Binding RNA RNA-Binding Proteins
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Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.

Briefings in bioinformatics Mar 10, 2022
Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins th...
Membrane Proteins Machine Learning Amino Acids Algorithms
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