Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.4.23.16 (HIV-1 protease)
 2,107 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Studies of the cyclin-dependent kinase inhibitors and HIV-1 protease inhibitors have confirmed that ligand-protein binding is dependent on desolvation effects. It has been found that a four parameter linear model incorporating desolvation energy, lipophilicity, dipole moment and molecular volume of the ligands is a good model to describe the binding between ligands and kinases or proteases. The resistance shown by MDR proteases to the anti-viral drugs is multi-faceted involving varying changes in desolvation, lipophilicity and dipole moment interaction compared to the non-resistant protease. Desolvation has been shown to be the dominant factor influencing the effect of inhibitors against the cyclin-dependent kinases, but lipophilicity and dipole moment are also significant factors. The model can differentiate between the inhibitory activity of CDK2/cycE, CDK1/cycB and CDK4/cycD enzymes.
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PMID:The effect of desolvation on the binding of inhibitors to HIV-1 protease and cyclin-dependent kinases: Causes of resistance. 2731 42

Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic coordinates of protein-ligand complexes. These new computational tools made application of a broad spectrum of machine-learning techniques to study protein-ligand interactions possible. The essential aspect of these machine-learning approaches is to train a new computational model by using technologies such as supervised machine-learning techniques, convolutional neural network, and random forest to mention the most commonly applied methods. In this chapter, we focus on supervised machine-learning techniques and their applications in the development of protein-targeted scoring functions for the prediction of binding affinity. We discuss the development of the program SAnDReS and its application to the creation of machine-learning models to predict inhibition of cyclin-dependent kinase and HIV-1 protease. Moreover, we describe the scoring function space, and how to use it to explain the development of targeted scoring functions.
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PMID:Machine Learning to Predict Binding Affinity. 3145 10