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Homology Modeling pp 399–414Cite as

Comparison of Common Homology Modeling Algorithms: Application of User-Defined Alignments

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 857))

Abstract

The number of known three-dimensional protein sequences is orders of magnitude higher than the number of known protein structures. This is a result of an increase in large-scale genomic sequencing projects, the inability of proteins to crystallize or crystals to diffract well, or a simple lack of resources. An alternative is to use one of a variety of available homology modeling programs to produce a computational model of a protein. Protein models are produced using information from known protein structures found to be similar. Here, we compare the ability of a number of popular homology modeling programs to produce quality models from user-defined target–template sequence alignments over a range of circumstances including low sequence identity, variable sequence length, and when interfaced with a protein or small molecule. Programs evaluated include Prime, SWISS-MODEL, MOE, MODELLER, ROSETTA, Composer, ORCHESTRAR, and I-TASSER. Proteins to be modeled were chosen to test a range of sequence identities, sequence lengths, and protein motifs and all are of scientific importance. These include HIV-1 protease, kinases, dihydrofolate reductase, a viral capsid protein, and factor Xa among others. For the most part, the programs produce results that are similar. For example, all programs are able to produce reasonable models when sequence identities are >30% and all programs have difficulties producing complete models when sequence identities are lower. However, certain programs fare slightly better than others in certain situations and we attempt to provide insight on this topic.

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Acknowledgments

The authors would like to thank Dr. Judith Hobrath for her technical assistance.

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Authors and Affiliations

  1. Bioinformatics and Computational Biosciences Branch, National Institute of Allergies and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Michael A. Dolan & Darrell Hurt

  2. Southern Research Institute, Birmingham, AL, USA

    James W. Noah

Authors
  1. Michael A. Dolan
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  2. James W. Noah
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  3. Darrell Hurt
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Corresponding author

Correspondence to Michael A. Dolan .

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Editors and Affiliations

  1. Molsoft L.L.C., Sorrento Valley Road 11199, S209 San Diego, 92121, California, USA

    Andrew J. W. Orry

  2. Skaggs School of Pharmacy &, Pharmaceutical Sciences, University of California, San Diego, Gilman Drive 9500, La Jolla, 92093, California, USA

    Ruben Abagyan

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Dolan, M.A., Noah, J.W., Hurt, D. (2011). Comparison of Common Homology Modeling Algorithms: Application of User-Defined Alignments. In: Orry, A., Abagyan, R. (eds) Homology Modeling. Methods in Molecular Biology, vol 857. Humana Press. https://doi.org/10.1007/978-1-61779-588-6_18

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  • DOI: https://doi.org/10.1007/978-1-61779-588-6_18

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-587-9

  • Online ISBN: 978-1-61779-588-6

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