Table Of ContentMethods in
Molecular Biology 1561
Ora Schueler-Furman
Nir London Editors
Modeling
Peptide-Protein
Interactions
Methods and Protocols
M M B
ethods in olecular iology
Series Editor
John M. Walker
School of Life and Medical Sciences
University of Hertfordshire
Hatfield, Hertfordshire, AL10 9AB, UK
For further volumes:
http://www.springer.com/series/7651
Modeling Peptide-Protein
Interactions
Methods and Protocols
Edited by
Ora Schueler-Furman
Microbiology and Molecular Genetics, Hebrew University Hadassah Medical School, Jerusalem, Israel
Nir London
Organic Chemistry, The Weizmann Institute of Science, Rehovot, Israel
Editors
Ora Schueler-Furman Nir London
Microbiology and Molecular Genetics Organic Chemistry
Hebrew University Hadassah Medical School The Weizmann Institute of Science
Jerusalem, Israel Rehovot, Israel
ISSN 1064-3745 ISSN 1940-6029 (electronic)
Methods in Molecular Biology
ISBN 978-1-4939-6796-4 ISBN 978-1-4939-6798-8 (eBook)
DOI 10.1007/978-1-4939-6798-8
Library of Congress Control Number: 2017933836
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Cover Caption: Peptide docking: Programs such as GalaxyPepDock can generate a refined model (dark green) that is
very similar to the native structure (yellow), starting from an initial similar model (green), or even farther. Image courtesy
of Hasup Lee and Chaok Seok.
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Preface
Peptide-mediated interactions play prominent roles in many cellular processes: their weak,
transient character, and their easy manipulation by targeted changes such as post-t ranslational
modifications, makes them especially amenable to versatile regulation.
The structure of a peptide-protein complex allows its detailed characterization and fine-
tuned manipulation and provides important leads for targeted inhibitor design. It is there-
fore not surprising that much effort has been put into the development of tailored tools for
the modeling of peptide-protein complex structures. However, until not long ago, such
approaches were significantly limited, mainly due to challenges of sampling (peptides pre-
dominantly do not adopt a defined conformation prior to binding, so that peptide docking
may be seen as a “fold-and-dock” challenge), but also scoring (peptide-protein interactions
are often transient and weak, and modeling of solvation can be particularly challenging for
these small interfaces).
The last few years have witnessed an unprecedented interest, and consequently advance,
in our abilities to model and manipulate peptide-mediated interactions. This started with
the development of dedicated protocols for local peptide-protein docking that apply a
range of different algorithms to tackle the sampling problem. These now generate on a
regular basis accurate, near-atom resolution models. It did not take long for the develop-
ment of a second wave of approaches that extend and complement these tools towards full
blind docking, without prior knowledge of the binding site, or an approximate starting
conformation for the peptide. Such global docking may be accomplished either by combin-
ing binding site prediction with subsequent peptide docking or, alternatively, by perform-
ing both together.
Another area of fruitful advance has been our improved ability to predict not only the
structure but also the binding affinity and specificity of peptide-protein interactions. These
come together with dramatic improvement in the design of inhibitory peptides for the fine-
tuned manipulation of protein interactions. Such advances bring us closer to be able to
perform peptide-protein modeling on proteomic scale.
It is truly impressive how, in a short time, peptide-protein modeling has risen from a
challenged side topic to an ever improving, buzzing field! Key to this improvement has
been benchmarks, in the form of curated datasets of peptide-protein complex structures
(such as PeptiDB), and last but not least, the CAPRI challenge for the assessment of the
modeling of protein interactions: CAPRI has enthusiastically embraced peptide docking
and included several peptide-protein docking targets over the past few years. This has fur-
ther spurred the development of peptide docking protocols; many of them have been dis-
cussed in detail at the latest CAPRI evaluation meeting in 2016 in Tel Aviv (www.cs.tau.
ac.il/conferences/CAPRI2016/).
In this book we have collected a series of chapters from the leading figures in the field
of peptide-protein docking. The chapters are bundled into four inter-related parts, includ-
ing (1) peptide binding site prediction; (2) peptide-protein docking; (3) prediction and
v
vi Preface
design of peptide binding specificity; and (4) the design of inhibitory peptides. In their
combination in this book, the chapters provide a diverse and unified state-of-the-art over-
view of this rapidly advancing field of major interest and applicability.
We look forward to seeing the many applications that will result from applying the
methodologies described in this book.
Jerusalem, Israel Ora Schueler-Furman
Rehovot, Israel Nir London
Contents
Preface.......................................................... v
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Part I PredIctIon of PePtIde BIndIng SIteS
1 The Usage of ACCLUSTER for Peptide Binding Site Prediction........... 3
Chengfei Yan, Xianjin Xu, and Xiaoqin Zou
2 Detection of Peptide-Binding Sites on Protein Surfaces
Using the Peptimap Server ....................................... 11
Tanggis Bohnuud, George Jones, Ora Schueler-Furman,
and Dima Kozakov
3 Peptide Suboptimal Conformation Sampling for the Prediction
of Protein-Peptide Interactions.................................... 21
Alexis Lamiable, Pierre Thévenet, Stephanie Eustache, Adrien Saladin,
Gautier Moroy, and Pierre Tuffery
Part II PePtIde dockIng
4 Template-Based Prediction of Protein-Peptide Interactions
by Using GalaxyPepDock ........................................ 37
Hasup Lee and Chaok Seok
5 Application of the ATTRACT Coarse-Grained Docking and Atomistic
Refinement for Predicting Peptide-Protein Interactions.................. 49
Christina Schindler and Martin Zacharias
6 Highly Flexible Protein-Peptide Docking Using CABS-Dock ............. 69
Maciej Paweł Ciemny, Mateusz Kurcinski, Konrad Jakub Kozak,
Andrzej Kolinski, and Sebastian Kmiecik
7 AnchorDock for Blind Flexible Docking of Peptides to Proteins ........... 95
Michal Slutzki, Avraham Ben-Shimon, and Masha Y. Niv
8 Information-Driven, Ensemble Flexible Peptide Docking
Using HADDOCK............................................. 109
Cunliang Geng, Siddarth Narasimhan, João P.G.L.M. Rodrigues,
and Alexandre M.J.J. Bonvin
9 Modeling Peptide-Protein Structure and Binding Using Monte
Carlo Sampling Approaches: Rosetta FlexPepDock and FlexPepBind........ 139
Nawsad Alam and Ora Schueler-Furman
Part III PredIctIon and deSIgn of PePtIde BIndIng SPecIfIcIty
10 Flexible Backbone Methods for Predicting and Designing Peptide Specificity .... 173
Noah Ollikainen
vii
viii Contents
11 Simplifying the Design of Protein-Peptide Interaction Specificity
with Sequence-Based Representations of Atomistic Models............... 189
Fan Zheng and Gevorg Grigoryan
12 Binding Specificity Profiles from Computational Peptide Screening......... 201
Stefan Wallin
13 Enriching Peptide Libraries for Binding Affinity and Specificity Through
Computationally Directed Library Design............................ 213
Glenna Wink Foight, T. Scott Chen, Daniel Richman,
and Amy E. Keating
Part IV deSIgn of InhIBItory PePtIdeS
14 Investigating Protein-Peptide Interactions Using the Schrödinger
Computational Suite............................................ 235
Jas Bhachoo and Thijs Beuming
15 Identifying Loop-Mediated Protein-Protein Interactions using LoopFinder..... 255
Timothy R. Siegert, Michael Bird, and Joshua A. Kritzer
16 Protein-Peptide Interaction Design: PepCrawler and PinaColada........... 279
Daniel Zaidman and Haim J. Wolfson
17 Modeling and Design of Peptidomimetics to Modulate
Protein–Protein Interactions...................................... 291
Andrew M. Watkins, Richard Bonneau, and Paramjit S. Arora
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
Contributors
nawSad alam • Department of Microbiology and Molecular Genetics, Institute for Medical
Research Israel-Canada, Faculty of Medicine, Hadassah Medical School, The Hebrew
University of Jerusalem, Jerusalem, Israel
ParamjIt S. arora • Department of Chemistry, New York University, New York, NY, USA
aVraham Ben-ShImon • The Institute of Biochemistry, Food Science and Nutrition, The
Robert H Smith Faculty of Agriculture, Food and Environment, and the Fritz Haber
Center for Molecular Dynamics, The Hebrew University, Jerusalem, Israel
thIjS BeumIng • Schrödinger, Inc ., New York, NY, USA
jaS Bhachoo • Schrödinger, Inc ., New York, NY, USA
mIchael BIrd • Department of Chemistry, Tufts University, Medford, MA, USA
tanggIS Bohnuud • Department of Biomedical Engineering, Boston University, Boston,
MA, USA
rIchard Bonneau • Department of Biology, Center for Genomics and Systems Biology,
New York University, New York, NY, USA; Computer Science Department, Courant
Institute of Mathematical Sciences, New York University, New York, NY, USA
alexandre m.j.j. BonVIn • Computational Structural Biology Group, Bijvoet Center
for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht,
The Netherlands
t. Scott chen • Department of Biology, Massachusetts Institute of Technology, Cambridge,
MA, USA; Google Inc ., Mountain View, CA , USA
macIej Paweł cIemny • Faculty of Chemistry, University of Warsaw, Warsaw, Poland
StePhanIe euStache • Université Paris Diderot, Sorbonne Paris Cité, Molécules
Thérapeutiques In Silico, Inserm UMR-S 973, Paris, France
glenna wInk foIght • Department of Biology, Massachusetts Institute of Technology,
Cambridge, MA, USA; Department of Chemistry, University of Washington, Seattle,
WA, USA
cunlIang geng • Computational Structural Biology Group, Bijvoet Center for
Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht,
The Netherlands
geVorg grIgoryan • Department of Biological Science, Dartmouth College, Hanover,
NH, USA; Department of Computer Sciences, Dartmouth College, Hanover, NH, USA
george joneS • Department of Applied Mathematics and Statistics, Stony Brook
University, New York, NY, USA
amy e. keatIng • Department of Biology, Massachusetts Institute of Technology,
Cambridge, MA, USA; Department of Biological Engineering, Massachusetts Institute
of Technology, Cambridge, MA, USA
SeBaStIan kmIecIk • Faculty of Chemistry, University of Warsaw, Warsaw, Poland
andrzej kolInSkI • Faculty of Chemistry, University of Warsaw, Warsaw, Poland
konrad jakuB kozak • Faculty of Chemistry, University of Warsaw, Warsaw, Poland
dIma kozakoV • Department of Biomedical Engineering, Boston University, Boston, MA,
USA; Department of Applied Mathematics and Statistics, Stony Brook University,
New York, NY, USA
ix
x Contributors
joShua a. krItzer • Department of Chemistry, Tufts University, Medford, MA, USA
mateuSz kurcInSkI • Faculty of Chemistry, University of Warsaw, Warsaw, Poland
alexIS lamIaBle • Uniersité Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques
In Silico, Inserm UMR-S 973, Paris, France
haSuP lee • Department of Chemistry, Seoul National University, Seoul, Republic of Korea
gautIer moroy • Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques
In Silico, Inserm UMR-S 973, Paris, France
SIddarth naraSImhan • Computational Structural Biology Group, Bijvoet Center
for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht,
The Netherlands
maSha y. nIV • The Institute of Biochemistry, Food Science and Nutrition, The Robert H
Smith Faculty of Agriculture, Food and Environment, and the Fritz Haber Center for
Molecular Dynamics, The Hebrew University, Jerusalem, Israel
noah ollIkaInen • Division of Biology and Biological Engineering, California Institute
of Technology, Pasadena, CA, USA
danIel rIchman • Department of Biology (T38), Massachusetts Institute of Technology,
Cambridge, MA, USA
joão P.g.l.m. rodrIgueS • Computational Structural Biology Group, Bijvoet Center
for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht,
The Netherlands; Department of Structural Biology, Stanford University School of
Medicine, Stanford, CA, USA
adrIen SaladIn • Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques
In Silico, Inserm UMR-S 973, Paris, France
chrIStIna SchIndler • Physics Department (T38), Center for Integrated Protein Science
Munich (CIPSM), Technical University of Munich, Garching, Germany
ora Schueler-furman • Department of Microbiology and Molecular Genetics, Institute
for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University
of Jerusalem, Jerusalem, Israel
chaok Seok • Department of Chemistry, Seoul National University, Seoul, Republic of
Korea
tImothy r. SIegert • Department of Chemistry, Tufts University, Medford, MA, USA
mIchal SlutzkI • The Institute of Biochemistry, Food Science and Nutrition, The Robert
H Smith Faculty of Agriculture, Food and Environment, and the Fritz Haber Center for
Molecular Dynamics, The Hebrew University, Jerusalem, Israel
PIerre théVenet • Université Paris Diderot, Sorbonne Paris Cité, Molécules
Thérapeutiques In Silico, Inserm UMR-S 973, Paris, France
PIerre tuffery • Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques
In Silico, Inserm UMR-S 973, Paris, France
Stefan wallIn • Department of Physics and Physical Oceanography, Memorial University,
St . John’s, NF, Canada
andrew m. watkInS • Department of Chemistry, New York University, New York, NY, USA
haIm j. wolfSon • Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv,
Israel
xIanjIn xu • Department of Physics and Astronomy, Department of Biochemistry, Dalton
Cardiovascular Research Center, Informatics Institute, University of Missouri,
Columbia, MO, USA
