AIMC Topic: Databases, Protein

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Partner-RBR: Predicting Multitype RNA-Binding Residues Based on Mutual Learning.

Journal of chemical information and modeling
RNA molecules play diverse and critical roles in various biological processes, including gene expression, post-transcriptional regulation, and disease pathogenesis. Understanding the interaction between proteins and RNA necessitates the precise ident...

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach.

Journal of chemical information and modeling
Phytocystatins are proteinaceous inhibitors found in plants that competitively target various classes of cysteine proteinases, including papain-like enzymes, cathepsins, and legumains. Based on structural characteristics and gene organization, phytoc...

Combining knowledge distillation and neural networks to predict protein secondary structure.

Scientific reports
The secondary structure of a protein serves as the foundation for constructing its three-dimensional (3D) structure, which in turn is critical for determining its function and role in biological processes. Therefore, accurately predicting secondary s...

Protein functional site annotation using local structure embeddings.

Proceedings of the National Academy of Sciences of the United States of America
The rapid expansion of protein sequence and structure databases has resulted in a significant number of proteins with ambiguous or unknown function. While advances in machine learning techniques hold great potential to fill this annotation gap, curre...

MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.

BMC biology
BACKGROUND: Protein-protein interactions (PPIs) play a critical role in essential biological processes such as signal transduction, enzyme activity regulation, cytoskeletal structure, immune responses, and gene regulation. However, current methods ma...

Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.

PloS one
Inferring protein function is a fundamental and long-standing problem in biology. Laboratory experiments in this field are often expensive, and therefore large-scale computational protein inference from readily available amino acid sequences is neede...

Off-site processing of data-dependent and data-independent acquisition shotgun proteomics data with MASSyPupX.

Journal of proteomics
The rapid pace of shotgun proteomics data generation presents challenges for timely data analysis. In parallel, the scientific community is creating novel data interpretation tools, such as artificial intelligence, that have not yet been integrated i...

Rprot-Vec: a deep learning approach for fast protein structure similarity calculation.

BMC bioinformatics
BACKGROUND: Predicting protein structural similarity and detecting homologous sequences remain fundamental and challenging tasks in computational biology. Accurate identification of structural homologs enables function inference for newly discovered ...

Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies.

Genome biology
BACKGROUND: Proteins self-organize in dynamic cellular environments by assembling into reversible biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates can comprise single or multiple proteins, with different roles...

Advancing the accuracy of clathrin protein prediction through multi-source protein language models.

Scientific reports
Clathrin is a key cytoplasmic protein that serves as the predominant structural element in the formation of coated vesicles. Specifically, clarithin enables the scission of newly formed vesicles from the plasma membrane's cytoplasmic face. Efficient ...