AIMC Topic: Proteins

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Harnessing Allostery to Modulate Protein-Protein Interactions: From Function to Therapeutic Innovations.

Journal of molecular biology
Protein-protein interactions (PPIs) are ubiquitous mediators of cellular functions, and their dysregulation is central to numerous pathological conditions. Traditional drug discovery strategies targeting PPIs directly have faced considerable obstacle...

Synthetic Biology for Designing Allostery and Its Potential Biomedical Applications.

Journal of molecular biology
Allosteric regulation of protein function, where a perturbation at one site induces a conformational shift or alters dynamics at a distal functional site, plays a key role in numerous biological processes. The ability to introduce allostery using syn...

How good is generative diffusion model for enhanced sampling of protein conformations across scales and in all-atom resolution?

The Journal of chemical physics
Molecular dynamics (MD) simulations are fundamental for probing the structural dynamics of biomolecules, yet their efficiency is limited by the high computational cost of exploring long-timescale events. Generative machine learning (ML) models, parti...

DeepPhosPPI: a deep learning framework with attention-CNN and transformer for predicting phosphorylation effects on protein-protein interactions.

Briefings in bioinformatics
Protein phosphorylation regulates protein function and cellular signaling pathways, and is strongly associated with diseases, including neurodegenerative disorders and cancer. Phosphorylation plays a critical role in regulating protein activity and c...

Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants.

NAR genomics and bioinformatics
Genetic variants can impact the structure of the corresponding protein, which can have detrimental effects on protein function. While the effect of protein-truncating variants is often easier to evaluate, most genetic variants that affect the protein...

DCBLSTM-Deep Convolutional Bidirectional Long Short-Term Memory neural network for Q8 secondary protein structure prediction.

Computers in biology and medicine
Protein secondary structure prediction involves determining a protein's secondary structure from its primary amino acid sequence, serving as a critical step toward tertiary structure prediction. This, in turn, is essential for applications in drug de...

ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra.

Interdisciplinary sciences, computational life sciences
In top-down proteomics, the accurate identification and characterization of proteoform through mass spectrometry represents a critical objective. As a result, achieving accuracy in identification results is essential. Multiple primary structure alter...

Transfer Learning for Predicting ncRNA-Protein Interactions.

Journal of chemical information and modeling
Noncoding RNAs (ncRNAs) interact with proteins, playing a crucial role in regulating gene expression and cellular functions. Accurate prediction of these interactions is essential for understanding biological processes and developing novel therapeuti...

Investigating the determinants of performance in machine learning for protein fitness prediction.

Protein science : a publication of the Protein Society
Machine learning (ML) has revolutionized protein biology, solving long-standing problems in protein folding, scaffold generation, and function design tasks. A range of architectures have shown success on supervised protein fitness prediction tasks. N...

DSSP 4: FAIR annotation of protein secondary structure.

Protein science : a publication of the Protein Society
Protein secondary structure annotation is essential for understanding protein architecture, serving as a cornerstone for structural classification, alignment, visualization, and machine learning applications. The Define Secondary Structure of Protein...