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Intrinsically Disordered Proteins

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DeepDRP: Prediction of intrinsically disordered regions based on integrated view deep learning architecture from transformer-enhanced and protein information.

International journal of biological macromolecules
Intrinsic disorder in proteins, a widely distributed phenomenon in nature, is related to many crucial biological processes and various diseases. Traditional determination methods tend to be costly and labor-intensive, therefore it is desirable to see...

MoRF_ESM: Prediction of MoRFs in disordered proteins based on a deep transformer protein language model.

Journal of bioinformatics and computational biology
Molecular recognition features (MoRFs) are particular functional segments of disordered proteins, which play crucial roles in regulating the phase transition of membrane-less organelles and frequently serve as central sites in cellular interaction ne...

Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.

Proteins
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed ...

Transferable deep generative modeling of intrinsically disordered protein conformations.

PLoS computational biology
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. ...

Protein embeddings predict binding residues in disordered regions.

Scientific reports
The identification of protein binding residues helps to understand their biological processes as protein function is often defined through ligand binding, such as to other proteins, small molecules, ions, or nucleotides. Methods predicting binding re...

AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder.

Nucleic acids research
Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play im...

Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning.

The journal of physical chemistry letters
Intrinsically disordered proteins and regions (IDP/IDRs) are ubiquitous across all domains of life. Characterized by a lack of a stable tertiary structure, IDP/IDRs populate a diverse set of transiently formed structural states that can promiscuously...

Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.

Nature communications
Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To ad...

Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational ensembles.

Current opinion in structural biology
Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions, challenging traditional structure-based prediction methods. This review explores how modern deep learning approaches, which have revolu...

Physics-Based Machine Learning Trains Hamiltonians and Decodes the Sequence-Conformation Relation in the Disordered Proteome.

Journal of chemical theory and computation
Intrinsically disordered proteins and regions (IDPs) are involved in vital biological processes. To understand the IDP function, often controlled by conformation, we need to find the link between sequence and conformation. We decode this link by inte...