AIMC Topic: Intrinsically Disordered Proteins

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Proteome-wide computational analyses reveal links between protein condensate formation and RNA biology.

Science advances
Biomolecular condensates mediate dynamic compartmentalization of cellular processes. The multivalent interactions that underlie biomolecular condensation are often promoted by intrinsically disordered regions (IDRs) within proteins. Although the role...

SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions.

Genome biology
Intrinsically disordered proteins and regions (IDRs) lack stable 3D structures, posing challenges for interaction prediction. We present SpatPPI, a geometric deep learning model tailored for IDPPI prediction. SpatPPI leverages structural cues from fo...

A comprehensive application of FiveFold for conformation ensemble-based protein structure prediction.

Scientific reports
The emergence of artificial intelligence in protein structure prediction has significantly advanced our understanding of protein folding. Yet, challenges remain in accurately modeling intrinsically disordered proteins (IDPs) and capturing conformatio...

When is Not Enough: Evaluating Simple Metrics for Predicting Phase Separation of Intrinsically Disordered Proteins.

The journal of physical chemistry. B
Understanding and predicting the phase behavior of intrinsically disordered proteins (IDPs) is of significant interest due to their role in many biological processes. However, effectively characterizing phase behavior and its complex dependence on pr...

Divergence in a eukaryotic transcription factor's co-TF dependence involves multiple intrinsically disordered regions.

Nature communications
Combinatorial control by transcription factors (TFs) is central to eukaryotic gene regulation, yet its mechanism, evolution, and regulatory impact are not well understood. Here we use natural variation in the yeast phosphate starvation (PHO) response...

Amino acid sequence-based IDR classification using ensemble machine learning and quantum neural networks.

Computational biology and chemistry
Biologically traditional methods, such as the Uversky plot, which rely on hydrophobicity and net charge, have inherent limitations in accurately distinguishing intrinsically disordered regions (IDRs) from ordered protein regions. To overcome these co...

Deciphering disordered regions controlling mRNA decay in high-throughput.

Nature
Intrinsically disordered regions within proteins drive specific molecular functions despite lacking a defined structure. Although disordered regions are integral to controlling mRNA stability and translation, the mechanisms underlying these regulator...

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 ...

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...

Machine learning methods to study sequence-ensemble-function relationships in disordered proteins.

Current opinion in structural biology
Recent years have seen tremendous developments in the use of machine learning models to link amino-acid sequence, structure, and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences th...