AIMC Topic: Databases, Protein

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: Advancing Proteomics Data Interpretation through GPT-4o Reannotated Topic Ontology and Data-Driven Analysis.

Analytical chemistry
The interpretation of proteomics data often relies on functional enrichment analysis, such as Gene Ontology (GO) enrichment, to uncover the biological functions of proteins, as well as the examination of protein expression patterns across data sets l...

ProtNote: a multimodal method for protein-function annotation.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding the protein sequence-function relationship is essential for advancing protein biology and engineering. However, <1% of known protein sequences have human-verified functions. While deep-learning methods have demonstrated prom...

Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcit...

Gated-GPS: enhancing protein-protein interaction site prediction with scalable learning and imbalance-aware optimization.

Briefings in bioinformatics
In protein-protein interaction site (PPIS) prediction, existing machine learning models struggle with small datasets, limiting their predictive accuracy for unseen proteins. Additionally, class imbalance in protein complexes, where binding residues c...

PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model.

Protein science : a publication of the Protein Society
Deep learning methods have played an increasingly pivotal role in advancing side-chain packing and mutation effect prediction (ΔΔG) for protein complexes. Although these two tasks are inherently closely related, they are typically treated separately ...

A robust ensemble framework for anticancer peptide classification using multi-model voting approach.

Computers in biology and medicine
Anticancer peptides (ACPs) hold great potential for cancer therapeutics, yet accurately identifying them remains a challenging task due to the complexity of peptide sequences and their interactions with biological systems. In this study, we propose a...

IDP-EDL: enhancing intrinsically disordered protein prediction by combining protein language model and ensemble deep learning.

Briefings in bioinformatics
Identification of intrinsically disordered regions (IDRs) in proteins is essential for understanding fundamental cellular processes. The IDRs can be divided into long disordered regions (LDRs) and short disordered regions (SDRs) according to their le...

TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.

Briefings in bioinformatics
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment ...

IAMPDB: A Knowledgebase of Manually Curated Insects-Derived Antimicrobial Peptides.

Journal of peptide science : an official publication of the European Peptide Society
Insects, a majority of animal species, rely on innate immunity and antimicrobial peptides (AMPs), which are a part of their innate immunity, to combat diverse parasites and pathogens. These peptides have applications ranging from agriculture to antim...

LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design.

Database : the journal of biological databases and curation
Enzymes, serving as eco-friendly catalysts, are progressively supplanting traditional chemical catalysts in light industry sectors such as feed, papermaking, textiles, detergents, leather, and sugar production. Despite this advancement, the variabili...