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Intrinsically Disordered Proteins

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PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network.

International journal of biological macromolecules
The involvement of protein intrinsic disorder in essential biological processes, it is well known in structural biology. However, experimental methods for detecting intrinsic structural disorder and directly measuring highly dynamic behavior of prote...

Emerging Frontiers in Conformational Exploration of Disordered Proteins: Integrating Autoencoder and Molecular Simulations.

ACS chemical neuroscience
Intrinsically disordered proteins (IDPs) are closely associated with a number of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease. Due to the highly dynamic nature of IDPs, their structural determination and conformatio...

An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein.

eLife
The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defi...

PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms.

Nature communications
Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins in cells to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards p...

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Journal of chemical information and modeling
Proteins are inherently dynamic, and their conformational ensembles play a crucial role in biological function. Large-scale motions may govern the protein structure-function relationship, and numerous transient but stable conformations of intrinsical...

IDP-EDL: enhancing intrinsically disordered protein prediction by combining protein language model and ensemble deep learning.

Briefings in bioinformatics
Identification of intrinsically disordered regions (IDRs) in proteins is essential for understanding fundamental cellular processes. The IDRs can be divided into long disordered regions (LDRs) and short disordered regions (SDRs) according to their le...

Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning.

Biomacromolecules
We use a combination of Brownian dynamics (BD) simulation results and deep learning (DL) strategies for the rapid identification of large structural changes caused by missense mutations in intrinsically disordered proteins (IDPs). We used ∼6500 IDP s...

PredIDR2: Improving accuracy of protein intrinsic disorder prediction by updating deep convolutional neural network and supplementing DisProt data.

International journal of biological macromolecules
Intrinsically disordered proteins (IDPs) or regions (IDRs) are widespread in proteomes, and involved in several important biological processes and implicated in many diseases. Many computational methods for IDR prediction are being developed to decre...

Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2.

International journal of molecular sciences
This study explores the role of intrinsically disordered regions (IDRs) in the SARS-CoV-2 proteome and their potential as targets for small-molecule drug discovery. Experimentally validated intrinsic disordered regions from the literature were utiliz...

Deep learning tools predict variants in disordered regions with lower sensitivity.

BMC genomics
BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined ...