AIMC Topic: Protein Binding

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Functional annotation and biological interpretation of proteomics data.

Biochimica et biophysica acta
Proteomics experiments often generate a vast amount of data. However, the simple identification and quantification of proteins from a cell proteome or subproteome is not sufficient for the full understanding of complex mechanisms occurring in the bio...

Developing an artificial intelligence-generated peptide targeting platelet-type von Willebrand disease.

Blood advances
Platelet-type von Willebrand disease (PT-VWD) refers to a rare bleeding disorder caused by gain-of-function mutations in platelet glycoprotein Ibα (GPIbα). These mutations lead to a hyperactive protein-protein interaction (PPI) with von Willebrand fa...

Harnessing Allostery to Modulate Protein-Protein Interactions: From Function to Therapeutic Innovations.

Journal of molecular biology
Protein-protein interactions (PPIs) are ubiquitous mediators of cellular functions, and their dysregulation is central to numerous pathological conditions. Traditional drug discovery strategies targeting PPIs directly have faced considerable obstacle...

Machine learning-driven discovery of antimicrobial peptides targeting the GAPDH-TPI protein-protein interaction in Schistosoma mansoni for novel antischistosomal therapeutics.

Computational biology and chemistry
Schistosomiasis, caused by Schistosoma mansoni, remains a significant public health burden, particularly in endemic regions with limited access to effective treatment. The emergence of resistance to praziquantel necessitates the urgent discovery of n...

NABP-LSTM-Att: Nanobody-Antigen binding prediction using bidirectional LSTM and soft attention mechanism.

Computational biology and chemistry
In vertebrates, antibody-mediated immunity is a vital component of the immune system, and antibodies have become a rapidly expanding class of therapeutic agents. Nanobodies, a distinct type of antibody, have recently emerged as a stable and cost-effe...

Computational modeling and experimental validation of the interaction between tumor biomarker mesothelin and an engineered targeting protein with therapeutic activity.

Protein science : a publication of the Protein Society
Mesothelin (MSLN) is a cell surface glycoprotein overexpressed in many solid tumors, which is known to interact with cancer antigen CA125/MUC16, promoting cancer cell adhesion and metastasis. MSLN has been used as a target of multiple antibody-based ...

BindUP-Alpha: A Webserver for Predicting DNA-and RNA-binding Proteins based on Experimental and Computational Structural Models☆.

Journal of molecular biology
Structural data provides important information on the proteins' function. Recent development of advanced machine learning and artificial intelligence tools, such as AlphaFold, have led to an explosion of predicted protein structures. However, many of...

Supervised machine learning and molecular docking modeling to identify potential Anti-Parkinson's agents.

Journal of molecular graphics & modelling
Parkinson's disease is a neurodegenerative condition that affects the brain's neurons, and causes malfunction of nerve cells and their death. A neurotransmitter called dopamine interacts with the part of the brain in charge of coordination and moveme...

Transfer Learning for Predicting ncRNA-Protein Interactions.

Journal of chemical information and modeling
Noncoding RNAs (ncRNAs) interact with proteins, playing a crucial role in regulating gene expression and cellular functions. Accurate prediction of these interactions is essential for understanding biological processes and developing novel therapeuti...

Multi-positive contrastive learning-based cross-attention model for T cell receptor-antigen binding prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: T cells play a vital role in the immune system by recognizing and eliminating infected or cancerous cells, thus driving adaptive immune responses. Their activation is triggered by the binding of T cell receptors (TCRs) to ep...