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Computational Challenges of Structure-Based Approaches Applied to HIV

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Book cover The Future of HIV-1 Therapeutics

Part of the book series: Current Topics in Microbiology and Immunology ((CT MICROBIOLOGY,volume 389))

Abstract

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.

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Abbreviations

BEDAM:

Binding energy distribution analysis method

CCD:

Catalytic core domain

BSI:

Backscattering interferometry

DSF:

Differential scanning fluorimetry

DTP:

Developmental Therapeutics Program

FA@H:

FightAids@Home

FBDD:

Fragment-based drug design

HAART:

Highly active antiretroviral therapy

HIV:

Human immunodeficiency virus

HTVS:

High-throughput virtual screening

IN:

Integrase

INSTI:

IN strand transfer inhibitor

LEGDF:

Lens epithelium-derived growth factor

MD:

Molecular dynamics

MW:

Molecular weight

NMA:

Normal mode analysis

PDB:

Protein Data Bank

PPI:

Protein–protein interaction

PR:

Protease

RC:

Relaxed complex

RH:

RNase H

RT:

Reverse transcriptase

SAMPL:

Statistical Assessment of Modeling of Proteins and Ligands

SBDD:

Structure-based drug design

WCG:

World Community Grid

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Acknowledgments

We thank IBM World Community Grid for the computational resource support provided to the FightAIDS@Home project. This work was supported by NIH R01 GM073087 and P50 GM103368 to AJO.

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Authors and Affiliations

  1. MGL, Department of Integrative Structural and Computational Biology and HIV Interaction and Viral Evolution Center, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA, 92037, USA

    Stefano Forli & Arthur J. Olson

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  1. Stefano Forli
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  2. Arthur J. Olson
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Correspondence to Arthur J. Olson .

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Editors and Affiliations

  1. Departments of Molecular and Experimental Medicine and Immunology and Microbial Science (MEM 131), The Scripps Research Institute, La Jolla, California, USA

    Bruce E. Torbett

  2. Department of Integrative Structural and Computational Biology, and RCSB Protein Data Bank, The Scripps Research Institute, La Jolla, California, USA

    David S. Goodsell

  3. Department of Pathology, University of California, San Diego, La Jolla, California, USA

    Douglas D. Richman

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Forli, S., Olson, A.J. (2015). Computational Challenges of Structure-Based Approaches Applied to HIV. In: Torbett, B., Goodsell, D., Richman, D. (eds) The Future of HIV-1 Therapeutics. Current Topics in Microbiology and Immunology, vol 389. Springer, Cham. https://doi.org/10.1007/82_2015_432

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