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Protein Binding

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Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

BMC bioinformatics
BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based meth...

TSSF-hERG: A machine-learning-based hERG potassium channel-specific scoring function for chemical cardiotoxicity prediction.

Toxicology
The human ether-à-go-go-related gene (hERG) encodes the Kv11.1 voltage-gated potassium ion (K) channel that conducts the rapidly activating delayed rectifier current (I) in cardiomyocytes to regulate the repolarization process. Some drugs, as blocker...

End-to-end learning for compound activity prediction based on binding pocket information.

BMC bioinformatics
BACKGROUND: Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, do...

Connecting MHC-I-binding motifs with HLA alleles via deep learning.

Communications biology
The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of bin...

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

Journal of chemical information and modeling
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to inclu...

miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

PloS one
Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in t...

Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics
Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery ...

MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

Structure (London, England : 1993)
The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances...

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches.

Journal of chemical information and modeling
The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket ...

FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection.

Journal of chemical theory and computation
Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. 2019, 59 (7), 3305-3315) proved that com...