AIMC Topic: Databases, Protein

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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...

Backyard Proteomics: A Case Study with the Black Widow Spider.

Journal of proteome research
Nearly all methods of mass-spectrometry-based proteomics rely on knowing the proteome of the species. In less studied organisms without annotated genomes, it can seem impossible to perform proteomic analysis. In this study, we sought to answer the qu...

AlphaFold modeling uncovers global structural features of class I and class II fungal hydrophobins.

Protein science : a publication of the Protein Society
Hydrophobins are a family of small fungal proteins that self-assemble at hydrophobic-hydrophilic interfaces. Hydrophobins not only play crucial roles in filamentous fungal growth and development but also have attracted substantial attention due to th...

Finding the dark matter: Large language model-based enzyme kinetic data extractor and its validation.

Protein science : a publication of the Protein Society
Despite the vast number of enzymatic kinetic measurements reported across decades of biochemical literature, the majority of relational enzyme kinetic data-linking amino acid sequence, substrate identity, kinetic parameters, and assay conditions-rema...

EnsemPred-ACP: Combining machine and deep learning to improve anticancer peptide prediction.

Computers in biology and medicine
Anticancer peptide (ACP) has emerged as potent therapeutic agents owing to its ability to selectively target cancer cells while minimising toxicity to healthy cells. However, the accurate computational prediction of ACP remains challenging because of...

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...

pLMMoRF: A Web Server That Accurately Predicts Membrane-interacting Molecular Recognition Features by Employing a Protein Language Model.

Journal of molecular biology
Interactions between proteins and lipids are crucial for numerous cellular processes. Some of the lipid interacting segments in protein sequences are intrinsically disordered regions (IDRs), which may gain secondary structures upon binding. We collec...

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...

PEGASUS: Prediction of MD-derived protein flexibility from sequence.

Protein science : a publication of the Protein Society
Protein flexibility is essential to its biological function. However, experimental methods for its assessment, such as X-ray crystallography and nuclear magnetic resonance spectroscopy, are often limited by experimental variability and high cost, lea...

Artificial intelligence and first-principle methods in protein redesign: A marriage of convenience?

Protein science : a publication of the Protein Society
Since AlphaFold2's rise, many deep learning methods for protein design have emerged. Here, we validate widely used and recognized tools, compare them with first-principle methods, and explore their combinations, focusing on their effectiveness in pro...