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Binding Sites

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Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.

Chemical biology & drug design
In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorisma...

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HTR Ligands.

Molecules (Basel, Switzerland)
The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HTR versus 5-HTR selectivity. Our approach employs the hierarchical...

High-Order Convolutional Neural Network Architecture for Predicting DNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics
Although Deep learning algorithms have outperformed conventional methods in predicting the sequence specificities of DNA-protein binding, they lack to consider the dependencies among nucleotides and the diverse binding lengths for different transcrip...

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

Proteins
Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neura...

ATPbind: Accurate Protein-ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons.

Journal of chemical information and modeling
Protein-ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. However, there i...

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

BMC bioinformatics
BACKGROUND: The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been ...

Development of CDK-targeted scoring functions for prediction of binding affinity.

Biophysical chemistry
Cyclin-dependent kinase (CDK) is an interesting biological macromolecule due to its role in cell cycle progression, transcription control, and neuronal development, to mention the most studied biological activities. Furthermore, the availability of h...

Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

Computational and mathematical methods in medicine
We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or si...