Chapter

The Future of HIV-1 Therapeutics

Volume 389 of the series Current Topics in Microbiology and Immunology pp 31-51

Date:

Computational Challenges of Structure-Based Approaches Applied to HIV

  • Stefano ForliAffiliated withMGL, Department of Integrative Structural and Computational Biology and HIV Interaction and Viral Evolution Center, The Scripps Research Institute
  • , Arthur J. OlsonAffiliated withMGL, Department of Integrative Structural and Computational Biology and HIV Interaction and Viral Evolution Center, The Scripps Research Institute Email author 

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Abstract

https://static-content.springer.com/image/chp%3A10.1007%2F82_2015_432/MediaObjects/183195_1_En_432_Figa_HTML.jpg

Three-dimensional model of mature HIV. A cutaway view of mature HIV includes the capsid (gray, with pentamers in yellow) and nucleocapsid (red), matrix protein (green), accessory proteins (magenta), membrane (white), and envelope protein (blue). The model was generated using CellPACK by Graham Johnson

Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.