AIMC Topic: Protein Binding

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Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2.

Current medicinal chemistry
BACKGROUND: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the developme...

A machine learning based intramolecular potential for a flexible organic molecule.

Faraday discussions
Quantum mechanical predictive modelling in chemistry and biology is often hindered by the long time scales and large system sizes required of the computational model. Here, we employ the kernel regression machine learning technique to construct an an...

Multitask deep networks with grid featurization achieve improved scoring performance for protein-ligand binding.

Chemical biology & drug design
Deep learning-based methods have been extensively developed to improve scoring performance in structure-based drug discovery. Extending multitask deep networks in addressing pharmaceutical problems shows remarkable improvements over single task netwo...

Study on the differentially expressed genes and signaling pathways in dermatomyositis using integrated bioinformatics method.

Medicine
Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyositis is a result of multiple factors, but its exact pathogenesis has not been fully elucidated. Here, we used biological information method to explore...

Artificial neural networks for the inverse design of nanoparticles with preferential nano-bio behaviors.

The Journal of chemical physics
Safe and efficient use of ultrasmall nanoparticles (NPs) in biomedicine requires numerous independent conditions to be met, including colloidal stability, selectivity for proteins and membranes, binding specificity, and low affinity for plasma protei...

DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleic acids research
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...

Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting...

Protein-ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data.

Bioinformatics (Oxford, England)
MOTIVATION: Knowledge of protein-ligand binding residues is important for understanding the functions of proteins and their interaction mechanisms. From experimentally solved protein structures, how to accurately identify its potential binding sites ...

Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...

Learning from the ligand: using ligand-based features to improve binding affinity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use fe...