AIMC Topic: Databases, Protein

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ANABAG: Annotated Antibody-Antigen Data Set with Unique Features for Antibody Engineering Applications.

Journal of chemical information and modeling
The analysis and prediction of antibody-antigen (Ab-Ag) interactions often overlook critical structural features such as glycosylation and important physicochemical conditions like pH and salt concentration. Additionally, the field lacks standardized...

DCMF-PPI: a protein-protein interaction predictor based on dynamic condition and multi-feature fusion.

BMC bioinformatics
BACKGROUND: The identification of protein-protein interaction (PPI) plays a crucial role in understanding the mechanisms of complex biological processes. Current research in predicting PPI has shown remarkable progress by integrating protein informat...

Empathi: embedding-based phage protein annotation tool by hierarchical assignment.

Nature communications
Bacteriophages, viruses infecting bacteria, are estimated to outnumber their cellular hosts by 10-fold, acting as key players in all microbial ecosystems. Under evolutionary pressure by their host, they evolve rapidly and encode a large diversity of ...

ProFlex as a linguistic bridge for decoding protein dynamics in normal mode analysis.

Nature communications
Artificial intelligence is revolutionizing structural bioinformatics, with AlphaFold arguably being the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological ...

AbDesign: database of point mutants of antibodies with associated structures reveals poor generalization of binding predictions from machine learning models.

mAbs
Antibodies are naturally evolved molecular recognition scaffolds that can bind a variety of surfaces. Their designability is crucial to the development of biologics, with computational methods holding promise in accelerating the delivery of medicines...

Enhancing peptide identification in metaproteomics through curriculum learning in deep learning.

Nature communications
Metaproteomics offers a powerful window into the active functions of microbial communities, but accurately identifying peptides remains challenging due to the size and incompleteness of protein databases derived from metagenomes. These databases ofte...

AI-driven protein pocket detection through integrating deep Q-networks for structural analysis.

Journal of computer-aided molecular design
Protein pockets, or small cavities on the protein surface, are critical sites for enzymatic catalysis, molecular recognition, and drug binding. Accurately identifying these pockets is crucial for understanding protein function and designing therapeut...

SARST2 high-throughput and resource-efficient protein structure alignment against massive databases.

Nature communications
The flood of protein structural Big Data is coming. With the belief that biotech researchers deserve powerful analysis engines to overcome the challenge of rapidly increasing computational demands, we are devoted to developing efficient protein struc...

BrainProt v3.0: An Integrative and Simplified Omics-Based Knowledge-Base About the Human Brain and Its Associated Diseases.

Journal of proteome research
The advancements in neuroscience research and omics technologies generate extensive data for brain-related diseases and disorders that are scattered across various manuscript repositories and databases, potentially hindering global initiatives to adv...

iBitter-Stack: A multi-representation ensemble learning model for accurate bitter peptide identification.

Journal of molecular biology
The identification of bitter peptides is crucial in various domains, including food science, drug discovery, and biochemical research. These peptides not only contribute to the undesirable taste of hydrolyzed proteins but also play key roles in physi...