AIMC Topic: Databases, Protein

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Deep learning-derived optimal annotation strategies to power the systematic mapping of peptide space.

Food chemistry
Rapid and reliable peptide identification techniques are essential for proteomics. High-resolution tandem mass spectrometry acquires a large amount of data through data-dependent acquisition (DDA) and data-independent acquisition (DIA), but tradition...

Ultradeep N-glycoproteome atlas of mouse reveals spatiotemporal signatures of brain aging and neurodegenerative diseases.

Nature communications
The current depth of site-specific N-glycoproteomics is insufficient to fully characterize glycosylation events in biological samples. Herein, we achieve an ultradeep and precision analysis of the N-glycoproteome of mouse tissues by integrating multi...

A fast (CNN + MCWS-transformer) based architecture for protein function prediction.

Statistical applications in genetics and molecular biology
The transformer model for sequence mining has brought a paradigmatic shift to many domains, including biological sequence mining. However, transformers suffer from quadratic complexity, i.e., O( ), where is the sequence length, which affects the tra...

CPPpred-En: Ensemble framework integrating a protein language model and conventional features for highly accurate cell-penetrating peptide prediction.

Computers in biology and medicine
Cell-penetrating peptides (CPPs) have gained significant attention for biomedical applications, including drug delivery and therapeutic development, due to their ability to penetrate cell membranes. The accurate prediction of CPPs is critical for acc...

M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling.

BMC bioinformatics
BACKGROUND: Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative effor...

Accurate identification and mechanistic evaluation of pathogenic missense variants with .

Proceedings of the National Academy of Sciences of the United States of America
Understanding the effects of missense mutations or single amino acid variants (SAVs) on protein function is crucial for elucidating the molecular basis of diseases/disorders and designing rational therapies. We introduce here , a machine learning too...

ProFun-SOM: Protein Function Prediction for Specific Ontology Based on Multiple Sequence Alignment Reconstruction.

IEEE transactions on neural networks and learning systems
Protein function prediction is crucial for understanding species evolution, including viral mutations. Gene ontology (GO) is a standardized representation framework for describing protein functions with annotated terms. Each ontology is a specific fu...

A new strategy for Cas protein recognition based on graph neural networks and SMILES encoding.

Scientific reports
The CRISPR-Cas system, an adaptive immune mechanism found in bacteria and archaea, has evolved into a promising genomic editing tool, with various types of Cas proteins playing a crucial role. In this study, we developed a set of strategies for minin...

Deep Learning-Based Prediction of Decoy Spectra for False Discovery Rate Estimation in Spectral Library Searching.

Journal of proteome research
With the advantage of extensive coverage, predicted spectral libraries are becoming an attractive alternative in proteomic data analysis. As a popular false discovery rate estimation method, target decoy search has been adopted in library search work...

Prediction and Evaluation of Coronavirus and Human Protein-Protein Interactions Integrating Five Different Computational Methods.

Proteins
The high lethality and infectiousness of coronaviruses, particularly SARS-Cov-2, pose a significant threat to human society. Understanding coronaviruses, especially the interactions between these viruses and humans, is crucial for mitigating the coro...