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Membrane Proteins

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ProteinMAE: masked autoencoder for protein surface self-supervised learning.

Bioinformatics (Oxford, England)
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...

Identification of membrane protein types via deep residual hypergraph neural network.

Mathematical biosciences and engineering : MBE
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...

Neural potentials of proteins extrapolate beyond training data.

The Journal of chemical physics
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...

GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning.

Nucleic acids research
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...

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.

Nucleic acids research
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...

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning.

Bioinformatics (Oxford, England)
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...

Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.

Briefings in bioinformatics
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...

Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning.

Briefings in bioinformatics
With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact/distance map through deep learning has become an...

A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor.

Briefings in bioinformatics
We present a concatenated deep-learning multiple neural network system for the analysis of single-molecule trajectories. We apply this machine learning-based analysis to characterize the translational diffusion of the nicotinic acetylcholine receptor...

Computational Approaches for Investigating Disease-causing Mutations in Membrane Proteins: Database Development, Analysis and Prediction.

Current topics in medicinal chemistry
Membrane proteins (MPs) play an essential role in a broad range of cellular functions, serving as transporters, enzymes, receptors, and communicators, and about ~60% of membrane proteins are primarily used as drug targets. These proteins adopt either...