Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease

Author(s): Val Oliveira Pintro, Walter Filgueira de Azevedo Jr*

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 20 , Issue 9 , 2017

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Background: One key step in the development of inhibitors for an enzyme is the application of computational methodologies to predict protein-ligand interactions. The abundance of structural and ligand-binding information for HIV-1 protease opens up the possibility to apply computational methods to develop scoring functions targeted to this enzyme.

Objective: Our goal here is to develop an integrated molecular docking approach to investigate protein-ligand interactions with a focus on the HIV-1 protease. In addition, with this methodology, we intend to build target-based scoring functions to predict inhibition constant (Ki) for ligands against the HIV-1 protease system.

Methods: Here, we described a computational methodology to build datasets with decoys and actives directly taken from crystallographic structures to be applied in evaluation of docking performance using the program SAnDReS. Furthermore, we built a novel function using as terms MolDock and PLANTS scoring functions to predict binding affinity. To build a scoring function targeted to the HIV-1 protease, we have used machine-learning techniques.

Results: The integrated approach reported here has been tested against a dataset comprised of 71 crystallographic structures of HIV protease, to our knowledge the largest HIV-1 protease dataset tested so far. Comparison of our docking simulations with benchmarks indicated that the present approach is able to generate results with improved accuracy.

Conclusion: We developed a scoring function with performance higher than previously published benchmarks for HIV-1 protease. Taken together, we believe that the approach here described has the potential to improve docking accuracy in drug design projects focused on HIV-1 protease.

Keywords: Docking, HIV-1 protease, machine learning, drug design, SAnDReS, virtual screening.

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Article Details

Year: 2017
Published on: 17 January, 2018
Page: [820 - 827]
Pages: 8
DOI: 10.2174/1386207320666171121110019
Price: $65

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