Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design

James A. Davey; Roberto A. Chica

doi:10.1002/pro.2618

journal article Open Access Jan 13, 2015

Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design

James A. Davey Roberto A. Chica

Protein Science Vol. 24 No. 4 pp. 545-560 · Wiley

View at Publisher Save 10.1002/pro.2618

Abstract

AbstractComputational protein design (CPD) predictions are highly dependent on the structure of the input template used. However, it is unclear how small differences in template geometry translate to large differences in stability prediction accuracy. Herein, we explored how structural changes to the input template affect the outcome of stability predictions by CPD. To do this, we prepared alternate templates by Rotamer Optimization followed by energy Minimization (ROM) and used them to recapitulate the stability of 84 protein G domain β1 mutant sequences. In the ROM process, side‐chain rotamers for wild‐type (WT) or mutant sequences are optimized on crystal or nuclear magnetic resonance (NMR) structures prior to template minimization, resulting in alternate structures termed ROM templates. We show that use of ROM templates prepared from sequences known to be stable results predominantly in improved prediction accuracy compared to using the minimized crystal or NMR structures. Conversely, ROM templates prepared from sequences that are less stable than the WT reduce prediction accuracy by increasing the number of false positives. These observed changes in prediction outcomes are attributed to differences in side‐chain contacts made by rotamers in ROM templates. Finally, we show that ROM templates prepared from sequences that are unfolded or that adopt a nonnative fold result in the selective enrichment of sequences that are also unfolded or that adopt a nonnative fold, respectively. Our results demonstrate the existence of a rotamer bias caused by the input template that can be harnessed to skew predictions toward sequences displaying desired characteristics.

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References

37

[1]

Computational Thermostabilization of an Enzyme

Aaron Korkegian, Margaret E. Black, David Baker et al.

Science 10.1126/science.1107387

[2]

Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles

Benjamin D. Allen, Alex Nisthal, Stephen L. Mayo

Proceedings of the National Academy of Sciences 10.1073/pnas.1012985107

[3]

Motif‐directed redesign of enzyme specificity

Benjamin Borgo, James J. Havranek

Protein Science 10.1002/pro.2417

[4]

Computational Design of an Enzyme Catalyst for a Stereoselective Bimolecular Diels-Alder Reaction

Justin B. Siegel, Alexandre Zanghellini, Helena M. Lovick et al.

Science 10.1126/science.1190239

[5]

Potential energy functions for protein design

F Edward Boas, Pehr B Harbury

Current Opinion in Structural Biology 10.1016/j.sbi.2007.03.006

[6]

Davey JA (2014)

[7]

Choi EJ (2010)

[8]

Probing the role of packing specificity in protein design

Bassil I. Dahiyat, Stephen L. Mayo

Proceedings of the National Academy of Sciences 10.1073/pnas.94.19.10172

[9]

Grigoryan G (2007)

[10]

Kellogg EH (2011)

[11]

Automated selection of stabilizing mutations in designed and natural proteins

Benjamin Borgo, James J. Havranek

Proceedings of the National Academy of Sciences 10.1073/pnas.1115172109

[12]

Improved side‐chain modeling for protein–protein docking

Chu Wang, Ora Schueler‐Furman, David Baker

Protein Science 10.1110/ps.041222905

[13]

Protein Design Using Continuous Rotamers

Pablo Gainza, Kyle E. Roberts, Bruce R. Donald

PLOS Computational Biology 10.1371/journal.pcbi.1002335

[14]

Increasing Sequence Diversity with Flexible Backbone Protein Design: The Complete Redesign of a Protein Hydrophobic Core

Grant S. Murphy, Jeffrey L. Mills, Michael J. Miley et al.

Structure 10.1016/j.str.2012.03.026

[15]

Flexible Backbone Sampling Methods to Model and Design Protein Alternative Conformations

Noah Ollikainen, Colin A. Smith, James S Fraser et al.

Methods in Enzymology 10.1016/b978-0-12-394292-0.00004-7

[16]

Predicting the Tolerated Sequences for Proteins and Protein Interfaces Using RosettaBackrub Flexible Backbone Design

Colin A. Smith, Tanja Kortemme

PLoS ONE 10.1371/journal.pone.0020451

[17]

Multistate approaches in computational protein design

James A. Davey, Roberto A. Chica

Protein Science 10.1002/pro.2128

[18]

Schneider M (2009)

[19]

10.1016/j.str.2013.10.019

[20]

Two Crystal Structures of the B1 Immunoglobulin-Binding Domain of Streptococcal Protein G and Comparison with NMR

Travis Gallagher, Patrick Alexander, Philip Bryan et al.

Biochemistry 10.1021/bi00181a032

[21]

A Novel, Highly Stable Fold of the Immunoglobulin Binding Domain of Streptococcal Protein G

Angela M. Gronenborn, David R. Filpula, Nina Z. Essig et al.

Science 10.1126/science.1871600

[22]

Amino acid substitution matrices from protein blocks.

S Henikoff, Jorja G. Henikoff

Proceedings of the National Academy of Sciences 10.1073/pnas.89.22.10915

[23]

ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions

M. D. S. Kumar

Nucleic Acids Research 10.1093/nar/gkj103

[24]

Control of protein signaling using a computationally designed GTPase/GEF orthogonal pair

Gregory T. Kapp, Sen Liu, Amelie Stein et al.

Proceedings of the National Academy of Sciences 10.1073/pnas.1114487109

[25]

Assessment of flexible backbone protein design methods for sequence library prediction in the therapeutic antibody Herceptin–HER2 interface

Mariana Babor, Daniel J. Mandell, Tanja Kortemme

Protein Science 10.1002/pro.632

[26]

Backrub-Like Backbone Simulation Recapitulates Natural Protein Conformational Variability and Improves Mutant Side-Chain Prediction

Colin A. Smith, Tanja Kortemme

Journal of Molecular Biology 10.1016/j.jmb.2008.05.023

[27]

Structure-Based Prediction of the Peptide Sequence Space Recognized by Natural and Synthetic PDZ Domains

Colin A. Smith, Tanja Kortemme

Journal of Molecular Biology 10.1016/j.jmb.2010.07.032

[28]

Labute P (2009)

[29]

Chemical Computing Group Inc (2011)

[30]

How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules?

Junmei Wang, Piotr Cieplak, Peter A. Kollman

Journal of Computational Chemistry 10.1002/1096-987x(200009)21:12<1049::aid-jcc3>3.0.co;2-f

[31]

Dramatic performance enhancements for the FASTER optimization algorithm

Benjamin D. Allen, Stephen L. Mayo

Journal of Computational Chemistry 10.1002/jcc.20420

[32]

Generation of longer emission wavelength red fluorescent proteins using computationally designed libraries

Roberto A. Chica, Matthew M. Moore, Benjamin D. Allen et al.

Proceedings of the National Academy of Sciences 10.1073/pnas.1013910107

[33]

Iterative approach to computational enzyme design

Heidi K. Privett, Gert Kiss, Tai-Sung Lee et al.

Proceedings of the National Academy of Sciences 10.1073/pnas.1118082108

[34]

Bayesian statistical analysis of protein side‐chain rotamer preferences

Roland L. Dunbrack, Fred E. Cohen

Protein Science 10.1002/pro.5560060807

[35]

DREIDING: a generic force field for molecular simulations

Stephen L. Mayo, Barry D. Olafson, William A. Goddard

The Journal of Physical Chemistry 10.1021/j100389a010

[36]

Lazaridis T (1999)

[37]

Pairwise calculation of protein solvent-accessible surface areas

Arthur G. Street, Stephen L. Mayo

Folding and Design 10.1016/s1359-0278(98)00036-4

Metrics

8

Citations

37

References

Details

Published: Jan 13, 2015
Vol/Issue: 24(4)
Pages: 545-560
License: View

Authors

J

James A. Davey

Department of Chemistry University of Ottawa Ottawa Ontario Canada K1N 6N5

R

Roberto A. Chica

Department of Chemistry University of Ottawa Ottawa Ontario Canada K1N 6N5

Funding

Canada Foundation for Innovation Award: 26503

Natural Sciences and Engineering Research Council of Canada Award: 386662-2011

Cite This Article

James A. Davey, Roberto A. Chica (2015). Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design. Protein Science, 24(4), 545-560. https://doi.org/10.1002/pro.2618

Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design

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