Computational Approaches to Protein Dynamics

283

Chapter 10

Coarse-Grained Simulation of

Intrinsically Disordered Proteins

David de Sancho, Christopher M. Baker, and Robert B. Best

INTRODUCTION

e important role played by intrinsically disordered proteins (IDPs) in protein–

protein interaction networks and cellular signaling is increasingly being rec-

ognized [1–3]. In these interactions, an intrinsically disordered polypeptide

CONTENTS

Introduction 283

Types of Coarse-GrainedModels Suitable for IDPs 285

Coarse-Grained Modeling of Protein–Protein Interactions 286

Standard Gō Models 287

Modications for the Study of IDPs 289

Local Structural Propensities 289

Nonnative and Electrostatic Interactions 289

Insights into Binding Mechanisms from Coarse-Grained Models 290

Conformational Selection and Induced Fit 292

Beyond Coarse-Grained Models 293

Coarse-Grained Modeling of Protein–Nucleic Acid Interactions 293

Intrinsic Disorder in Nucleic Acid Binding 293

Coarse-Grained Models of DNA 294

Coarse-Grained Models of IDP–DNA Complexes 296

Coupled Folding and Binding in Protein–Nucleic Acid Complexes 296

Intrinsically Disordered Regions Not Directly Involved in Sequence-

Specic Binding 297

Outlook 299

References 299

Coarse-Grained Simulation of Intrinsically Disordered Proteins

284

(often an intrinsically disordered region of a multidomain protein) binds to

another macromolecule, be it a folded protein, another intrinsically disordered

protein, or a nucleic acid. Frequently, but not always, the disordered region

undergoes a local folding transition coupled to binding [2]. A rich variety of

possible binding scenarios is thus generated (in comparison with the simpler

case of two folded proteins associating) and consequently many interesting

questions arise regarding the binding mechanism, the bound state, and pos-

sible advantages of disordered regions versus folded proteins in fullling a

given role in an interaction network [4]. While bioinformatics provides pow-

erful tools for hypothesis testing by correlation [5], it does not provide direct

physical insight into the origin of any observed eects.

Physics-based models do not have this deciency. Fully atomistic simula-

tions of coupled folding-binding with explicit solvent in principle provide the

highest accuracy feasible, albeit requiring large computational resources in

addition to enhanced sampling methods in order to sample binding. However,

there are a number of reasons why atomistic simulations may not be the best

choice for studying folding-binding transitions at the time of writing. e rst

is that the energy functions, or force elds, themselves have some limitations,

which may be particularly detrimental to an accurate treatment of unfolded

or disordered proteins. For example, unfolded states using current protein

force elds and water models are too collapsed and structured in comparison

to any experimental measure [6], and nonspecic protein–protein association

appears to be too favorable [7]. Since this deciency could clearly bias any bind-

ing mechanism obtained in simulation, use of such force elds for studying

coupled folding and binding may be questionable. e second issue relates to

the sort of questions that are being asked. If a set of binding events at atomistic

detail is needed, certainly all atom simulations are the only feasible approach.

However, for a great number of interesting questions, this level of detail is

unnecessary and may even make it harder to identify the eects that are really

important. Lastly, the computational cost for studying binding events, even

with enhanced sampling, is still very high. is limits the possibility of varying

the protein sequence or other conditions in order to test hypotheses. Coarse-

grained simulation models, in which the number of congurational degrees

of freedom as well as the complexity of the energy function are reduced, can

help to overcome some of these deciencies. ey make it easier to tune both

sequence and interactions, and, in some cases, to make accurate predictions of

binding mechanism. eir major drawback is their limited predictive value for

specic cases, particularly if a structure of the bound complex is not known.

Nonetheless, there are many generic and specic questions that can be more

straightforwardly investigated with the aid of coarse-grained models.

In this chapter, we will give an overview of the types of coarse-grained mod-

els that may be useful for investigating intrinsic structure formation as well as